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Knowledge-Based Systems 18 (2005) 107–115 www.elsevier.com/locate/knosys

Adoption and diffusion of knowledge management systems: field studies of factors and variables Mohammed Quaddusa, Jun Xub,* b

a Graduate School of Business, Curtin University of Technology, 78 Murray Street, Perth, WA 6000, Australia Graduate College of Management, Southern Cross University, Tweed Gold Coast campus, Brett Street, Tweed Heads, P.O. Box 42, Tweed Heads, NSW 2485, Australia

Received 29 May 2003; accepted 2 November 2004 Available online 8 December 2004

Abstract The concept of knowledge and knowledge management is not new. Researchers identified the practice of knowledge management as early as 4000 years ago. However, knowledge management systems (KMS), which involve the application of IT systems and other organizational resources to manage knowledge strategically, are a relatively recent phenomenon. While the literature on knowledge management covers various issues, it lacks comprehensive studies of factors and variables of adoption and diffusion of KMS. This paper studies these factors and variables in the context of some Australian organizations. A qualitative field study is undertaken in this research, where six organizations of various sizes, all in various stages of KMS adoption and diffusion, are studied via interviews with key personnel. Content analysis is then performed to extract the factors and variables and a comprehensive model of KMS adoption and diffusion is developed. The results of the interviews identify four major variables affecting KMS diffusion as: organizational culture, top management support, benefits to individuals, and dream of KMS. The paper also highlights the research and managerial implications of the KMS diffusion model. q 2004 Elsevier B.V. All rights reserved. Keywords: Knowledge management systems; Adoption and diffusion; Qualitative method; Content analysis

1. Introduction

“Much of the Knowledge of the Greeks and Persians was preserved in Arabic translations, following the fall of these empires to the expanding Islamic Empire. This knowledge eventually reached the monasteries of Europe where monks, who could be termed knowledge specialists, preserved and translated these works for contemporary scholars and future generations.” [1, pp. 23] The above quote highlights that the practice of knowledge management is not new. Human civilizations have been preserving and passing knowledge from generation to generation for a better understanding of the past and therefore, the future. In today’s dynamic and complex * Corresponding author. Tel.: C61 7 5506 9320; fax: C61 7 5506 9301. E-mail addresses: quaddus@gsb.curtin.edu.au (M. Quaddus), jxu@ scu.edu.au (J. Xu). 0950-7051/$ - see front matter q 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.knosys.2004.11.001

business environment, the thirst for knowledge has increased even more and the scope and content of knowledge have changed dramatically, often spreading outside of the organization. Information technology and the Internet have brought new challenges in creating, preserving and managing knowledge. The term ‘Knowledge Management (KM)’ has been defined in a number of ways ([1–5]; among many others). In this study we have adopted the definition of Ruggles [6], which is as follows: “KM is.an approach to adding or creating value by more actively leveraging the know-how, experience, and judgment resident within and, in many cases, outside of an organization.” ([6]) The above definition highlights important elements of knowledge management. The ‘know-how’ aspect of KM emphasizes the ‘explicit’ knowledge, which can be easily captured and codified ([4]). On the other hand,


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the ‘experience’ and ‘judgment’ aspects of KM reflect the ‘tacit’ or ‘implicit’ knowledge, which is difficult to capture and formalize ([4]). The definition also emphasizes that the primary purpose of knowledge management is to add or create ‘value’. To add value with knowledge management, we need knowledge management systems (KMS), which facilitate the generation, preservation and sharing of knowledge ([1,4]). Like KM, KMS has also been defined in a number of ways ([5]). However, we take a broader information systems perspective and define KMS as ‘specialized information systems which deal with the generation, preservation and sharing of knowledge within and outside of the organization’. Although KMS has been studied widely over the last several years, literature ([3,7]) suggests that there is a scarcity of empirical studies on KMS. Like any information system, the success of KMS depends on its effective use by the users. A number of case studies are available in the literature ([3,4]) which presents various success and failure factors of KMS projects. However, no comprehensive study on the diffusion (spread in sustained usage) of KMS is available. What must be done to develop or adopt KMS? What factors are important in the diffusion of KMS? These are the natural questions to investigate in the context of KMS adoption and diffusion. This paper investigates the above questions in the context of some Australian organizations. Basic premise of the study is the extensive literature on the adoption and diffusion of generic technologies and a high level model of innovation diffusion (presented in Section 2). The primary objectives of this paper are two-fold: (i) to identify various factors and variables of KMS diffusion, and (ii) to explore and develop a model of KMS diffusion based on the high level model of innovation diffusion. We use qualitative field study as the research method and use structured interview techniques to collect relevant data. In the next several sections we first present relevant background literature on KMS and the adoption/diffusion process. The research method is presented next which describes the process of data collection via interview and data analysis via a combination of inductive and deductive approaches of content analysis ([8]). Results of the study are then presented in detail, in the form of factors and variables of KMS diffusion, and a comprehensive adoption/diffusion model of KMS as obtained from the field study. Finally, conclusions and future directions are presented.

2. Background There has been an exponential growth of knowledge management activity in organizations all over the world.

Bonner [4] mentions that in early 1999 about one-half of US companies had some form of knowledge management activity already underway. The author also mentions that corporate America’s spending on KM activities increased almost ten-fold from $410 million in 1994 to $4.5 billion in 1999. In Australia, there also had been a major shift from a resource-based economy to a knowledge-based economy ([9,10]). In the list of top 10 companies in Australia, the number of knowledge-based companies increased from two in 1980 to eight in 1998 ([10]). In this section, we first briefly look into the evolution and use of knowledge management systems. We then briefly review the background to the diffusion of technology in order to prepare a sketchy workable model of KMS diffusion for further exploration in Australian organizations. 2.1. Knowledge management systems (KMS) KMS provides the necessary infrastructure for organizations to implement the knowledge management process ([11]). The existing research on KMS consists primarily of general and conceptual principles of KMS and case descriptions of such systems in a handful of leading organizations ([12–17]). The majority of research ([11,18–20]) has only covered the issues concerning the building/creating and implementing of knowledge management systems. Knowledge management systems have appeared in various forms and formats in different industries. Indeed, there is no single model for knowledge management systems. There is no single role of IT in knowledge management, just as there is no single technology comprising KMS ([18]). Chait [19] mentions that KMS should include the key elements of organizations’ knowledge capital such as: (i) information about staff which improve organizations’ ability to identify people with needed skills and knowledge, (ii) information about customers and clients which help organizations to support and serve them, (iii) information about methodologies and tools which allow organizations to deliver quality and consistent service in an efficient and effective manner, and (iv) information about practices and groups which keep every one in organization up-to-date any time and anywhere. Information Technology (IT) is only one element of the knowledge management system. In addition to the hardware and software, organizations must pay much more attention to the issues of content, culture, and infrastructure ([19]). Literature ([11,18,20,21]) provides descriptions of some KMS applications in leading organizations such as BP, Xerox, KPMG, AMP, Ernst & Young, etc. These case descriptions mainly focus on the issues of the process of implementing KMS, objectives of KMS, critical factors of the successful KMS, and the characteristics of KMS leaders and laggards. The diffusion process of KMS is not addressed in these case studies. Limited empirical studies on KMS also do not deal with the diffusion process, except the study by


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Scarbrough and Swan [22]. The authors explain the role of fashion in KM diffusion and conclude that it has only a partial impact. 2.2. Diffusion process Diffusion is ‘the process by which an innovation is communicated through certain channels over time among the members of a social system’ ([23]). In the context of KMS diffusion, the innovation is KMS and the social system is the group of prospective users of KMS in an organization. According to the innovation diffusion research, the organization first has to make a decision on the adoption of the innovation (e.g. new technology). Information about the innovation is collected. This information then leads to the formation of perceptions about the innovation. In line with these perceptions, a decision to adopt or reject the innovation is made ([23]). Mere adoption of technology, however, does not provide the expected benefits until sustained diffusion (spread in usage) is achieved. It is necessary for the end-users to infuse, routinize and implement the innovation into their daily tasks in a routine manner ([23–25]). The entire process of diffusion, therefore, starts from the adoption of a technology and continues through various stages of infusion, routinization, adaptation, etc. until the technology becomes obsolete. Over the last three decades researchers have studied different stages of the diffusion process. For a description of these stages see [26]. Literature on diffusion analysis is plentiful, for example see [23,27–29]; among many others. Applications range from administrative innovation (soft-tech) diffusion ([30]) to the diffusion of high-tech products (ICs) ([31]). Past research on the diffusion process has identified a number of factors which influence the diffusion of an innovation. Most of these studies use the model(s) proposed by Ajzen and Fishbein [32] and Davis [33]. Basically, the authors have suggested that some external factors affect the perceptions about an innovation, which in turn impact the diffusion of the innovation, i.e. ‘External Factors’/‘Perceptions’/‘Diffusion’. This simple model is generic in nature and is likely to be applicable, with some adjustments, in various innovation diffusion processes. As mentioned before, the aim of this research is to study, via field study, various factors and variables affecting KMS diffusion and to investigate to what extent the above generic model is applicable in developing a comprehensive model of KMS diffusion.

3. Research method The paradigm of the research in this study is qualitative, in which the field study has been used as the research method ([34,35]). Since we are interested in exploring the real companies involved in KMS, the field study is

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the appropriate method. Like any other research method field study involves choosing a sample of companies using either random or non-random methods ([35]). Data in a field study can also be collected via different methods. As we shall explain later we have chosen the interview method to collect the required data ([35]). The details of our field study research process are presented below. 3.1. Sample A convenient sampling procedure was undertaken to select six companies who were willing to be included in the field study. It is noted that convenience sampling is frequently undertaken in business research ([35]). The main selection criterion was that the companies must be involved in various stages of the diffusion of some form of KMS. Five companies were selected from the list of companies who took part in the Curtin Graduate School of Business’s lunchtime discussion on knowledge management. The sixth company was selected based on personal contacts. A key person in the company, who is dealing with the KMS, was contacted via telephone and objectives of the study were made clear. All companies took part in the study voluntarily. Table 1 shows the demographic information on the companies together with the details of key persons who took part in the interview. 3.2. Data collection A semi-structured interview technique was used as the primary vehicle to collect data, as it was possible to structure the interview questions based on the basic model of innovation diffusion of ‘External Factors’/‘PercepPerceptions’/‘Diffusion’. Our interview plan followed the guidelines of Whiteley et al. [36], and Patton [34]. The semi-structured interview questions focused on the areas of information needed in this research. These are: (i) general perceptions and understanding of KMS, (ii) the adoption and diffusion process of KMS, (iii) the motivations of adopting a KMS, (iv) the main factors that influence the establishment of a KMS in an organization and the links between those factors, (v) barriers to putting a KMS into place in an organization, (vi) things that would encourage people to use a KMS, (vii) things that would prevent people from using a KMS, (viii) required resources and facilitating factors of implementing a KMS, and (ix) the management’s and individual’s view on KMS. Before the final interviews were carried out a pre-test of the interview questions was conducted with a company. Interview questions proved to be working well in getting the required information. However, minor adjustments were made based on the feedback. The final interviews were scheduled as per the convenience of the interviewees, so that there would be minimum disruption and interruption to their working schedules. A preinterview session was conducted first via telephone,


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Table 1 Demographic information

Nature of business

Company 1

Company 2

Company 3

Company 4

Company 5

Company 6

Public Service (Resources preservation)

Mineral Resources (WA-based) 593 staff (BRW 1000, 2000) Managing Director

Consulting (International)

Public Service (Justice)

Software Development

Interview participant’s position

Director of Strategic Development & Corporate Affairs

Interview participant’s education

Bachelor of Arts

Bachelor of Engineering

KMS ??

Has some parts of KMS No

Has some parts of KMS No

Has some parts of KMS Yes

Has some parts of KMS Yes

Engineering & Construction (Multinational) 4000 staff (BRW 1000, 2000). Director & Chief Financial Officer; Director of Business Development & Director of Corporate Affairs; 3. Manager-Business Proposal MBA, MPhil, BCom; Qualifications in EE; BEng Has some parts of KMS No

Web-based System (Intranet) & Computing Network

Well-structured Data (knowledge) Storage System

Database & Intranet

E-mail & Issue Management System, Document Management System, Expert systems, Internet & Intranet

Intranet, Computing Networks & Knowledge Systems (such as Lessons-learnt database)

Size

Knowledge Manager or Chief Knowledge Officer? KMS technologies

4500 staff (Australia) (BRW 1000, 2000) National Board Member & Partner of Perth Office

providing each interviewee with an understanding of the interview process and giving them some food for thought. In total we conducted eight interviews. One company allowed us to interview three key persons involved in KMS (see Table 1). Each interview lasted for about 1 h. Each interview was transcribed the following day in order to reflect on the body language and other cues fresh from memory. 3.3. Data analysis One of the challenges in qualitative research is data analysis. A number of tools and techniques are available in the literature ([37]). These tool(s) must be selected based on the objectives of the research. Since our research is more exploratory than confirmatory in nature, we have chosen ‘content analysis’ in analyzing our interview transcripts ([8]). Although only eight interviews were conducted there were over a hundred pages of interview scripts to analyze. Content analyses were carried out in two stages. Stage one dealt with single interview transcripts, while stage two dealt with cross interview transcripts ([37]). It is noted that all content analyses were done manually and a combination of inductive and deductive approaches was used to categorize the factors and variables ([8]). The stepwise

Change & Knowledge Manager

20 staff Team Leader & Software Engineer

Bachelor of Science

Has some parts of KMS No

Email, Databases & Interactive Websites

procedure of single interview content analysis (stage one) was as follows: 1. Manually review the transcripts, line by line and sentence by sentence, to uncover key patterns/themes and produce key words/phrases (inductive process). 2. Produce labels/categories of these key words/phrases. Identify high-level factors and corresponding variables. 3. Look for relationships among the factors from each interview transcript. 4. Match these factors and variables with those from the literature. Revise and update accordingly without sacrificing any factors and variables obtained from the interview (deductive process). 5. Develop raw tables of factors, variables and their links for each interview. The second stage of the content analyses primarily aimed at integrating all the individual factors, variables and their relationships to come up with a final list of factors and variables and their links. The stepwise procedure was as follows: 1. Revisit the company specific transcripts with the developed factors, variables and their links from stage 1.


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2. Identify the similarities and differences in the variables under each factor. 3. Use the concept of ‘union’ in integrating the variables, i.e. combine the similar variables and give it a common name. Retain the unique variable(s). 4. Use the concept of ‘union’ in integrating the links among the factors. 5. Develop the final tables of factors, variables and their links. 6. Develop the combined model of KMS diffusion.

4. Results and implications 4.1. Demography Table 1 presents the demographic information on the companies involved in the field study. It is noted that there are two government organizations and four private companies (one mineral resource, one consulting, one engineering and one software development). The size of the company varies from 20 staff to over 4000 staff. All companies are involved in various stages of the diffusion of KMS. One private company and one public organization have a knowledge manager or chief knowledge officer on board. The last row of Table 1 shows various technologies which are in use in KMS. They vary widely, which of course depends on the purpose and objectives of corresponding KMS. 4.2. Factors and variables of KMS diffusion Altogether 17 factors and 90 variables of KMS diffusion were identified from different companies via extensive content analyses as described earlier. Comprehensive list of these factors and variables can be obtained from Xu [38]. Out of the 17 factors, 11 are primary factors. These are: External inspiring factor, Task complexity factor, Individual factor, Organizational factor, Management support factor, KMS characteristics, Perceived usefulness, Perceived userfriendly, Perceived voluntariness, Subject norm, and Diffusion of KMS. The factor Diffusion of KMS has six more sub-factors such as: Initiation, Adoption, Pilot implementation, Organic growth, Organizational implementation, and Sustained use. It is noted that, where possible, we have labeled the factors in line with the literature ([23,25,32,33,39–41]; among many others). However, the variables within each factor and their meanings are different from earlier studies and more specific to knowledge management. The subfactors of the Diffusion of KMS are worth mentioning. Our study brings out Pilot implementation of KMS as distinct from Organizational implementation. The majority of the companies highlighted the need for ‘organizational adjustments’ and ‘Pilot implementation’ before full-scale

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‘organizational implementation’. All companies also emphasized the need for dream of KMS as part of the Initiation sub-factor, which included thinking about KMS and what benefits it can bring to the company. In other words, the companies emphasized the need for internally ‘pulled’ type KMS diffusion as opposed to externally ‘push” type diffusion. It is interesting to note that out of 90 variables only four variables are mentioned by all six companies and 13 variables are mentioned by five companies. The variables under task complexity factor have only been mentioned by company 5, an engineering and construction company. The four variables, chosen by all six companies, are: Organizational culture, Top management support, Benefits to individuals, and Dream of KMS. We call them the major significant (not in statistical sense) variables. Dream of KMS has been discussed earlier. Organizational culture is one of the most significant variables affecting successful IT (and other technology) diffusion ([42,43]; among many others). A perfect ‘fit’ of culture with KMS is needed for successful diffusion ([42]). Like any other technology, success of KMS diffusion depends on the top management taking the initiatives and supporting the diffusion stages all the way through. However, top management support is extremely necessary, but it is not sufficient. In a recent study, Quaddus and Intrapairot [44] have shown that once the top management provides the necessary support for the adoption and diffusion of a technology, the ultimate success of diffusion then depends more on other factors and requisite policies than just the top management support. The variable, benefits to individuals, is perhaps the most important for the success of KMS diffusion. ‘What’s in it for me?’ is always a popular comment by individuals when any new venture is initiated in an organization. Individuals will not take KMS as an integral part of their daily routine if they cannot identify clear benefits in using it. In terms of the number of variables, companies 1 and 5 have mentioned 51 variables followed by company 2 with 41 variables, company 3 with 38 variables and company 4 with 35 variables. It must be noted that company 1, a public service organization (see Table 1), is relatively less involved in KMS compared to others, while company 4, another public service organization (see Table 1), is relatively more involved in KMS compared to others. Company 4 has a dedicated ‘Change & Knowledge Manager’ in place. 4.3. Causal links among the factors Table 2 presents the causal links among the factors of KMS diffusion. Information on perceived causal links was sought during the interview process and was extracted from the interview scripts via content analysis. Column 1 of Table 2 presents the pairs of factors and corresponding direction of links. For example, ‘EI & PU’ in row 1 of Table 2 represents that ‘External Inspiring factor’ (EI) impacts ‘Perceived Usefulness factor’ (PU), and this link


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Table 2 Causal links among the factors Links between factors

Company 1

EI & PU TSCPX & PU IND & PU ORG & PU MGMS & PU KMS & PU PU & DF PUFRD & PU PUFRD & DF SUBNM & DF PLVTN& DF

#

Company 2

# # # # #

# # # # #

# # #

# #

Company 3

Company 4

Company 5

Company 6

#

#

#

# # # # #

# # # # #

# # # # # # #

# #

# #

# # #

# # # # # # # # #

EI, External Inspiring Factor; TSCPX, Task Complexity Factor; IND, Individual Factor; ORG, Organizational Factor; MGMS, Management Support Factor; KMS, KMS Characteristics; PU, Perceived Usefulness; PURFD, Perceived User-Friendly; DF, Diffusion of KMS; SUBNM, Subject Norms; PVLTN, Perceived Voluntariness.

has been identified in companies 1, 3, 4, 5, and 6. Table 2 can be used to trace a causal model of KMS diffusion. For example, Figs. 1 and 2 present the causal models of KMS diffusion as perceived by companies 1 and 4, respectively. It is noted that Fig. 1 represents the causal model of KMS diffusion for company 1, which mentioned 51 variables (the maximum) related to KMS diffusion. This model is, therefore, more comprehensive than Fig. 2 which represents the KMS diffusion model of company 4, which had only 35 variables mentioned (the minimum). Comparing Figs.1 and 2

one observes that, although the labels of the factors are similar the variables in them are quite different. The subfactors of the ‘Diffusion of KMS’ differ significantly between companies 1 and 4. Company 1, being in early stages of KMS diffusion, elaborated more on the diffusion of KMS than company 4, which is more involved in KMS diffusion than company 1. Company 4, being well into KMS diffusion emphasized the need for Pilot implementation, while company 1 went beyond the Pilot implementation to Sustained use (see Figs. 1 and 2).

Fig. 1. KMS diffusion model of company 1.


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Fig. 2. KMS diffusion model of company 4.

Fig. 3 presents the combined model of KMS diffusion which has been developed selecting the variables and links mentioned by at least two companies. This model can be used in a number of ways as elaborated below. Looking at Figs. 1–3, it is observed that the basic diffusion model of External Factors/Perceptions/Diffusion, which was obtained from the literature, applies quite effectively for KMS diffusion. However, it must be highlighted that the factors and variables are different and very specific to KMS diffusion. 4.4. Research implications Fig. 3 presents the comprehensive model of KMS diffusion. This model is unique in the sense that it has been developed based on the data obtained from eight interviews in six different companies. Although no formal propositions are developed in this paper, the model can still be taken as a research model for further investigation. For example, a causal modeling approach (structural equation modeling) can be undertaken to test the model ([45]). The combined model (Fig. 3) has 16 factors/subfactors and 64 variables. It could be too big (in terms of sample size needed) to test the model in its entirety. The measurement parts of the model can first be tested separately (for example, ‘perceived usefulness’) and

replaced by an equivalent index. The resulting model can then be tested ([45]). Alternatively, only parts of the model can be extracted and investigated in detail. For example, the roles of ‘perceived usefulness’ and ‘subject norms’ can be tested to investigate their impact on ‘adoption’ and ‘Sustained use’ parts of the KMS diffusion. Similarly other parts of the combined model can also be tested. Fig. 3 also shows the detailed stages of KMS diffusion from ‘initiation’ to ‘Sustained use’. The direction of the arrow indicates the sequence of the KMS diffusion stages. A separate study can also be undertaken to test these stages and their sequence. It must be noted that in all of the above proposed research, Fig. 3 would serve as the basic research model. Further exploratory research is needed to develop appropriate research hypotheses in order to carry on with the above research. 4.5. Managerial implications Fig. 3 also presents a practical model of KMS diffusion. All the factors/sub-factors and variables have been obtained from the real world. A close examination of the model reveals that all the factors/sub-factors ultimately lead to ‘Sustained use’ of KMS. Companies planning to embark on KMS can consider the variables of Fig. 3 as ‘criteria’ of


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Fig. 3. Combined KMS diffusion model.

successful diffusion of KMS. It must be noted that not all criteria of Fig. 3 will be applicable for all companies. A careful analysis is first needed to select the appropriate criteria for the company. A multiple criteria modeling approach can then be undertaken to assess the suitability of the company for KMS adoption and diffusion. A similar modeling approach can also be undertaken to find the type of KMS suitable for the company ([46]).

5. Conclusions and future study This paper presents a comprehensive study to determine the factors and variables of KMS diffusion. In doing so it takes a qualitative field study approach. Six companies took part in the study, which resulted in eight interviews with key person(s) in the companies. The participating companies were in various stages of KMS diffusion. The interviews were transcribed by the researchers and the contents were analyzed thoroughly using a structured process. The analyses resulted in 17 factors and 90 unique variables. Company-specific individual diffusion models were first developed which were then combined to develop a comprehensive KMS diffusion model.

Four variables identified to be significant for KMS diffusion were organizational culture, top management support, benefits to individuals, and dream of KMS. These variables were mentioned by all six companies. Organizations planning to embark on KMS or currently using parts of KMS should look into these variables carefully for successful adoption and diffusion of KMS. This study contributes to the KMS literature in the following ways. It used a qualitative research method to develop the factors, variables and comprehensive model. The research was thus exploratory in nature. It must be mentioned that most of the existing research on adoption/ diffusion area is quantitative in nature, i.e. hypothesis testing confirmatory type. The comprehensive model can be used to undertake further research and thus add value to the literature on knowledge management. The paper elaborated on how the combined model can be used to undertake further research and how it can also be used for practical applications in companies which are embarking on KMS adoption and diffusion. Our immediate future plan is to study the combined model further using a structural equation modeling approach. This part of the research will use a quantitative approach, which will test a number of hypotheses and the model itself.


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