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Prepress: Jelena Petrović Design: Aleksandar Mihajlović ISSN: 2406-2588 The European Journal of Applied Economics is published twice a year. Contact us: The European Journal of Applied Economics 32 Danijelova Street, 11010 Belgrade, Serbia Phone No. +381 11 3094046, +381 11 3093284 Fax. +381 11 3093294 E-mail: journal@singidunum.ac.rs Web: www.journal.singidunum.ac.rs Printed by: Caligraph, Belgrade

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CONTENTS

1 - 19 20 - 29 30 - 40

41 - 58 59 -76

77 - 98

Population Age Structure Change and Labour Productivity: Evidence from Tunisia Olfa Frini, Khoutem Ben Jedidia

Antecedents of Mobile Banking: UTAUT Model Jovana Savić, Aleksandra Pešterac

Causal Link between Employment and Renewable Energy Consumption: Evidence from Nigeria Mukhtar Wakil Lawan, Matthew Oladapo Gidigbi

Gold in Investment Portfolio from Perspective of European Investor Tijana Šoja

Defining the Need for and Proposing How to Transform Traditional into Digital Banks with the Support of Information and Mobile Technologies

Mirko Sajić, Zlatko Bundalo, Dušanka Bundalo

Resultant Effect of Crisis-Driven HR Strategies Applied During Current Economic Crisis in Oman – An HR Manager’s Perspective

Venkat Ram Raj Thumiki, Ana Jovancai Stakić, Rayaan Said Sulaiman Al Barwani

III


EJAE 2019, 16(1): 1-19 ISSN 2406-2588 UDK: 331.101.6(611) 331.445:159.922.6 DOI: 10.5937/EJAE15-18209 Original paper/Originalni naučni rad

POPULATION AGE STRUCTURE CHANGE AND LABOUR PRODUCTIVITY: EVIDENCE FROM TUNISIA Olfa Frini*, Khoutem Ben Jedidia University of Manouba, Manouba, Tunisia

Abstract: Relying on a macroeconomic view, this paper investigated the population ageing effect on the aggregate labour productivity. It examined the effects of the labour force participation rate through three broad age ranges: young adulthood (15-29), prime age (30-49) and old age (5064). It computed the labour force participation rate by age considering the working-age of the same age range. Using Tunisian data covering the years 1965-2014, the cointegration method testified for a long-run relationship with a progressive adjustment process towards equilibrium. Unlike the conventional approach outcome, the age-productivity profile in our study did not follow an inverted U-shape. Labour productivity edged down for young workers, rose for the prime age adults, and kept on rising for older people. Accordingly, population ageing did not alter the Tunisian labour market performance. Thus, to achieve better productivity gains and enhance the country’s economic growth, delaying the retirement age beyond 60 was advocated.

Article info: Received: July 11, 2018 Correction: September 19, 2018 Accepted: November 19, 2018

Keywords: population age structure change, labour force participation rate, labour productivity, error correction model, Tunisia.

INTRODUCTION Population ageing may be dramatic for the economy affecting the labour market features through the slowdown of labour force population growth, and eventually causing its contraction (Cadiou et al., 2002; Peng, 2006; Bloom & Sousa-Poza, 2013). The population ageing process influences the structure and performance of the labour market in two ways: (1) directly−via the supply and demand of labour and productivity, and (2) indirectly−via shifting the aggregate demand structure towards more services and products for the elderly. Labour force ageing might influence workers’ mobility, employment, productivity and, consequently, labour market performance and flexibility. Thus, it is understood that the age-productivity profile is relevant for an ageing society. Several studies have focused on how the *E-mail: frini.olfa@planet.tn

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individual’s productivity changes with age to reveal an inverted U-shaped profile as the aged are less productive than the young (Haltivanger et al., 1999; Crépon et al., 2002). However, these individual effects cannot be automatically assumed to apply collectively (Lindh & Malmberg, 1999; Chawla et al., 2007; Brunow & Hirte, 2006, 2008; Van Ours & Stoeldraijer, 2010). It is difficult to systematically conclude, at a macroeconomic level, that an ageing population may lower the aggregate productivity and economic growth. Therefore, reviewing how the aggregate labour productivity changes with age also remains an important hot issue. Within this framework, this paper investigated the population ageing effects on the labour market productivity from a macroeconomic perspective. To this end, and unlike previous studies which concentrated either on the total population (e.g. Barro & Sala-i-Martin, 1992 and Lindh & Malmberg, 1999), or on the working-age population (Mankiw, Romer & Weil, 1992) or also on the employees (Brunow & Hirte, 2006), we focused our empirical study on the labour force population. However, similar to Frini and Ben Jedidia (2018), we assessed the labour force according to the age structure effect, taking into account three age ranges: young adulthood (15-29 years), prime age (30-49), and old age (50-64). Nevertheless, our novelty lies in the fact that we estimated the labour force participation rate by age, defining it as a share of the labour force in the working-age population of the same age range. Additionally, in order to predict the outcomes of the intended policy of postponing the retirement age, we extended it to 65 years, instead of 60. This issue has been weakly addressed in the context of Arab countries before. However, we limited our study to the Tunisian case for the years 1965-2014, as it's well-advanced in population ageing. To check the labour age-productivity profile, we applied the time series modelling approach using the cointegration technique to find out about the long-run equilibrium relationship between the variables and the Error Correction Model, in order to capture the short-run adjustment mechanism. Our methodology is rather standard, but is extensively used in macroeconomic analysis to check a dynamic long-run relationship. The remainder of the paper was organized as follows: Section 2 developed a literature review. Section 3 depicted an overview of the demographic change and its consequences on the labour force age structure. Section 4 specified the applied model for our estimations. Section 5 detailed the econometric methodology and discussed the results before concluding and suggesting some policy recommendations in Section 6.

LITERATURE REVIEW Demographic change modifies the population age distribution, the size of different age ranges of the working-age population, and, consequently, the labour force age structure which, in the long-run, may influence aggregate and age group-specific labour productivity (Dixon, 2003; Börsch-Supan, 2003; and Vodopivec & Arunatilake, 2008). There are microeconomic and macroeconomic effects of ageing on labour productivity. However, to deal with some central macroeconomic issues about an ageing labour force productivity, our major concern in this paper requires a good understanding at the microeconomic level. At this level, several studies, some of which are quoted in the table below, have displayed an inverted U-shaped age productivity profile; rising as workers enter prime age, and then declining as they approach retirement.

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Region/Country and Period

Productivity Indicator

Age Productivity Profile

France 1994-2000

Firm’s value added

Productivity increases with age in the first part of working life, remains stable around 40-45 or uncertain thereafter.

German 2000

The regional value-added

The most promoting age group for growth is 45-55; those over 55 years reduce it.

France 1994-1997

Firm’s output

Productivity peaks at 25-34, and decreases for those aged over 50.

Haltiwanger et al. (1999)

Maryland US

Sales per employee

Productivity increases until 55, and decreases slightly after.

Ilmakunnas and Maliranta (2004)

Finland 1995-2003

Firm’s value added

Productivity peaks at around 40, and decreases for those older.

Prskawetz et al. (2007)

Austria

Firm’s value added

Productivity peaks 30-49.

Author Aubert and Crépon (2004) Bruno and Hirte (2006) Crépon et al. (2002)

Table 1. An Empirical Overview of Age-Productivity in an Inverted U-Shape.

This negative ageing impact may be explained by the introduction and swift development of the new technologies (Bös & Weizsäcker, 1989). Older workers have difficulties adjusting to new ways of working, which in return hampers their productivity. Additionally, they suffer from an ageing knowledge stock, skill obsolescence (De Grip & Vanloo, 2002), declining cognitive abilities (notably by the age of 50, as stressed by Verbaegen and Salthouse (1997), and qualifications depreciation. This age-related reduction in cognitive abilities is an important cause of the age-related productivity decline (Skirbekk, 2003). Moreover, the financial spurs to acquire new skills decline gradually with age, which lowers productivity. The recent study of Rožman et al. (2016) comparing older and younger employees in Slovenian companies concludes that older workers are less productive, less motivated, and less innovative and energetic. Moreover, the increase of health and infirmity incidences undermines labour productivity (Tanner, 1997). In contrast, young workers demonstrate a better ability for learning new skills, and a greater adaptability to new jobs. This inverted U-shaped age-productivity profile is, however, not irrefutable and incontestable. Positive correlations between older workers and productivity were reached according to several studies (table 2 below). As argued by both Disney (1996) and Dixon (2003), older workers may have a higher average level of work experience, and a positive effect on productivity. They were consistently rated as having more positive attitudes, being more reliable, and possessing better skills than average workers. For instance, learning stimulates productivity as related to seniority (Aubert & Crépon, 2003). Furthermore, older workers tend to have stable relationships with their employers, while young workers tend to frequently change jobs and employers (Gregg &Wadsworth, 1999). The decline in voluntary job mobility may reduce the turnover costs for employers, including recruitment and initial training costs, which would have a favourable impact on overhead labour costs and profitability (Dixon, 2003). In addition, the older workers’ contribution to firm-level productivity exceeds their contribution to the wage bill, as revealed by Cardoso et al. (2011).

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Author

Region/Country and Period

Productivity Indicator

Age Productivity Profile

Cardoso et al. (2011)

Portugal longitudinal employer-employee data 1986-2008

Share of total number Productivity increases until the age of worker-hour range of 50-54

Goebel & Zwick (2009)

Germany employeremployee data over 1997-2005

Marginal productivity

Productivity increases until age 55 and decreases slightly after that.

Malmberg et al. (2008)

Sweden 1985-1996

Value added per employee

Older workers are more associated with higher productivity than younger ones.

Austria 2002-2005

Average labour productivity across industrial sectors

A positive correlation exists between the share of older employees and productivity, but no evidence for a significant relationship between the share of younger employees and productivity was found.

Netherlands 2000-2005

Firm’s value added

Increasing productivity up to age 57.

Mahlberg et al. (2013b)

Van Ours & Stoeldraijer (2010)

Table 2. An Empirical Overview of Productive Older Workers

From the above brief review, we can deduce that there is no agreement about the ageing-productivity nexus as related to the diversity of the required skills and individuals’ capacities. In fact, this relationship depends on the nature of the work, education level, and physical demands. An age productivity profile is not necessarily static, but depends on labour market requirements, as suggested by Skirbekk (2008). Diminishing labour productivity at older ages seems to be particularly strong for work tasks where physical abilities, learning, and the speed of carrying out tasks are needed. Nevertheless, for jobs where experience and verbal abilities are important, older workers maintain a relatively high productivity level. On the other hand, the empirical literature suggests that there might be differences in the ageproductivity profiles between/among sectors. For example, Aubert and Crépon (2006) conclude that relative productivity increases until the age of 35 for French manufacturing, trading, and services sectors. In trade, however, workers 40 to 59 are significantly more productive, and those between 45 and 54 are more productive than younger workers in services. Nevertheless, the authors showed that there are no differences in manufacturing between older workers and the 35-39 group. Van Ours and Stoeldraijer (2010) show significant differences in the age productivity patterns between sectors in the Netherlands. However, Mahlberg’s and Prskawetz (2013b) study, dealing with mining, manufacturing, and market-oriented services sectors in Austria, proves a positive correlation between the older employees and productivity, but not a significant relationship with the younger ones. Therefore, taking into account the fact that an ageing labour force differently influences productivity according to sectors, the total impact of ageing will depend on the industrial structure of an economy (Göbel & Zwick, 2012). Accordingly, it is hard to systematically conclude at a macroeconomic level that ageing working population may lower the aggregate productivity and, consequently, the country’s economic growth. At the macroeconomic level, the ageing population effect is to reduce the relative size of labour force as a share of the total population. From this viewpoint, labour becomes relatively scarce, while capital becomes relatively more abundant. This engenders changes in the relative price of labour, and leads to a higher capital intensity. This labour force change affects economic growth. In details, as per 4


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capita output ( Y ) (where Y denotes the output and N is the total population) is a function of capital (K), N labour (L) and total factor productivity (A) as follows ( Y ) = A f ( L , K , L ) ; a change in total population N N L N (N) (in its size and structure) changes the labour (L) structure and subsequently affects growth output L (Bloom & Williamson, 1998). A decline in N induces an increase of both the labour force ratio ( ) and N K L capital intensity ( ) . Moreover, considering the working-age population (WAP), this labour ratio ( ) N L L L WAP can be expressed as a multiplication of two components ( N ) = (WAP ) ( N ) (Mankiw et al., 1992; Barro & Sala-I-Martin, 1995). Henceforth, per capita output expression becomes a function of the labour L Y Y L WAP ); ( = ( )( )) . This expression highlights the effects of the labour force participation rate (WAP N L WAP N force participation rate, and therefore of its age structure, on economic growth. Additionally, the elderly share increase in the working-age population is likely to reduce the geographical mobility and the national migration, all things being equal (GreenWood, 1997). Reduced voluntary mobility between/among jobs, as well as the older workers’ geographical mobility, may reduce employment and productivity. It generally causes fewer matching people to jobs in which their skills may be used efficiently to diminish disparities in economic performance across regions. Thus, these mobility and flexibility issues may affect labour market performance, and therefore the economic dynamism. A good deal of empirical evidence proved a positive effect of ageing working population on aggregate productivity. For instance, using five-year data from the OECD countries 1950-1990, Lindh, Malmberg (1999) demonstrated that the 50-64 age group has a positive influence on productivity (defined as GDP/Worker), and that the above 65 contribute negatively, while younger age groups have ambiguous effects. In addition, when estimated in the Tunisian context over the period 1965-2014, Frini and Ben Jedidia (2018) found that productivity declines at a young age (15-29), and rises at old age (50-64). However, the mechanism behind these age effects has not been resolved. The Tang and Macleod (2006) study on Canadian provinces 1981-2001, however, revealed that older workers have a modest negative impact on productivity.

TUNISIAN DEMOGRAPHIC CHANGE AND CONSEQUENCES ON LABOUR MARKET Demographic Shifts and Age Structure Change1 A drastic demographic change has occurred in Tunisia following the decline of both mortality and fertility rates. During the period 1966-2016, the mortality rate fell from 35-40% to a fairly constant low rate of 5.5%. Fertility, which was close to eight children per woman in the early 60’s, has dropped below the renewal threshold (2.05 children per woman) since 1999. However, a slight increase has been recorded since 2010 to attain 2.4 children per woman in 2015. Life expectancy, which hardly exceeded 40 years in 1950, reached 75.4 years (78.1 years among women and 74.5 years among men) in 2016. This demographic transition has brought a deep change in the population age structure toward an irreversible ageing process. The age groups’ proportions of 0-4 and 5-14 have become less important. Over 1966-2015, they shifted from respectively 18.6% to 8.5% and from 27.9% to 14.9%. In contrast, the share of the working-age population 15-59 has increased from 48% to 64.4%. However, this noteworthy change has affected the proportion of the over 60-year-olds, which has further increased by more than two-fold to rise from 5.5% to 12.2%. Accordingly, these demographic changes have brought about a change in the labour force size and age structure. Meanwhile, the working-age population size growth declined from 2.5% to 1.7%, while the growth rate of labour force went down from 1.8% during 2004-2009 to 0.8% during 2014-2017. However, the labour force participation rate has increased from about 44.9% to 49.6% during 19662014. Concurrently, the labour force average age rose progressively; the modal age evolved from the 1 Source of all quoted statistics is the Tunisian annual statistics of the National Institute of Statistics (NIS) from 1957-2016.

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25-29-year-olds in 2004 to the 30-34-year-olds in 2014. During 1984-2014, the share of the young labour force (15-29-year-olds) shrank significantly (from 49.8% to 30.3%), the prime-aged adults share (30-49-year-olds) increased significantly (from 33.7% to 52.7%) and the share of the older labour force (50-64-year-olds) rose slightly (16.5% to 17%). Consequently, the share of the young employed population declined (from 35.6% to 29.7%) while that of both prime-aged adults and elderly increased (respectively from 46.7% to 52.7% and from 17.6% to 17.6%). The employed population is becoming older and older; the modal age has evolved from 20-24 to 30-34-year-olds. Likewise, the unemployed are getting older; the modal age shifted from the 15-19-year-olds to 25-29-year-olds. In 2011, about 50% of the unemployed were 25-34-year-olds (34% for those between 25 and 29, and 16% who were aged 30-34), and 38% were younger than 29 (10% were aged 15-19 and 28% were aged 25-29).

Labour Productivity Shift Labour productivity is viewed to be below its potential level. As shown in diagram 1, the labour productivity growth has evolved irregularly over the past fifty years. The long-term productivity growth (over 1980-2010) has been estimated to be about 2.25%. In the post-revolutionary period 2011-2014, it has reached its lowest levels due to the economic and social instability, which includes the low growth and job creation, and the sit-ins that have crippled the productive units. In 2013, the productivity loss was about -0.6%, as job creation was higher than economic growth (3.5% against 2.8%).2

Diagram 1. Tunisian labour productivity growth change (1962-2014)

MODEL AND DATA SPECIFICATION Empirical model specification The previous literature review has allowed us to build our aggregate labour productivity model that refers to the augmented Solow model based on the work of Mankiw et al. (1992). Labour productivity (Prod) can, generally, be calculated in several ways, such as the added value per number of workers or per worked hours, or as a marginal productivity. For our, estimate, we used the average labour productivity, as in the studies of Alexander (1993), Lindh and Malmberg (1999), Wakeford (2004), Tang and Macleod (2006) and Frini and Ben Jedidia (2018), because the marginal productivity or labour output per hour data are not available in Tunisia. It reflects labour productivity in terms of personal capacities of workers or the intensity of their effort. Its change reflects the combined effect of changes on both capital and technical efficiency, as well as the influence of economics of scale. 2 Data sources−the Tunisian Institute of Competitively and Quantitative Study (ITCQS) 2014.

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In order to assess the influence of ageing on labour productivity, we estimated the labour force age structure, as it is wholly economically involved in the labour market, which is not the case for the working-age population. Unlike the previous works, we were interested in the labour force rate by age structure to better capture the age effect over time. Different from the common definition of the labour force participation rate by age as a ratio of the labour force of age range per the overall working-age population (Mankiw, Romer & Weil, 1992) or per total population (e.g. Barro & Sala-i-Martin, 1992, Lindh & Malmberg, 1999, Frini & Ben Jedidia, 2018) or per employees (Brunow & Hirte, 2006); we defined it for an age range as a share of the labour force per working-age population within the same age range. Explicitly, we distinguished three broad age ranges: young adulthood (15-29), prime-age adults (30-49) and old age (50-64), as in Frini and Ben Jedidia (2018) study on the Tunisian case but unlike them we reported to the working population of the same age range. Thus, we treated three labour force participation rates: that of the young (YL), adults (PL) and elderly (AL) as illustrated below. YL =

Labor force aged 15 − 29 Population aged 15 − 29

; PL =

Labor force aged 30 − 49 Population aged 30 − 49

; AL =

Labor force aged 50 − 64 Population aged 50 − 64

;

Together, these measurements reflect most of the age structure variation to allow the identification of distinct age effects. The age ranges that are not related to the labour market were considered as a reference age range. It should be noted that we considered 65 as the retirement age instead of 60 in order to foresee the impact of the retirement age delay as suggested by the government. Additionally, to make sure that the elderly who continue to work are not likely to be the most productive and those who have left are not the least productive, we undertook a robustness test by estimating a model with older workers aged 50-59. As we got the same result, we assumed that age retirement postponement would not artificially raise productivity. Furthermore, as we were rather interested in labour productivity as a whole, we did not distinguish the labour force rate by gender. In determining our economic variables, we estimated the influence of education, trade openness, investment, wage, and unemployment. By estimating the influence of education (E), we looked at a part of the human capital effect on productivity growth, the stock or accumulation of knowledge effect, and through the age distribution we looked at the other part, the transfer and implementation of new knowledge, through training or accumulated experience. We especially considered the enrolment rate at secondary education, for three reasons: (1) The unavailability of education level data for employees for all the period of study, (2) The enrolment rate data are only available for the population 5-11-yearolds, which is not suitable for our case study, and (3) The secondary level gives the most statistically significant result. We therefore chose it in order to win a freedom degree and overcome the multicollinearity problem. This is consistent with a labour market specificity characterized by a low human capital of employees.3 As in Lindh and Malmberg (1999), we considered trade openness (OP) to look into technology diffusion effect on productivity as stressed by literature. Following Mankiw et al. (1992), we appraised the long-run gain in productivity of the capital accumulation (K) by considering the gross fixed capital formation (GFCF) at constant domestic prices. In addition, we looked at the long-run dynamics relationship between labour productivity and wages, since it has been constantly a salient economic and legal concern. As a measure, we used the guaranteed industrial minimum wage (for the 40-hour regime) (W). 3 The average number of years−study of employees has evolved from 1.6 to 7.5 years during 1965-2014.

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Furthermore, because of the ambivalence relative to the connection between productivity growth and employment, we estimated the unemployment rate (U) in order to specify its nature for the Tunisian case (e.g. Blanchard et al., 1995; Gordon, 1997). Finally, the baseline estimation equation takes the following form: Prodt = α + θ1YLt + θ2 PLt + θ3 ALt + θ 4 Et + θ5OPt + θ6Wt + θ7 K t + β8U t + Zt

Where, zt is the error term. In order to refine our empirical analysis, we estimated another model (model 2) with a time variable (DATE) to find out the effect of the structural changes that occurred after the revolution on the 14th of January 2011.

Data Construction Our annual time series were gathered from the National Institution of Statistics (NIS) and the Tunisian Institute of Competitively and Quantitative Study (ITCQS) data sources. Since their databases started after 1960 and were not up to date for at least two years, the longest possible time series covers the period 1965-2014. Moreover, these institutes could not provide a full series for all our variables. For this reason, we constructed our series for the labour force participation rate according to the three relevant age ranges and for education enrolment rate by level. For labour force participation rate by age range, we firstly calculated the size of the labour force and the size of the working-age population corresponding to each age range considered. Then, we divided the labour force population per workingage population for each age range. For education, we reconsidered our data series computed in our previous published empirical work (as indicated in the Appendix) (Frini & Muller, 2012). For trade openness, and as generally defined, we divided the sum of import and export by the GDP per capita at constant domestic prices. The GFCF per capita at constant price measuring the capital accumulation (K) was computed by dividing the GFCF per capita at current price per the consumption price index (base 1990) to avoid the prices effects. Finally, we defined DATE as a dummy time variable equal to one if upper to 2010 and zero otherwise. All variables are stated in logarithm so that the coefficients are interpreted as elasticities. Their primary statistical characteristics are displayed in Table 3 (in the Appendix). The model specification does not exhibit either correlation or multicollinearity problems as proved by the several check tests.4 Also, it does not lead to a heteroskedasticity issue, as the homoskedasticity is not rejected by the results of ARCH test (P-value of 0.65 for model 1 and of 0.49 for model 2). Likewise, this estimate does not imply a non-normal error as the Jarque-Berra test on the estimated residual does not reject the normality (P-value is of 0.986 for model 1 and of 0.856 for model 2).

ECONOMETRIC METHODOLOGY AND ESTIMATION RESULTS Before performing our time series estimation, we tested the reliability of our time series data by testing the unit roots existence. The results of the Augmented Dickey-Fuller and of Philip-Perron tests 4 The Durbin-Watson test is inconclusive, as the test statistic value lies between dL and dU for the reference model (dL =1.20<DW=1.687< dU=1.93). As Durbin-Watson test is not powerful in a statistical acceptance, we applied the Breusch Godfrey test, which presents a probability greater than 10 % (p-value of 0.17 for model 1) and a low R2. Thus, we did not reject the null hypothesis of non-autocorrelated errors and consequently the model is free of autocorrelation. The same evidence is observed for model 2.

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used (Table 4, in the Appendix) ascertain that all the variables are integrated I (1). A cointegration VAR model, which required the variables be integrated of the same order, is then appropriate.5

Cointegration Estimation Johansen and Jesulius (1990) maximum eigenvalue test was used to determine the cointegration ranks. We chose the model with no trend in the cointegration relation and the presence of a constant in the VEC, since such long-run equilibrium relationship between series does not have trends. The lag one was used referring to the VAR lag order selection by the Akaike information criterion. It was found that the maximum Eigen value test result (Table 5 in the Appendix) rejects the null hypothesis of no cointegration relationship at one percent level.6 There is a unique cointegration equation binding the variables together in a long-run equilibrium relationship characterized by a common trend. Prod = 0.755 − 0.780YL + 0.577 PL + 1.06 AL + 0.604 E + 0.386OP − 0.115W + 0.227 K + 0.308U + Zt 3.09  5.14  10.32 4.34  12.51 8.31 7.40 11.15

Where, zt is the error term. T-statistics are presented in parentheses. The long-run empirical evidence testified that the aggregate labour productivity in Tunisia is influenced by both economic and demographic factors. Even though we used a dissimilar measurement of labour force participation rate per age range compared to previous studies on Tunisia particularly Frini and Ben Jedidia (2018), we found the same evidence. Age structure impact on productivity is significant and non-monotonic. In Tunisia, productivity edges down at young age it increases for the prime age adult, and rises more toward the end of one’s career. Thus, the overall age-productivity profile does not follow an inverted U-shape. In line with Dixon (2003), Cardoso et al. (2011), and Göbel and Zwick (2013), older workers are found to be productive. The aggregate labour productivity is positively affected by both prime-aged adult and old age. Better yet, the gains of labour productivity are rather boosted by the elder range of the labour force. The older workers seem to have been efficiently adapted to technological changes since they have experienced greater growth in tasks with an intense use of cognitive abilities (Autor et al., 2003). They have skills and capacities based on experience that many youngsters lack. Therefore, the older labourers may have higher average levels of work experience and positive effect on productivity thanks to skills and capacities. Such a result is consistent with the Tunisian productive system specificity, characterized mainly by the service sector, which does not require a high technological development. This is in line with Skirbekk (2003) conclusion stating that job performance increases when experience and verbal abilities are important. However, analogous to Mahlberg et al. (2013a) findings, young workers weaken the labour productivity level. Although young workers have capabilities to become accustomed to technical progress, they require time to acquire the high skill (learning and training). Some years of experience are required to highlight the educational skills and gain significant education return. This fact is amplified by the low synergy between the educational system outcomes and the labour market needs. Consequently, this empirical evidence shows an increasing productivity with age, which enables us to predict that labour productivity will not be adversely altered by the ageing process. 5 The cointegration technique is, however, built in a linear context. This linearity characteristic is considered restrictive insofar, as it implies a single long-run equilibrium and a symmetric adjustment to long-run target by the error correction model. 6 For model 2, two cointegration vectors were found. However, it exhibited the same results (note 2 in the Appendix).

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The findings related to the economic variables are, generally, in coherence with the theoretical expectations. Results display increasing returns of education on productivity. A potential productivity gain is embodied in the workers who accumulated human capital as emphasized by the human capital theory. Education is likely to raise workers productivity by providing useful knowledge and skills and the workers become more receptive to the new production processes. Thus, a higher educational attainment should help to maintain productivity as the labour force ages. Furthermore, investment infers long-run gains in productivity. Capital accumulation improves the labour productivity, as it provides more capital per unit of labour, facilitates the effective use of new and powerful technologies, and raises workers productivity. Moreover, it could be pinpointed that the advent of new technology in the long-run, in turn, replaces labour and increases productivity. Thereby, contrary to Mankiw et al. (1992), results of the empirical evidence point out a small weight of the physical capital but a large weight of human capital in explaining the output per worker variation. In agreement with Alcala’s and Ciccone (2004) interpretations, these potential productivity gains through physical and human capital accumulation are, also, reinforced by trade openness. Trade openness stimulates productivity in an environment of international competition thanks to technology transfer, gains stemming from economies of scale, and knowledge flows between countries. Additionally, similar to Gordon’s (1997) study results, there is a link between labour productivity and unemployment. A less volatile and more persistent positive correlation between productivity and unemployment was found as in Uhlig’s (2006) work. Such a result confirms the neoclassical view, suggesting that a decline in labour demand increases productivity given the technical progress and wage setting. Nevertheless, the wage policy is likely to decrease productivity. This may be due to the Tunisian policy of “low wages”, which promotes the rotation of the workforce and, consequently, presents a negative influence on the labour productivity in the long-run. Therefore, a reconsideration of the level of the minimum guaranteed wage of the industrial sector should be achieved in order to motivate workers to be more productive. Interestingly, over the long-run, the structural and political change leads to a positive effect on productivity evolution (model 2).

Estimation Vector Error Correction Thanks to the Vector Error Correction model, we estimated the diffusion speed of the labour force ageing on labour productivity by examining the adjustment mechanisms of the long-run relationships across variables (Engle & Granger, 1987). The results (Tables 6 and 7 in the Appendix) show that the error correction term derived from the long-run cointegration relationship is highly significant and negative in the productivity equation. The short-run productivity evolution tends to join the long-run equilibrium. The adjustment towards equilibrium is swift, with a coefficient of -0.468 for model 1 and of -0.535 for model 2. In the short-run, the labour productivity is independent of its lagged value, of labour age structure, and of the economic factors. Unlike the long-run, a negative short-run effect of the change brought about by the revolution of January 2011 was observed (Model 2). Such a finding denotes the dramatic economic situation resulting from the sit-ins and strikes that occurred in the productive sectors (mining industry). In addition, we noted that only the lagged education variable influences labour productivity with an instantaneous negative effect. Education development did not efficiently contribute to the shortrun labour productivity growth process owing to three major raisons. Firstly, the Tunisian productive system is characterized by a low−educated labour force. With few education years, the labour force requires a long time to acquire the necessary skill and experience to be productive. Secondly, the 10


EJAE 2019  16 (1)  1-19

FRINI, O., JEDIDIA, K. B.  POPULATION AGE STRUCTURE CHANGE AND LABOUR PRODUCTIVITY: EVIDENCE FROM TUNISIA

educational system is inconsistent with the labour market requirement. As noted by Frini and Muller (2012), there is a low synergy between the educational system and labour market needs. Thirdly, the inability of the labour market to absorb the skilled labour force as revealed by the high unemployment rate of highly educated.7 Overall, this determinism between demographic, economic and productivity variables does not occur overnight but progressively; it is a long-run process. Consequently, labour productivity improvement requires a structural change in both labour force and economic conditions. Indeed, time is required for workers to adapt and acquire new skills and consequently to improve his productivity.

CONCLUSION This paper underscored the population ageing impact on labour productivity in a macroeconomic perspective. It depicted the age-productivity profile in the Tunisian labour market by assessing the effects of three broad age ranges of the labour force participation rate of young adulthood, prime age and old age over the period 1965-2014. The achieved findings pointed out that labour productivity is boosted thanks to economic factors (education, trade openness, capital accumulation and unemployment rate). But the appealing result is that productivity is, also, affected by demographic factors. Changes in the relative size of different age ranges have a noteworthy impact on the aggregate labour productivity. The results confirmed a strong long-run equilibrium relationship between labour productivity and labour force age structure. Interestingly, opposite to the widespread belief, older workers were consistently rated as having a more positive attitude, being more reliable, and displaying greater skills than young workers. Thus, the age-productivity profile does not follow an inverted U-shape. Productivity declines for young workers and rises when they enter the prime-adult age and go up further toward the end of their career. In this respect, ageing does not seem to lead to a low performance for the Tunisian labour market. Nevertheless, the unfavourable scenario may come true with the arrival of the “baby-boom generation” to the retirement age after about a decade, if policy-makers do not manage the situation. If the Tunisian government does not respond appropriately to these demographic changes, it will face the risk that labour supply will shrink and labour productivity may not grow as quickly as needed to boost economic growth and increase living standards. Finally, policies that affect labour market regulation and wage setting practices, retirement, pension rules, health care system, training, and education will be particularly critical for improving labour productivity. In light of our results, it appears that the retirement age delay beyond 60 years-old, as suggested by the government, is advised to gain more in labour productivity and enhance economic growth. Moreover, to keep a higher productivity level, older workers should be engaged only in jobs where experience and verbal ability are needed, and develop incentives for their training. Firms will have no choice but to expand their training programs to invest more in older employees and reorient the programs to meet the needs of those workers and strengthen the effectiveness of the professional training system. Similarly, policy-makers should invest in the workers’ healthcare, and foster work environments in order to promptly take advantage of an ageing labour force, and enhance a continued productive participation of older workers.

7 Over 1966-2014, the graduates' unemployment rate has increased from 0.8% to 33.1%.

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Dixon, S. (2003). Implications of population ageing for the labour Market. Labour Market Division, Office for National Statistics. Labour Market trends. Engle, R. F., & Granger, C. W. J. (1987). Co-integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276. Frini, O., & Ben Jedidia, K. (2018). The age structure change of population and labour productivity impact. Economics Bulletin, 38(4), 1831-1844. Frini, O., & Muller, C. (2012). Demographic Transition, Education and Economic Growth in Tunisia. Economic Systems, 36(3), 351-371. DOI:10.1016/j.ecosys.2012.04.002 Göbel, C., & Zwick, T. (2009). Age and productivity - evidence from linked employer employee data. ZEW - Centre for European Economic Research Discussion Paper No. 09-020. DOI:10.2139/ssrn.1431878 Göbel, C., & Zwick, T. (2012). Age and Productivity: Sector Differences. De Economist, 160(1), 35-57. DOI:10.1007/ s10645-011-9173-6 Göbel, C., & Zwick, T. (2013). Are Personnel Measures Effective in Increasing Productivity of Old Workers? Labour Economic, 22(C), 80-93. Gordon, R. J. (1997). Is there a trade-off between unemployment and productivity growth? In D. Snower and G. de la Dehesa (Ed.), Unemployment policy: Government options for the labour market (pp. 433-463). Cambridge: Cambridge University Press. Greenwood, M. (1997). Internal migration in developed countries. In M. R. Rosenzweig and O. Stark (Ed.), Handbook of population and family economics (pp. 647-720). Amsterdam: Elsevier. Gregg, P., & Wadsworth, J. (1999). Job tenure, 1975-1998. In P. Gregg and J. Wadsworth (Ed.), The state of working Britain (pp. 109-126). New York: Manchaster University Press. Haltivanger, J. C., Lane, J. I., & Spletzer, J. R. (1999). Productivity Differences Across Employers. The Roles of Employer Size, Age and Human Capital. American Economic Review Papers and Proceedings, 89(2), 94-98. Ilmakunnas, P., Maliranta, M. & Vainiomäki, J. (2004). The roles of employer and employee characteristics for plant productivity. Journal of Productivity Analysis, 21(3), 249-276. DOI:10.1023/B:PROD.0000022093.59352.5e Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration - with applications to money demand. Oxford Bulletin of Economics and Statistics, 52(2), 169-210. DOI:10.1111 /j.1468-0084.1990.mp52002003.x Lindh, T., & Malmberg, B. (1999). Age structure effects and growth in the OECD, 1950-1990. Journal of Population Economics, 12(3), 431-449. Mahlberg, B., Freund, I., & Prskawetz, A. (2013a). Ageing, productivity and wages in Austria: Sector level evidence. Empirica, 40(4), 561-584. Mahlberg, B., Inga, F., Cuaresma, J-C., & Prskawetz, L. (2013b). Ageing, productivity and wages in Austria. Labour Economics, 22, 5-15. DOI:10.1016/j.labeco.2012.09.005 Malmberg, B., Lindh,T. & Halvarsson, M. (2008). Productivity consequences of workforce ageing - Stagnation or a Horndal effect? In A. Prskawetz, D. Bloom, and W. Lutz (Ed.), Population Aging, Human Capital Accumulation and Productivity Growth, Population and Development Review (pp. 238-256). New York: Population Council. Mankiw, N. G., Romer, D., & Weil, D. N. (1992). A contribution to the empirics of economic growth. The Quarterly Journal of Economics, 107(2), 407-437. Peng, X. (2006). Macroeconomic Consequences of Population Ageing in China-A Computable General Equilibrium Analysis. Journal of Population Research, 30(4), 12-22. Prskawetz, A., Mahlberg, B., & Skirbekk. V. (2007). Firm Productivity, Workforce Age and Educational Structure in Austrian industries in 2001. In R. Clark, N. Ogawa and A. Mason (Ed.), Population Aging, Intergenerational Transfers and the Macroeconomy (pp. 38-66). Cheltenham: Edward Elgar Publishing. Rožman, M., Treven,T., & Cancer, V. (2016). Stereotypes of older employees compared to younger employees in Slovanian companies. Management, 21(1), 165-179. 13


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Skirbekk, V. (2003). Age and individual productivity: a literature survey. Vienna Yearbook of Population Research, 2, 133-153. Skirbekk, V. (2008). Age and Productivity Capacity: Descriptions, Causes and Policy Options. Ageing Horizons, 8, 4-12. Tang, J., & MacLeod, C. (2006). Labour force ageing and productivity performance in Canada. Canadian Journal of Economics, 39(2), 582-603. Tanner, S. (1997). The dynamics of retirement behaviour. In The Dynamics of Retirement: Analyses of the Retirement Surveys. R. Disney, E. Grundy, and P. Johnson (Ed.), Department of Social Security Research Report No. 72. London: Stationery Office. Uhlig, H. (2006). Regional Labor Markets, Network Externalities and Migration: The Case of German Reunification. American Economic Review Papers & Proceedings, 96(2), 383-387. Van Ours, J. C., & Stoeldraijer, L. (2010). Age, Wage and Productivity. Bonn: IZA. Verhaeghen, P., & Salthouse, T. A. (1997). Meta-Analyses of Age-Cognition Relations in Adulthood: Estimates of Linear and Nonlinear Age Effects and Structural Models. Psychological Bulletin, 122(3), 231-249. Vodopivec, M., & Arunatilake, N. (2008). Population aging and the labor market: the case of Srilanka. Retrieved September 30, 2018, from http://siteresources.worldbank.org/SOCIALPROTECTION/Resources/SPDiscussion-papers/Labor-Market-DP/0821.pdf Wakeford, J. (2004). The productivity-wage relationship in South Africa: an empirical investigation. Development Southern Africa, 21(1), 109-132.

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APPENDIX PROD

YL

PL

AL

K

OP

E

W

U

Mean

8.649

3.847

4.060

3.791

8.015

4.307

3.664

4.350

2.726

Median

8.650

3.847

4.074

3.792

8.071

4.419

3.710

4.677

2.747

Maximum

9.280

3.956

4.188

3.933

8.787

4.738

4.254

5.615

2.923

Minimum

7.854

3.668

3.854

3.682

7.006

3.459

2.737

2.578

2.517

Std. Dev.

0.392

0.061

0.083

0.070

0.506

0.330

0.491

1.001

0.074

50

50

50

50

50

50

50

50

50

Observations

Table 3. Descriptive Statistics Variables The Probability value of the unit roots tests (P-value) Augmented Dickey Fuller (ADF) Model

Model (1)

Model (2)

Model (3)

Phillips Perron (PP) Model (1)

Model (2)

Model (3)

1.000 0.833 0.987 0.376 0.999 0.999 0.981 0.973 0.660

0.577 0.049 0.265 0.761 0.381 0.432 0.661 0.125 0.033

0.516 0.005 0.049 0.239 0.815 0.968 0.607 0.535 0.081

0.000 0.000 0.000 0.000 0.000 0.002 0.000 0.000 0.000

0.000 0.003 0.000 0.000 0.001 0.000 0.004 0.000 0.000

0.000 0.008 0.000 0.000 0.008 0.001 0.018 0.000 0.000

Level Prod YL PL AL E W K OP U

1.000 0.518 0.999 0.335 0.986 0.995 0.961 0.970 0.421

0.479 0.241 0.999 0.761 0.752 0.273 0.236 0.146 0.033

0.496 0.025 0.049 0.452 0.980 0.987 0.157 0.384 0.654 First difference (∆)

Prod YL PL AL E W K OP U

0.117 0.000 0.000 0.000 0.026 0.029 0.000 0.000 0.000

0.000 0.018 0.000 0.000 0.001 0.011 0.004 0.000 0.000

0.000 0.048 0.000 0.001 0.008 0.018 0.020 0.000 0.000

Table 4. Unit Root Tests*

k + ∑ θ ΔX +ε ρ − 1) X *Model (1) with no intercept and no deterministic trend: ΔX = ( t t −1 j t−j t j k Model (2) with intercept and no deterministic trend: ∆Xt = ( ρ − 1) Xt −1 + υ + ∑θ j ∆Xt − j + εt j

Model (3) with intercept and deterministic trend: ∆Xt =

k

( ρ − 1) Xt −1 + λ + δt + ∑ θ j∆Xt − j + εt j

Both the ADF and the PP tests take the unit root as the null hypothesis H0: ρ =1. This null hypothesis is tested against the one side alternative H1 ρ <0. 15


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Base regression: Model 1

Model 2

H0: r or fewer cointegration vectors

Eigen Value

P-value**

None* At most 1 At most 2 At most 3 At most 4 At most 5 At most 6 At most 7 At most 8

0.823 0.654 0.561 0.527 0.486 0.331 0.290 0.150 0.068

0.000 0.068 0.217 0.133 0.082 0.387 0.198 0.395 0.065

H0: r or fewer cointegration vectors

Eigen Value

P-value**

None * At most 1 * At most 2 At most 3 At most 4* At most 5 At most 6 At most 7*

0.679 0.618 0.525 0.496 0.449 0.260 0.229 0.111

0.029 0.049 0.141 0.065 0.036 0.328 0.093 0.017

Table 5. Maximum Eigenvalue Test Max-eigenvalue test indicates 1 cointegrating eqn (s) at the 0.05 level for model 1 Max-eigenvalue test indicates 2 cointegrating eqn (s) at the 0.05 level for model 2 *denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values D(Prod)

D(YL)

D(PL)

D(AL)

D(E)

D(W)

D(K)

D(OP)

D(U)

Error Correction term ECT1

-0.468 (-3.595)

-0.160 (-2.132)

-0.019 (-0.156)

0.202 (2.149)

-0.113 (-0.765)

-0.066 (-0.233)

0.306 (0.891)

1.082 (2.636)

0.381 (1.246)

Regressors D(Prod (-1))

C

0.041 (0.280) 0.360 (1.650) -0.175 (-0.832) -0.430 (-1.868) -0.666 (-4.481) -0.060 (-0.872) -0.078 (-1.222) 0.038 (0.716) -0.034 (-0.491) 0.053 (6.297)

0.166 (1.967) 0.403 (3.193) 0.205 (1.679) 0.223 (1.674) -0.001 (-0.014) -0.023 (-0.577) -0.031 (-0.845) -0.015 (-0.481) -0.029 (-0.717) 0.001 (-0.369)

0.093 (0.663) 0.084 (0.404) -0.431 (-2.135) -0.308 (-1.400) 0.068 (0.478) -0.090 (1.373) -0.039 (-0.650) 0.033 (0.649) 0.014 (0.215) 0.009 (1.213)

-0.034 (-0.329) 0.038 (0.381) 0.482 (3.158) 0.106 (0.635) -0.042 (-0.390) 0.086 (1.737) -0.003 (-0.070) 0.026 (0.669) 0.011 (0.223) -0.009 (-1.554)

0.030 (0.186) 0.120 (0.487) -0.314 (-1.315) -0.088 (-0.339) 0.410 (2.432) 0.036 (0.406) -0.027 (-0.371) -0.007 (-1.144) -0.014 (-0.181) 0.016 (1.730)

-0.232 (-0.726) -0.165 (-0.347) 0.147 (0.320) -0.534 (-1.064) -0.038 (-0.117) 0.346 (2.304) 0.213 (1.525) 0.102 (0.867) 0.023 (0.151) 0.035 (1.910)

0.082 (0.213) 0.242 (0.419) -0.158 (-0.284) -0.158 (-0.284) 0.045 (0.312) -0.029 (-0.160) 0.494 (2.920) 0.266 (1.864) 0.089 (0.486) 0.008 (0.390)

-0.167 (-0.362) -0.054 (-0.079) 0.397 (0.596) 0.189 (0.261) 0.660 (1.408) -0.229 (-1.053) 0.071 (0.353) 0.256 (1.504) 0.200 (0.907) 0.010 (0.373)

-0.171 (-0.499) 0.037 (0.072) -0.224 (-0.452) 0.061 (0.114) 0.224 (0.642) -0.006 (-0.037) 0.049 (0.327) 0.186 (1.468) -0.075 (-0.458) -0.006 (-0.317)

R2

0.484

0.480

0.184

0.363

0.400

0.306

0.370

0.214

0.103

D(YL(-1)) D(PL(-1)) D(AL(-1)) D(E(-1)) D(W(-1)) D(K(-1)) D(OP(-1)) D(U(-1))

Table 6. Vector Error Correction base regression: Model 1 Notes: Students’ t is in parentheses.

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Note2: Model (2) First cointegration equation Model 2 Prod = 0.659 − 0.565 YL + 0.494 PL + 0.70 AL + 0.620 E − 0.143 W + 0.202 K + 0.043OP + 0.3176U + 0.074 DATE + Zt 6.58 

D (Prod) Error Correction term ECT1

2.72 

D (YL)

3.56

D (PL)

11.41

D (AL)

-0.535 -0.151 -0.110 0.154 (-4.500) (-2.008) (-0.901) (1.621)

5.79 

D (E)

10.95

D (W)

C

0.148 (1.780) (0.336) (2.495) 0.194 (1.514) 0.235 (1.735) 0.027 (0.339) -0.020 (-0.497) -0.020 (-0.563) -0.018 (-0.575) -0.019 (-0.250) -0.003 (-0.104) -0.002 (-0.458)

0.123 (0.913) 0.0173 (0.079) -0.499 (-2.402) -0.316 (-1.436) 0.018 (0.143) -0.089 (-1.358) -0.052 (-0.888) 0.020 (0.389) 0.078 (0.631) -0.038 (-0.709) 0.0125 (1.525)

0.004 (0.038) 0.124 (0.732) 0.501 (3.102) 0.102 (0.597) -0.090 (-0.880) 0.084 (1.645) -0.021 (-0.471) 0.021 (0.523) -0.097 (-1.016) 0.050 (1.200) -0.009 (-1.494)

-0.046 (-0.297) 0.107 (0.421) -0.293 (-1.216) -0.107 (-0.421) 0.497 (3.241) 0.034 (0.448) 0.001 (0.017) -0.038 (-0.633) 0.218 (1.525) -0.104 (-1.676) 0.016 (1.709)

-0.258 (-0.826) -0.194 (-0.385) 0.137 (0.287) -0.543 (-1.067) -0.011 (-0.038) 0.346 (2.275) 0.223 (1.647) 0.111 (0.913) 0.136 (0.480) -0.053 (-0.430) 0.035 (1.889)

R2

0.568

0.476

0.208

0.347

0.441

0.309

D(YL(-1)) D(PL(-1)) D(AL(-1)) D(E(-1)) D(W(-1)) D(K(-1)) D(OP(-1)) D(U(-1)) D(DATE (-1))

D (K)

0.054 -0.019 0.212 (0.379) (-0.069) (0.615)

0.0182 (0.138) 0.079 (0.375) -0.307 (-1.523) -0.419 (-1.959) -0.644 (-5.019) -0.051 (-0.812) -0.061 (-1.072) 0.019 (0.379) 0.179 (1.493) -0.108 (-2.090) 0.056 (7.078)

Regressors D(Prod (-1))

9.38

3.01

3.36 

D (OP)

D (U)

D (DATE)

1.026 (2.491)

0.447 (1.485)

1.294 (1.875)

-0.162 -0.615 -0.045 0.145 (0.381) (-0.100) (-0.490) (-0.807) 0.371 0.217 0.396 0.323 (0.524) (0.539) (0.403) (0.301) 0.218 -0.179 0.470 -0.190 (0.186) (-0.350) (0.673) (-0.324) -0.150 0.018 0.103 0.015 (0.024) (0.139) (0.034) (-0.120) 0.208 0.193 0.467 0.024 (0.065) (1.049) (0.594) (0.279) -0.014 -0.152 -0.248 -0.033 (-0.183) (-1.120) (-0.090) (-0.411) 0.215 0.034 -0.002 0.462 (2.804) (-0.011) (0.238) (0.652) 0.493 0.213 0.283 0.262 (1.761) (1.589) (1.639) (1.651) -0.203 0.011 0.145 0.111 (0.320) (0.351) (0.037) (-0.292) 0.143 -0.041 0.017 -0.019 (-0.126) (0.095) (-0.311) (0.475) 0.016 -0.005 0.013 0.011 (0.512) (0.489) (-0.261) (0.363) 0.365

0.207

0.127

0.137

Table 7. Vector Error Correction: Model 2 Notes: Students’ t is in parentheses.

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FRINI, O., JEDIDIA, K. B.  POPULATION AGE STRUCTURE CHANGE AND LABOUR PRODUCTIVITY: EVIDENCE FROM TUNISIA

18


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FRINI, O., JEDIDIA, K. B.  POPULATION AGE STRUCTURE CHANGE AND LABOUR PRODUCTIVITY: EVIDENCE FROM TUNISIA

PROMENE U STAROSNOJ STRUKTURI POPULACIJE I RADNA PRODUKTIVNOST: PRIMER TUNISA

Rezime: Oslanjajući se na makroekonomsku perspektivu, ovaj rad analizira uticaj starosne dobi populacije na produktivnost u poslu. U vezi sa tim, posmatrani su efekti nivoa učešća radne snage kroz tri starosne grupe – pripadnici mlađe populacije (15-29 godina), oni koji su dosegli punu zrelost (30-49 godina), starija populacija (50-64 godina). Nivo učešća radne snage – a po osnovu godina, posmatran je na način da se porede radno aktivni pripadnici iste starosne dobi. Koristeći podatke iz Tunisa, koji se odnose na vremenski okvir 1965-2014. godine, metod kointegracije je potvrdio dugoročni odnos sa procesom progresivnog prilagođavanja, na putu ka uspostavljanju ravnoteže. Za razliku od ishoda do kojih dovodi konvencionalni pristup, profil produktivnosti zasnovan na parametru starosne dobi, nije dobio obrnuti U-oblik. Radna produktivnost smanjivala se kada su u pitanju mladi radnici, rasla za one u zrelom dobu, te nastavila da raste – kada su u pitanju pripadnici starije populacije. U skladu sa tim, starenje populacije nije uticalo na učinak u okvirima tržišta rada u Tunisu. Otuda, kako bi se pospešila produktivnost, ali i unapredio ekonomski rast zemlje, preporučljivo je odlaganje penzionisanja populacije nakon šezdesete godine.

Ključne reči: promene u starosnoj strukturi populacije, nivo učešća radne snage, radna produktivnost, model korigovanja greške, Tunis.

19


EJAE 2019, 16(1): 20-29 ISSN 2406-2588 UDK: 336.71:[621.395.721.5:004.77 336.717:336.745 DOI: 10.5937/EJAE15-19381 Original paper/Originalni naučni rad

ANTECEDENTS OF MOBILE BANKING: UTAUT MODEL Jovana Savić*, Aleksandra Pešterac Faculty of Economics, University of Kragujevac, PhD students Kragujevac, Serbia

Abstract: The development of modern information and communication technologies enabled banks to rely on mobile banking as an important distribution channel in their businesses. Given that investments in the development of mobile banking systems are extremely high, knowledge of which factors affect the intentions of individuals to use mobile banking services can be of great importance. For this purpose, empirical research was conducted and 313 respondents were surveyed in the territory of Sumadija, Central Serbia. The collected primary data were analyzed using the statistical software SPSS v. 20. To examine the factors in the work, the UTAUT model (Unified Theory of Acceptance and Use of Technology) was used. The results of empirical research indicate that all components of the UTAUT model have statistically significant influence on intention to use mobile banking, with performance expectancy singled out as the most important antecedent, while effort expectancy has the weakest impact. The paper confirms the success of the UTAUT model for testing mobile banking antecedents, and gains new insights regarding the intention of using mobile banking in Serbia that can serve for managerial purposes.

Article info: Received: November 1, 2018 Correction: November 15, 2018 Accepted: December 10, 2018

Keywords: modern technologies, mobile banking, intention to use mobile banking, UTAUT model.

INTRODUCTION The rapid development of modern information technology and an increase in the number of mobile users have caused the emergence of a new trend in banking operations, known as mobile banking. Mobile banking was developed as an extension of Internet banking, and is based on the use of modern mobile technology to provide clients with various banking and financial services (Yao & Zhong, 2011). On the other hand, mobile banking is a part of mobile commerce, and can therefore be defined as the evolution of the e-commerce paradigm from fixed line networks to wireless data networks (Samudra 20

*E-mail: jsavic@kg.ac.rs


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& Phadtare, 2012, p. 51). Using the benefits of mobile devices, mobile banking allows clients to conduct banking transactions at any time and from any place. Activities that can be performed by mobile banking include paying bills, transferring money, finding ATM locations, information inquiry, account management, etc. (Afshan & Sharif, 2016). Providing quality and diverse mobile banking services to clients is a priority for today’s banks. However, since mobile banking implies the development of complex systems that require very high investments, for the banking sector it is particularly important to learn about the factors that influence the intentions of clients to use mobile banking services, as it can help them when deciding on investments in mobile banking. As mobile banking is a trend in the banking industry that is still developing, especially in the territory of Serbia, the number of mobile banking users is still small (Yao & Zhong, 2011; Alalwan et al., 2017). Stated reasons for this include mistrust in the security of service delivery, risks, the danger of fraud, lack of awareness, and technical issues during the realization of banking transactions (Sanader, 2014; Bhatt & Bhatt, 2016). In this regard, it is concluded that new research on the antecedents of mobile banking is necessary in order to provide the banking sector with better and more complete information that can serve as a good basis for making optimal business decisions. Starting from the abovementioned, the paper presents the results of the empirical research conducted in order to identify key antecedents behind the intentions of clients in the territory of Sumadija, Central Serbia, to use mobile banking services. The UTAUT model was used as the initial research model, which has become very popular in research literature for testing the process of adopting technology, but which, to the authors' knowledge, was not used too much in the research of domestic authors when it comes to segment of mobile banking. Therefore, the contribution of the work is also reflected in the practical testing of the UTAUT model in this segment, apart from the knowledge related to the antecedents of mobile banking.

LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT Understanding the factors that influence technology acceptance has become the subject of research for a large number of authors. For these purposes, based on psychological and sociological theories, many models have been developed, where the most widely used was the technology acceptance model. Using the foundations of research by authors in the field of technology acceptance, Venkatesh et al. (2003) developed the UTAUT model (Unified Theory of Acceptance and Use of Technology). The UTAUT model was created as a result of the integration of eight models used in previous research to explain the process of adopting technology, such as The Theory of Reasoned Action, The Technology Acceptance Model, The Motivational Model, The Theory of Planned Behavior, The Combined Theory of Planned Behavior/Technology Acceptance Model, The Model of Personal Computer Utilization, The Diffusion of Innovation Theory and Social Cognitive Theory (Samudra & Phadtare, 2012). The UTAUT model has attracted the attention of a large number of researchers and the success of its application has been confirmed in plenty of empirical research (Venkatesh et al., 2003, 2012; Venkatesh & Zhang 2010; Yu, 2012; Alkhunaizan & Love, 2012; Baptista & Oliveira, 2015). Its importance is reflected in not only allowing to analyze the most important antecedents of technology use, but also in allowing the analysis of moderators that amplify or constrain the effects of core determinants (Yu, 2012). The UTAUT model includes four constructs: performance expectancy, effort expectancy, social influence, and facilitating conditions (Venkatesh et al., 2011). In addition, the UTAUT model includes gender, age, experience and voluntariness of use as moderating factors, which explain the behavioral differences of different groups of people (Min et al., 2008). Performance expectancy is defined as the degree to which using a technology will provide benefits to consumers in performing certain activities (Adapted from: Venkatesh et al., 2003). Performance 21


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SAVIĆ, J., PEŠTERAC, A.  ANTECEDENTS OF MOBILE BANKING: UTAUT MODEL

expectancy actually measures the degree to which a person believes that using mobile banking services will help them in performing banking transactions (Adapted from: Tarhini et al., 2016). Oliveira et al. (2014) and Sarfaraz (2017) have come to the conclusion that performance expectancy has a total effect on behavioral intentions towards mobile banking. Baptista & Oliveira (2015) and Basri (2018) have empirically shown that mobile banking users believe that performance expectancy is one of the most important antecedents of behavioral intention. In this regard, the following hypothesis will be tested in this paper: H1: Performance expectancy has a statistically significant effect on behavioral intention to use mobile banking services. The second construct which builds UTAUT model is effort expectancy. Venkatesh et al. (2003, p. 450) define effort expectancy as the degree of ease associated with the use of the system. The easier the mobile banking is to use, the greater the likelihood that clients will use it to conduct their banking transactions. In their research, Bankole et al. (2011), exploring the antecedents of mobile banking in Nigeria, have proven that the effort expectancy has a positive impact on the behavioral intention to use mobile banking services. Bhatiasevi (2016) came to the same conclusion in his research conducted to identify the factors leading to the adoption of mobile banking in Thailand, as did Albashrawi et al. (2017) by observing a sample of U.S. bank clients. Starting from the above, one can assume the following: H2: Effort expectancy has a statistically significant effect on behavioral intention to use mobile banking services. The following construct refers to social influence. Social influence refers to the degree to which an individual perceives that important others believe he or she should use the new system (Venkatesh et al., 2003, p. 451), and is particularly important in the early stages of new technology development when most users do not have experience or information about technology, and therefore rely on public opinion (Marinkovic & Kalinic, 2017). In fact, it concerns the influence of people from the immediate surroundings of the individual (family, friends, superiors) on his or her perceptions and behavior related to a certain activity. Many studies have confirmed that social influence is directly related to the intention of an individual to use mobile banking services (Bhatiasevi, 2015; Tan & Leby Lau, 2016). Moreover, in some research this factor has been singled out as the most significant when it comes to the intention of using mobile banking (Venkatesh & Zhang 2010; Yu, 2012). On the basis of the above results, the hypothesis is posed: H3: Social influence has a statistically significant effect on behavioral intention to use mobile banking services. The last, but not the least important construct are facilitating conditions. Facilitating conditions are defined as the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of the system (Venkatesh et al., 2003, p. 453). Since the use of mobile banking services requires the availability of appropriate resources, knowledge, and technology infrastructure, it is logical to assume that of these conditions a considerable extent depends the intention of an individual to use mobile banking. This assumption was empirically proven by Zhou et al. (2010), Witeepanich et al. (2013), as well as Afshan & Sharif (2016). Consequently, the following hypothesis will be tested in the paper: H4: Facilitating conditions have a statistically significant effect on behavioral intention to use mobile banking services. 22


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RESEARCH METHODOLOGY The conducted empirical research is based on the primary data collected on the territory of Sumadija, Central Serbia, by interviewing 313 respondents with different demographic characteristics. The questionnaire technique was used to collect data, and was distributed to respondents personally and online in the period from August 17, 2018 until September 10, 2018.1 The questionnaire includes 18 statements measured on the seven-point Likert scale related to the antecedents of mobile banking according to the UTAUT model and intention to use mobile banking services, where respondents rounded out a score of 1 (I absolutely disagree) to 7 (I absolutely agree) to express their agreement with given statements. Statements are taken from relevant domestic and foreign literature, and are grouped in five variables. Along with statements, the questionnaire includes four questions related to respondent data. The analysis of the collected primary data was performed using the statistical software SPSS v. 20, where the descriptive statistical analysis for sample structure (Table 1), reliability analysis by calculating Cronbach’s alpha coefficient and correlation analysis were conducted. Starting from the work of Venkatesh et al. (2003), who used multiple regression in their research to examine the impact of constructs on behavior intentions, the same analysis was carried out in this paper, also using SPSS v. 20. Demographic characteristics Gender

Age

Level of education

Working status

Number

Percentage

Female

180

57.5%

Male

133

42.5%

18-24

69

22%

25-44

158

50.5%

45-54

60

19.2%

55 and more

26

8.3%

Secondary education

95

30.4%

Higher education

59

18.8%

University degree

159

50.8%

Employee

153

48.9%

Unemployee

81

25.9%

Student

67

21.4%

Pensioner

12

3.8%

Table 1. Sample structure Source: Authors

Based on the results obtained, it is evidenced that the majority of the sample are female respondents (57.5%), while men represent 42.5% of the sample. Respondents are predominantly aged 25 to 44 years (50.5% of the sample), the percentage of respondents aged 18 to 24 (22%) and 45 and 54 years (19.2%) is approximately equal, while the smallest percentage of respondents are those aged 55 and up (8.3%). More than half of the sample includes respondents who have obtained a university degree (50.8%), followed by those with secondary education (30.4%) and the smallest amount being 1 Raw data used for analysis are available at the following URL address: https://data.mendeley.com/datasets/dhh4mmw3f3/1/ files/95d39de7-22ad-4b3e-945f de84a4f3329e/Antecedents%20of%20mobile%20banking%20%20UTAUT%20model. xlsx?dl=1.

23


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those with higher education (formal education between secondary education and university degree) (18.8%). When it comes to working status, as the last demographic characteristic, the sample has the highest number of employed respondents (48.9%), followed by unemployed respondents (25.9%), with fewer students (21.4%), and pensioners representing the smallest group (3.8%).

RESEARCH RESULTS The reliability analysis was performed to test the reliability of the UTAUT variables, as well as the intention to use, i.e., the dependent variable. The results of the analysis are shown in Table 2: Variable

Cronbach’s Alpha

Performance expectancy

0.895

Effort expectancy

0.954

Social influence

0.954

Facilitating conditions

0.937

Intention to use

0.973

Table 2. Reliability analysis Source: Authors

Table 2 gives the values of the Cronbach’s Alpha reliability coefficient. Since all the values obtained are greater than 0.7, it is concluded that all observed variables are reliable, with the highest reliability of the variable intention to use, with performance expectancy being the variable with the lowest degree of reliability. The correlation analysis determines the degree of linear dependence between the variables of the research expressed as the value of the Pearson correlation coefficient. The values of this coefficient are shown in Table 3: Performance expectancy

Effort expectancy

Social influence

Facilitating conditions

Intention to use

1

0.815**

0.527**

0.668**

0.754**

Effort expectancy

0.815**

1

0.555**

0.755**

0.749**

Social influence

0.527**

0.555**

1

0.706**

0.709**

Facilitating conditions

0.668**

0.755**

0.706**

1

0.764**

Intention to use

0.754**

0.749**

0.709**

0.764**

1

Performance expectancy

** Correlation is significant at the 0.01 level Table 3. Correlation analysis Source: Authors

The results of the correlation analysis indicate that there is a statistically significant correlation, with a probability of 99%, among all pairs of variables. A strong correlation exists between the majority of variables (performance expectancy and effort expectancy, performance expectancy and facilitating conditions, performance expectancy and intention to use, effort expectancy and facilitating conditions, 24


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effort expectancy and intention to use, social influence and facilitating conditions, social influence and intention to use, facilitating conditions and intention to use), while a moderate correlation occurs between the variables of performance expectancy and social influence, and between effort expectancy and social influence. Regression analysis is carried out to test the set of research hypotheses that relate to the impact of independent variables (variables of the UTAUT model) on the dependent variable Intention to use. The results of this analysis are shown in Table 4: Independent variable

β

Sig

VIF

Performance expectancy

0.328

0.000

3.060

Effort expectancy

0.149

0.010

3.910

Social influence

0.296

0.000

2.017

Facilitating conditions

0.224

0.000

3.236

Rsquare (R2)=0.740; F=218.862 (p<0.05) Table 4. Multiple regression analysis (dependent variable Intention to use) Source: Authors

Based on the value of the VIF coefficient, it can be seen that the data are suitable for carrying out multiple regression analysis (VIF less than 5). The value of the Rsquare determination coefficient indicates that 74% of the variability of the dependent variable intention to use is explained by the given regression model. Sig value from the third column of the table shows that all independent UTAUT model variables have a statistically significant effect on the clients’ intention to use mobile banking services, with the strongest impact of the variable performance expectancy (β = 0.328, p<0.05), followed by the variable social influence (β = 0.296, p<0.05), followed by facilitating conditions (β = 0.224, p<0.05), with the weakest effect being that of effort expectancy (β = 0.149, p<0.05).

CONCLUSIONS The aim of the conducted research is to identify the key antecedents of the intention of clients to use mobile banking services, emphasizing the components of the UTAUT model and their influence on intention to use mobile banking. Research hypotheses were tested using a multiple regression analysis, whose results indicate that all four components of the UTAUT model (performance expectancy, effort expectancy, social influence, and facilitating conditions) determine intention to use mobile banking, and it is therefore concluded that all the tested hypotheses have been proven. The performance expectancy has been highlighted as the strongest antecedent, which is consistent with the results of previous research (Baptista & Oliveira, 2015; Basri, 2018), while the weakest antecedent is that of effort expectancy. The significance of the conducted research is based on the fact that its results enable us to gain new relevant knowledge of mobile banking antecedents, a good starting point for future research has been created and the UTAUT model has been practically tested in this segment. On the basis of the obtained results, bank managements can make optimal business decisions related to investments in the development of mobile banking. Research limitations relate to a small sample of respondents, with a sample limited to clients in Central Serbia, and neglecting the moderator’s effects when it comes to the demographic characteristics of the respondents. Furthermore, the multiple regression analysis is used for testing the relationships of independent and dependent variables. It is therefore recommended to increase the sample of respondents for future papers, since results cannot be generalized, 25


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as the sample structure does not represent the Serbian average, from demographic and educational point of view. For future research, it might be desirable to include demographic characteristics, such as gender and age, in the research model in order to examine their moderator effect. When it comes to testing relationships between variables, the SEM (Structural Equation Modeling) approach would be more appropriate for analysis, since multiple regression analysis has limitations, such as the use of a small number of indicators, omission of measurement errors, one or more independent variables are included in the analysis but only one dependent variable etc. (Jeon, 2015). It is desirable to conduct a t-test or one-way Anova, in order to obtain more precise results when considering the demographic characteristics of the respondents. In addition, future research may rely on an extended version of the research model, by adding variables such as trust, perceived risk, dimensions of national culture, and so on. The banks are recommended to put the greatest emphasis on the performances that clients expect when it comes to mobile banking to, during the promotion of their services take into account the social influences to which their target markets are exposed, and to use those influences to make a more convincing promotional message. It is also necessary to provide good technical infrastructure and support, in order for clients to use mobile banking services without any difficulties. Finally, as effort expectancy has proven to be an important antecedent of the intention to use mobile banking, it is recommended to let clients know about the availability of the appropriate instructions or info lines for free calls to inform themselves about the correct way to access and use the mobile banking system.

REFERENCES Afshan, S., & Sharif, A. (2016). Acceptance of mobile banking framework in Pakistan. Telematics and Informatics, 33(2), 370-387. DOI:10.1016/j.tele.2015.09.005 Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management, 37(3), 99-110. DOI:10.1016/j.ijinfomgt.2017.01.002 Albashrawi, M., Kartal, H., Oztekin, A., & Motiwalla, L. (2017). The Impact of Subjective and Objective Experience on Mobile Banking Usage: An Analytical Approach. In Proceedings of the 50th Hawaii International Conference on System Sciences. HICSS Conference Office. 4-7 January 2017 (pp.1161-1170). DOI:10.24251/ hicss.2017.137 Alkhunaizan, A., & Love, S. (2012). What drives mobile commerce?, An empirical evaluation of the revised UTAUT model. International Journal of Management and Marketing Academy, 2(1), 82-99. Bankole, F. O., Bankole, O. O., & Brown, I. (2011). Mobile Banking Adoption in Nigeria. The Electronic Journal of Information Systems in Developing Countries, 47(1), 1-23. DOI:10.1002/j.1681-4835.2011.tb00330.x Baptista, G., & Oliveira, T. (2015). Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators. Computers in Human Behavior, 50, 418-430. DOI:10.1016/j. chb.2015.04.024 Basri, S. (2018). Determinants of adoption of mobile banking: evidence from rural Karnataka in India. International Journal of Trade and Global Markets, 11(1/2), 77-86. DOI:10.1504/ijtgm.2018.092490 Bhatiasevi, V. (2016). An extended UTAUT model to explain the adoption of mobile banking. Information Development, 32(4), 799-814. DOI:10.1177/0266666915570764 Bhatt, A., & Bhatt, S. (2016). Factors Affecting Customer‘s Adoption of Mobile Banking Services. Journal of Internet Banking and Commerce, 21(1), 1-22. Jeon, J. (2015). The strengths and limitations of the statistical modeling of complex social phenomenon: Focusing on SEM, path analysis, or multiple regression models. International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 9(5), 1559-1567. DOI:10.5281/zenodo.1105869

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Marinkovic, V., & Kalinic, Z. (2017). Antecedents of customer satisfaction in mobile commerce: exploring the moderating effect of customization. Online Information Review, 41(2), 138-154. DOI:10.1108/oir-112015-0364 Min, Q., Ji, S., & Qu, G. (2008). Mobile commerce user acceptance study in China: A revised UTAUT model. Tsinghua Science and Technology, 13(3), 257-264. DOI:10.1016/s1007-0214(08)70042-7 Oliveira, T., Faria, M., Thomas, M.A., & Popovič, A. (2014). Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM. International Journal of Information Management, 34(5), 689-703. DOI:10.1016/j.ijinfomgt.2014.06.004 Samudra, M.S., & Phadtare, M. (2012). Factors Influencing the Adoption of Mobile Banking with Special Reference to Pune City. ASCI Journal of Management, 42(1), 51-65. DOI:10.12691/jbms-6-1-2 Sanader, D. (2014). Mobile banking: New trend in thecontemporary banking sector. Bankarstvo, 43(5), 86-109. DOI:10.5937/bankarstvo1405086s Sarfaraz, J. (2017). Unified Theory of Acceptance and Use of Technology (UTAUT) Model-Mobile Banking. Journal of Internet Banking and Commerce, 22(3), 1-20. Tarhini, A., El-Masri, M., Ali, M., & Serrano, A. (2016). Extending the UTAUT model to understand the customers’ acceptance and use of internet banking in Lebanon. Information Technology and People, 29(4), 830-849. DOI:10.1108/itp-02-2014-0034 Tan, E., & Leby, L. J. (2016). Behavioural intention to adopt mobile banking among the millennial generation. Young Consumers, 17(1), 18-31. DOI:10.1108/yc-07-2015-00537 Venkatesh, V., Morris, G.M., Davis, B.G., & Davis, D.F. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425-478. DOI:10.2307/30036540 Venkatesh, V., & Zhang, X. (2010). Unified Theory of Acceptance and Use of Technology: U.S. Vs. China. Journal of Global Information Technology Management, 13(1), 5-27. DOI:10.1080/1097198x.2010.10856507 Venkatesh, V., Thong, J.Y.L., Chan, F. K. Y., Hu, P. J. H., & Brown, S. A. (2011). Extending the two-stage information systems continuance model: incorporating UTAUT predictors and the role of context. Information Systems Journal, 21(6), 527-555. DOI:10.1111/j.1365-2575.2011.00373.x Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157-178. DOI:10.2307/41410412 Witeepanich, C., Emklang, N., Matsmak, J., Kanokviriyasanti, P., & Chanvarasuth, P. (2013). Understanding the Adoption of Mobile Banking Services: an Empirical Study. In Proceedings of the 4th International Conference on Engineering, Project, and Production Management. Bangkok, Thailand: The Sukosol. 2013 (pp. 282-291). October; 23-25. Yao, H., & Zhong, C. (2011). The Analysis of Influencing Factors and Promotion Strategy for the Use of Mobile Banking. Canadian Social Science, 7(2), 60-63. DOI:10.3968/j.css.1923669720110702.008 Yu, C. (2012). Factors affecting individuals to adopt mobile banking: Empirical evidence from the UTAUT model. Journal of Electronic Commerce Research, 13(2), 104-121. Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760-767. DOI:10.1016/j.chb.2010.01.013

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APPENDIX Variable

Statements

Source

Performance Expectance

1. Using mobile banking services helps me accomplish things more quickly. 2. Using mobile banking would make it easier for me to carry out my tasks. 3. I find mobile banking services useful in my daily life.

Addapted from: Al-Jabri (2015); Venkatesh et al., (2012)

Effort Expectance

4. I find mobile banking services easy to use. 5. Learning how to use mobile banking services is easy for me. 6. I think the interaction with mobile banking does not require a lot of mental effort. 7. Mobile banking services are easily accessible.

Addapted from: Samudra, Phadtare, 2012 Venkatesh et al., (2012); Al-Jabri (2015); Gašević et al., (2016)

Social Influence

8. People who are important to me think that I should use mobile banking services. 9. People who are familiar with me think that I should use mobile banking. 10. People who influence my behaviour think that I should use mobile banking services. 11. Most people surrounding with me use mobile banking.

Addapted from: Venkatesh et al., (2012); Yu (2012)

Facilitating Conditions

12. My living environment supports me to use mobile banking. 13. My working environment supports me to use mobile banking. 14. I can get help from others when I have difficulties using mobile banking services.

Addapted from: Venkatesh et al., (2012); Yu (2012)

Behavioral Intention

15. I intend to use mobile banking. 16. I would use mobile banking. 17. I would see myself using mobile banking for handling my banking transactions. 18. I think it is a wise idea to use mobile banking services.

Yu (2012); Al-Jabri (2015); Dasgupta et al., (2011)

Table 5. Variables and corresponding statements Source: Authors

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SAVIĆ, J., PEŠTERAC, A.  ANTECEDENTS OF MOBILE BANKING: UTAUT MODEL

ANTECEDENTE MOBILNOG BANKARSTVA: UTAUT MODEL

Rezime: Razvoj savremenih informaciono-komunikacionih tehnologija omogućio je bankama da se u svom poslovanju oslone na mobilno bankarstvo kao važan distributivni kanal. S obzirom na to da su ulaganja u razvoj mobilnih bankarskih sistema izuzetno velika, saznanja o tome koji faktori utiču na namere pojedinaca da koriste usluge mobilnog bankarstva mogu biti od velikog značaja. U te svrhe, sprovedeno je empirijsko istraživanje i anketirano je 313 ispitanika na teritoriji Šumadije, centralna Srbija. Prikupljeni primarni podaci analizirani su u statističkom softveru SPSS v. 20. Za ispitivanje faktora u radu se koristi UTAUT model (eng. The unified theory of acceptance and use of technology). Rezultati empirijskog istraživanja ukazuju na to da sve komponente UTAUT modela imaju statistički značajan uticaj na nameru korišćenja mobilnog bankarstva, pri čemu su se kao najvažnije antecedente izdvojile očekivane performanse, dok najslabiji uticaj ima očekivani napor. U radu se potvrđuje uspešnost primene UTAUT modela za ispitivanje antecedenti mobilnog bankarstva i stečena su nova saznanja u vezi sa namerom korišćenja mobilnog bankarstva u Srbiji koja mogu poslužiti u menadžerske svrhe.

Ključne reči: savremene tehnologije, mobilno bankarstvo, namera korišćenja mobilnog bankarstva, UTAUT model.

29


EJAE 2019, 16(1): 30-40 ISSN 2406-2588 UDK: 338.23:620.92(669)"1991/2014" 331.5.024.5:620.92 DOI: 10.5937/EJAE15-19730 Original paper/Originalni nauÄ?ni rad

CAUSAL LINK BETWEEN EMPLOYMENT AND RENEWABLE ENERGY CONSUMPTION: EVIDENCE FROM NIGERIA Mukhtar Wakil Lawan*, Matthew Oladapo Gidigbi School of Management and Information Technology, Modibbo Adama University of Technology, Yola, Nigeria

Abstract: The paper examined the causal link between employment and renewable energy consumption in Nigeria for the period of 1991 to 2014 using annual time series data. The Toda-Yamamoto Granger Causality approach was used for causal analysis and Johansen cointegration technique to verify the long-run relationship between model variables. The results supported the conservation hypothesis with unidirectional causality from employment to energy consumption, but no evidence of cointegration was established. The study recommends that Nigeria should review its energy policy in order to promote private investment in renewable energy projects towards finding a sustainable solution to help solve the energy crisis, create employment opportunities, and minimise environmental pollution peculiar to long-term use of fossil fuel.

Article info: Received: November 30, 2018 Correction: February 8, 2019 Accepted: March 21, 2019

Keywords: renewable, causality, employment, cointegration, energy policy.

INTRODUCTION For more than a decade, the Nigerian government has been pushing for the adaptation of renewable energy into its energy generation mix in response to the persistent energy crisis in the power sector. The existing energy policy under the Multi-Year Tariff Order (MYTO) for the power sector and existing energy laws in the petroleum industry has failed to attract the desired level of investment. The existing gas-fired power plants are largely energy inefficient, and face recurrent gas supply shortages from gas supply hubs situated in the gas-rich Niger Delta region of the country. Rising concerns and debates on the importance of environmental conservation and the reduction of air pollution, particularly from gas flaring, have also provided a strong basis for the government to propel investment in renewable energy. The government strongly believes that this strategy will also provide employment opportunities, while helping to address rising environmental concerns associated with global warming. 30

*E-mail: mukhtar.lawan@mautech.edu.ng


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LAWAN, M. W., GIDIGBI , M. O.  CAUSAL LINK BETWEEN EMPLOYMENT AND RENEWABLE ENERGY CONSUMPTION: EVIDENCE FROM NIGERIA

Numerous earlier country-specific studies for Nigeria analysed the causality pattern between economic growth and aggregate energy/electricity consumption, and some used the traditional Granger causality approach. Therefore, this paper will contribute to the existing literature by analysing causal linkages between renewable energy consumption and employment by using the Toda-Yamamoto (1995) augmented VAR approach due to its statistical advantages over the order of integration and cointegration properties of time series.

LITERATURE REVIEW Renewable energy sources, like solar, wind, and water, are distinct from fossil fuels due to the absence of the carbon dioxide (CO2) gas notably identified by a number of studies (Nisbet & Myers, 2007; Schelling, 1992; Warrick & Farmer, 1990) as one of the green gases that contribute to global warming. Ariouri et al. (2014) have identified six economic effects that help in the understanding of the dynamic links between energy consumption and employment. The links consisted of price, substitution, democratic, structural, income, and technological effects. According to Papapetrou (2001), the price effect relates to the impact of external shocks on the prices of energy sources, like oil, gas, and coal, which can stimulate economic growth in certain economies and improve employment levels. Notable examples here are most of the OPEC member countries, which rely extensively on the export of energy resources for government revenue. The substitution effect explains how shortages or constraint in energy supply may be substituted by labour in certain macroeconomic production scenarios and vice versa. The demographic effect explains the impact of a population boom on the short-run domestic demand for energy from households, and long-run aggregate demand for energy due to changes in the level of the work-force within an economy. The structural effect describes an impact in the form of less energy consumption due to transition from manufacturing to a service-driven economy with minimal impact on unemployment. However, Jespersen (1999) argues that this might not be attainable in economies that have an energy-intensive private service sector. The income effect relates to the simultaneous growth of employment and energy consumption, which may be at different rates usually preceded by rapid economic expansion. The more income households get through employment, the higher the demand for goods, services, and subsequently energy. In terms of technological effect, Çetin and Eğrican (2011) suggest that changes from traditional energy technology to modern energy technology, such as solar energy, can create direct and indirect employment opportunities depending on a nation’s development level. Several empirical studies have tested the direction of causality between energy consumption and economic growth/employment since its introduction in Granger (1969). These studies established support for at least one of four hypotheses; conservative, growth, feedback, and neutrality hypothesis. Bilgili and Ozturk (2015) in a study of G7 countries over 1980-2009 applied panel cointegration, conventional, and dynamic OLS to establish the positive effects of biomass energy consumption on economic growth. Ocal and Aslan (2013) applied the ARDL and Toda-Yamamoto causality approach using time series covering 1990-2010 for Turkey and found evidence of causal flow from economic growth to renewable energy consumption. Kahia, Aïssa and Lanouar (2017) analysed panel data covering the period 19802012 for MENA net oil importing countries using the panel vector error correction model (PVECM). The study found evidence of simultaneous causality between economic growth and both renewable and non-renewable energy use. Payne (2009) employed the Toda-Yamamoto causality test for U.S. data for the period 1949-2006 and found no evidence of a causal link between renewable, non-renewable energy consumption, and real GDP. However, the empirical results of causal studies have mostly been mixed with no dominant prevailing theory both for the case of developed and developing countries. Awokuse (2003) and Bahmani-Oskooee and Alse (1993) have suggested that variation in causal pattern is influenced by several factors including theoretical framework, model variables, foreign policy, country specific characteristics, time period, data frequency, and methodology adopted for the causality model. 31


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LAWAN, M. W., GIDIGBI , M. O.  CAUSAL LINK BETWEEN EMPLOYMENT AND RENEWABLE ENERGY CONSUMPTION: EVIDENCE FROM NIGERIA

The conservative, growth, feedback, and neutrality hypothesis are typically used to analyse the direction of causality between energy consumption and economic growth/employment. The growth hypothesis represents unidirectional causality from energy consumption to economic growth/employment. It suggests that increases in energy consumption lead to higher economic growth/employment level. A number of studies (Altinay & Karagol, 2005; Ighodaro, 2010; Inglesi-Lotz, 2016; Ozturk & Bilgili, 2015) found evidence in support of the growth hypothesis. The conservative hypothesis indicates a one-way link from economic growth/employment to energy consumption. It implies that higher economic growth/employment will lead to more energy consumption. The empirical findings of some studies (Lise, 2007; Matthew et al., 2018; Menyah & Wolde-Rufael, 2010; Ocal & Aslan, 2013; Sadorsky, 2009) supported the conservative hypothesis. The feedback hypothesis indicates a bidirectional causal link between economic growth/employment and energy consumption. Numerous studies (Apergis & Payne, 2011; Chang et al., 2001; Ebohon, 1996; Kahia et al., 2017; Koçak & Sarkgünes, 2017; Lin & Moubarak, 2014; Osigwe & Arawomo, 2015; Sebri & Ben-Salha, 2014) established support for the feedback hypothesis. The neutrality hypothesis indicates the absence of causality in either direction between economic growth/employment and energy consumption. Some past empirical studies (Akpan & Akpan, 2012; Esso, 2010; Menegaki & Tugcu, 2016, 2017; Payne, 2009; Vaona, 2012) provided support for the neutrality hypothesis for the countries under study. The use of Granger’s (1969) or Sim’s (1972) Granger causality approach in the early set of studies were particularly criticised due to use of non-stationary and sometimes cointegrated series. In such instances, the Wald test statistic under the null does not follow its usual asymptotic chi-square distribution. Hence, other techniques, such as the ARDL bounds testing, Johansen and Juselius (1990) cointegration technique, restricted VAR with imposed cointegration restrictions, instrumental regression, potential outcome framework, and the surplus-lag approach introduced by Toda and Yamamoto (1995) along with similar alternatives by Dolado and Lütkepohl (1996) and Bauer and Maynard (2012), were applied in later studies. The addition of surplus-lag in the causality model is meant to ensure the Wald test statistic follows its usual chi-square asymptotic null distribution in a non-stationary process. The approach also helps to minimise the pre-test bias from unit root and cointegration test at the cost of a decrease in model efficiency. Nonetheless, Clarke and Mirsa (2006) argued that the loss of power in the surplus-lag approach is less when compared to alternatives like VECM due to cointegration restrictions. The latter approach could lead to severe over-rejecting of the null hypothesis of Granger non-causality from preliminary test of cointegration. This paper focuses on analysing causal pattern, and employs the Toda-Yamamoto (1995) surplus-lag approach in the causality model.

METHODOLOGY The study used annual time series data1 for the period 1991 to 2014 obtained from the World Development Indicators (WDI) of the World Bank for the model variables of employment (EMP) and renewable energy consumption (REC). The EMP variable expressed in percentage measures the proportion of people aged 15 and above that are employed in Nigeria, while the REC variable measures renewable energy consumption as a percentage of total final energy consumption.

Order of Integration and Optimal Lag Selection The preliminary step for the Toda-Yamamoto (T-Y) Granger causality approach is to establish the maximum order of integration (dmax) for model variables and the appropriate lag length (k) for the VAR System. Hence, the order of integration for this study was established using the Augmented 1 Data set available at http://dx.doi.org/10.17632/3pp84yf7yf.1

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EJAE 2019  16 (1)  30-40

LAWAN, M. W., GIDIGBI , M. O.  CAUSAL LINK BETWEEN EMPLOYMENT AND RENEWABLE ENERGY CONSUMPTION: EVIDENCE FROM NIGERIA

Dicky-Fuller (ADF) test, while the appropriate lag length (k) for the VAR system was determined using four lag selection criteria. This consisted of the Akaike Information Criterion (AIC), Schwarz Information Criterion (SC), Hannan-Quinn Information Criterion (HQ), and the Final Prediction Error (FPE).

Model Diagnostic In order to ensure the VAR model is well specified, the serial correlation LM test is used to ensure the residuals are serially independent, and model dynamic stability is verified by analysing the inverse roots of the associated characteristic equation.

Cointegration Test Although the cointegration test does not affect the end result of the Toda-Yamamoto Granger causality approach, the study will use the Johansen and Juselius (1990) maximum likelihood cointegration technique. This helps to provide information on the potential long-run association between model variables, and serves as a useful check for our causality model, since cointegrated variables will be expected to exhibit at least a unidirectional causal pattern. The Johansen method relies on testing the likelihood ratio of the trace test (λtrace) and max eigenvalue test (λmax) to determine the number of cointegrating vectors for the VAR equation (1). ΔZ t = μ + Σip=-11 Гi ∆Z t -i + ПZ t - p + ε t

(1)

Where Zt is (n x 1) vector of I(1) variables, µ is (n x1) vector of constants, Γ and Π are (n x n) matrix of coefficients and parameters, ∆ is difference operator, and εt is (n x 1) vector of error terms.

λtrace = − T Σni = r +1 ln (1 −  i )

(2)

Where T stands for the number of observations usable, Ln is natural log, and  is characteristic root estimated (eigenvalue). The trace test in equation (2) test the null hypothesis that the rank of Π is less than or equal to r cointegrating vector(s).

λmax = − T ln (1 −  i )

(3)

The max eigenvalue test in equation (3) test the null hypothesis for the presence of exactly r cointegrating vectors in Zt in the VAR equation (1).

Granger Causality Model The Granger causality model is based on the Toda-Yamamoto (1995) approach. The causal test follows a standard asymptotic distribution for the Wald statistic regardless of the order of integration and cointegration between model variables (Bosupeng, 2016). The Granger non-causality model is expressed in equation (4) and (5).

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EJAE 2019  16 (1)  30-40

LAWAN, M. W., GIDIGBI , M. O.  CAUSAL LINK BETWEEN EMPLOYMENT AND RENEWABLE ENERGY CONSUMPTION: EVIDENCE FROM NIGERIA

EMPt= δ= + Σik +1dmax Ψ1i EMPt= + Σik +1dmax λ1i RECt −1 + μ1t 1 −1

(4)

RECt= δ= + Σik +1dmax Ψ 2i REC= + Σik +1dmax λ2i EMPt −1 + μ 2t 2 t −1

(5)

In equations (4-5), δ , ψ and λ are model parameters and k is the appropriate lag length while dmax is maximum integration order. The symbol µt is model uncorrelated error term while EMP and REC stand for variables of employment and renewable energy consumption respectively.

EMPIRICAL RESULTS AND DISCUSSION Unit Root Test and Optimal Lag Selection Results The results of the unit root test for determining the order of integration for variables of employment (EMP) and renewable energy consumption (REC) based on the ADF test is shown in table 1.

Variables

Level

5% Critical Value

First Difference

5% Critical Value

Remark

EMP

-1.59

-2.99

-3.38*

-3.00

I (1)

REC

-2.34

-2.99

-5.37*

-3.00

I (1)

Note: * denotes null rejection of non-stationarity at 5% level Table 1. Augmented Dickey-Fuller (ADF) Unit Root Test Results

The results in table 1 suggest that the variables of renewable energy consumption (REC) and employment (EMP) are both stationary after first differencing and thus, maximum order of integration (dmax) is 1. This also fulfils the condition to check for the presence of cointegration between the two variables. Lag

FPE

AIC

SC

HQ

0

0.086287

3.225445

3.324375

3.239086

1

0.037757*

2.392903*

2.689694*

2.433827*

2

0.047615

2.601179

3.095830

2.669384

3

0.050764

2.608780

3.301291

2.704268

4

0.077526

2.921383

3.811755

3.044153

5

0.089162

2.860121

3.948353

3.010174

Note: * indicates lag order selected by each criterion Table 2. VAR Lag Order Selection Results

The appropriate lag length (k) for the VAR causality model based on the four information criteria is lag 1 as shown in table 2 results. The model is also found to be dynamically stable and the residuals serially independent. Hence, the optimal lag for the Granger causality model, VAR (k + dmax) is 2.

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LAWAN, M. W., GIDIGBI , M. O.  CAUSAL LINK BETWEEN EMPLOYMENT AND RENEWABLE ENERGY CONSUMPTION: EVIDENCE FROM NIGERIA

Model Diagnostic Test Results The result of the serial correlation LM test is shown in table 3 and the inverse roots of the characteristic equation is shown in figure 1. Lag

LRE* stat

Prob.

Rao F-stat

Prob.

1

6.719831

0.1515

1.825352

0.1520

2

0.525369

0.9710

0.127969

0.9710

3

0.981194

0.9126

0.240892

0.9127

4

3.255813

0.5160

0.831700

0.5164

5

5.162243

0.2711

1.363847

0.2716

6

3.279710

0.5122

0.838156

0.5126

Note: * Edgeworth expansion likelihood ratio statistic Table 3. VAR Residual Serial Correlation LM Test

The results in table 3 indicates that serial correlation is removed from the model at 5% level.

Figure 1. Inverse Roots of AR characteristic Polynomial

The results in figure 1 established that the VAR model is dynamically stable as all points lie within the unit circle.

Cointegration Test Results The results of the Johansen cointegration test for the two I(1) model variables are shown in table 4.

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Vector

Trace Statistics

Critical Value

Probability

Max-Eigen Statistics

Critical Value

Probability

Ho

H1

λtrace

5%

P

λmax

5%

P

r=0

r >0

12.22

15.49

0.15

10.26

14.26

0.20

r <= 1

r >1

1.97

3.84

0.16

1.97

3.84

0.16

Note: r indicates number of cointegrating vector (s) Table 4. Johansen Cointegration Test Results

The trace and Max-Eigen statistics in table 4 indicate the absence of cointegration between the two variables of REC and EMP. The null hypothesis of no cointegration cannot be rejected at the 5% significance level. The absence of cointegration over the study period lend credence to the energy policy in Nigeria that has promoted investment in the petroleum sector to sustain the economy through revenue generation and employment. As a result, most studies (Akpan & Akpan, 2012; Ighodaro, 2010; Matthew et al., 2018) for Nigeria that used electricity as proxy for energy consumption found evidence of longrun relationship with economic growth. A further emphasis on the dominant use of non-renewables (particularly natural gas) in power generating plants for several decades across the country.

Granger Causality Test Results The results of the Granger non-causality model using the set of equations (4-5) in the augmented level VAR (k + dmax) for the Wald test is shown in table 5. EMP

REC

EMP

_

5.54* (0.019)

REC

0.082 (0.77)

_

Note: * denotes null rejection of Granger non-causality at 5% level Probability reported in parenthesis Table 5. T-Y Granger Non-Causality Test Results

The results in table 5 indicate a one-way causality from employment (EMP) to renewable energy consumption (REC) as the null hypothesis of Granger non-causality is rejected at 5% level. It implies that renewable energy consumption will continue to rise so long as employment levels continue to grow in Nigeria. Hence, execution of renewable energy projects towards power generation will help to create both direct and indirect employment which should induce an income effect, technological effect and lead to higher energy consumption in the Nigerian economy. Direct jobs can be created in the short-term during construction due to building, procurement, and installation of equipment at renewable based power stations. Likewise, additional jobs are created during the course of operation and maintenance of power stations in the long-term. Indirect jobs can be created during the course of transmission and distribution of electrical power. These employment opportunities will increase the potential for household spending on goods and services, which will have to be compensated by higher energy consumption, especially by firms, in order to meet those demands. The causal pattern observed in this paper differs from the findings of other studies (Akpan & Akpan, 2012; Ebohon, 1996; Esso, 2010; Ighodaro, 2010; Osigwe & Arawomo, 2015) for Nigeria largely due to 36


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variation in methodological approach. The unrestricted test adopted in this paper minimise the prefiltering bias likely to affect some of those papers. However, this study is limited by the availability of data on renewable energy consumption, thereby limiting the time period for the causal analysis which could also account for variation in outcomes across these set of related studies.

CONCLUSION AND POLICY RECOMMENDATION The study used annual time series data for the period 1991–2014 to analyse the causal pattern between employment and renewable energy consumption in Nigeria. The Toda-Yamamoto Granger non-causality technique was employed for the causality model, and the Johansen cointegration technique was used to check for the long-run relationship between model variables. The findings support the conservative hypothesis as a one-way causal link was found from employment to renewable energy consumption in Nigeria for the period under consideration. As a result, employment can be created during the construction phase and in the long-run during operations and maintenance of renewable energy projects. This will result in higher supply and consumption of energy by residential consumers and firms. Due to the large abundance of reserve for conventional energy sources in Nigeria, the energy policy has mostly been geared towards promoting investment in infrastructure to harness oil and gas resources. However, rising population figures, oil price volatility, and air pollution, particularly from gas flaring in the oil industry, necessitates finding an economic and environmentally sustainable solution to the energy crisis, and unemployment through the adaptation of renewable energy resources. The energy crisis that has persisted for several decades has led to the collapse of many industries and business enterprises, resulting in alarming levels of unemployment figures. The study recommends that policy makers should introduce incentives such as import duty exemption and a tax holiday in order to propel private investment in renewables particularly in the powergenerating segment of the economy. Longe et al. (2018) established evidence of positive short-run and long-run relationship between carbon dioxide emissions and energy use pattern in Nigeria. Diversification into renewables will provide employment opportunities, particularly in the rural areas, and eliminate the health-related costs associated with air pollution notable with the use of carbon-rich fossil fuels. Furthermore, small and medium scale local production of alternative vehicle fuels, such as biodiesel, should be supported by energy policy in Nigeria. The Nigerian Electricity Regulatory Commission (NERC) should seek to introduce and implement a feed-in tariff scheme in order to facilitate the adaptation of renewables and reduce the perceived producer economic risk in renewable energy production. It is widely believed that Nigeria, like many oil producing countries, has passed its peaked oil production and, as such, it is imperative the country exploit its vast renewable energy resources in order to sustain economic growth going into the future. Lastly, policymakers should seek to implement a strategic plan towards ensuring that new and existing hydropower plants are well equipped with modern technology in order to facilitate energy efficiency, and create employment in the combined effort towards promoting an eco-friendly environment. Although the findings are meant to complement other empirical studies on the causal link between employment and energy consumption for Nigeria, there are still ways to expand on the research in further studies. One such way is to disaggregate renewable energy consumption into its different components leading to a multivariate causality model, which can provide further insight on the source of renewable energy that exacts greater employment effects on the economy. Moreover, another methodology other than the surplus-lag approach, such as the bootstrapped p-value, could potentially be used to improve the efficiency of the Granger non-causality test.

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Kahia, M., Aïssa, M. S. B., & Lanouar, C. (2017). Renewable and Non-renewable Energy use - Economic growth Nexus: The Case of MENA Net Oil Importing Countries. Renewable and Sustainable Energy Reviews, 71, 127-140. DOI:10.1016/j.rser.2017.01.010 Koçak, E., & Şarkgüneşi, A. (2017). The Renewable Energy and Economic Growth Nexus in Black Sea and Balkan Countries. Energy Policy, 100, 51-57. DOI:10.1016/j.enpol.2016.10.007 Lin, B., & Moubarak, M. (2014). Renewable Energy Consumption - Economic Growth Nexus for China. Renewable and Sustainable Energy Reviews, 40, 111-117. DOI:10.1016/j.rser.2014.07.128 Lise, W., & Van Montfort, K. (2007). Energy Consumption and GDP in Turkey: Is there a Co‐Integration Relationship? Energy Economics, 29(6), 1166-1178. DOI:10.1016/j.eneco.2006.08.010 Longe, A. E., Ajulo, K. D., Omitogun, O., & Adebayo, E. O. (2018). Trade, Transportation and Environment Nexus in Nigeria. The European Journal of Applied Economics, 15(2), 29-42. DOI:10.5937/EJAE15-17360 Matthew, O. A., Ede, C. U., Osabohien, R., Ejemeyovwi, J., Fasina, F. F., & Akinpelumi, D. (2018). Electricity Consumption and Human Capital Development in Nigeria: Exploring the Implications for Economic Growth. International Journal of Energy Economics and Policy, 8(6), 8-15. DOI:10.32479/ijeep.6758 Menegaki, A. N., & Tugcu, C. T. (2016). Rethinking the Energy-Growth Nexus: Proposing an Index of Sustainable Economic Welfare for Sub-Saharan Africa. Energy Research & Social Science, 17, 147-159. DOI:10.1016/j. erss.2016.04.009 Menegaki, A. N., & Tugcu, C. T. (2017). Energy Consumption and Sustainable Economic Welfare in G7 Countries; A Comparison with the Conventional Nexus. Renewable and Sustainable Energy Reviews, 69, 892-901. DOI:10.1016/j.rser.2016.11.133 Menyah, K., & Wolde-Rufael, Y. (2010). CO2 Emissions, Nuclear Energy, Renewable Energy and Economic Growth in the Us. Energy Policy, 38, 2911-2915. DOI:10.1016/j.enpol.2010.01.024 Nisbet, M. C., & Myers, T. (2007). Trends: Twenty Years of Public Opinion About Global Warming. The Public Opinion Quarterly, 71(3), 444-470. Ocal, O., & Aslan, A. (2013). Renewable Energy Consumption–Economic Growth Nexus in Turkey. Renewable and Sustainable Energy Reviews, 28, 494-499. DOI:10.1016/j.rser.2013.08.036 Osigwe, A. C., & Arawomo, D. F. (2015). Energy Consumption, Energy Prices and Economic Growth: Causal Relationships based on Error Correction Model. International Journal of Energy Economics and Policy, 5(2), 408-414. Ozturk, I., & Bilgili, F. (2015). Economic Growth and Biomass Consumption Nexus: Dynamic Panel Analysis for Sub-Sahara African Countries. Applied Energy, 137, 110-116. DOI:10.1016/j.apenergy.2014.10.017 Papapetrou, E. (2001). Oil Price Shocks, Stock Market, Economic Activity and Employment in Greece. Energy Economics, 23(5), 511-532. DOI:10.1016/S0140-9883(01)00078-0 Payne, J. E. (2009). On the Dynamics of Energy Consumption and Output in the US. Applied Energy, 86, 575-577. DOI:10.1016/j.apenergy.2008.07.003 Sadorsky, P. (2009). Renewable Energy Consumption and Income in Emerging Economies. Energy Policy, 37(10), 4021-4028. DOI:10.1016/j.enpol.2009.05.003 Schelling, T. C. (1992). Some Economics of Global Warming. The American Economic Review, 82(1), 1-14. Sebri, M., & Ben-Salha, O. (2014). On the Causal Dynamics between Economic Growth, Renewable Energy Consumption, CO2 Emissions and Trade Openness: Fresh Evidence from BRICS Countries. Renewable and Sustainable Energy Reviews, 39, 14-23. DOI:10.1016/j.rser.2014.07.033 Sims, C. A. (1972). Money, Income and Causality. American Economic Review, 62(4), 540-552. Toda, H. Y., & Yamamoto, T. (1995). Statistical Inference in Vector Autoregressions with Possibly Integrated Processes. Journal of Econometrics, 66(1-2), 225-250. DOI:10.1016/0304-4076(94)01616-8 Vaona, A. (2012). Granger Non-Causality Tests between (Non)Renewable Energy Consumption and Output in Italy Since 1861: The (Ir)Relevance of Structural Breaks. Energy Policy, 45, 226-236. DOI:10.1016/j.enpol.2012.02.023 Warrick, R., & Farmer, G. (1990). The Greenhouse Effect, Climatic Change and Rising Sea Level: Implications for Development. Transactions of the Institute of British Geographers, 15(1), 5-20. DOI:10.2307/623089 39


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UZROČNA VEZA IZMEĐU ZAPOŠLJAVANJA I POTROŠNJE OBNOVLJIVE ENERGIJE: PRIMER NIGERIJE

Rezime: Rad je ispitao uzročnu vezu između zapošljavanja, s jedne strane i potrošnje energije iz obnovljivih izvora, s druge strane – u Nigeriji, za period 1991-2014. godine, a oslanjajući se na godišnje podatke o vremenskim serijama. Pristup Toda-Yamamoto Granger je korišćen za uzročnu analizu, a Johansen tehnika kointegracije je upotrebljena da bi se potvrdila dugoročna veza između varijabli modela. Rezultati su podržali hipotezu o očuvanju jednosmerne uzročnosti između zapošljavanja i potrošnje energije, ali nije utvrđen nikakav dokaz o kointegraciji. Studija preporučuje da Nigerija treba da analizira svoju energetsku politiku, kako bi promovisala privatna ulaganja u projekte obnovljivih izvora energije u pronalaženju održivog rešenja u vezi sa energetskom krizom, kao i stvaranju mogućnosti za zapošljavanje i smanjenju zagađenja životne sredine, koji karakterišu dugoročnu upotrebu fosilnih goriva.

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Ključne reči: obnovljiva energija, uzročnost, zaposlenje, kointegracija, energetska politika.


EJAE 2019, 16(1): 41-58 ISSN 2406-2588 UDK: 330.322:339.722(4) 336.76 DOI: 10.5937/EJAE15-19652 Original paper/Originalni naučni rad

GOLD IN INVESTMENT PORTFOLIO FROM PERSPECTIVE OF EUROPEAN INVESTOR Tijana Šoja Central Bank of Bosnia and Herzegovina, Sarajevo, Bosnia and Herzegovina

Abstract: Gold is a unique asset, highly liquid, but scarce and limited. It is a luxury good and can be considered an investment opportunity. Gold is an asset which does not carry counterparty risk – there is no associated credit risk. Due to these characteristics, gold represents a significant asset, and has a fundamental role in investment portfolios. These circumstances increase the interests of investors to include gold in investment portfolios, especially during times of financial crisis. If an investor decides to include gold in investment portfolio, it is necessary to evaluate the portion of gold in the portfolio considering risk aspect, return and diversification. In this research, a hypothesis was tested and confirmed that gold offers good diversification for the investment portfolio, which implies that gold is a desirable asset in the investment portfolio. This research is focused on developing an optimal portfolio that combines the Eurozone bond index with the investment grade rating from 1 to 10 years (EG05), the stock index Euro Stoxx50 and gold using the Markowitz methodology. The result showed that optimal portfolio should include gold with a share between 1% to 9%, depending on the risk that the investor is willing to accept.

Article info: Received: November 23, 2018 Correction: December 28, 2018 Accepted: January 31, 2019

Keywords: gold, investment, portfolio, crisis.

INTRODUCTION Throughout history, gold has always had a noticeable reputation and different characteristics: it held the role of a global currency, it was sometimes seen as goods, sometimes as a financial asset and, of course, as jewellery. During the rapid development of the global financial market in the 1980’s and 1990’s, gold became an increasingly attractive investment. Moreover, the last financial crisis in 2007/8 and the debt crisis in Europe in 2010 increased the interest of investors for gold. Investors want gold in their portfolios for many reasons. Some investors invest in gold in order to realise profit from the *E-mail: tijana_soja@yahoo.com

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ŠOJA, T.  GOLD IN INVESTMENT PORTFOLIO FROM PERSPECTIVE OF EUROPEAN INVESTOR

growth of the price of gold and take into account the limited supply of gold that can affect the price increase. Other gold investors consider it a strategic asset and a long-term investment because of the specific characteristics of this investment asset. Financial crises, such as the global crisis 2007/8, increase the demand for safe assets like gold, as it is perceived as an asset that preserves its value and provides a high level of security in times of crisis. In addition, gold plays an important role in improving portfolio performance, portfolio diversification, and could reduce overall portfolio risk. Due to these characteristics, it is argued that gold is a desirable instrument in financial portfolios. Previous research claim that asset class commodity could be the answer to many problems in investment activity today, and investment problems that the investment world faces. Because of the low correlation between commodity and bonds, equities and inflation, investment in commodity, such as gold, could be an valuable component in investment portfolios (Idzorek, 2006; Conover et al., 2010). Gold is also considered an asset which can offer inflation hedge due to its low or negative correlation with inflation and currency devaluation. During times of inflationary pressures, the price of gold generally increases in order to balance inflationary consequences and hold purchasing power (Erb & Harvey, 2013). Academic studies have also shown that gold is an attractive investment, especially as an investment vehicle to diversify portfolios. Conover et al., (2009) but also Daskalaki & Skiadopoulos (2011) have shown that investment portfolios which include precious metals, like gold or silver, demonstrate a better rate of performance than portfolios without them. Obviously, there is evidence that gold, as a financial asset, has an important role in diversifying investment portfolios and can improve their performance. In such circumstances, it is useful to research the role of gold in European portfolios during and after the financial crisis of 2007/8. It was found that there is a gap in research, which examined the optimal share of gold in investment portfolios during the pre-crisis and post-crisis period, but also research papers that mainly focused on European investors. The aim of this study is to explore the optimal share of gold in investment portfolios from the perspective of a European investor. The optimal share of gold in the investment portfolio is being examined from a European investor perspective that invests in portfolios which contain three instruments: Eurozone government bonds, shares of European companies, and gold. Previous research has shown that a low correlation between these assets could improve portfolio performance, and we will therefore explore what an optimal share of gold in such an investment portfolio would be. Bearing this in mind, the hypothesis that gold offers a good diversification for investment portfolios, thus implying that gold is a desirable asset in portfolios, will be examined in this research. The starting point is the assumption that the investor does not have a high-risk appetite, and prefers a lower risk portfolio. The empirical research is focused on the period from January 2000 to December 2017. Monthly data were used for the analyzed instruments, as follows: ◆◆ The EG05 index, which includes investment grade government bond maturity from 1 to 10 years, ◆◆ The Euro index Stoxx50, representing the top 50 shares of the best-performing companies from eleven Eurozone countries, and ◆◆ The price of gold expressed in dollars. The desired share of gold in investment portfolios is estimated throughout two periods: from January 2000 to December 2017, and during the global crisis from January 2007 to December 2017. For portfolio construction and examining the optimal share of gold in investment portfolios, the modern portfolio theory proposed by Markowitz will be used. This research is divided into several thematic units: literature review, research methodology, research findings, analysis, and concluding remarks. 42


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ŠOJA, T.  GOLD IN INVESTMENT PORTFOLIO FROM PERSPECTIVE OF EUROPEAN INVESTOR

LITERATURE REVIEW The role of gold in investment portfolios and the need to be included in investment portfolios, are interesting and current topics. According to Hillier et al. (2006), the existing literature that treats the role of gold in portfolios and the role of gold as an investment, can generally be classified into several areas, thus, gold is analyzed as a form of hedging, as an instrument of portfolio diversification, its connection with macroeconomic factors and gold production and its characteristics. From the perspective of this research, our focus will be on the role of gold in investment portfolios, so gold will be observed as a financial asset. Usually, investors diversify portfolios through two key assets, i.e. stocks and bonds (Idzorek, 2006). However, with increased globalization, research has shown that the correlation among primary assets has observed a steady growth (Idzorek, 2005; Johnson & Soenen, 1997). As a result,, investors did not enjoy a high degree of portfolio diversification and their investments were not protected enough from the financial market turbulence (Ratner & Klein, 2008; Bernhart et al., 2011). Economic and political crises have influenced investors to include assets with a lower correlation with shares and bonds in portfolios, in order to diversify them. In this context, the idea of including new assets, such as gold, in portfolios with the aim of diversification, is desirable. Numerous analysts consider gold a good alternative to diversification, due to its low correlation with traditional assets (Idzorek, 2006; Conover et al., 2010). Gold is often perceived as a “safe haven” and as an asset that protects wealth and value in times of inflation, resulting from a low correlation of gold with market trends (Clapperton, 2010; Conover et al., 2009). During periods of global uncertainty, many investors choose to invest in gold because it is regarded as a safe investment. In addition to this, the rise in the price of gold, which has been present since 1999 through 2012, has led to a 15.4% annual return on gold investment. This is a far greater return than offered by shares in the U.S. (return of 1.5%) and bonds (return of 6.4%) over the same period (Fernando, 2017). Researchers have a different stance towards gold as an investment option. Some researchers highlight negative attitudes to gold in investment portfolios, while some have a positive view about gold and its role in portfolios. Investors, such as Warren Buffett, believe that gold is a non-productive asset that increases fears among its investors. He considers that the rise in gold prices from 2010 to 2012 was a “balloon”, and compares it to the 17th-century Tulipomania, the dot-com crisis in the 1990’s and the latest crisis of 2008. Numerous sceptics of gold generally support Buffett’s claims (Fernando, 2017). The World Gold Council−or WGC (2018) highlights that gold is a highly liquid, scarce asset and is no one’s liability. Moreover, gold is a luxury good, but also an investment. Because of these characteristics, gold can have a very important, even fundamental, role in investment portfolios. By adding gold to investment portfolios, investors can increase diversification but also enhance risk-adjusted returns. The WGC (2018) found that US dollar institutional investors, by adding 2%, 5% or 10% in gold, have increased returns and reduced volatility. Their analysis also showed that, for most US dollar investors, holdings between 2% to 10% of gold can improve portfolio performance. The majority of researchers and analysts point out that gold is an attractive asset, and represents a good basis for portfolio diversification. Researchers have shown that portfolios containing precious metals, such as gold and platinum, have recorded significantly better performances than standard stock portfolios without gold (Conover et al., 2009; Daskalaki & Skiadopoulos, 2011; Hillier et al., 2006). Nevertheless, advocates of gold-inclusive portfolios suggest that gold can minimize the standard deviation of the total portfolio risk, reduce volatility, and boost returns (Merk Investments, 2012). Gold can be the valuable asset for diversification - even a small share of gold in the portfolio, between 1% and 3%, can significantly reduce the overall portfolio risk (Michaud et al., 2011). Research 43


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conducted during the 1980s’, such as Sherman (1982), suggest that the 5% share of gold in the stock portfolio resulted in lower risk and higher returns. Lucey et al. (2006) examined the structure of portfolios, investors, and their focus on finding optimal portfolios, and demonstrated that the optimal portfolio has 6% to 25% of gold, depending on the period of investment. Numerous evidence suggest that gold can serve as a safe haven and an asset that provides a high degree of protection against inflation and currency depreciation, as observed over a long period of time (Baur & Lucey, 2010; Conover et al., 2009; Ghosh et al., 2004; Capie et al., 2005; Joy, 2011). Similarly, Pullen et al, (2014) demonstrate and confirm that gold represents good protection in periods of financial disasters. Baur and Lucey (2010) studied constant and time-varying relations between the U.S., the U.K., and German stock and bond returns and gold returns in order to explore gold as a hedge and safe haven. They found that gold, on average, is a safe haven in extreme stock market conditions, but did not found that gold is a safe haven for bonds at any analyzed market. As we can see from previous research, there is a lot of evidence that gold represents a good basis for diversification portfolios. Furthermore, there are many papers on U.S. portfolios and role of gold, but not those on European investing in EUR financial instruments (bonds and shares). Additionally, there is lack of research on the optimal role of gold in EUR portfolios during and after crisis 2007/8. Considering all the abovementioned, this research will show that gold has a significant role in EUR investment portfolios, and can be used for diversification.

METHODOLOGY The role and share of gold in portfolios was analyzed for the period from January 2000 to December 2017. The analysis includes the following instruments: Eurozone government bond index (EG05), Euro area stocks (Euro Stoxx50), and gold. All data is on a monthly basis, i.e., the analysis includes the value of each instrument at the end of the month. Firstly, a monthly return for each instrument is calculated using the following expression (Bodie, et al 2014): Rt = ln (

pt ) pt −1

Rt represents a return, ln is the natural logarithm, pt is the value in the current period and pt-1 is the value in the previous period. Complete portfolio optimization was carried out based on the data on the monthly return of analyzed instruments. The average of return, necessary for the analysis, is calculated as follows: x=

x1 + ... xn n

The standard deviation as a measure of the dispersion of the return, i.e., the deviation of the individual return from the middle value, was calculated using the following expression (Bodie, et al 2014):

σ=

1 n Σ (xi − x )2 n i =1

For calculating the VaR it is important to consider the observation period, as well as the confidence interval. Parameter VaR is known as the method of variance and covariance, and the formula for calculating parametric VaR is the following (Bodie, et al 2014): 44


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ŠOJA, T.  GOLD IN INVESTMENT PORTFOLIO FROM PERSPECTIVE OF EUROPEAN INVESTOR

VaRx ,a = − za ⋅ σ ⋅ S In the above formula, za represent the quantile order α of standardized random variables, S is the value of the position being calculated. VaR was calculated with a confidence interval of 95%. The optimal portfolio is determined using the Markowitz method, or a modern portfolio theory. This theory involves the calculation of correlation and covariation among the instruments included in the portfolio, as well as the calculation of the expected return and portfolio risk. The expected return of the portfolio is calculated using the following expression (Levišauskait, 2010): n

Er ( p ) = Σ w1 ⋅ Ei (r ) = w1 ⋅ E1(r ) + w2 ⋅ E2(r ) + ... + wn ⋅ En(r ) i =1

where the following applies:

E r ( p ) - Expected portfolios return Ei (r ) - The expected rate of return on a financial instrument wi - The share of the value of the portfolio that is invested in financial instrument i n - Number of financial instruments in the portfolio In order to deploy an efficient portfolio, it is necessary to calculate the covariance that measures how two variables move together – whether to move in the same direction, so they have a positive covariance, or move in a different, opposite direction, so they have a negative covariance. Covariance is calculated as follows (Bodie, et al 2014): Covariance ( A, B) =

Σ (RA − RA )(RB − RB ) N

where the following applies: RA – Return of instrument A (the same holds for instrument B) RA – Average return for instrument A (the same hold for instrument B) For the next step, it is necessary to calculate the correlation between the instruments in order to determine the strength of the relationship between the instruments in a portfolio. The correlation should be used in conjunction with the covariance, and is represented as follow (Bodie, et al 2014): Correlation= p=

Cov ( A, B)

σA σB

where the following applies: Cov (A,B) – Covariance between instrument A and instrument B

σ A σ B - Standard deviation of A and B The portfolio risk that consists of three securities (A, B and C) can be calculated as follows (Levišauskait, 2010):

σ P = (w A2 ⋅ σ A2 + w B2 ⋅ σ B2 + wC2 ⋅ σ C2 + 2 w Aw Bw AB + 2 w AwC w AC + 2 w BwC w BC )1/2 45


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ŠOJA, T.  GOLD IN INVESTMENT PORTFOLIO FROM PERSPECTIVE OF EUROPEAN INVESTOR

where the following applies: wA, wB, wC - Share of initially invested values of financial instruments A,B and C (wA+ wB+wC= 1) σ A , σ B , σ C - Standard deviation of financial instruments

RESULTS OF RESEARCH This research has observed and analyzed during the period from January 2000 to December 2017, and the period from January 2007 to December 2017. All calculations are made for these two periods in Excel. In the first step, all data are analyzed and demonstrated using descriptive statistics. Var, as an initial measure of risk, was calculated. The results are shown in Table 1: EG05

EURO STOXX50

Gold

Mean

0.36%

-0.02%

0.82%

StDev

0.81%

5.25%

4.87%

Freq<0

31.94%

46.30%

44.91%

Parametric VaR

-0.97%

-8.66%

-7.19%

Table 1. Descriptive statistics and VaR for the period 01.01.2000 - 31.12.2017 Source: Authors’ processing

Descriptive statistics were calculated in Excel. All data are calculated for the period from 01.01.2000 until 31.12.2017. The same date were used for the calculated parametric VaR. It is important to emphasize that parametric VaR assume normal distribution. During the period from January 2000 to December 2017, the average return on the index of government bonds EG05 was 0.36%, the Euro Stoxx50 had a negative return of 0.02%, while gold had the highest return of 0.82%. The standard deviation, as a risk measure, was observed at the highest level with shares at 5.25%, followed by gold at 4.87%. Bonds carry a significantly lower standard deviation; thus, they are considered less risky instruments. Frequency (Freq) shows the number of observations that are less than 0, and demonstrates returns that are less than 0%, divided by total observations. This implies that this data shows a return share that is less than 0% during the analyzed period. The data show that the lowest returns below 0% in the analyzed period were recorded in bonds (EG05), where the frequency of negative return has a share of 31.94%, while the largest share of negative returns was recorded in the stock index, where this figure is 46.30%. VaR as a risk measure is at the lowest level in bonds, which is further evidence that it is one of the safest instruments of investment. Descriptive analysis and VaR for the period from January 2007 to December 2017 was calculated in the same way as a previous analysis. The data are presented in Table 2: EG05

EURO STOXX50

Gold

Mean

0.33%

0.01%

0.69%

StDev

0.85%

5.14%

5.33%

Freq<0

34.09%

46.97%

46.21%

Parametric VaR

-1.07%

-8.44%

-8.09%

Table 2. Descriptive statistics and VaR for the period 01.01.2007-31.12.2017 Source: Authors’ processing 46


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ŠOJA, T.  GOLD IN INVESTMENT PORTFOLIO FROM PERSPECTIVE OF EUROPEAN INVESTOR

During the crisis period, gold again had the highest average return of 0.69%, while the shares again had the lowest return of 0.01%. Bonds, once more, showed the highest degree of security, considering the data on standard deviation, VaR, and the frequency of negative returns. For the next step, a return correlation is calculated among the analyzed instruments, as shown in Table 3: EG05

EURO STOXX50

EG05

1

EURO STOXX50

-0.22

1

Gold

0.08

-0.09

Gold

1

Table 3. Correlation for the period 31.01.2000 - 31.12.2017 Source: Authors’ processing

Correlation analysis shows almost no correlation amid gold and other financial assets. The difference between the bond index EG05 and gold correlation is only 0.08, while between the stock index EuroStoxx50 and gold correlation it is slightly negative, at -0.09. The correlation between the analyzed data for the crisis and post-crisis period is shown in Table 4: EG05

EURO STOXX50

EG05

1

EURO STOXX50

-0.07

1

Gold

0.09

-0.09

Gold

1

Table 4. Correlation for the period 31.01.2007 - 31.12.2017 Source: Authors’ processing

In this period, the correlation is almost 0 for all analyzed instruments. Moreover, there is a similar correlation over a longer period of time, as shown in Table 3. After calculating the correlation, the covariance among the analyzed instruments was taken into consideration. The covariance matrix is shown in Tables 5 and 6: EG05

EURO STOXX50

Gold

EG05

0.0078%

-0.0093%

0.0062%

EURO STOXX50

-0.0093%

0.2749%

-0.0221%

Gold

0.0062%

-0.0221%

0.2427%

Table 5. Covariance matrix for the period of 01.01.2000 - 31.12.2017 Source: Authors’ processing EG05

EURO STOXX50

Gold

EG05

0.0083%

-0.0029%

0.0065%

EURO STOXX50

-0.0029%

0.2620%

-0.0255%

Gold

0.0065%

-0.0255%

0.2871%

Table 6: Covariance matrix for the period of 01.01.2007 - 31.12.2017 Source: Authors’ processing

47


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ŠOJA, T.  GOLD IN INVESTMENT PORTFOLIO FROM PERSPECTIVE OF EUROPEAN INVESTOR

Different portfolios were made using the covariant matrix by taking into consideration the different participation of individual instruments in the portfolio, and with the aim of finding the optimal portfolio using the Markowitz methodology. An overview of the portfolio offering the minimum standard deviation and the appropriate return for a given level of risk or an effective set is shown in Table 7. When calculating the optimal portfolio, the risk-free rate is fixed at 0% (German Council of Economic Experts – Annual Report 2016/17). In the current low and negative yield environment in EMU, especially in Germany, which are at the moment negative, up to 7 years. Table 7 shows data for slope, which represents the slope of the capital market line (CML) and demonstrates the point where CML has a tangency in line with the efficient set. The optimal portfolio is the one with the highest slope. At that point, there is a portfolio that has the lowest standard deviation and the lowest risk, and such is acceptable from the risk-averse investor’s perspective. Return

0.25%

0.30%

0.35%

0.39%

0.40%

0.45%

0.50%

0.55%

0.60%

0.65%

0.70%

0.75%

0.80%

Risk

1.53%

1.00%

0.83%

2.3%

0.97%

1.28%

1.71%

2.18%

2.67%

3.18%

3.69%

4.21%

4.73%

Slope

0.1634

0.3012

0.4140

0.17

0.4125

0.3506

0.2928

0.2524

0.2245

0.2045

0.1197

0.1782

0.1692

EG05

70.86%

84.21%

93.31%

33.33%

90.18%

80.24%

69.37%

58.50%

47.62%

36.75%

25.88%

15.01%

4.14%

EURO 29.14% STOXX50

15.79%

5.73%

33.33%

0.51%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.0%

0.96%

33.34%

9.30%

19.76%

30.63%

41.50%

52.38%

63.25%

74.12%

84.99%

95.86%

Gold

0.00%

Table 7. An overview of different portfolios, and optimal portfolio for a period of 01.01.2000-31.12.2017 Source: Authors’ processing in Excel

All the calculations are made in Excel, using the Solver function. At the first step it was calculated portfolio who has the lowest standard deviation or the lowest risk. For the second step, we chose a different rate of the returns, between 0.2% up to 0.8%, which was the maximum returns that gold had during the analyzed period (from 01.01.2000 until 31.12.2017). We therefore used returns from 0.250% and increased it up to a 5 basis point, as shown in Table 7. When determining the target return, we used Solver function to find the instrument share that offer desirable return. The average portfolio return was calculated considering the average returns of each instrument and their share in portfolios compared with the risk-free rate, which was set to 0%. The slope was calculated just as a ratio between portfolio return and portfolio risk. As we can see, we calculated a different returns rate, and show a share of each instrument in corresponding returns. We also calculated returns and risk in case that portfolio consist the same share of each instrument. In that case portfolio has a returns of 0.357% but the risk of this portfolio is 2.3% which is quite high. The same level of return investor can have with a lower risk, as it is shown in Table 7. The results demonstrate that, from the standpoint of the risk-averse investor, the acceptable portfolio is one that offers an expected return of 0.3% with a standard deviation of 0.80%, which is represented by the tangent of the efficient set line, as shown in Figure 1:

48


EJAE 2019  16 (1)  41-58

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Figure 1. Efficient set for a period of 01.01.2000 - 31.12.2017 Source: Authors’ processing

If an investor is risk averse, then the optimal portfolio should contain 93.3% of bonds, 5.7% shares, and the share of gold should be 1%. On the other hand, the portfolio with an expected return of 0.4% and a standard deviation of 1% can be considered an optimal portfolio. It is a portfolio made up with 90.2% of bonds, 0.5% of shares and 9.3% gold. This portfolio has a higher expected return, but also a slightly higher risk. However, the structure of the portfolio that the investor will adopt depending on investor's risk appetite. This means that a portfolio that includes 9.3% of gold could be considered as optimal. Likewise, if an investor is ready to take on more risk, the possibility exists of including a larger share of gold in the investment portfolio. The following phase included testing of an optimal portfolio using the same instruments for the period during the global crisis and beyond, from January 2007 to December 2017. The same calculations were used in this case. The efficient set is given in Table 8 and Figure 2: Return

0.15%

0.20%

0.25%

0.32%

0.34%

0.35%

0.40%

0.45%

0.50%

0.55%

0.60%

0.65%

0.68%

Risk

2.86%

2.09%

1.38%

0,88%

2.39%

0.96%

1.39%

2.01%

2.68%

3.39%

4.10%

4.82%

5.26%

Slope

0.0524

0.0957

0.1810

0.3590

0.14

0.3643

0.2880

0.2244

0.1863

0.1624

0.1462

0.1347

0.1293

EG05

44.11%

59.94%

75.77%

94.96%

33.33%

93.51%

79.60%

65.72%

51.84%

37.96%

24.08%

10.20%

1.87%

EURO STOXX50

55.89%

40.06%

24.23%

4.11%

33.33%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

Gold

0.00%

0.00%

0.00%

0.93%

33.34%

6.49%

20.40%

34.28%

48.16%

62.04%

75.92%

89.80%

98.13%

Table 8. Overview of different portfolios and optimal portfolio for a period of 01.01.2007 - 31.12.2017 Source: Authors’ processing

49


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ŠOJA, T.  GOLD IN INVESTMENT PORTFOLIO FROM PERSPECTIVE OF EUROPEAN INVESTOR

Efficient set is shown in Figure 2:

Figure 2. Efficient set for a period of 01.01.2007 - 31.12.2017 Source: Authors’ processing

An optimal portfolio comprises 93.5% of bonds, 6.5% of gold, and no stock. This portfolio has an expected return of 0.35% and a risk of 0.96%. If the investor favours a somewhat lower risk, the portfolio can consist of 95% of bonds, 4.1% of shares and 0.9% of gold. These findings confirm that, during the crisis, it was optimal to include gold in portfolios.

CONCLUSION Economic crises, turbulence, and uncertainties in financial markets emphasize the importance of risk management strategies and portfolio diversification. At the same time, it is evident that the correlation between traditional assets, shares, and bonds is increasing. If these circumstances are considered in combination with poor returns offered by the stock, it can be concluded that investors are more interested in pursuing other investment assets, such as monetary gold. Previous research has indicated that investors have different attitudes towards the role of gold in the portfolio. Some advocate its inclusion in portfolios, while others consider it is not beneficial. Decisions to include monetary gold in the portfolio will ultimately depend on the investor, investment objectives, and the interest in portfolio diversification. The purpose of this study was to examine whether gold has an important role in investment portfolios from the perspective of European investors that invest in traditional assets, stocks, and bonds. Three instruments were combined - bonds, stocks and gold, whereas the optimal portfolio was found by Markowitz portfolio theory. The analyzed period was from January 2000 to December 2017, while the observation period was segmented into two periods covering the entire analyzed period, from January 2000 to December 2017, and the period of global crisis and the post-crisis period, from January 2007 to December 2017. This study tested the hypothesis that gold represents a useful instrument for portfolio diversification and, as such, is a desirable instrument in investment portfolios. 50


EJAE 2019  16 (1)  41-58

ŠOJA, T.  GOLD IN INVESTMENT PORTFOLIO FROM PERSPECTIVE OF EUROPEAN INVESTOR

Following the research results, it can be concluded that gold represents an important instrument for portfolio diversification and, therefore, shares between 1% and 9% are recommended for inclusion in portfolios. It can also be argued that it is justifiable to include gold in investment portfolios if the portfolio is combined with European bonds and stocks. Gold is a good basis for diversification portfolios, both from the standpoint of the risk-averse investor and from the aspect of the investor prepared to take a higher risk.

REFERENCES Baur, D., & Lucey, B. (2010). Is gold a hedge or a safe haven? An analysis of stocks, bonds and gold. Financial Review, 45(2), 217-229. Bernhart, G., Höcht, S., Neugebauer, M., Neumann, M., & Zagst, R. (2011). Asset correlations in turbulent markets and the impact of different regimes on asset management. Asia-Pacific Journal of Operational Research, 28(1), 1-23. DOI:10.1142/S0217595911003028 Bodie, Z., Kane, A., Marcus, A. (2014). Investment. New York: McGraw-Hill Education. Capie, F., Mills, T. C., & Wood, G. (2005). Gold as a hedge against the dollar. Journal of International Financial Markets Institutions and Money, 15(4), 343-352. Clapperton, G. (2010). Raising the gold standard. Engineering & Technology, 5(15), 66-69. DOI:10.1049/et.2010.1514 Conover, C. M., Jensen, G. R., Johnson, R. R., & Mercer, J. M. (2009). Can precious metals make your portfolio shine? Journal of Investing, 18(1), 75-86. DOI:10.3905/JOI.2009.18.1.075 Conover, C. M., Jensen, G. R., Johnson, R. R., & Mercer, J. M. (2010). Is now the time to add commodities to your portfolio? The Journal of Investing, 19(3), 10-19. DOI:10.3905/joi.2010.19.3.010 Daskalaki, C., & Skiadopoulos, G. (2011). Should investors include commodities in their portfolios after all? New evidence. Journal of Banking and Finance, 35(10), 2606-2626. DOI:10.1016/j.jbankfin.2011.02.02 Erb, C. B., & Harvey, C. R. (2013). The Golden Dilemma. Financial Analysts Journal, 69(4), 10-42. DOI:10.2139/ ssrn.2078535 Fernandno, N. (2017). The Role of Gold in an Investment Portfolio: An empirical study on diversification benefits of gold from the perspective of Swedish investors. Umeå: University German Council of Economic Experts. Ghosh, D., Levin, E. J., Macmillan, P., & Wright, R. E. (2004). Gold as an inflation hedge. Studies in Economics and Finance, 22, 1-25. Hillier, D., Draper, P., & Faff, R. (2006). Do precious metals shine? An investment perspective. Financial Analysts Journal, 62(2), 98-106. DOI:10.2469/faj.v62.n2.4085 Idzorek, T. M. (2005). Portfolio diversification with Gold, Silver, and Platinum. Chicago: Ibbotson Associates. Idzorek, T. M. (2006). Strategic asset allocation and commodities. Chicago: Ibbotson Associates. Johnson, R., & Soenen, L. (1997). Gold as an investment asset: Perspectives from different countries. Journal of Investing, 6(3), 91-99. DOI: 10.3905/joi.1997.408427 Joy, M. (2011). Gold and the US dollar: hedge or haven? Finance Research Letters, 8(3), 120-131. DOI:10.1016/j. frl.2011.01.001 Levišauskait, K. (2010). Investment Analysis and Portfolio Management. Lithuania: Vytautas Magnus University Kaunas. Lucey, B., Poti,V., Tully, E. (2006). International Portfolio Formation, Skewness and the Role of Gold. DOI:10.2139/ ssrn.452482 Merk Investments, LLC. (2012). The Case for Gold: Portfolio Benefits of the Ultimate Currency. Retrieved October 28, 2018, from https://www.merkinvestments.com/gold/white-papers/downloadcaseforgold-portfoliobenefits.php 51


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Michaud, R., Michaud, R., Ecsh, D., Schroeder, E. (2011). Gold as strategic assets for European investor. New York: World Gold Council. Pullen, T., Bensen, K., & Faff, R. (2014). A comparative analysis of the investment characteristics of alternative gold assets. Abacus, 50(1), 76-92. DOI:10.2139/ssrn.1928591 Ratner, M., & Klein, S. (2008). The portfolio implications of gold investment. The Journal of Investing, 17(1), 77-87. DOI:10.3905/joi.2008.701958 Sherman, E. (1982). Gold: A Conservative, Prudent Diversifier. Journal of Portfolio Management, 8(3), 21-27. DOI:10.3905/jpm.1982.408850 World Gold Council. (2018). Investment Update: IMF report highlights gold’s relevance. Retrieved October 28, 2018, from https://www.gold.org/goldhub/research/imf-report-highlights-golds-relevance

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APPENDIX Date

EG05

EURO STOXX50

Gold (in USD)

31.12.2017

638.241

3503.96

1302.8

30.11.2017

641.495

3569.93

1271.72

31.10.2017

640.889

3673.95

1270.34

29.9.2017

637.124

3594.85

1279.75

31.8.2017

638.318

3421.47

1316.96

31.7.2017

635.338

3449.36

1267.9

30.6.2017

633.954

3441.88

1241.61

31.5.2017

636.571

3554.59

1269.86

28.4.2017

633.943

3559.59

1268.28

31.3.2017

631.627

3500.93

1249.2

28.2.2017

633.707

3319.61

1256.37

31.1.2017

629.714

3230.68

1210.59

30.12.2016

636.44

3290.52

1147.5

30.11.2016

632.685

3051.61

1174.94

31.10.2016

637.026

3055.25

1273.76

30.9.2016

643.661

3002.24

1315.87

31.8.2016

642.57

3023.13

1308.17

29.7.2016

642.626

2990.76

1351.28

30.6.2016

640.275

2864.74

1316.13

31.5.2016

635.125

3063.48

1214.88

29.4.2016

631.786

3028.21

1293.53

31.3.2016

634.553

3004.93

1232.44

29.2.2016

633.492

2945.75

1232.07

29.1.2016

630.91

3045.09

1118.21

31.12.2015

624.272

3267.52

1062.19

30.11.2015

627.997

3506.45

1064.17

30.10.2015

624.822

3418.23

1142.11

30.9.2015

620.819

3100.67

1114.9

31.8.2015

616.142

3269.63

1133.72

31.7.2015

619.113

3600.69

1095.8

30.6.2015

611.707

3424.3

1173.76

29.5.2015

618.451

3570.78

1190.58

30.4.2015

621.706

3615.59

1181.44

31.3.2015

624.871

3697.38

1183.88

27.2.2015

623.269

3599

1213.18

30.1.2015

619.8

3351.44

1283.79

31.12.2014

615.374

3146.43

1187.96 53


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54

28.11.2014

612.503

3250.93

1167.38

31.10.2014

608.391

3113.32

1172.94

30.9.2014

608.153

3225.93

1208.67

29.8.2014

606.714

3172.63

1287.32

31.7.2014

600.64

3115.51

1282.28

30.6.2014

597.679

3228.24

1318.35

30.5.2014

592.667

3244.6

1249.68

30.4.2014

588.373

3198.39

1293.5

31.3.2014

585.007

3161.6

1286.92

28.2.2014

581.198

3149.23

1326.39

31.1.2014

578.525

3013.96

1244.55

31.12.2013

569.519

3109

1204.99

29.11.2013

572.521

3086.64

1253.35

31.10.2013

570.194

3067.95

1323.66

30.9.2013

563.645

2893.15

1331.77

30.8.2013

560.243

2721.37

1395.27

31.7.2013

561.747

2768.15

1308.29

28.6.2013

557.649

2602.59

1234.53

31.5.2013

563.957

2769.64

1387.8

30.4.2013

568.492

2712

1471.96

29.3.2013

558.774

2624.02

1597.5

28.2.2013

556.154

2633.55

1581.4

31.1.2013

553.812

2702.98

1662.51

31.12.2012

555.636

2635.93

1676.23

30.11.2012

553.361

2575.25

1714.98

31.10.2012

547.385

2503.64

1719.35

28.9.2012

543.972

2454.26

1772.25

31.8.2012

537.781

2440.71

1691.85

31.7.2012

533.277

2325.72

1615.73

29.6.2012

527.468

2264.72

1597.45

31.5.2012

527.342

2118.94

1566.84

30.4.2012

526.463

2306.43

1663.81

30.3.2012

526.888

2477.28

1668.15

29.2.2012

526.85

2512.11

1721.9

31.1.2012

519.543

2416.66

1730.91

30.12.2011

509.712

2316.55

1564.91

30.11.2011

493.849

2330.43

1745.59

31.10.2011

504.583

2385.22

1724.48

30.9.2011

511.382

2179.66

1623.79

31.8.2011

509.716

2302.08

1834.99


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29.7.2011

495.494

2670.37

1627.05

30.6.2011

495.81

2848.53

1504.72

31.5.2011

496.187

2861.92

1536.23

29.4.2011

492.119

3011.25

1563.7

31.3.2011

490.879

2910.91

1437.78

28.2.2011

493.412

3013.09

1411.88

31.1.2011

492.856

2953.63

1337.14

31.12.2010

494.774

2792.82

1421.4

30.11.2010

494.947

2650.99

1388.53

29.10.2010

504.495

2844.99

1359.4

30.9.2010

505.976

2747.9

1307.6

31.8.2010

509.88

2622.95

1248.45

30.7.2010

503.02

2742.14

1181

30.6.2010

499.319

2573.32

1241.68

31.5.2010

502.363

2610.26

1216,45

30.4.2010

495.374

2816.86

1179.03

31.3.2010

499.629

2931.16

1114.49

26.2.2010

496.943

2728.47

1117.59

29.1.2010

491.722

2776.3

1081.2

31.12.2009

490.316

2964.96

1098.65

30.11.2009

493.412

2797.25

1173.38

30.10.2009

491.131

2743.5

1045.45

30.9.2009

489.72

2872.63

1007.6

31.8.2009

487.214

2775.17

950.94

31.7.2009

485.857

2638.13

954

30.6.2009

480.278

2401.69

930

29.5.2009

475.594

2451.24

975.75

30.4.2009

477.869

2375.34

887.95

31.3.2009

476.288

2071.13

919.9

27.2.2009

471.51

1976.23

942.32

30.1.2009

466.974

2236.98

927.85

31.12.2008

467.308

2447.62

875.43

28.11.2008

462.422

2430.31

817.68

31.10.2008

451.291

2591.76

721.8

30.9.2008

445.312

3038.2

875.55

29.8.2008

441.124

3365.63

831.86

31.7.2008

436.608

3367.82

917.43

30.6.2008

429.084

3352.81

923.56

30.5.2008

433.308

3777.85

885.43

30.4.2008

437.87

3825.02

867.03 55


EJAE 2019  16 (1)  41-58

ŠOJA, T.  GOLD IN INVESTMENT PORTFOLIO FROM PERSPECTIVE OF EUROPEAN INVESTOR

56

31.3.2008

440.356

3628.06

920.96

29.2.2008

443.148

3724.5

973.08

31.1.2008

439.559

3792.8

924.49

31.12.2007

430.095

4399.72

833.05

30.11.2007

431.422

4394.95

782.92

31.10.2007

427.392

4489.79

790.5

28.9.2007

425.132

4381.71

743.75

31.8.2007

424.446

4294.56

673

31.7.2007

419.959

4315.69

665.15

29.6.2007

415.629

4489.77

650.9

31.5.2007

416.385

4512.65

659.45

30.4.2007

419.29

4392.34

680.85

30.3.2007

419.548

4181.03

665.05

28.2.2007

420.04

4087.12

667.89

31.1.2007

416.732

4178.54

650.53

29.12.2006

417.009

4119.94

635.7

30.11.2006

420.368

3987.23

647.5

31.10.2006

418.359

4004.8

604.8

29.9.2006

417.856

3899.41

596.55

31.8.2006

416.63

3808.7

626.28

31.7.2006

413.59

3691.87

633.36

30.6.2006

410.221

3648.92

613.99

31.5.2006

411.224

3637.17

642

28.4.2006

409.208

3839.9

653.23

31.3.2006

410.779

3853.74

582.85

28.2.2006

414.24

3774.51

561.2

31.1.2006

413.913

3691.41

571.95

30.12.2005

415.407

3578.93

517

30.11.2005

413.302

3447.07

494.7

31.10.2005

413.777

3320.15

467.4

30.9.2005

416.944

3428.51

469

31.8.2005

417.7

3263.78

434.53

29.7.2005

414.953

3326.51

430.55

30.6.2005

416.616

3181.54

435.88

31.5.2005

413.095

3076.7

417.1

29.4.2005

409.742

2930.1

436.01

31.3.2005

404.74

3055.73

428.24

28.2.2005

402.807

3058.32

436

31.1.2005

404.033

2984.59

421.5

31.12.2004

401.059

2951.01

438.05


EJAE 2019  16 (1)  41-58

ŠOJA, T.  GOLD IN INVESTMENT PORTFOLIO FROM PERSPECTIVE OF EUROPEAN INVESTOR

30.11.2004

399.46

2876.39

450.65

29.10.2004

396.423

2811.72

427.04

30.9.2004

393.015

2726.3

418.1

31.8.2004

391.957

2670.79

409.7

30.7.2004

387.505

2720.05

390.2

30.6.2004

384.768

2811.08

394

31.5.2004

384.321

2749.62

395.35

30.4.2004

384.74

2787.48

389.3

31.3.2004

388.056

2787.49

425.1

27.2.2004

384.969

2893.18

397

30.1.2004

379.994

2839.13

401.2

31.12.2003

377.864

2760.66

415.05

28.11.2003

373.375

2630.47

397.75

31.10.2003

374.508

2575.04

383.05

30.9.2003

379.013

2395.87

387.28

29.8.2003

373.316

2556.71

375.55

31.7.2003

373.502

2519.79

355.95

30.6.2003

378.124

2419.51

346.35

30.5.2003

377.63

2330.06

364.55

30.4.2003

371.016

2324.23

339.15

31.3.2003

370.291

2036.86

335.95

28.2.2003

371.142

2140.73

349.75

31.1.2003

366.924

2248.17

368.05

31.12.2002

363.57

2398.65

347.85

29.11.2002

357.251

2656.85

318.95

31.10.2002

355.617

2518.99

317.55

30.9.2002

356.676

2204.39

324.05

30.8.2002

350.043

2709.29

312.75

31.7.2002

346.321

2685.79

302.43

28.6.2002

341.548

3133.39

317.25

31.5.2002

336.716

3425.79

325.25

30.4.2002

336.189

3574.23

308.15

29.3.2002

332.727

3784.05

302.5

28.2.2002

335.481

3624.74

296.65

31.1.2002

334.303

3670.26

281.85

31.12.2001

333.723

3806.13

278.95

30.11.2001

337.01

3658.27

275.05

31.10.2001

338.616

3478.63

279.85

28.9.2001

332.602

3296.66

292.55

31.8.2001

329.468

3743.97

274.45 57


EJAE 2019  16 (1)  41-58

ŠOJA, T.  GOLD IN INVESTMENT PORTFOLIO FROM PERSPECTIVE OF EUROPEAN INVESTOR

31.7.2001

326.218

4091.38

266.15

29.6.2001

322.34

4243.91

271.55

31.5.2001

319.845

4426.24

266.6

30.4.2001

318.476

4525.01

263.48

30.3.2001

321.816

4185

257.95

28.2.2001

319.136

4318.88

267.15

31.1.2001

317.688

4779.9

265.2

29.12.2000

315.543

4772.39

272.25

30.11.2000

311.145

4790.08

269

31.10.2000

307.107

5057.46

264.68

29.9.2000

305.635

4915.18

274.25

31.8.2000

302.854

5175.12

277.25

31.7.2000

302.772

5122.8

277.25

30.6.2000

302.037

5145.35

289.15

31.5.2000

300.393

5200.89

272.6

28.4.2000

300.332

5303.95

274.5

31.3.2000

300.485

5249.55

279.73

29.2.2000

296.822

5182.62

293.3

31.1.2000

295.425

4684.48

283.6

31.12.1999

296.306

4904.46

288

Table 9. Raw data, monthly values for all variables Source: Bloomberg

ZLATO U PORTFOLIO INVESTICIJAMA IZ UGLA EVROPSKOG ULAGAČA Rezime: Zlato je jedinstven vid imovine, velike vrednosti, ali, u isto vreme, vid kojeg nema dovoljno na raspolaganju. U pitanju je imovina luksuzne prirode, koja se može smatrati sjajnom prilikom za ulaganje. Takođe, zlato je imovina koja ne nosi rizik druge strane – odnosno, ne uključuje povezani kreditni rizik. Upravo zbog ovih osobina, zlato predstavlja značajan oblik imovine i ima izuzetno važnu ulogu u portfolio investicijama. Navedene okolnosti uvećavaju interese ulagača da uvrste zlato u portfolio investicije, posebno tokom perioda finansijskih kriza. Ukoliko ulagač odluči da to učini, neophodno je proceniti udeo zlata u portfoliju, uzimajući u obzir apskte rizika, povraćaja i diversifikacije. U ovom radu, testirana je, i potvrđena hipoteza na osnovu koje zlato omogućava diversifikaciju za portfolio investicije, što implicira da je zlato poželjan oblik imovine u ovom kontekstu. Istraživanje se fokusiralo na razvoj optimalnog portfolija, koji kombinuje indeks obveznica Evrozone sa investicionim rejtingom 1-10 godina (EG 05), indeks Euro Stoxx 50 i zlato, uz upotrebu metodologije autora Markowitz. Rezultati su pokazali da optimalan portfolio treba da uključuje zlato sa udelom 1-9%, u zavisnosti od rizika koji je ulagač spreman da prihvati. 58

Ključne reči: zlato, investicije, portfolio, kriza.


EJAE 2019, 16(1): 59-76 ISSN 2406-2588 UDK: 336.71:[004:007 336.71:[621.395.721.5:004.77 DOI: 10.5937/EJAE15-18751 Original paper/Originalni naučni rad

DEFINING THE NEED FOR AND PROPOSING HOW TO TRANSFORM TRADITIONAL INTO DIGITAL BANKS WITH THE SUPPORT OF INFORMATION AND MOBILE TECHNOLOGIES Mirko Sajić1,*, Zlatko Bundalo2, Dušanka Bundalo3 Sberbank a.d., Banja Luka, Bosnia and Herzegovina 2 Faculty of Electrical Engineering, University of Banja Luka, Banja Luka, Bosnia and Herzegovina 3 Faculty of Philosophy, University of Banja Luka, Banja Luka, Bosnia and Herzegovina 1

Abstract: The need to practically and effectively implement the transformation of standard traditional banks into digital ones and how to transform them using information and mobile technologies are analyzed and described in the paper. The aim of this paper is to provide clear evidence about the need to transform banks from their present, traditional form into a new one that organizes and provides services, through a so-called digital bank. The great influence of mobile digital information technologies on the entire financial sector is further emphasized. The problems that appear because of the divergence of the existing concept of bank development and way in which modern clients want to obtain services, according to the possibilities of modern mobile digital technologies, are additionally explained. Proposals are also given for how to practically perform these changes and transformations, together with reasons that require changes. The basic principles for transitioning banks from traditional to digital formats through the so-called hybrid transformation period are presented, and several concrete solutions are proposed.

*E-mail: mirko.sajic@gmail.com

Article info: Received: September 2, 2018 Correction: October 18, 2018 Accepted: October 31, 2018

Keywords: bank transformation, information and mobile technologies, traditional retail bank and digital bank, hybrid period of bank transformation, digital bank creation period.

59


EJAE 2019  16 (1)  59-76

SAJIĆ, M., BUNDALO, Z., BUNDALO, D.  DEFINING THE NEED FOR AND PROPOSING HOW TO TRANSFORM TRADITIONAL INTO DIGITAL BANKS WITH THE SUPPORT OF INFORMATION AND MOBILE TECHNOLOGIES

INTRODUCTION

It has been noticed and written about in professional and scientific literature that the expansion of information and digital technologies has greatly contributed to the rise of so-called uncontrolled banking. State institutions and governments are trying to strengthen their banking regulations through their agencies for banking, but the achieved effects have not shown great efficiency. More and more often theses like the following can be found: “The banking system has been turned into a dysfunctional public-private project. Banking institutions achieve a huge profit by taking excessive risks in good times, while the government absorbs their losses in bad times.” (McMillan, 2014). Modern technology has drastically changed entire areas of life and interest of human population, of creation, of trade, and so on. One only needs to notice what happened to books (from their writing and publishing to how they are sold), what happened to bookstore chains (for example, Barnes & Noble). Something similar happened to the music industry (for example, LP, cassettes, CD players, mp3 players, iTunes) and to movies and TV. A very similar situation is now also occurring with banks and in the banking sector. The modern information, telecommunication, and mobile technologies lead to the fact that the modern banking client wants and demands that banks be faster, more efficient, and cheaper. Modern clients also wants that he/she realize his/her needs for banking services from his/her home or work, without needing to personally enter into bank premises. All that forces banks to be adapted to such requests and needs. However, all this in practice goes quite slowly and unorganized, without any systematic approach. This paper analyzes, proposes, and describes a systematized approach to transforming standard traditional banks into modern digital banks. It could contribute to a faster and better quality of practical transformation of banks and their adaptation to modern trends and client requirements and needs.

DISHARMONY BETWEEN DEVELOPMENT OF BANKS AND MOBILE INFORMATION TECHNOLOGIES It is a well-known fact that it is very difficult for traditional banks to adapt to the growing expansion of the use of mobile digital technologies and mobile devices. Banks were one of the leaders in introducing new technologies in the time when the first computers, servers, databases, and communication links appeared. However, reception of the latest expansion and boom in the development and use of digital mobile technologies and mobile devices banks was quite unprepared (Turban, McLean & Wetherbe, 2004; Accenture, 2015). Figure 1. demonstrates the nearly identical matching of the structure of a traditional bank information system (Figure 1a.) and the structure of the bank itself (Figure 1b.) when client-server architecture was actual information technology. That structure is with the central office and the network of branch offices, agencies, and forward (remote) work places. The main reason behind the banks’ unpreparedness and delay concerning the speed of development and introduction of information and mobile technologies lies in the traditional form of structure of the so-called Retail Bank. A real existing example of such an structure is shown in Figure 2. It is not possible to adapt such an structure, with small changes, to new technology in such a way that the technology could be used to its full capacity and taking advantage of the possibilities they provide (Sajić, Bundalo, Bundalo & Pašalić, 2017). The base of the traditional bank consists of the Central Unit (Central Office), which includes the strategic and operational management of the bank, IT (Information Technology) centre, marketing, the Call Centre, the Back Office, accounting and controlling, legal services, and other support services. The Central Unit dictates work procedures , creates and launches products on the market, and manages network operations. The network consists of relevant branches, agencies, 60


EJAE 2019  16 (1)  59-76

SAJIĆ, M., BUNDALO, Z., BUNDALO, D.  DEFINING THE NEED FOR AND PROPOSING HOW TO TRANSFORM TRADITIONAL INTO DIGITAL BANKS WITH THE SUPPORT OF INFORMATION AND MOBILE TECHNOLOGIES

and counters. This network was recently was expanded to include ATM (Automated Teller Machine) and POS (Point Of Sale) devices. That, in a sense, represented the limited use of modern technologies. The main problem of such forms of bank structure lies in the fact that modern clients, using modern mobile information technologies, mobile smart phones and tablets, receive an increasing amount of information and services through these mobile devices. As such, the need for clients to personally enter banks for services continues to disappear. On the other hand, the basic concept behind traditional banks is to attract clients to come into their bank branches and offices.

a)

b)

Figure 1. Similarity of client-server architecture (a) and network structure (b) of bank

Figure 2. Example of the traditional structure of bank 61


EJAE 2019  16 (1)  59-76

SAJIĆ, M., BUNDALO, Z., BUNDALO, D.  DEFINING THE NEED FOR AND PROPOSING HOW TO TRANSFORM TRADITIONAL INTO DIGITAL BANKS WITH THE SUPPORT OF INFORMATION AND MOBILE TECHNOLOGIES

Banks have tried to use modern IT technologies, but quite incorrectly. Banks send e-mails and SMS messages to clients and publish a variety of information on their websites. Yet all this is done to attract clients to enter their offices. That is a real problem of this approach. Modern clients would like to obtain the banking services they need on their mobile devices and that they can realize these services from home, from work, while traveling, etc. However, the basic concept of a bank is still to do everything to attract clients to come into their office and to present and sell their banking products to clients there. Banks have begun to develop and provide electronic (eBanking) and mobile banking (mBanking) services as products in order to somehow follow in line with their clients’ desires and needs. .Products, when sufficiently popularized and distributed, demonstrate the thesis is correct of thesis concerning the way the structure of banks is outdated. Clients can perform many services by using their mobile devices. It practically demonstrates that the bank network of branches, agencies, and remote counters becomes unnecessary in the form that it is now. The simplest cost/benefit analysis would show that branch offices are more and more expensive, and that they are becoming less and less profitable. Clients’ habits of are increasingly moving in other directions. The only thing that still retains branches in practice is the legal regulation of the Banking Agencies. They still require a client’s physical presence in the realization of certain banking services (physical identification, signature on paper, etc.). Figure 3. demonstrates the typical architecture of modern banks and a block diagram of the information system of a modern bank. In addition to the central area, essentially two channels, those of communication and bank operation performance with clients, physical channel and mobile channel, are shown. Physical contact with clients is realized through the network of branches/agencies/counters where there is physical personal contact between bank employees and clients. Through personal conversation, clients request and bank employees provide needed services. On the other hand, a number of clients access banks via the Internet, and use their mobile devices at their disposal. They perform their operations with the bank through the appropriate software support. Though not specifically shown here, ATM’s and the network of POS devices basically belong to this group.

Figure 3. Typical architecture and block diagram of information system of modern bank. 62


EJAE 2019  16 (1)  59-76

SAJIĆ, M., BUNDALO, Z., BUNDALO, D.  DEFINING THE NEED FOR AND PROPOSING HOW TO TRANSFORM TRADITIONAL INTO DIGITAL BANKS WITH THE SUPPORT OF INFORMATION AND MOBILE TECHNOLOGIES

Figure 3. clearly shows that, during the time in question, more and more customers will use remote access with the help of mobile devices for performing banking operations. The so-called Client migration path can be seen in Figure 3. As such, there will be more and more weaken power and importance of using certain branches/agencies/counters and of parts of their networks. Based on this, it can be concluded that the networks of branches/agencies/remote counters will collapse by themselves when a sufficient number of clients begins to use almost exclusively work with bank at distance. It will happen under pressure of insolvency if before of that is not performed bank transformation, adequately and on time. Prediction and proposal is that this will lead to transformation of complete banking network and transformation of purpose of its physical locations. In that process, some of that places will be converted into combination of devices for self-service during performing banking operations and reception offices. That offices will serve as suitable places where trained bank employees will perform work of banking financial advisors to clients who want to do this via physical contact and who need this kind of help. Some of that places will be completely closed. The latter is particularly related to rented places, non-bank-owned places. It is clear that sustainability of certain locations in which bank branches/ agencies/counters are located, in terms of cost-effectiveness, will no longer be possible after migration of sufficient number of clients to mobile technologies. That means that the main reason for banking problems during the migration from using digital mobile technology and during the transformation into digital banks is, in fact, that branch networks and agencies become unnecessary (King, 2012; Accenture, 2015). Moreover, the traditional structure of the bank implies traditional way of dealing with clients. However, all analyses demonstrate that if success with clients is desired then in some way it should become part of them (banks should become part of clients). That is especially demonstrated on the basis of development of social networks.

THE DISADVANTAGES OF THE TRADITIONAL APPROACH TOWARDS CLIENTS AND HOW INFORMATION TECHNOLOGIES HELP With the birth of high-quality CRM (Customer Relationship Management) solutions and the building of their own Knowledge Data Bases in banks it is evidently that orientation to financial data only is insufficient to provide quality banking services to clients. One of the most common descriptions of the term CRM that can be found in the literature is: “CRM - Customer Relationship Management is the harmonization of business strategies, structural structure and culture of company, information about clients and information technology, with the purpose that in all contact with clients satisfy their needs and achieve business benefit and profit. CRM can be understood as a set of tools for management of business and relationship with clients, that enable full connectivity of clients with all processes that are performed, from tracking orders, offers and contracts, to tracking working tasks. It also represents an integrated marketing, service and sales strategy that requires the common work of all departments of the company. This business strategy is based on the philosophy “the buyer is king” and focus is on the client. Management with relationship with clients is reflected through people, processes and information technologies. The tool that is used to achieve the goals of this strategy is CRM technology. CRM is not just a tool or solution, it is also a special model of thinking.” (Haase, 2016). Figure 4. shows the basic principles of CRM (Haase, 2016). Development of information and mobile digital technologies, in particular expansion of eCommerce sales via the Internet, was demonstrated to clients what are all possibilities and options in this regard. Customers now know that it is possible to buy television set, music devices, furniture, kitchen devices 63


EJAE 2019  16 (1)  59-76

SAJIĆ, M., BUNDALO, Z., BUNDALO, D.  DEFINING THE NEED FOR AND PROPOSING HOW TO TRANSFORM TRADITIONAL INTO DIGITAL BANKS WITH THE SUPPORT OF INFORMATION AND MOBILE TECHNOLOGIES

via the Internet. Then, customers correctly thing why would not it be possible to get a loan in the same way to achieve a particular goal of retail shopping. Many of them are thinking even further, why not perform entire retail shopping in this way from one place and immediately. It is realistic to predict that in the near future significant number of customers will perform complete purchase process from their homes or from their workplaces. For example, customer could purchase a car even though he/ she did not even sit in it.

Figure 4. Basic principles of CRM.

The Knowledge Data Bases have been incorporated in large banks in last ten years or more (Turban, McLean & Wetherbe, 2004; Accenture, 2015). They were originally intended to provide promptly and efficiently answers to questions asked in the structure (in the bank). However, it was appeared idea that this knowledge base, which every day increases and becomes more quality, can be efficiently used also in providing adequate answers to clients. For this purpose, with the help of so-called Business Intelligence, were made robots that respond to customers questions. It is most often performed on Web pages, although there are also cases of developed and used speech robots. Therefore, modern client raises very logical question that if this was possible to create and organize what is with possibility of automated loan offering and granting and with other automated banking services. Proposals what should be performed in this regard in order to reduce lacks of traditional approach and to use possibilities of modern information technologies: ◆◆ Collect data from all available resources It should be collecting data from all resources, especially on the Internet. Collect data such as data that can be found on social networks and various other resources. That should be data that contain information about existing but also potential clients. Locally it could be CIPS, Statistics, APIF, Registry of loans, data bases of water supply companies, electricity distribution companies, local telecom providers, etc. That data should be processed well. Organize own data bases, based on the Big Data concept, and connect them to existing CRM solution in order to obtain 64


EJAE 2019  16 (1)  59-76

SAJIĆ, M., BUNDALO, Z., BUNDALO, D.  DEFINING THE NEED FOR AND PROPOSING HOW TO TRANSFORM TRADITIONAL INTO DIGITAL BANKS WITH THE SUPPORT OF INFORMATION AND MOBILE TECHNOLOGIES

as much as possible useful client data. Such could better organize services and create products that will satisfy customers as much as possible. Based on that knowledge, choose appropriate products for appropriate user groups, address them appropriately, and perform predictions of their wishes and needs.

◆◆ Understand the power of mobile systems Power of mobile systems could be illustrated, for example, with the information that the Barclays Bank needed 13 years to collect 2 million clients in its Internet banking application and only 2 months for the same number of clients on the mobile application. ◆◆ Understand the power of social networks The most banks not only do not use social networks, such as are for example Facebook, Twitter, etc., but intentionally avoid them. That is practically big mistake. On the contrary, banks should stimulate “chatting” through that social networks. They should to open their accounts and profiles on social networks and to obtain their followers. In that way banks would easier perform marketing and expansion of information about their products. Also, those followers and others who would give their comments on social networks can serve to bank, especially to department for new products, as free of charge but very powerful testers of new products. It could be also performed surveys on existing products, collection of helpful advices for improving products and services, and similar. But, it should be very careful that all this be performed in accordance with relevant laws and regulative about personal data. For example, the GDPR (General Data Protection Regulation) is regulation of European Union that prescribes way of manipulation with personal data of citizens (EU Regulation 2016/679) (European Parliament and Council of Europe, 2016).

THE INFLUENCE OF SOCIAL NETWORKS ON THE DEVELOPMENT OF BANKING PRODUCTS Large modern companies, such as Google, Apple, Facebook and similar, with their approach and appropriate social networks, achieved that clients share their information with others. Banks are either not trying to participate in social networks, or are doing it in a rather rigid and incorrect way, trying to control them. It has a bad effect for banks. Banks should stimulate engagement and the openness of their clients in order to obtain as much and as precise information as possible from the clients for the banks’ own needs. Below is a proposal for what should be done in order to use social networks to significantly improve bank operations: ◆◆ Harness the power of the masses by organizing surveys about their own current and future banking products Use mass of bank active and potential users present on social networks for testing and as source of future ideas. Such, it is simply possible to bank to convert that mass of clients into a kind of department for new products, the department with great efficiency and with small investments. But, wrong approach to that mass of clients can cause loss of clients confidence, permanently or for a longer period of time. Therefore, all this should be performed very systematically and very responsibly.

65


EJAE 2019  16 (1)  59-76

SAJIĆ, M., BUNDALO, Z., BUNDALO, D.  DEFINING THE NEED FOR AND PROPOSING HOW TO TRANSFORM TRADITIONAL INTO DIGITAL BANKS WITH THE SUPPORT OF INFORMATION AND MOBILE TECHNOLOGIES

IMPROVING KNOWLEDGE AND SOCIALIZATION OF CLIENTS USING MODERN INFORMATION TECHNOLOGIES

Let us consider the following situation. Let someone is bank client for 10 years and they ask him/ her in the bank for name of his/her father. Two problems may arise in such case. The first problem is that, on the basis of a rigid too formal approach, it can be provoked reaction of the client of type “Why are you interested in that?”. On the other hand, many companies such as are Google, Amazon, Apple, Facebook, Instagram, LinkedIn and others, already know this information for long time period. They were properly and unobtrusively obtained that information from the user, mainly through a hidden question when creating user names, passwords, and similar. Another problem, possibly even bigger, could be client reaction of style: “I am your client for ten years and you do not know anything about me. You asked me this question many times.” Thus, the client correctly concludes that this bank does not care about him/her. His/her natural reaction is to look for another bank what will be better financial advisor for him/her. It is evidently that companies Google, Amazon and similar companies know more about us than the banks in what we are clients. It is clear that the problem is in the technology of approach and access to the clients. Proposal what should be performed to better use knowledge and socialization of clients and modern information technology to improve bank operations: ◆◆ Change behaviour towards the client, be more accessible and open It should allow even more relaxed way of bank staff dressing in order that also in this way be closer to clients. A rigid, repulsive and too formal approach and treatment towards clients refuses them and certainly does not contribute to the trust and closeness with bank, that the modern client is looking for. One of examples is way of dressing of bank staff, suit, tie, and similar. Analyses show that this generally could influence more refusing than that it attracts modern client. Also, it is needed to look more in the eyes of the client during contact and less to look at computer screen to concentrate on correct data entry. However, in order that bank employee be able to perform all of mentioned in a quality way, it should be enabled to him/her by technology. The bank should strive towards appropriate so-called “Front End” solution (an application used by bank employees when serving client). That solution should to allow that with a few movements of mouse employee can find and enter appropriate data, that use of keyboard be as less as possible, that the application itself offers appropriate products according to characteristics of specific client that adequate CRM will get either from CBS (Core Banking System) or from Big Data system. This means that banks needs to know their clients, and to use the KYC (Know Your Client) principle. There is no bank that does not insist on it. But the big question is whether it is the right way it wants to perform it. Proposal what should be performed to better know bank clients in order to improve operations of banks: ◆◆ Gain more knowledge about clients through sophisticated built CRM solution, based on Data Warehouse and Business Intelligence (Sajić, Bundalo, Bundalo & Pašalić, 2017) The main goal is to have accurate and on time information about client, both financial and nonfinancial, about his/her needs, wishes, and even also about his/her behaviour. That will enable necessary information to those bank employees who come at the counter in contact with the client, as well as to those who work on segmentation, who create and realize campaigns, according to which they will act towards client in an adequate way. Also, it should enable to those clients who want their activities with bank to perform remotely, by mobile devices, from home, from work, when are at travelling, to can perform it in such a way. 66


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It should be also used possibilities of so-called socialization of clients. It means that it should be find ways how to convince client to believe that bank employee will offer him/her the best solution. Proposals what should be performed to better use possibilities of socialization of clients in order to improve operations of banks: ◆◆ Create mutual trust

It should be understood that power of the Internet and mobile devices is such that client can to obtain adequate information, in a very simple way, very quickly, almost on the spot, about same or similar products that are offered to him/her from other banks. This practically means that it should be offered products to client with reasonable prices and other conditions. It should not try at any price that product be sold to client, especially when it is known in advance that client will not use it and that product is not suitable for client. In practice it is very often case with sale of the package products. Managers in banks should stop practice of stimulating sales of products at all costs. For such attempts of sales, on the contrary, they have to determine penalty counter-measures. Client will realize that bank sold him some product unnecessarily, that he/ she does not use it, but pays as is using it. Client will then feel cheated and perhaps decide to change the bank. That is very often case in banking, selling products without previous analysis of needs of clients. The most common case is with electronic payment cards where to client are issued and sold, for example, both Visa and Master credit cards, but client uses only one of the cards. Similar situation is also with issuing and selling revolving and instalment cards that are usually not used by client. Very similar things were also done by mobile providers who offered the package services. They were, for example, to user who is their client for about decade or more in the package among others offered up to 3000 SMS messages per month. But, if they were performed statistical analysis of amount of messages that client sends monthly they would see that it was only about 10. ◆◆ Stop practice of treating clients as they are goods, but try to satisfy real needs of client When bank obtains customer trusts in such a way then it can count on long-term cooperation. Client will not leave bank so easily just because someone else offers some more financially favourable services at a certain moment, because the trust is created for years.

TRANSFORMATION OF BANKS AND THE INCREASED NEEDS OF CLIENTS The most surveys that are carried out and that can be found in literature show that younger clients mostly say that banks do not know their needs. Also, the elder population of clients is not satisfied with degree of assistance of bank in creating their financial needs and strategies. As one recent and interesting example of that can be seen conclusion of one study. According to research of Cisco company with the title “Reimagining Digital Bank” it can be seen that 43% of clients in USA believe that their bank does not know them and therefore cannot provide them personalized service. In addition, 31% of clients believe that their bank does not help them to achieve their primary financial goals. Also, 20% of examined said that they will change banks and traditional financial institutions with personalized services of so-called “Internet of Everything (IoE)” type. Proposal what should be performed to better coordinate activities of banks with growing customer needs: ◆◆ Accelerate transformation of banks Accelerate bank transformation even at cost of achieving worse financial results in transformation period. That could be problem if owners of bank do not take clear position about the transformation. It is well known that owners of large number of banks are small shareholders 67


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and that there is no one recognizable owner who would have significant number of shares. That is more common for European banks than for US banks. The problem with banks with such type of capital could be when it is necessary to influence on Bank Management to begin faster transformation. Namely, mandates of Bank Managements are mainly on 4 year period. For such large bank transformations as is the one proposed and described here 4 years is short period. Also, effectiveness of success of existing Bank Management is measured in the period of their mandate. Therefore, any Bank Management will very difficultly and unwillingly decide to go on more radical transformation moves what may give worse results in initial series of years, even if later successes would far exceed previous temporarily weaker results. That really slows down processes of transformation and can lead banks to large problems. It is therefore necessary to look for effective ways how Bank Managements could be free of existing fear and be stimulated to accelerate unavoidable processes of bank transformation.

MODERN RETAIL Modern retail of products and services, such as are performed by big companies Apple, Amazon, Google, eBay, etc., no longer implies only sale in the stores. It implies also use of such stores as means of creating followers of a brand, creating loyalty and belonging to a particular brand. Philosophy of so-called physical presence in the store is more and more becoming an outdated category, economically unprofitable and unsustainable, especially when products are becoming more and more “virtual.” Practice of obtaining new clients at all costs, while neglecting keeping of existing ones, returns to banks as a boomerang. Modern IT solutions, advanced CRM and Front End solutions, mobile applications, Big Data and similar, and modern tools for achieving KYC approach are needed for keeping clients and obtaining clients loyalty. It is necessary to maintain close relationship with existing clients and to increase total number of clients. This can be achieved by mobile applications, remotely. It is proposed that it be accomplished in the following way: ◆◆ Working, as best as possible, and as profiled as possible, on the segmentation of clients It should be performed with help of Big Data, social networks and strategies of addressing to different groups of clients, ◆◆ Working on permanent improvement of speech robots Robots that answer on clients questions (so-called Chat boot) should be permanently developed and improved, ◆◆ Gathering in the Call Centre experts for the products That experts should to answer live to clients by chatting, video and phone calls or postponed by e-mails, video help clips, etc.

MODERN BANKS AND THE CONCEPT OF THE DIGITAL BANK The main task of standard Retail Bank in the past was to distribute live money (so-called cash) through network of bank branches. The basic task of modern bank now is ability to be constantly on service to customers with all bank products on the principle of availability of 24/7/365 basis. It can be shortly said for fully digital bank that the entire banking business that bank has in its scope of activities digital bank performs in the cloud, via the Internet. There are no offices (physical places and locations) in what the bank would receive and serve clients. Accessibility of the bank and services 68


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is permanent and really of 24/7/365 type. Many banks, still traditionally oriented, usually post on their Web pages information about availability of 24/7/365 type. But, it is only partially correct and is valid only for certain types of services and products. Mobile phones and other types of mobile devices are no longer just technological fashion trend. They are means that connect people on distance, that connect friends, relatives, business partners. Thus, the mobile devices become unavoidable also as means for realizing of banking operations. It is obvious that the mobile devices will very soon become primary means for realizing banking operations, as in some spheres of human activities they already are. Also, it is obvious that thus bank also will soon become digital bank (Marous, 2013; Accenture, 2015). The digital bank is partly also social bank (Skinner, 2014). For example, it is interesting how many newspapers are being purchased and reading in this time. It is clear that this is less and less because their news, while they be printed and reach users, are mostly already out dated and worthless. Various types of Internet social mobile media have already published them. The social media long ago do not exist and live from subscriptions and similar things. They mostly serve to stimulate clients engagement and activities, through which, most often in an indirect way, they come to financial benefit. It is, therefore, clear necessity for digital bank to be connected with social media and social networks in finding useful information and in finding some of future ways for realization of financial earnings and profit. So, there is need to create links between banks and various social networks in order to exchange useful information and to obtain new useful information and knowledge about clients and groups of clients. Banks can use social media and social networks in different ways: ◆◆ As a sources of information about clients, ◆◆ As a sources of information about different trends, ◆◆ As a polygon for testing of new ideas and new products, ◆◆ As a polygon for development of new products by launching information about new products and gathering reactions and opinions about it, ◆◆ As a place for popularization of their products and services However, bank should here act with extreme care because social media were proven as very powerful in popularizing of any topic. So, it should take care not to get the reverse effect, not to start an avalanche of bad criticism on account of services and products of the bank, what could have great negative consequences for that bank, ◆◆ As a source and an example of new ways of performing operations and providing services.

TRANSFORMATION OF BANK STRUCTURE AND ROLE OF MOBILE INFORMATION TECHNOLOGIES Development and application of information technologies significantly influence on needs and way of transformation of bank from traditional into digital one. It is clear that this will also cause significant changes in structure of bank. It is proposal here and it is also expectation that in process of transformation the structure of banks should be transferred from vertical to horizontal one. Therefore, it is proposed here that in structure of bank should be performed transformation of sectors and departments into teams, organized and gathered around individual banking products. The teams should look more like project teams than like current banking sectors. There would be very few workers outside of these teams, mostly common services and management. Figure 5. shows proposed model of structure of modern digital bank. It is more similar to some production structure than to the current bank (Sajić, Bundalo, Bundalo & Pašalić, 2017). 69


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With banking experts for each particular product in the teams will also be experts from IT technologies who are connected with that product, experts from so-called data science, experts for marketing of that product type, experts from Call Centre. Proposal is that, with all of them, in the team also be engaged mathematician/statistician and psychologist/sociologist. They should statistically and psychologically/sociologically shape and from the expert point of view interpret obtained data. Based on that they will propose guidelines to the marketing and others in the team. It is clear that in the transformation of structure of bank the IT sector will also go through significant changes. It is proposed here that the most part of the IT sector be simply divided by product teams. As some specific sector or department it should remain technicians/operators and administrators who work on general activities (e-mail, the Internet, active directory environment, etc.). It is also evident that in such new structure of digital bank outsourcing of the IT sector becomes impossible. It should be already clear to everyone that technology on what are based all more important jobs and work places in the bank can not be outsourced. IT sector for long time is no more bank service but it acts more like its bloodstream. It may sound strange but it is easier to outsource the Corporate part of bank than the IT part. Something just like that will most probably happen in near future. For example, the banks for what it will no longer be profitable will transfer business of payment transactions to someone else with whom they will make contract. It could be for example some FinTech company, PayPal for example. However, it is absurd and impossible to expect that bank can perform outsourcing of operations of IT sector in the future. If the bank would even succeeded to do it, having in the mind nature of digital bank and ways of its operation, nothing more would prevent such an outsourcing partner from becoming bank itself, because it was already taken over all important bank jobs.

Figure 5. Proposed model of structure of modern digital bank.

Also, it is evident and clear that the Bank Managements in the modern digital banks will have to have IT experts in their composition. Or the Bank Management will post them as so-called Chief Information Officers. They will not formally be in the Bank Management but will have authority as well 70


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as the Bank Management in the areas of IT technologies that they cover. It is already evident in present banks that there is huge lack of IT experts, especially in part for organizing and performing projects. The great majority of banking projects as a very significant have IT component. That creates difficulties for those who initiate, organize and implement projects but do not have necessary IT knowledge. When such project comes to the head of IT sector it is usually then too late to correct created errors. Also, the head of IT sector does not have sufficient authority to impose an opinion that he/she considers to be correct based on his/her expertise and expertise of his/her team.

MODERN BANKING APPLICATIONS AND CORE BANKING SYSTEMS IN THE FUTURE The biggest transformation in information technologies and in information system (IS) in bank will not happen either in interface nor in some special technology. It will be in approach which must enable easier anticipation of needs of clients and their habits and customs. It is proposed here that in the future it more goes to build such API functions that will be easily fitted into various heterogeneous systems. It is proposed that the IS of the bank be decomposed into the components, into the products. On the basis of it, the banks could be also profiled and could perform only some types of banking operations, that are profitable for them. With other jobs bank will not deal at all or will, through the API interface, connect with other banks or FinTech companies, specialists for that type of services. The bank will transfer them concrete work while taking its share of financial compensation in distribution chain. Only members of large banking groups will, maybe, remain to deal with complete banking operations as it is also situation now in practice. Regarding the API functions, in one survey can be seen that 59% of banks declared that so-called “open API” technology will have great impact on modelling their products. Approximately 67% of banks stated that API already has an impact or that it is expected in the next 2 years. While 39% examined stated that they already allocate large percentage of investments in the implementation of API technology. The proposal of structure of the IS of modern digital bank is shown in figure 6. (Sajić, Bundalo, Bundalo & Pašalić, 2017). It can be seen from figure 6 that here is proposed and predicted that communication with data that is coming from clients first realizes the CRM and then the CRM forwards data to the CBS. It is ensured in this way that the CRM in the real time analyzes incoming data and creates some conclusions based on it or only forward data to the CBS.

Figure 6. Proposal of structure of information system of digital bank.

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PROPOSAL OF WAY OF BANK TRANSFORMATION FROM TRADITIONAL TO DIGITAL BANK

In addition to already mentioned arguments that are caused by huge population of digital mobile technology and changes of habits and wishes of clients, there is also one more important fact why it is natural process of transformation of traditional into digital (cloud) bank. If it is looked carefully, it can be seen that almost all banking products and services have already become of virtual character. With withdrawal of “the cash” from circulation the last “solid” banking product is also lost. It is known that every virtual product and service can be algorithmically described and therefore software processed and automated (API functions). That is why a logical question arises in relation to fact that banks usually have large number of branches, most often in their possession. The proposed solution is in performing transformation of bank from traditional to modern (digital) bank. Intermediate phase in that would be so-called hybrid period. One representative example of failed attempt of transformation is transformation of traditional banking branches into places for sitting, drinking coffee and talking about banking activities, according to model applied for bookstores. Clients that come into bank offices come with concrete intention and do not want to spend more time there than it is needed. They want as soon as possible to complete activities they have planned. They probably do not have enough time and have some other obligations. This is basic difference in comparison to similar, but successful, model of transformation of bookstores. Even some conveniences, such as free Wi-Fi, free coffee and similar, do not hold clients in bank more than it is needed. Simply, clients did not come with intention to relax but to perform necessary activity in the bank as quickly as possible. It is proposed here one completely different way of transformation of bank branches. Some of reasons for that transformation are: ◆◆ There is less and less money in the branches, the money becomes data, and data must be centralized to be usable. ◆◆ Branches will be less and less sales and transaction centres, and more and more will become centres where consultations on more complex products are performed. ◆◆ The products of the bank, except of the cash, by their nature are virtual and then it is much easier to organize sales and perform services via the Internet. Newly formed banks and small banks with a few branches will much easier perform transformation into hybrid or digital banks. Large banks will have many problems while be performing transformation. What banks are bigger the transition process will last longer. Some of the world largest banks are, probably calculating about needed time for transition, decided that for them is more profitable to create completely new digital bank that will appear on the market immediately. That bank will acquire part of new clientele that is already ready for such type of bank and prevent leaving of part of the existing clientele who will automatically move to the digital bank. It is proposed that the process of bank transformation be divided into two periods: hybrid period (Weber, 2014) and period of creation of the digital bank, and that the process be realized in that order.

Hybrid period It is proposed here that the hybrid period of transformation of banks includes the following phases and activities: ◆◆ Replace traditional banking counters in the bank branches with multifunctional ATM devices Besides issuing money that devices can also receive money, perform payments and can gradually increase number of banking services. At the start, it would be desirable that this devices have 72


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microphone handsets and ability to reproduce video clips that are essentially help reproductions. Such equipped that devices would be able at a later stage, when will no more be employees at place of installation of that devices, to have possibility for connection with the Call Centre for purpose of providing assistance and consultations to clients.

◆◆ Train employees to become consultants and providers of services Train them to be more consultants and providers of services and less sellers of services. ◆◆ At places where the settings of described devices creates sufficient surplus of employees shorten working hours of employees and organize work in more shifts It should be performed in order to provide as quality as possible and as approximate as possible services of the 24/7/365 type, and in order to keep as much as possible employees. ◆◆ ATM devices, located in separate locations outside the branches, attempt to transfer to ownership of specialized firms Such firms will necessarily appear on the market and their primary business will be working with ATMs. It is already known that ATMs are becoming more cost than they itself earn. Reasons for that are increases in cost of renting space, insurance and transport of the money. If it is realized by one specialized firm, its costs will be the same or lower than for the bank, but it will be used the same device for several banks with which it makes contracts. On the other hand, bank will such expand its network of ATM services and at same time reduce costs. ◆◆ Transform branches and agencies into places to provide consultations It should be performed for purpose of better selling products and raising level of bank services. ◆◆ Try to sell or rent selling places that appear insufficiently visited It should try to sell or rent places that are non profitable in terms of contribution to the overall image and operation of the bank. ◆◆ Introduce and permanently upgrade CRM solutions in information system of the bank It should be performed in order of better and faster segmentation of clients, connected in groups according to some characteristics and, accordingly, creating successful campaigns for purpose of selling and popularizing banking products and services. Good CRM solutions will enable more efficient usage of information about clients financial habits and achievement of KYC strategy in full sense of that word. ◆◆ Introduce and permanently upgrade DMS solution of the bank It should be performed in order of more efficient archiving of banking documents, their faster searching and savings in physical space, paper and everything else that includes the archive in physical sense. DMS is very useful also for possibility of automating of banking jobs. ◆◆ Permanently work on the automation of banking operations It should try to perform it in all parts of the banking business.

Period of creation of digital bank It is proposed here that the period of creation of a digital bank includes following phases and activities: ◆◆ Synchronously with phases of hybrid period, strengthen centralization of bank with core in Call Centre The reason for this is that through the Call Centre will be provided consulting and help services to clients. It will be performed either through educational video clips, by telephone, via live video, by chatting or by email correspondence. By contact via the Call Centre clients will be able to get 73


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answers on their questions and consult themselves from the first hand, from the most specialized people and experts related to the particular product. In the traditional bank system the client received information from the Account Managers or Personal Bankers, who went to trainings to mentioned experts. Although they can be very capable, they are still so-called second hand experts. Such, it is lost on the quality of presentation of a particular product.

◆◆ eBanking and mobile banking applications merge into one, at least as far as users are concerned The goal is that user when is using eBanking or mobile application not notice difference at all, that can to start at one and to end on the other, that training for using is the same for both, and similar. ◆◆ As tempo of harmonization of legislation will allow, change products and services in a way that they can be executed and used remotely Products and services should also be transferred on mobile applications. ◆◆ Create a flexible information system It should be performed through the API functions. It should be performed such that it be easily compatible with other applications, especially with those already offered by the so-called FinTech firms, because that firms are leading in innovative services. There are very few of banks that have such development teams that can parry to FinTech firms. If they have them, it is only in certain banking business domains. It is more profitable to have the ability to provide some service on time, than at all costs to develop it. The PSD2 EU directive supports this conclusion (Backbase, 2017). ◆◆ Reorganize bank such that it has far less vertical and far more horizontal elements It can be seen in figure 1. problem and complexity of organizing cooperation between sectors in traditional bank, because all cooperation goes through the central parts. It is therefore necessary to reorganize current banking sectors (Retail, Corporate, IT, etc.) into more efficient teams concentrated around products or product groups. Only the most necessary personnel that serves to provide common internal services to that teams should be leaved in the vertical part of the structure. Also, very small number of managing posts that serve to effectively connect that teams into the structural unit should be in the vertical part. Programmers, development teams, marketing, sales teams should be grouped by that product teams. Thus will be provided narrow specialization of professional personnel, their greater efficiency and better teamwork. Such new proposed structure of the digital bank is shown in figure 5. (Sajić, Bundalo, Bundalo & Pašalić, 2017). ◆◆ Introduce the “Big Data” concept into the information system of bank It should be performed in a way to enable better collection of both financial and other data about clients of the bank, according to the model of the world known search engines, Internet shops, social networks, etc. For providing adequate services to clients, it is not enough only knowledge about the financial characteristics of the client but also other knowledge, especially related to its purchasing habits. So, it is necessary and desirable to perform as soon as possible transformation of banks from financial data banks into data banks. This will also cause employment of some of previously unused employee profiles in banks. Beside “data science” specialists that would be experts for Big Data, it will be need to classify and statistically process the data. Such, it opens places for profiles of employees of mathematician/statistician type and psychologist/sociologist (or of similar education) type. They will provide correct interpretations of statistically processed data. ◆◆ Permanently work on development and introduction of new banking services and products It should be performed on application of information and mobile technologies (Sajić, Bundalo, Bundalo & Pašalić, 2017; Sajić, Bundalo, Bundalo & Pašalić, 2018; Sajić, Bundalo, Bundalo, Stojanović & Sajić, 2018; Sajić, Bundalo, Bundalo, Sajić & Kuzmić, 2018). 74


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CONCLUSIONS

The need for anticipated changes in the banking sector is no longer questioned. The way that this will be performed can vary in accordance with specifics of concrete bank. One general principle for transformation of banks from traditional into digital banks that is practically the most expected and the most realistic possible way is proposed and described in this paper. Tempo of implementation of all activities of the transformation will depend mostly on tempo of adoption of necessary legal regulations related to the digital business. Such regulations are digital signature and other similar regulations that will remove need for physical visits of clients to bank branches. Also, it will depend on degree of pressure of clients on banks for gaining ability to use bank services remotely. Proposal is that banks already now perform all activities that they are able to do in order to more prepared meet that changes. Also, banks should even to initiate that changes in accordance with their possibilities. Cooperation and symbioses of banks and telecom operators also have great chances to provide good results in these areas, especially in the case of local and regional structures. The process of transformation will be more complex and slower if bank is more diversified, if it has larger network of branches and agencies. From that reason, some large world banks use another method in the transformation. They were established totally new digital bank. That digital bank in parallel with traditional bank operates on the market. It tries to satisfy all those clients who are already prepared to move to new way of operation with bank (remote operation). At the same time, they will also get some new clients from some other banks that still do not offer such conditions of operation. It is expected that, over time, this newly established digital banks will take over leadership of their traditional banks founders and that huge majority of clients will move over time to that new digital bank. The trend among clients to complete the entire centralized retail business has become more and more noticeable. For example, a buyer wants to buy new kitchen. He/she wants that from one place select model of kitchen that suits to him/her and that at the same place resolves commercial conditions (to choose loan and perform payment). If banks do not set themselves as leaders in this process, someone else will set up and take over that business. Companies that have successfully developed social networks, search engines, and Internet shopping could become new retail leaders. Such companies have greater potential and possibility to take over the retail business and, therefore, retail banking in the future. Banks need to keep this in the mind, to develop their own appropriate strategy, and to transform themselves in that direction as soon as possible.

REFERENCES Accenture (2015). Being digital: Digital strategy execution drives a new era of banking. Retrieved Jun 30, 2018, from https://www.accenture.com/us-en/insight-digital-strategy-new-era-banking Backbase (2017). The PSD2 Playbook - Backbase. Retrieved Jun 30, 2018, from https://backbase.com/wp-content/ uploads/2017/04/Backbase_The_PSD2_Playbook.pdf European Parliament and Council of Europe. (2016). Regulation (EU) 2016/679 of the European Parliament and of the Council of Europe of 27 April 2016 on the protection of individuals with regard to the processing of personal data and on the free movement of such data and on putting out of force of Directive 95/46/EC (General Data Protection Regulation). Retrieved Jun 30, 2018, from https://eur-lex.europa.eu/eli/reg/2016/679/oj Haase, S. (2016). 4 unconventional ways to use your CRM software. Retrieved Jun 10, 2018, from https://www. entrepreneur.com/article/279015 King, B. (2012). BANK 3.0: Why Banking Is No Longer Somewhere You Go But Something You Do. Hoboken: Wiley. Marous, J. (2013). Top 10 Retail Banking Trends and Predictions for 2014. Retrieved Jun 3, 2018, from https://thefinancialbrand.com/36367/2014-top-bank-trends-predictions-forecast-digital-disruption. 75


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SAJIĆ, M., BUNDALO, Z., BUNDALO, D.  DEFINING THE NEED FOR AND PROPOSING HOW TO TRANSFORM TRADITIONAL INTO DIGITAL BANKS WITH THE SUPPORT OF INFORMATION AND MOBILE TECHNOLOGIES

McMillan, J. (2014). The End of Banking Money, Credit, and the digital Revolution. Zurich: Zero/One Economics. Sajić, M., Bundalo, D., Bundalo, Z., & Pašalić, D. (2017). Digital Technologies in Transformation of Classical Retail Bank into Digital Bank. In 25th Telecommunications forum TELFOR 2017. 21-22 November 2017 (pp. 1-4). Belgrade: Telecommunications Society. Sajić, M., Bundalo, D., Bundalo, Z., & Pašalić, D. (2018). Using Digital and Mobile Technologies for Increasing Efficiency of Financial Institutions. Acta Technica Corviniensis-Bulletin of Engineering, 11(3), 39-42. Sajić, M., Bundalo, D., Bundalo, Z., Stojanović, R., & Sajić, L. (2018). Design of Digital Modular Bank Safety Deposit Box Using Modern Information and Communication Technologies. In 7th Mediterranean Conference on Embedded Computing MECO 2018. 10-14 June 2018 (pp. 107-112). Budva: IEEE. Sajić, M., Bundalo, D., Bundalo, Z., Sajić, L., & Kuzmić, G. (2018). Programmable Electronic Payment Card Transaction Limit Implemented Using Mobile Electronic Technologies. In 7th Mediterranean Conference on Embedded Computing MECO 2018. 10-14 June 2018 (pp. 186-190). Budva: IEEE Skinner, C. (2014). Digital Bank: Strategies for Launching or Becoming a Digital Bank. Singapore: Marshall Cavendish Business. Turban, E., McLean, E., & Wetherbe, J. (2004). Information Technology for Management - Transforming Business in the Digital Economy. Hoboken: Wiley. Weber, M. (2014). 5 Tips for Your Next Branch Transformation Project. Retrieved Jun 5, 2018, from https://thefinancialbrand.com/39641/bank-credit-union-branch-design-tips.

POTREBA I PREDLOG NAČINA TRANSFORMACIJE KLASIČNE U DIGITALNU BANKU UZ POMOĆ INFORMACIONIH I MOBILNIH TEHNOLOGIJA

Rezime: U radu se razmatra i opisuje potreba da se praktično i efikasno realizuje transformacija standardne klasične banke u digitalnu banku i način transformacije korišćenjem informacionih i mobilnih tehnologija. Rad ima za cilj da pruži jasne dokaze o nužnosti transformacije banaka iz sadašnjeg klasičnog oblika u novi oblik organizovanja i pružanja usluga, u tzv. digitalnu banku. Pri tome je takođe naglašen veliki uticaj mobilnih digitalnih informacionih tehnologija na celokupni finansijski sektor. Objašnjeni su problemi koji nastaju zbog razmimoilaženja postojeće koncepcije razvoja banke i načina na koji moderni klijent želi da mu se pruži usluga, shodno mogućnostima modernih mobilnih informacionih tehnologija. Zajedno sa razlozima koji zahtevaju promene, dati su i predlozi kako da se praktično izvrše te promene i transformacija. Predstavljena su osnovna načela prelaska banke iz klasičnog u digitalni oblik, preko tzv. hibridnog perioda transformacije, te predložena neka konkretna rešenja.

76

Ključne reči: transformacija banke, informacione i mobilne tehnologije, klasična retail banka i digitalna banka, hibridni period transformacije banke, period stvaranja digitalne banke.


EJAE 2019, 16(1): 77-98 ISSN 2406-2588 UDK: 005.334:009.96 338.124.4(596.2) DOI: 10.5937/EJAE15-19262 Original paper/Originalni naučni rad

RESULTANT EFFECT OF CRISIS-DRIVEN HR STRATEGIES APPLIED DURING CURRENT ECONOMIC CRISIS IN OMAN – AN HR MANAGER’S PERSPECTIVE Venkat Ram Raj Thumiki1, Ana Jovancai Stakić1,*, Rayaan Said Sulaiman Al Barwani2 Modern College of Business and Science, Muscat, Sultanate of Oman 2 Franklin University, Columbus, Ohio 1

Abstract: During an economic crisis, companies redesign their functional strategies for survival and growth. This paper aims at identifying HR practices adopted during the current economic crisis in Oman, and explains the resultant effect of crisis-driven HR strategies from an HR managers’ perspective. Primary data was collected online from 112 HR managers representing various manufacturing and service organizations in Muscat, Oman. It was found that HR managers perceive a greater impact of economic crisis on their businesses rather than other types of crises such as natural and technology crises. They think that managing employees during an economic crisis is relatively easier than managing other resources and other stakeholders. Changing HR practices during economic crises include, abeyance of employee benefits and cutting costs on employee recreation. Knowledge management activities have been increased and non-monetary motivation techniques are being adopted as a part of crisis-driven HR management. Increased employee engagement and enhanced corporate image among employees were identified as the resultant effect. Testing the hypothesis revealed that cost cutting on employee recreation is significantly higher in large organizations, job redesign activity is significantly higher in small organizations, while large organizations find it difficult to deal with employees during periods of crisis more so than small and medium sized organizations do.

*E-mail: anastakic@mcbs.edu.om

Article info: Received: October 23, 2018 Correction: December 5, 2018 Accepted: April 8, 2019

Keywords: economic crisis, abeyance, job redesign, non-monetary motivation, knowledge management.

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THUMIKI, V. R. R., STAKIĆ, J. A., AL BARWANI, R. S. S.  RESULTANT EFFECT OF CRISIS-DRIVEN HR STRATEGIES APPLIED DURING CURRENT ECONOMIC CRISIS IN OMAN – AN HR MANAGER’S PERSPECTIVE

INTRODUCTION

In the business world, where crisis is common, how an organization acts and responds to the crisis and how it motivates its employees (Silverthorne, 2018) speaks about how successfully it will survive (Argenti, 2002). Crisis management is a process that includes, understanding crises before they occurs, preventing crisis situations whenever possible, managing business functions during crises and handling post-crisis situations (Augustine, 1995). There are various types of crises, such as natural crises, technological crises, economic crises, crises of malevolence, etc. (Hart et al., 1993). Cyclone Gonu created a natural crisis in Oman in 2007 that resulted in a temporary shut-down of the Sohar and Qaboos ports and lowering operational level of oil refinery petrochemical plant in Sohar (Fritz et al., 2010). Emma Storm caused disruption to the supplies and affected the retail industry in England (Woods, 2018). A malevolence crisis was recorded in Sony Picture Entertainment in 2014, due to a cyberattack that leaked information regarding the release of its upcoming films (Peterson, 2014). Another example of a malevolence crisis is the ‘Ransomware’ attack committed by a hacker using the ‘WannaCry’ malware (Graham, 2017). Along with macro environmental factors, such as oil price fluctuations, internal factors such as organizational misdeeds, mismanagement and mishandling of situations could also cause crisis in a company (Coombs & Holladay, 2002). An important aspect to note is that, whatever the type of crisis may be, the impact will be on one of the key stakeholders, i.e., employees (Coombs, 2004) and lack of knowledge of HR policies among employees could lead to aggravating the situation (Narayanan et al., 2018). Current research addresses the role of HRM in times of economic crisis through capturing the crisis-driven HR strategies and further understanding the resultant effect of those strategies on human resources from the HR managers’ perspective. The current economic crisis in Oman started in 2014 with the reduction in oil prices (Elrich, 2015) as Oman depends on oil for more than two thirds of its budget (ibid). This lowered the investments in the oil sector and reduced the spending by the Government (The National, 5 March, 2015) which resulted in decelerating economic growth in the Sultanate in 2016 (fanack.com, 21 Feb, 2018). Though the available literature indicates betterment of the economic situation (Focus economics, 10 April, 2018), forecasts and experts’ opinions indicate that the Omani economy has not yet entered into a post-crisis situation (Focus Economics,) and is still in an economic crisis (Times of Oman, 10 January, 2018).

LITERATURE REVIEW According to a research by Fink (1986) which was further emphasized by Bergstrom (2018), among organizations that have not planned for a crisis and are unprepared for potential crises, the crisis lasted two-and-a-half times longer than those companies that did have a plan in place. Spillan and Hough (2003), who conducted a study in Pennsylvania and New York found that 15% of companies had crisis management teams. An important finding from their study, which is relevant to Oman, is that the small business owners showed a slight apprehension towards crises which could lead to unpreparedness. The following sections present the reviewed literature relating to economic crises, and the role of HRM and HRM strategies pre, during, and post crisis. As HRM is one of the most crucial functions that affect the organizational efficiency (Noe et al., 2011; Ochetan & Ochetan, 2012) and employment relations (Kirov & Thill, 2018), it is important to emphasize its role in crisis management (Barton, 2000; Luxford, 2008). Functional managers need to devise crisis-specific strategies in their respective functions in which the HR department’s involvement is imperative (Fodor & Poor, 2009). During a crisis, as employees may not voice their opinion (Prouska & Psychogios, 2016), the role of the HR department becomes crucial (Wooten & James, 2008) because 78


EJAE 2019  16 (1)  77-98

THUMIKI, V. R. R., STAKIĆ, J. A., AL BARWANI, R. S. S.  RESULTANT EFFECT OF CRISIS-DRIVEN HR STRATEGIES APPLIED DURING CURRENT ECONOMIC CRISIS IN OMAN – AN HR MANAGER’S PERSPECTIVE

this department is in-charge of handling employee affairs, formulating rules and regulations, employee training and development, etc. (Burma, 2014). Some of the commonly applied HR strategies during a crisis include, downsizing, cost cutting, postponing benefits, etc. (Howard, 2013; HRMA, 2014), which could influence employee perception towards work (Coombs, 2007). Pre-crisis HR strategies: The HR department needs to be environmentaly sensitive (Vardarlier, 2016) and may have to identify a crisis before it occurs because it will certainly affect their human resources (Baubion, 2013). After estimating and defining the potential crisis, the HR manager needs to devise appropriate crisis response plans, crisis communication plans, crisis management teams; the manager may have to revisit HR policies and strategies (Workplace Info Writers, 2006). The HR department in association with various functional heads must engage its personnel in multiple operational training programs that enable them to prepare for the potential crisis from all dimensions (Coombs, 2007). HR strategies during crisis: According to Hendry and Pettigrew (1986), companies can survive a crisis through effective management of human resources. In-crisis HRM strategies include, redesigning organization structure, providing necessary knowledge and training to employees regarding performing during crisis, etc. (Ochetan & Ochetan, 2012). HR departments need to ensure their employees wellbeing, job security, and enhance the value of human capital as a part of in-crisis HR strategy (HRMA, 2014; Howard, 2013). Crisis communication plays an important role, as it requires expertise and special skill (Coombs & Holladay, 1996). The HR department is the contact-point between a company and its employees (Burma, 2014). The first step towards crisis communication with employees during a crisis is to communicate with the right people in the team about the situation so that they can handle the relevant issues (Coombs & Holladay, 1996). Employees of HR departments who speak on behalf of the company need to be trained in crisis communication (Fener & Cevik, 2015) so that they can effectively communicate with the external stakeholders during a crisis. Crises influence organizations’ outlooks on managing situations during a crisis (Argenti, 2002), and the result is that the companies started building crisis management teams (Kondrasuk, 2004). Post-crisis HR strategies: By being environmentally sensitive, the HR managers need to identify the end of the crisis, and may have to plan for expansion and growth strategies. Post-crisis HR strategies include, recovery plans, increased recruitment of manpower, redesigning the organizational structure, etc. (Matsuka, 2010), along with effective supervisor support that could positively influence the employees’ perception of the the organization (Straub et al., 2018).

RESEARCH METHODOLOGY Primary Data-Questionnaire design: Primary data was collected through administering an online questionnaire created using Google Forms. It contained a question on the number of employees along with a question on the sector in which the organization was operating. Questions were aimed at capturing the awareness level of HR managers of the current economic crisis, their perception of impact of economic crisis on human resources of the organization, their HR practices during the current economic crisis, along with the problems that they had been encountering as a result of crisis-driven HR strategies. A monadic rating scale (Smith & Albaum, 2010) was used to understand HR managers’ rating of difficulty level of dealing with various resources during crises along with identifying their perception of the resultant effect of their crisis-driven HR strategies. A semantic differential scale was used to identify HR managers’ level of implementation of various HR strategies during the current crisis (Saunders et al., 2007).1 1 The dataset is publicly available via following web link: https://data.mendeley.com/datasets/bdbntgy6fc/draft?a=cb2e3e9b516b-4189-9894-8095fb00f9f0

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THUMIKI, V. R. R., STAKIĆ, J. A., AL BARWANI, R. S. S.  RESULTANT EFFECT OF CRISIS-DRIVEN HR STRATEGIES APPLIED DURING CURRENT ECONOMIC CRISIS IN OMAN – AN HR MANAGER’S PERSPECTIVE

Reliability and Validity of the Questionnaire: The instrument used to collect primary data from HR managers was tested (Kothari, 2004) using two methods, firstly, the test of face validity (Saunders et al., 2007) where in an expert has reviewed the questionnaire, and suggested changes and secondly, through calculating Cronbach’s Alpha (Cronbach, 1951), which is presented in the reliability section.

Sample Plan: An online questionnaire was distributed to more than 250 respondents using snowball sampling technique (Smith & Albaum, 2010) in which the respondents (HR managers in this case) were requested to send the questionnaire to their contacts. However, due to a low response rate (Nulty, 2008), only 116 valid responses were received out of which 112 responses were used in data analysis. Four responses were excluded from the analysis, as they are identified as a ‘non-representative’ sample. Reason for Excluding Four Responses: On the questionnaire, the item measuring HR Managers’ perception of impact of different types of crises on their business as ‘Not applicable’ as one of its options. Out of a total of seven types of crises presented, one or few of the crises may not be applicable to some types of businesses. Hence, the ‘Non-applicable’ option was included. Out of the 116 responses received, four respondents indicated that all seven types of crises were not applicable to them. Thus, they become a non-representative sample. The reason could be either their businesses or the organizations are not impacted by the crisis, or that they did not understand the question. In both cases, the responses do not fall under the scope of the research. Hence, are not included in the analysis. Statistical Analysis: The research output of the current research is explained through quantitative analyses using frequency distribution and percentages. Descriptive statistics, including mean, median, and standard deviation are used to describe the nature of the findings. Hypotheses were tested using ANOVA to find the differences between multiple groups and a T-test to find the differences between two groups of respondents. Secondary Data: Various sources of secondary data that are used for this study include, websites of companies and newspapers (Times of Oman, Oman Observer, The National). As part of secondary data information regarding HR strategies during crises was collected from published journal articles and reports by experts (Saunders et al., 2007). Data Analysis Software: Data analysis software, SPSS (17.0) was used to analyse the collected data using all relevant statistical tools and techniques (Field, 2012).

RESULTS AND DISCUSSION The following section focuses on analyzing the data collected from 112 respondents who are HR managers in various organizations in Muscat. It provides the findings related to HR practices during the current economic crisis and the HR managers’ perception of the resultant effect of their crisisdriven HR strategies. Sample Characteristics Nearly half (49.1%) of the respondents represented organizations with an employee-size of 101 to 500. The majority of the respondents (72%) were from the service sector representing, telecomunications, airlines, construction, banking, insurance, retail, education and health sectors. Due to the increase in non-oil diversification strategy in Oman (Times of Oman, 7 Feb, 2018), more jobs are now created in the service sector (Oman Observer, 25 Feb, 2018). The sample, therefore, becomes valid for the study. HR Managers’ Perception of the ‘impact of different types of crises on their businesses’ Seven types of crises were presented to the respondents viz., 1) natural crisis: floods, 2) economic crisis: economic downturn, 3) technological crisis: computer malware attack, 4) malevolence crisis: extreme tactics by miscreant individuals, 5) deception crisis: deliberate wrong actions taken by 80


EJAE 2019  16 (1)  77-98

THUMIKI, V. R. R., STAKIĆ, J. A., AL BARWANI, R. S. S.  RESULTANT EFFECT OF CRISIS-DRIVEN HR STRATEGIES APPLIED DURING CURRENT ECONOMIC CRISIS IN OMAN – AN HR MANAGER’S PERSPECTIVE

management, 6) workplace violence: strikes or other related problems, and 7) rumors: about the company or its products spread in society. Respondents were asked to rate the impact of each type of crisis on their businesses. The measurement scale ranged from 1 to 5, 1 being no impact at all and 5 having significantly high impact with an additional ‘not applicable’ option. While calculating averages, ‘not applicable’ responses were not considered as they were not included in the ‘population of interest’ (Smith & Albaum, 2010).

N

Not applicable

Average rating (scale 1-5)

Std. Dev.

Perceived impact of natural crisis on business

104

8

2.90

1.383

Perceived impact of economic crisis on business

108

4

3.53

1.080

Perceived impact of technology crisis on business

108

4

3.37

1.173

Perceived impact of malevolence crisis on business

91

21

3.01

1.378

Perceived impact of deception crisis on business

89

23

3.20

1.208

Perceived impact of workplace violence crisis on business

81

31

3.00

1.423

Perceived impact of rumors based crisis on business

88

24

3.00

1.232

Table 1. HR Managers’ Perception of Impact of Different Types of Crises on their Businesses

Findings related to HR managers’ perception of impact of different types of crises on their respective businesses can be seen in Table 1. It was found that HR managers perceived greater impact of economic crisis (3.53) and technology crisis (3.37) over other crises on their businesses. The low standard deviation (1.080) indicates closeness of the opinions. This finding empirically proves the importance given to economic crisis by the companies. Furthermore, as presented in Table 2, more than 97% of the respondents were knowledgeable about the current economic crisis. Frequency

Percent

Unknown to me

0

0%

I know a little from others

3

2.7%

I read about it a number of times

37

33%

I read about it regularly

53

47.3%

I have extensive knowledge about it

19

17%

Total

112

100%

97.3%

Table 2. Level of Awareness of Current Economic Crisis

Impact of Current Economic Crisis on the Organization The majority of the respondents (80.4%) specified that the current economic crisis had an impact on their organization which can be interpreted as ‘it affected their HR strategies’. Though a similar finding is presented in Table 1, it addresses the impact of the economic crisis in general whereas this finding is specific to the current economic crisis and also with reference to HR function. These two questions validate the findings and can be treated as evidence of validity and reliability of the questionnaire. 81


EJAE 2019  16 (1)  77-98

THUMIKI, V. R. R., STAKIĆ, J. A., AL BARWANI, R. S. S.  RESULTANT EFFECT OF CRISIS-DRIVEN HR STRATEGIES APPLIED DURING CURRENT ECONOMIC CRISIS IN OMAN – AN HR MANAGER’S PERSPECTIVE

Possible ways of the impact of the economic crisis on HR could be downsizing, reducing costs, hiring of trainees or contractors to pay less wages, suspending promotions and extra privileges, changing HR policies, rules and regulations, etc. Difficulty in Dealing with Resources and Stakeholders During Economic Crisis This question was aimed at discovering HR managers’ perception of difficulty in dealing with human resources during economic crises. The findings are presented in Table 3. Six items were presented out, of which three are resources: employees, machines and money, while three are parties: investors, dealers & suppliers, and Government. Among these options, the HR managers directly dealt with employees and with Government regarding HR issues only, and did not directly deal with machines, investors, and suppliers. Hence, their perception of difficulty in dealing with employees is understood as result of their experience, while their perception of difficulty in dealing with other resources and stakeholders is understood as a result of their comprehension of the situation. The measurement scale contained 5 points, with 1 being no problem at all to deal with and 5 being highly complicated to deal with, with an average point of 3 being moderately difficult to deal with. A high average indicates more difficult to deal with. Average rating (scale of 1-5) Difficulty of dealing with Government during crisis

3.21

Difficulty of dealing with external parties during crisis

3.06

Difficulty of dealing with money during crisis

3.03

Difficulty of dealing with investors during crisis

2.88

Difficulty of dealing with employees during crisis

2.74

Difficulty of dealing with machines during crisis

2.32

Table 3. HR Managers’ Perception of Difficulty in Dealing with Various Resources and Stakeholders During a Crisis

According to the respondents, dealing with Government during a crisis is more difficult. An interesting finding is that the HR managers think that dealing with employees is easier during crisis compared to dealing with other resources and other parties (Table 3). Possible reasons could be that the employees become vulnerable due to the ‘fear factor’. Probable reasons for difficulty in dealing with external parties might be due to the change of communication and change of contract terms during crises. A reason for difficulty in dealing with money could be need for saving and cost cutting. Data presented in Table 1 presents the HR managers’ perception of impact of economic crisis on the organization from the general perspective, while the finding presented in Table 4 presents their perception of the effect of economic crisis on the organization from the HR perspective. A higher average represents the perception of a greater impact on company’s HR. An average of 3.42 indicates moderate to high impact of the current economic crisis on employees’ jobs and their work life.

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THUMIKI, V. R. R., STAKIĆ, J. A., AL BARWANI, R. S. S.  RESULTANT EFFECT OF CRISIS-DRIVEN HR STRATEGIES APPLIED DURING CURRENT ECONOMIC CRISIS IN OMAN – AN HR MANAGER’S PERSPECTIVE

Points on scale

Frequency

Percent

Unaffected

1

9

8.0

Little effect on the jobs & work life of employees

2

14

12.5

Moderate effect

3

36

32.1

More effect

4

27

24.1

Significant effect

5

26

23.2

112

100.0

Total Average rating of effect of current economic crisis on HR

3.42

Table 4. HR Managers’ Perception of Effect of Current Economic Crisis on HR

HR Practices During the Economic Crisis This is one of the key findings of the current research. Respondents were asked to mention a level of implementation of the given HR strategies during a crisis on a measurement scale of 1 to 5, with an additional option of ‘non-applicable’. The scale contained, 1) no plan of doing it, 2) will think of doing it, 3) planning of doing it, 4) just started doing it and 5) already started and is in full implementation.

No.

N

ImplemenNot Std. tation level applicable Dev. (scale 1-5)

1

Increased knowledge management activities

102

10

3.45

1.340

2

Using non-monetary motivation techniques

104

8

3.43

1.453

3

Cost cutting on entertainment & recreational activities

108

4

3.41

1.582

4

Redesign jobs: Job enlargement

106

6

3.27

1.515

5

Abeyance of rewards and incentives

107

5

3.22

1.690

6

Cost cutting on employee training and development activities

104

8

3.12

1.554

7

Increased communication with employees

107

5

3.07

1.574

8

Redesign jobs: Job enrichment

103

9

2.83

1.498

9

Increased dependency on outsourcing

108

4

2.81

1.548

10 Appointment of crisis team

108

4

2.73

1.562

11 Training employees in working during crisis

108

4

2.69

1.609

12 Reducing number of employees

107

5

2.66

1.590

13 Lowering income options for workers

99

13

1.93

1.409

14 Sending employees on un-paid leaves

92

20

1.89

1.370

15 Reducing number of working hours

84

28

1.57

1.056

Table 5. HR Practices Adopted During Economic Crisis Arranged According to Level of Implementation

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THUMIKI, V. R. R., STAKIĆ, J. A., AL BARWANI, R. S. S.  RESULTANT EFFECT OF CRISIS-DRIVEN HR STRATEGIES APPLIED DURING CURRENT ECONOMIC CRISIS IN OMAN – AN HR MANAGER’S PERSPECTIVE

The findings presented in Table 5 are interesting. HR managers indicated that they have increased ‘knowledge management’ activities in the organization (3.45). The highest rating of this item speaks of the ‘professionalism’ of HR managers, as they are aware of the fact that learning organizations survive crises. The next strategy being implemented during the current economic crisis is an increased use of non-monetary motivation techniques (3.43) HR managers are aware that motivation and recognition are important during crisis to ensure productivity (O’Connor, 1987). Managers now apply non-monetary techniques to motivate their employees viz., recognition letters, motivational mailers, appreciation letters, redesigning the job titles without increase in pay, promotions without increase in pay, etc. Managers have cut costs on employee entertainment activities (implementation rating of 3.41) and not on the employee developmental activities (3.12). This data speaks of the professional approach of the managers. The budget for training and development activities is usually assumed to be greater than the entertainment budget, and the managers can save more by cutting training and development costs. However, they were cutting entertainment and recreation costs, instead of cutting training and development costs. HR strategies during economic crisis include job enlargement (3.27), i.e., adding tasks at no additional costs as this strategy can motivate employees by making their jobs more interesting and important (Noe et al., 2011). Promotions, bonuses and other benefits were kept under abeyance or postponed (3.22) which is one of the strategies usually adopted during an economic crisis (Feng, 2018; Merhan & Tracy, 2016). The lowest rated HR strategy in implementation is reducing the number of working hours for employees (the lowest average of 1.57). In fact, 25% of the respondents specified that this strategy is not applicable to them. Mostly presumed strategy, ‘downsizing’ is also rated low in implementation with an average of 2.66. Thus, this finding disproves the myth that downsizing is the first HR strategy that is implemented during a crisis. This empirical survey proves most of the commonly presumed strategies as myths. For example, some of the myths from a lay-man’s perspective are that HR managers reduce the number of jobs during crisis, they cut even the training and development costs, and they force employees to go on un-paid leaves. But the current research empirically proved that, in reality, these strategies are given the least amount of importance in implementation. Thus, this research attains significance. HR Managers’ Perception of Resultant Effect of their Crisis-Driven HR Strategies This is another key finding of the current research. The respondents (HR managers) were presented seven resultant effects of crisis-driven HR strategies and were required to rate each of them on a scale of 1 to 5 in terms of effect on their human resources according to their perception (presented in Table 6). Though the scale was uniform (5-point scale), the scale description varied according to the type of effect, which is clearly explained in individual description analyses. Effect on Motivation Level The perceived resultant effect of crisis-driven HR strategies on the motivation level of employees was measured on a scale of 1 to 5, starting from highly reduced motivation levels to highly increased motivation levels with a center-point of moderate effect. The average of 3.11 indicates a moderate effect of crisis-driven HR strategies on employees, according to the HR managers. As this finding is from the HR managers’ perspective, it needs to be validated through studying the employee behavior. However, from the HR managers’ view-point, their crisis-driven HR strategies did not demotivate their employees and the reasons could be the preparedness of the employees due to awareness of the current economic crisis.

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THUMIKI, V. R. R., STAKIĆ, J. A., AL BARWANI, R. S. S.  RESULTANT EFFECT OF CRISIS-DRIVEN HR STRATEGIES APPLIED DURING CURRENT ECONOMIC CRISIS IN OMAN – AN HR MANAGER’S PERSPECTIVE

N

Mean

Std. Deviation

Motivation level of employees

112

3.11

1.269

Commitment level of employees

112

3.05

1.199

Rumors and informal communication among employees

112

3.13

0.988

Employee engagement & involvement of employees

112

3.21

1.086

Active/ Passive behavior of employees at the workplace

111

3.44

1.076

Employee turnover

112

3.20

1.184

Corporate image among employees

112

3.15

1.172

Table 6: HR Managers’ Perception of Resultant Effect of Crisis-Driven HR Strategies

Effect on Commitment Level The measurement scale contained 5 points starting from a highly reduced commitment level to a highly increased commitment level. The opinion was that the commitment levels of employees were reduced due to stringent measures, and cost cutting needed to be validated through research. The current research attempted to find an answer to this research question. According to HR managers, their crisis-driven HR strategies did not reduce the commitment levels of their employees, and has shown moderate effect (3.05). Rumors and Informal Communication The crisis-driven HR strategies might encourage informal communication and generate rumors as the employees may not openly communicate or comment on the management’s decisions during a crisis (Coombs & Holladay, 2002). According to the HR managers, there is a moderate impact on communication among employees (3.13 on a scale of 1 to 5). Though informal communication is common in any organization (Bovee & Thill, 2011), the current research proved that the crisis-driven HR strategies have a moderate effect on informal communication among employees and create room for rumors. Active and Passive Behavior at the Workplace This is the highest rated item on the questionnaire, with an average of 3.44 on a scale of 1 to 5. The measurement scale contained 1) visible passive behavior, 3) moderate active and passive behavior, and 5) visible active behavior. An average of more than 3 indicates that the employees have become active in the workplace, as they are now aware of current situation. The HR managers’ have noticed active behavior in the form of actively participating in meetings, coming forward for solving problems at the workplace, etc. Moreover, a low standard deviation (1.076) indicates similarity in the responses of HR managers. Employee Turnover According to the HR managers. the crisis-driven HR strategies have not resulted in employee loss. The measurement scale contained 1) increased employee turnover, 3) moderate employee turnover and 5) reduced employee turnover, and the respondents were required to provide their perception of the resultant effect of their HR strategies during a crisis on employee turnover. An average of more than 3 gave a good signal, as it indicated reduced employee turnover. In this study, an average of 3.20 indicated that the crisis-driven HR strategies had, in fact, reduced employee turnover as against the common opinion or myth of increased employee turnover during a crisis (Vintisa, 2010). 85


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Employee Engagement and Involvement of Employees

The crisis-driven HR strategies had created an active workplace environment. According to HR managers, employees involved more in constructive activities participated more actively in meetings and contributed willingly in decision-making, which contributed to enhanced employee engagement. Employee engagement created a healthy relationship between the employee and the organization (Ellis & Sorensen, 2007). An average of 3.21 indicated moderate to high employee engagement at the workplace which was a positive resultant effect of crisis-driven HR strategies. Corporate Image Among Employees The assumption of lowered corporate image among employees during a crisis (Wright, 2009) could be a myth (Hansen, 2005). The current research has empirically proved that corporate image did not suffer a decrease among employees due to stringent and difficult crisis-driven HR strategies. The respondents were required to present their perception of the resultant effect of their HR strategies taken-up during crisis on corporate image of their employees. The measurement scale contained 1) reduced corporate image among employees, 3) moderate effect on corporate image, and 5) increased corporate image. An average of more than 3 can be considered as a good indicator as it indicates an increase in corporate image among employees after crisis-driven HR strategies have been implemented. The HR managers perceived (3.15) that their employees do regard and respect their organization and that the corporate image had increased among employees. Differences in Application of HR Strategies Between Small, Medium and Large Organizations Ho: There is no significant difference in the application of HR strategies during a crisis between small, medium, and large organizations H1: There is a significant difference in the application of HR strategies during crisis between small, medium, and large organizations

HR strategy

Conclusion (Post Hoc Test)

Significance

Increased knowledge management activities

.510

Not significant Ho: Not rejected

***

Using non-monetary motivation techniques

.238

Not significant Ho: Not rejected

***

.038

Significant Ho: Rejected

Application by large organizations is significantly higher than small and medium organizations (Appendix 1)

Redesign jobs: Job enlargement (adding tasks to a job at no additional pay)

.035

Significant Ho: Rejected

Application by small organizations is significantly higher than medium organizations (Appendix 2)

Abeyance of rewards and incentives

.101

Not significant Ho: Not rejected

***

.017

Significant Ho: Rejected

Application by large organizations is significantly higher than small and medium organizations (Appendix 3)

Cost cutting on entertainment & recreational activities

Cost cutting on employee training and development activities 86

p-value


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Increased communication with employees

.114

Not significant Ho: Not rejected

***

Redesign jobs: Job enrichment

.615

Not significant Ho: Not rejected

***

Increased dependency on outsourcing

.254

Not significant Ho: Not rejected

***

Appointment of crisis team

.724

Not significant Ho: Not rejected

***

Training employees in working during crisis

.523

Not significant Ho: Not rejected

***

Reducing number of employees

.331

Not significant Ho: Not rejected

***

Lowering income options for workers

.594

Not significant Ho: Not rejected

***

Sending employees on un-paid leaves

.752

Not significant Ho: Not rejected

***

Reducing number of working hours

.331

Not significant Ho: Not rejected

***

Grouping Variable: Number of Employees in the Organization Small=Below 100, Medium=101 to 500, Large=More than 500 employees Table 7. Summary of ANOVA Test Results

The organizations in the study were divided into small (under 100 employees), medium (101 to 500 employees) and large (more than 500 employees) for the purpose of hypotheses testing and further analysis. Out of 15 crisis-driven HR strategies, only three strategies namely, cost cutting on employee recreational activities, job enlargement and cost cutting on employee training and development were found to be applied at different levels in the three categories of organizations. HR managers representing large organizations have given an average implementation rating of 4.28 (on a scale of 1 to 5) to ‘cost cutting on employee entertainment & recreational activities’. Whereas HR managers of small organizations gave an average implementation rating of 3.24 and HR managers of medium organizations gave an average implementation rating of 3.25 to the same variable. An ANOVA test was conducted to identify the difference of means between groups indicated that there is a significant difference (Table 7). Later, the Post Hoc Tests (Appendix 1) indicated that the application of this strategy by large organizations is significantly higher than small and medium organizations. The reasons could be more spending on employee recreation by larger organizations compared to small and medium-sized organizations. Moreover, it can be interpreted that small organizations may not be saving significant amount of money by cutting costs on employee recreation. The next finding is related to redesigning jobs, more specifically, job enlargement which involves adding tasks to a job with no additional pay to the employee. The ANOVA test revealed that there is a significant difference between the implementation of this strategy by large, medium and small organizations (Table 7 & Appendix 2). With an average of 3.67, job enlargement strategy is applied more by small organizations compared to medium-sized organizations. Otherwise, the difference is not 87


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significant compared to large organizations. The reasons could be that it is easier to redesign jobs in small organizations whereas, in large organizations with hundreds and thousands of employees, it is a difficult task. Hence, HR managers of large-sized organizations might not be showing more interest in applying job enlargement activity. The last hypothesis under this section is related to cost cutting on employee training and development. The ANOVA test revealed that large sized organizations’ implementation of this strategy has been significantly higher than in small and medium sized organizations (Table 7 & Post Hoc Tests presented in Appendix 3). It can be interpreted, that as the budget for employee training and development in large sized organizations is usually higher, they can save significant amount of money on cost cutting compared to small and medium sized organizations. Differences in HR Managers’ Perception of the Resultant Effect of their HR Strategies Between Small, Medium, and Large organizations Ho: There is no significant difference in the resultant effect of crisis-driven HR strategies between small, medium, and large organizations H1: There is a significant difference in resultant effect of crisis-driven HR strategies between small, medium, and large organizations p-value

Significance

Conclusion (Post Hoc Test)

Effect on motivation level

.956

Not significant Ho: Not rejected

***

Effect on commitment level

.937

Not significant Ho: Not rejected

***

Rumors and informal communication

.942

Not significant Ho: Not rejected

***

Employee engagement & involvement of employees

.349

Not significant Ho: Not rejected

***

Active/passive behavior at workplace

.893

Not significant Ho: Not rejected

***

Employee turnover

.292

Not significant Ho: Not rejected

***

Corporate image among employees

.880

Not significant Ho: Not rejected

***

Resultant effect

Grouping variable: Number of Employees in the Organization Small=Below 100, Medium=101 to 500, Large=More than 500 employees Table 8. Summary of ANOVA Test Results

This analysis proves that the resultant effect does not depend upon the size of the organization (Table 8). The HR managers of small, medium and large organizations equally perceive the effect of their crisisdriven HR strategies. With reference to the motivation levels of their employees after certain changes to HR strategies, all three types of HR managers mentioned that the effect is moderate to high ranging between the average values of 3.06 to 3.15 (scale 1 to 5). Similarly, with reference to active and passive behavior at the workplace, the HR managers of all three types of organizations felt that the employees 88


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in their respective organizations became active players at the workplace with averages of 3.49 (small), 4.39 (medium), and 3.39 (large). Thus, it can be interpreted that the resultant effect of crisis-driven HR strategies will be same, irrespective of the manpower size of the organization. Differences in HR Managers’ Perception of Resultant Effect of their HR Strategies Between the Manufacturing and Service Sectors Ho: There is no significant difference in the resultant effect of crisis-driven HR strategies between the manufacturing and service sector organizations H1: There is a significant difference in the resultant effect of crisis-driven HR strategies between the manufacturing and service sector organizations p-value

Significance

Conclusion

Effect on motivation level

.872

Not significant Ho: Not rejected

***

Effect on commitment level

.985

Not significant Ho: Not rejected

***

Rumors and informal communication

.002

Significant Ho: Rejected

Significantly higher in service sector (3.16) than manufacturing sector (3.03)

Employee engagement & involvement of employees

.023

Significant Ho: Rejected

Significantly higher in service sector (3.22) than manufacturing sector (3.19)

Active/passive behavior at workplace

.908

Not significant Ho: Not rejected

***

Employee turnover

.048

Significant Ho: Rejected

Significantly higher in service sector (3.32) than manufacturing sector (2.87)

Corporate image among employees

0.76

Not significant Ho: Not rejected

***

Resultant effect

Grouping Variable: Sector of the Organization - Manufacturing Sector & Service Sector Table 9. Summary of t-Test Results

Another relevant thing for this study is to understand the differences between the manufacturing and service sector organizations. Though the current crisis started with the manufacturing sector (petroleum), the service sector was also suffering equally or even more. The current survey included service sector organizations like telecommunications, airlines, construction, banking, insurance, retail, education, and health, while the manufacturing sector included petroleum, food, etc. The findings of the hypotheses testing revealed that the impact had been greater in the service sector than in the manufacturing sector (Table 9). 3 out of 7 resultant effects were found to be significantly different in the two sectors and, interestingly, the impact has been significantly higher in the service sector than in the manufacturing sector. Managers from the service sector have indicated a relatively lower job loss (3.32) than HR managers from the manufacturing sector (2.87).

89


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Differences Between HR Managers’ Perceived Difficulty in Dealing with Employees Between Small, Medium and Large Organizations Ho: There is no significant difference between the perceived difficulty in dealing with employees during a crisis between small, medium, and large organizations H1: There is no significant difference between the perceived difficulty in dealing with employees during a crisis between small, medium, and large organizations p-value

Testing variable

Difficulty in dealing with employees during crisis

.040

Significance

Conclusion (Post Hoc Test)

Significant Ho: Rejected

• HR managers of small organizations perceive less difficulty in dealing with employees during a crisis than HR managers of medium and large organizations • HR managers of medium organizations perceive less difficulty in dealing with employees during a crisis than HR managers of large organizations (Appendix 4).

Grouping Variable: Number of Employees in the Organization Small=Below 100, Medium=101 to 500, Large=More than 500 employees Table 10. Summary of ANOVA Test Results

The general opinion that managing smaller organizations with fewer employees is relatively easier than managing larger organizations (Noe et al., 2011; Morgan, 2015) is proved to be true through this empirical research. HR managers of smaller organizations with less than 100 employees indicated that it is easier to deal with employees during a crisis with an average of 2.54 on a scale of 1, which indicates no problem at all in dealing with employees during a crisis and 5 being highly complicated to deal with. This average is significantly lower than perceptions of HR managers representing medium-sized organizations and larger-sized organizations. Thus, it can be interpreted that, the larger the organization in terms of number of employees, the more difficult it is in dealing with them during a crisis. Reliability Analysis Cronbach’s Alpha

N of Items

.871

38

Table 11. Reliability Statistics

The reliability analysis which is expected to be as close as possible to 1, indicates that the questionnaire is valid and the data collected can be used in further analyses (Reynaldo & Santos, 1999). Cronbach’s Alpha for the current research is calculated at .871 (Table 11) for 38 items on the questionnaire. This allows the researcher to arrive at reliable outputs and valid conclusions.

CONCLUSIONS AND SUGGESTIONS Economic crisis affects all aspects of business, forcing managers to alter their functional strategies. During a crisis, dealing with human resources is a complicated function when compared with managing other business functions. Hence, managers are advised to revise their HR practices in the current 90


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economic crisis. This paper addresses the vital issue of the application of crisis-driven HR strategies, as well as the resultant effect through empirical research conducted in Muscat. HR practices that are being applied now can be called as ‘crisis-driven HR strategies’, as they are influenced by the current economic crisis and the need to be looked at from a different perspective. As findings revealed that HR managers perceive a greater impact of the economic crisis on their respective businesses compared with other types of crises, it is recommended that they redesign their HR strategies to ensure success in the current economic crisis. The research findings disproved many myths about HR strategies adopted during economic crisis. For example, instead of emphasizing cutting cost and employee separations, the HR managers emphasized on enhancing knowledge management activities in the company as they knew that learning organizations survive during difficult times. It is recommended that they further increase their knowledge management activities and communicate the same to their employees. One of the resultant effects of crisis-driven HR strategies was that the employee engagement increased, as the employees were now more involved in constructive activities and exhibited active behavior at the workplace. This further increased corporate image among employees which was earlier a myth that crisis-driven HR strategies lowerd the corporate image. During the current economic crisis, large organizations had cut costs on employee recreation, rather than the costs related to employee training and development. It was found that small organizations performed job enlargement tasks (by adding more tasks at no additional costs) while large organizations found it difficult to deal with their employees during a crisis (due to the large size of their work force). The survey revealed a moderate increase in communication with employees, and it is recommended that the HR should increase communication with its employees and should discuss various issues and crises with them. Furthermore, it is recommended that managers and team leaders adopt more overt and planned motivation techniques. A need to appoint crisis management teams and train employees during crisis has also been identified. To perform effectively during the current economic crisis, the HR managers, may have to equip themselves with more information and acquire more knowledge in the HR domain. While this research presented a broad overview of human resource management during the current economic crisis, it calls for further research thorough inquiry into specific aspects, such as changes in job analysis and design, changes in recruitment and selection procedures, etc. Thus, through more narrowed-down research, organizations can enhance the effectiveness of their HR strategies for the overall success of their organizations.

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Muscat Daily (2017). Central Bank of Oman Annual Report Unveiled. Retreived March 20, 2018, from http:// www.muscatdaily.com/archive/business/central-bank-of-oman-annual-report-unveiled-52b1 Narayan, V., John-Stewart, G., O’Malley, G., & Gage, G. (2018). “If I had known, I would have applied”: Poor communication, job dissatisfaction, and attrition of rural health workers in Sierra Leone. Human Resources for Health, 16(1). DOI:10.1186/s12960-018-0311-y Noe, R. A., & Noe, R. A. (2011). Fundamentals of human resource management. New York: McGraw-Hill Irwin. Nulty, D. D. (2008). The adequacy of response rates to online and paper surveys: what can be done? Assessment & Evaluation in Higher Education, 33(3), 301-314. DOI:10.1080/02602930701293231 Ochetan, C. M. T., & Ochetan, D. A. (2012). The Influence of Economic and Financial Crisis on Human Resources Management. Procedia Economics and Finance, 3, 769-774. O’Connor, J. R. (1987). The meaning of crisis: A theoretical introduction. Oxford: Blackwell. Oman Observer (2018). Jobs for 80,000 Omanis in Logistics Sector by 2020. Retreived March 20, 2018, from http:// www.omanobserver.om/jobs-80000-omanis-logistics-sector-2020/ Peterson, A. (18 Dec, 2014). The Sony Pictures Hack, Explained. Retrieved February 28, 2018, from https:// www.washingtonpost.com/news/the-switch/wp/2014/12/18/the-sony-pictures-hack-explained/?utm_ term=.392c92bb6bd8 Prouska, R., & Psychogios, A. (2016). Do not say a word! Conceptualizing employee silence in a long-term crisis context. The International Journal Of Human Resource Management, 29(5), 885-914. DOI:10.1080/0958 5192.2016.1212913. Reynaldo, J.A., & Santos, A. (1999) Cronbach’s Alpha: A Tool for Assessing the Reliability of Scales. Journal of Extension, 37, 1-4. Saunders, M., Lewis, P., & Thornhill, A. (2007). Research Methods for Business Students. Harlow: Pearson Education. Silverthorne, S. (2018). The Dark Side of Performance Bonuses. Retrieved April 28, 2018, from HBS Working Knowledge https://hbswk.hbs.edu/item/the-dark-side-of-performance-bonuses?cid=wk-rss Smith, S. M. & Albaum, G. S. (2010). An Introduction to Marketing Research. Qualtrics, 123, 130, 234. Spillan, J., & Hough, D. M. (2003). Crisis Planning in Small Businesses: Importance, Impetus and Indifference. European Management Journal, 21(3), 398-407. DOI:10.1016/S0263-2373(03)00046-X Straub, C., Vinkenburg, C. J., Van Kleef, M., & Hofmans, J. (2018). Effective HR implementation: the impact of supervisor support for policy use on employee perceptions and attitudes. International Journal Of Human Resource Management, 29(22), 3115-3135. DOI: 10.1080/09585192.2018.1457555 Vardarlier, P. (2016). Strategic approach to human resources management during crisis. Procedia - Social And Behavioral Sciences, 235(24), 463-472. Vintisa, K. (2010). The Impact of Economic Crisis on Human Resources Management of Public Sector. Human Resources Management & Ergonomics, 6(1), 1-13. Woods, B. (2018). Snow to melt high street sales as retailers brace for more pain. Retrieved March 1, 2018, from https://www.independent.ie/world-news/snow-to-melt-high-street-sales-as-retailers-brace-for-morepain-36658839.html Wooten, L. P., & James, E. H. (2008). Linking Crisis Management And Leadership Competencies: The Role Of Human Resource Development. Advances In Developing Human Resources, 20(10), 1-28. DOI:10. 1177/1523422308316450 Workplace Info Writers (2006). HR’s role in crisis management. Retrieved February 28, 2018, from http://workplaceinfo.com.au/hr-management/performance-management/analysis/hr-s-role-in-crisis-management#. WRPLJ9RUBIU Wright, C. (2009). Responding to crises: A test of the situational crisis communication theory. Graduate Thesis and Dissertations, University of South Florida. Retrieved February 2, 2018, from https://scholarcommons.usf.edu/CGI/VIEWCONTENT.CGI?ARTICLE=1090&CONTEXT=ETD 93


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APPENDICES

Appendix 1 DESCRIPTIVES FOR ANOVA TEST 1 Cost cutting on employee entertainment & recreational activities N

Mean

Std. Dev.

Below 100 employees (small)

38

3.24

1.584

101 to 500 employees (medium)

53

3.25

1.628

More than 500 employees (large)

18

4.28

1.179

Total

109

3.41

1.582

ANOVA Cost cutting on employee entertainment & recreational activities Sum of Squares

Df

Mean Square

F

Sig.

Between Groups

16.131

2

8.066

3.362

.038

Within Groups

254.291

106

2.399

Total

270.422

108

Post Hoc Tests Multiple Comparisons Cost cutting on employee entertainment & recreational activities LSD (I) Number of employees

Below 100 employees (small)

101 to 500 employees (medium)

More than 500 employees (Large)

(J) Number of employees

Mean Difference (I-J)

Std. Error

Sig.

101 to 500 employees (medium)

-.008

.329

More than 500 employees (Large)

-1.041*

Below 100 employees (small)

Lower Bound

Upper Bound

.980

-.66

.64

.443

.021

-1.92

-.16

.008

.329

.980

-.64

.66

More than 500 employees (Large)

-1.032*

.423

.016

-1.87

-.19

Below 100 employees (small)

1.041*

.443

.021

.16

1.92

101 to 500 employees (medium)

1.032*

.423

.016

.19

1.87

*. The mean difference is significant at the 0.05 level. 94

95% Confidence Interval


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Appendix 2 DESCRIPTIVES FOR ANOVA TEST 2 Redesign jobs: Job enlargement (adding tasks at no additional payments)

N

Mean

Std. Dev.

Below 100 employees (small)

39

3.67

1.383

101 to 500 employees (medium)

51

2.88

More than 500 employees (Large)

16

Total

106

Std. Error

95% Confidence Interval for Mean

Min

Max

4.11

1

5

2.46

3.30

1

5

.418

2.67

4.45

1

5

.147

2.98

3.57

1

5

Lower Bound

Upper Bound

.221

3.22

1.492

.209

3.56

1.672

3.27

1.515

ANOVA Redesign jobs: Job enlargement (adding tasks at no additional payments) Sum of Squares

Df

Mean Square

F

Sig.

Between Groups

15.168

2

7.584

3.458

.035

Within Groups

225.898

103

2.193

Total

241.066

105

Post Hoc Tests Multiple Comparisons Redesign jobs: Job enlargement (adding tasks at no additional payments)LSD (I) Number of employees

Below 100 employees (small)

101 to 500 employees (medium)

More than 500 employees (Large)

95% Confidence Interval

(J) Number of employees

Mean Difference (I-J)

Std. Error

Sig.

Lower Bound

Upper Bound

101 to 500 employees (medium)

.784*

.315

.014

.16

1.41

More than 500 employees (Large)

.104

.440

.813

-.77

.98

Below 100 employees (small)

-.784*

.315

.014

-1.41

-.16

More than 500 employees (Large)

-.680

.424

.112

-1.52

.16

Below 100 employees (small)

-.104

.440

.813

-.98

.77

101 to 500 employees (medium)

.680

.424

.112

-.16

1.52

*. The mean difference is significant at the 0.05 level.

95


EJAE 2019  16 (1)  77-98

THUMIKI, V. R. R., STAKIĆ, J. A., AL BARWANI, R. S. S.  RESULTANT EFFECT OF CRISIS-DRIVEN HR STRATEGIES APPLIED DURING CURRENT ECONOMIC CRISIS IN OMAN – AN HR MANAGER’S PERSPECTIVE

Appendix 3 DESCRIPTIVES FOR ANOVA TEST 3 Cost cutting on employee training and development activities

N

Mean

Std. Dev.

Below 100 employees (small)

38

3.13

1.455

101 to 500 employees (medium)

50

2.80

More than 500 employees (Large)

16

Total

104

Std. Error

95% Confidence Interval for Mean

Min

Max

3.61

1

5

2.36

3.24

1

5

.359

3.30

4.83

1

5

.152

2.81

3.42

1

5

Lower Bound

Upper Bound

.236

2.65

1.565

.221

4.06

1.436

3.12

1.554

ANOVA Cost cutting on employee training and development activities Sum of Squares

df

Mean Square

F

Sig.

Between Groups

19.336

2

9.668

4.259

.017

Within Groups

229.280

101

2.270

Total

248.615

103

Post Hoc Tests Multiple Comparisons Cost cutting on employee training and development activities LSD (I) Number of employees

Below 100 employees (small)

101 to 500 employees (medium)

More than 500 employees (Large)

(J) Number of employees

Mean Difference (I-J)

Std. Error

Sig.

101 to 500 mployees (medium)

.332

.324

More than 500 employees (Large)

-.931*

Below 100 employees (small)

Lower Bound

Upper Bound

.309

-.31

.97

.449

.041

-1.82

-.04

-.332

.324

.309

-.97

.31

More than 500 employees (Large)

-1.263*

.433

.004

-2.12

-.40

Below 100 employees (small)

.931*

.449

.041

.04

1.82

101 to 500 employees (medium)

1.263*

.433

.004

.40

2.12

*. The mean difference is significant at the 0.05 level. 96

95% Confidence Interval


EJAE 2019  16 (1)  77-98

THUMIKI, V. R. R., STAKIĆ, J. A., AL BARWANI, R. S. S.  RESULTANT EFFECT OF CRISIS-DRIVEN HR STRATEGIES APPLIED DURING CURRENT ECONOMIC CRISIS IN OMAN – AN HR MANAGER’S PERSPECTIVE

Appendix 4 DESCRIPTIVES FOR ANOVA TEST 4 Difficulty of dealing with employees during crisis

N

Mean

Std. Dev.

Below 100 employees (small)

39

2.54

1.144

101 to 500 employees (medium)

55

2.71

More than 500 employees (Large)

18

Total

112

Std. Error

95% Confidence Interval for Mean

Min

Max

2.91

1

5

2.46

2.96

1

5

.240

2.77

3.78

2

5

.098

2.55

2.94

1

5

Lower Bound

Upper Bound

.183

2.17

.916

.124

3.28

1.018

2.74

1.038

ANOVA Difficulty of dealing with employees during crisis Sum of Squares

df

Mean Square

F

Sig.

Between Groups

6.842

2

3.421

3.310

.040

Within Groups

112.649

109

1.033

Total

119.491

111

Post Hoc Tests Multiple Comparisons Difficulty of dealing with employees during crisis LSD (I) Number of employees

Below 100 employees (small)

101 to 500 employees (medium)

More than 500 employees (Large)

(J) Number of employees

Mean Difference (I-J)

Std. Error

Sig.

101 to 500 employees (medium)

-.171

.213

More than 500 employees (Large)

-.739*

Below 100 employees (small)

95% Confidence Interval Lower Bound

Upper Bound

.424

-.59

.25

.290

.012

-1.31

-.17

.171

.213

.424

-.25

.59

More than 500 employees (Large)

-.569*

.276

.042

-1.12

-.02

Below 100 employees (small)

.739*

.290

.012

.17

1.31

101 to 500 employees (medium)

.569*

.276

.042

.02

1.12

*. The mean difference is significant at the 0.05 level. 97


EJAE 2019  16 (1)  77-98

THUMIKI, V. R. R., STAKIĆ, J. A., AL BARWANI, R. S. S.  RESULTANT EFFECT OF CRISIS-DRIVEN HR STRATEGIES APPLIED DURING CURRENT ECONOMIC CRISIS IN OMAN – AN HR MANAGER’S PERSPECTIVE

EFEKTI KRIZNIH HR STRATEGIJA PRIMENJENIH ZA VREME TRENUTNE EKONOMSKE KRIZE U OMANU – STANOVIŠTE JEDNOG HR MENADŽERA

Rezime: Tokom ekonomske krize, kompanije nastoje da preoblikuju svoj način rada, a sa ciljem opstanka i napretka. Ovaj rad teži da osvetli praksu iz okvira ljudskih resursa koja je primenjena za vreme trenutne ekonomske krize u Omanu, istovremeno ilustrujući efekte kriznih strategija, iz ugla jednog menažera za ljudske resurse. Osnovni podaci dobijeni su posredstvom interneta, od strane 112 HR menadžera, iz različitih organizacija koje se bave proizvodnjom i uslugama u Muskatu, Oman. Utvrđeno je da HR menadžeri ekonomsku krizu doživljavaju kao činioca koji ostavlja značajnije efekte na njihovo poslovanje od efekata krize druge prirode, kao što su prirodna ili tehnološka. Oni veruju da je, tokom kriznog perioda, relativno jednostavnije upravljati zaposlenima nego drugim izvorima i ostalim zainteresovanim stranama. Izmene u HR praksi tokom perioda ekonomske krize uključuju ukidanje povlastica namenjenih zaposlenima, kao i smanjenje troškova u vezi sa njihovim rekreativnim aktivnostima. Umesto toga, krizni HR menadžment dovodi do umnožavanja aktivnosti u vezi sa menadžmentom znanja, ali i usvajanja tehnika motivacije koja ne uključuje novčana sredstva. Kao rezultat, primećeno je značajnije učešće zaposlenih u aktivnostima, te unapređena korporativna slika među njima. Proveravanje polazne hipoteze dovelo je do zaključka da je smanjivanje troškova, a u vezi sa rekreativnim aktivnostima zaposlenih, značajno više u velikim organizacijama, da je preoblikovanje aktivnosti značajno više u manjim organizacijama, ali i da velike organizacije imaju poteškoće u odnosu i radu sa zaposlenima tokom perioda krize, u većoj meri u odnosu na male i organizacije srednje veličine.

98

Ključne reči: ekonomska kriza, ukidanje, preoblikovanje aktivnosti, ne-novčana motivacija, menadžment znanja


CIP - Каталогизација у публикацији Народна библиотека Србије, Београд 33 The EUROPEAN Journal of Applied Economics / editor-in-chief Nemanja Stanišić. Vol. 12, No. 1 (2015)- . - Belgrade : Singidunum University, 2015- (Belgrade : Caligraph). - 28 cm Polugodišnje. - Је наставак: Singidunum Journal of Applied Sciences = ISSN 2217-8090 ISSN 2406-2588 = The European Journal of Applied Economics COBISS.SR-ID 214758924

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The European journal 2019 vol 16 no 1  

The European journal 2019 vol 16 no 1  

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