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Industrial Marketing Management xx (2006) xxx – xxx

Unmet adoption expectation as the key to e-marketplace failure: A case of Taiwan's steel industry Yu-Hui Tao a,⁎, Chia-Ping Chen b,1 , Chia-Ren Chang c,2 a

National University of Kaohsiung, Department of Information Management, 700 l Kaohsiung University Road, Nan-Tzu District, 811, Kaohsiung, Taiwan, ROC b National Chung Chen University, Department of Business Administration, 168 University Road, Min-Hsiung, Chia-Yi, Taiwan, ROC c Metal Industries Research and Development Center, Industrial Information and Planning Section, 1001 Kaonan Highway, Kaohsiung, Taiwan, ROC Received 5 July 2004; received in revised form 26 May 2006; accepted 8 August 2006

Abstract Many e-marketplaces rose and fell in recent years. In this study, we argue that unmet expectation was the main reason why Taiwan's steel emarketplaces failed. To confirm this statement, we examined the discrepancy between the original adoption factors and the factors that lead to the current satisfaction as perceived by e-marketplace participants. Adoption factors were identified by the factor analysis using data collected from medium-sized steel enterprises via a questionnaire based on a preliminary literature review and a focus group interview. The importance rank of the nine adoption factors suggested that the customers desired a safe and secured transaction-oriented platform and that they were less concerned on the cost-saving or management efficiency for steel e-marketplaces. However, Taiwan's steel e-marketplaces failed to meet users' expectations as negative scores appeared on all but “industry information” satisfaction indicators. In other words, more efforts are needed to focus on transforming the visionary e-marketplace into a practical and secured trading environment to earn the user's satisfaction and support. An implication drawn from concerns regarding security to privacy and trust is also discussed. © 2006 Elsevier Inc. All rights reserved. Keywords: e-Marketplace; Adoption factors; Steel industry; Factor analysis

1. Introduction Visioning the importance of electronic marketplace (emarketplace) to Taiwan's international competitive advantage, the Industrial Development Bureau of Ministry of Economic Affairs (IDB, 2001) of Taiwan announced a project in May 2001 to fund Taiwan Industrial Marketplace (TIM) as a common platform for connecting more than ten Taiwan industries to the world, such as mechanics, automobile, electronics, food, and steel. In 2002, one year after TIM was established, an official public announcement revealed that Taiwan had about twenty emarketplaces which generated US$1.2 billion worth of transac-

⁎ Corresponding author. Tel.: +886 7 5919220; fax: +886 7 5919328. E-mail addresses: ytao@nuk.edu.tw (Y.-H. Tao), bmacpc@ccu.edu.tw (C.-P. Chen), jonathan@mail.mirdc.org.tw (C.-R. Chang). 1 Tel.: +886 5 2720411 34318. 2 Tel.: +886 7 3513121.

tions and US$38.9 million worth of earnings as of 2002, and it was expected in 2005 to have over seventy e-marketplaces with US$4.4 billion worth of transactions and US$146.1 million worth of earnings (Tsang, 2002). In the official announcement, Taiwan's e-marketplaces seemed to be prosperous, but in reality, not all e-marketplaces were performing equally well. According to our investigation, as of the third quarter of 2004, the remaining two steel e-marketplaces (Professional Steel Net in Chinese Language-http://steelnet.com and Universal Exchange-http:// steel.uxb2b.com) were not profitable due to low actual transaction volumes, and thus have been downgraded to information exchange oriented sites. There were over 200 steel e-marketplaces at peak time (Schneider & Perry, 2003), including some well-known ones like Metalsite (http://www.metalsite.com) and e-STEEL (http://www. newview.com) in the U.S., isteelasia (http://www.isteelasia.com) and worldmetal (http://www.worldmetal.com) in Hong Kong and Mysteel in China (http://www.mysteel.com). One major difference between the U.S. and the Asian e-marketplaces is that the

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U.S. e-marketplaces focus more on the transaction activities while the Asian e-marketplaces further expand their efforts into information exchange. Metal Industries Research and Development Center (MIRDC, 2001a) has done a critical analysis on Taiwan's steel industry as follows: Taiwan's raw steel production ranks number 14 worldwide with a two percent of market share. Furthermore, Taiwan owns a superior environmental factor being the second largest steel consumption (per person ratio) country and many nearby Asian counties are either on top of the list or have the biggest growth of consumption. Because the average exporting rate of steel products is smaller than 50% and Taiwan has been a free steel trading country with custom duty closed to WTO level of 3.5%, these factors present a great opportunity for Taiwan's steel industry growth by increasing steel export. Therefore, business to business (B2B) electronic commerce (EC) is one strategy that can easily unify other strategies such as national alliances in marketing or R&D, and information sharing. Majority of Taiwan's medium-sized steel enterprises who are major members of Taiwan's steel e-marketplaces share the same characteristics of small and medium enterprises (SMEs) while evolving into larger capital-base enterprises. In principle, a typical Taiwan manufacturing SME has a capital of less than US $2.6 million, or an employee size of less than 200 people (SMEA, 2000–2002). One major problem being encountered by small and medium business is the lack of quality people and financial resources, which especially limited their capability on IT and information-system (IS) investment and implementation, and more critically, the awareness of IT as an enabler (SMEA, 2000–2002). This problem echoes to some SME e-marketplace barriers summarized in Stockdale and Standing (2004). Steel e-marketplaces do offer some attractiveness to the majority of their members which are medium-size enterprises and have been suffering a long period of economic recession as well as the recent impact of Taiwan entering the World Trade Organization. An industrial report by MIRDC (2001a) identified several benefits for Taiwan's steel industry to participate in electronic business. These include lower purchasing costs, better inventory management, shorter expected production cycle, lower marketing and sale costs, and limitless geographical boundary for accessing international customers. However, these potential benefits also pose great challenges to the steel emarketplace providers, and barriers to the steel industry, which may be linked to the present failure of Taiwan's steel emarketplaces. Combining the above descriptions of Taiwan's steel industry and e-marketplace potentials, an interesting question immediately follows: why did the steel industry perform so poorly compared to other Taiwan's traditional or high-technology industries in the realm of e-marketplace? Grewal, Comer, and Mehta (2001) pointed out that motivation could affect the longterm activity of participation, which Stockdale and Standing (2002) believe to have a forceful effect on the will to succeed in the electronic environment. In responding to many e-marketplace failures, companies such as Chemdex, MetalSpectrum, GoFish and E-Chemicals (Daniel, Hoxmeier, White, & Smart, 2004), Brunn, Jensen, and Skovgaard (2002) proposed a theoretical framework to explain how an e-marketplace could

achieve success by creating a powerful setup and meeting the challenge of building liquidity and capturing value. The great comeback of Hong Kong's isteelasia e-marketplace (http:// www.isteelasia.com) had demonstrated such a good application of focus, governance, functionality, technology and partnership in Brunn et al.'s framework. Based on the study of Grewal et al. (2001), we assume that Taiwan's steel e-marketplaces failed to focus appropriately on their members' motivation for participation. Therefore, instead of investigating the after-failure factors, this study aims to explore the original adoption factors of the participated members. In collaboration with MIRDC, we intend to validate our assumption by examining the perception of the emarketplace members in Taiwan's medium-sized steel enterprises on the original adoption factors. The remaining sections of this paper provide background information in Section 2, describe the formal research objectives and methods in Section 3, present data analyses and discussion in Section 4, and draw conclusions in Section 5. 2. E-marketplace and adoption factors 2.1. E-marketplace A general definition of B2B EC is “under specific transaction scope, both the suppliers and buyers are willing to proceed with exchanging of money, and distribution of products and information through the mechanism and norms provided by the Internet” (Deloitte Consulting, 2000). A major classification for B2B EC is determined by its owner, such that it is an edistribution if it comes from the seller's side, an e-procurement if it is on the buyer's side, or an e-marketplace if it is intermediary (Gebauer, 1996; Weller, 2000). However, scholars have extended the scope of the intermediary e-marketplace to include both the e-distribution and the e-procurement. For example, Kaplan and Sawhney (2000) classified the B2B emarketplace into maintenance/repair/operating center (MRO) hubs, catalog hubs, and yield manager exchanges based on procurement and product types. There are also other B2B EC classifications such as bonding game, trading game, morphing game, and value-added game based on purchasing relationship and industry fragmentation by McKinsey and Company (2000), commerce hub, dynamic marketplace, channel enabler and content/community portal by Piccinelli, Vitantonio, and Mokrushin (2001), aggregators, trading hubs, post and browse markets, auction markets and fully automated exchanges by Sculley and Woods (2001), and static vs. dynamic or established vs. discovered by Gottschalk and Abrahamsen (2001). E-marketplace can be further classified based on industry type into horizontal, vertical, and diagonal market (De Figueriredo, 2000). The major difference between horizontal and vertical markets is that a vertical market targets specific products and services such as e-steel while a horizontal market targets multi-industries such as VerticalNet that covers over 40 vertical industries, including communication, environmental services and food services. See Daniel et al. (2004) for further references of e-marketplace classification.

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In terms of functionality, Weller's framework (2000) indicates that a complete e-marketplace portal includes at least the following eight categories: content, catalog management, auctions, transaction services, logistics, back-office integration, supply chain management and value-added services. Additional functionalities may be considered, such as e-marketplace of engineering information exchange and management, in which a customer can post product-engineering diagrams to communicate the specification as well as to manage engineering data. In the related literature, Daniel et al. (2004) noted that there has been an evolution between e-marketplace models from thirdparty to either consortia-based and then private e-marketplaces, or to private and then consortia-based marketplaces. This developmental evolution has also transformed the functionality of e-marketplaces from being an early online catalogue to subsequent online auction. Laseter, Long, and Capers (2001) made an observational remark that only a minority of emarketplaces actually offer services beyond procurement, in which online actions might be the only transaction activity they had achieved (Smart & Harrison 2001).

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tation, access to new revenue potential, and increased IT effectiveness are supplier advantages. Hsiao (2003) categorized the challenges of e-marketplace adoption into removing adoption barriers, chasing the shadow of fear (online trust building), and persisting culture (trust-production mechanisms). By focusing on SMEs, Stockdale and Standing (2004) identified the benefits and barriers of e-marketplaces from the literature. The benefits to be gained from the participation of SMEs in e-marketplaces include access to a wider range of markets, greater potential for partnerships, flexibility in administration and communication, convenience (24/7 accessibility), information, improved customer services, updating of information, lower transaction costs, differentiation of products and services/customization, and ability to enter supply chain for larger companies. In contrast, the barriers to e-marketplace participation include lack of resources and knowledge, the skill levels of business operators, lack of trust in the IT industry, lack of e-commerce readiness in some industry sectors, lack of recognition of the potential to improve business appropriate to the effort and costs of adoption, and lack of understanding of the realizable benefits.

2.2. Adoption factors 3. Research methods The majority of technology adoption literature applied Technology Acceptance Model (TAM) by Davis (1989) or Diffusion of Innovation (DOI) theory by Rogers (1995). However, TAM was criticized by King and Gribbins (2003) as having the individual attitude-behavior rather than the firm behavior, and DOI was criticized by Lundblad (2003) as being unsuitable for explaining a firm's adoption decision. Therefore, this research surveyed more generalized adoption factors from the firm's perspective instead of directly applying TAM or DOI models. Generally speaking, the adoption factors for EC and information technology (IT) can be classified into six items such as organization, technology, industry environment, relative advantage, compatibility and testing (CommerceNet, 2000; Kendall, 2001; Lin, 2001; PricewaterhouseCoopers, 1999). Six benefits can also be summarized. These include transparent information and market control, reduction of transaction cost, increasing transaction opportunities, avoidance of human manipulation or mistakes, privacy, and smooth product flow (Detourn, Fischer, & Larson, 2000; Hsu, 2001; PricewaterhouseCoopers, 1999; Yuan, 2001). Because measuring e-marketplace success is a complex issue, scholars discuss the adoption factors from different alternatives, such as sustainability, value proposition, and challenges. Daniel et al. (2004) studied the sustainability of emarketplaces from the macroeconomic and regulatory level, the industry level that includes power of buyers and suppliers, characteristics of the product, industry's IT readiness, and the individual level that includes strategic intent and culture. Bテシyテシkテカzkan (2004) summarized the e-marketplace value proposition of both buyers and suppliers, referring to lower transaction costs, better prices, and increased IT effectiveness as buyer advantages, while lower transaction costs, improved marketing, improved customer relationship, back-office facili-

There are no specific hypotheses to be tested in this research. Instead, in order to quantify the failure causes of Taiwan's steel e-marketplaces from the original adoption factors, the specific objectives of this research include O1) to derive the distinct factors that mainly affect the adoption of steel e-marketplaces; O2) to establish the disconnection between current membership status and previous adoption factors; and O3) to measure the current satisfaction levels of steel e-marketplace functionalities. These objectives were accomplished based on the participants' perception of medium-sized enterprises that compose the majority of steel e-marketplaces adopters. The analysis and discussion on the outcomes of the three objectives provide a good justification for the causes of steel e-marketplace failure in Taiwan. Accordingly, a three-step adoption-satisfaction approach was adopted as shown in Fig. 1. The first step was to extract steel emarketplace adoption variables via the preliminary literature review and the focus group interview method. A small group of industrial representatives provided in-depth e-marketplace experiences and opinions, which helped to screen and compile the adoption variables from the literature review. These extracted variables were then used to design a questionnaire for the survey on the medium-sized steel enterprises. With the survey data available, the second step was a series of preliminary analyses. A t-test and a chi-square test were used to inspect the group difference between the respondents and the non-respondents while descriptive statistics were used to describe the respondents. Then, the satisfaction factors were calculated and ranked to assess their relative importance to the medium-sized steel enterprises as well as assess (Hair, Anderson, Tatham, & Black, 1998) the impacts of the eight individual satisfaction factors on the overall satisfaction level by the multiple regression analysis.

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Fig. 1. Research approach diagram.

The third step consisted of two formal analyses: the first was the factor analysis to extract the e-marketplace adoption factors, which can be seen in many studies such as Yasin, Correia, and Lisboa (2004), Sarker, Valacich, and Sarker (2003), and Scott and Shieff (1993); and then the binary logistic regression analysis to assess the influence of adoption factors on the current membership status of the steel e-marketplaces, which can also be seen in studies like Kuan and Chau (2001) and Laitinen (1999). 4. Data and analyses The process of our data analyses followed the route illustrated in Fig. 1 by first extracting the adoption variables in Section 4.1, and then conducting preliminary analyses as shown in Section 4.2 and formal analyses in Section 4.3. 4.1. Extraction of adoption variables As described in the first step of the research method in Section 3, a focus group and a preliminary literature review were combined to extract the e-marketplace adoption variables for further analyses. Nine executive managers from mediumsized steel enterprise for the focus group were selected and approached with the assistance of a Steelnet e-marketplace marketing manager, and seven actually participated. Before the focus group took place, basic information on e-marketplace was provided together with the invitation letter for participants' references. The complete focus group discussion was audio taped, transcribed, and coded for further analyses. Twenty-four variables (V1窶天24) of e-marketplace adoption were extracted as shown in Table 1. These were further used to design the questionnaire for determining the adoption factors of steel companies. The eight categories of services (V25窶天32) from Weller (2000) were also included to measure the satisfaction with Taiwan's steel e-marketplaces. The Taiwan Association of Steel Industry (TASI) which targets traditional steel business scope in welding, casting, forging, stamping and so on has about 330 members. Nevertheless, the majority of the e-marketplace members fall between large enterprises and SMEs under the categories of secondary manufacturing and waste steel, which in this paper are called medium-sized enterprises, as referenced by Birou and

Fawcett (1993), Massey (2004), Tao, Ho, and Yeh (2001), and Kamel and Hussein (2004). Therefore, a broader scope of steel business considering the secondary manufacturing and the waste steel was adopted, which added to the TASI total of over 1000 members (MIRDC, 2001b). The sources of the survey population include the 2002 membership records of the association of steel industry and the two steel e-marketplaces. After screening out redundant candidates and big steel corporations, three hundred and thirteen medium-sized enterprises that had registered as free members in either of the two e-marketplaces remained in the list. Since the targeted population of the qualified steel enterprises was only three hundred and thirteen, the questionnaire was sent to all these steel enterprises instead of further sampling a small subset for the survey. The questionnaire was divided into three parts: 1) the company profile and status of e-marketplace membership, 2) the importance of adoption variables, and 3) the satisfaction level of e-marketplace experiences. Parts 2 and 3 contained questions to be answered based on a five-point Likert scale starting with 1 for very important up to 5 for very unimportant. 4.2. Preliminary analyses The preliminary analyses, as described in the second step of the research method in Section 3, include the sample inspection in Section 4.2.1, sample profile in Section 4.2.2, and user satisfaction in Section 4.2.3. 4.2.1. Sample inspection As designed, the questionnaires were sent out to the total census size of three hundred and thirteen companies, and fifty-one Table 1 Adoption variables (translated from Chinese) Adoption variables V1. Increasing business publicity V2. Shortening A/P collecting length V3. Stimulating business changes V4. Increasing credit limit

V13. Uncertainty of Internet V14. Fair Web bid V15. Authority of human signature V16. Completeness of e-marketplace laws V5. Building decision efficiency with V17. Safety of money flow gains in formation V6. Emerging trend of e-commerce V18. Inventory information leaks V7. Membership fees V19. Value-added services V8. Support of e-business V20. Differences between specification and actual product V9. Qualified employees and V21. Appropriateness of Web steel equipment trading V10. Stability of buyers and suppliers V22. Perfection of money and product flows V11. Opportunities for foreign trades V23. Human manipulations of emarketplace V12. Compatibility of computer V24. Government interfering systems marketplace V25. Forming business advantage V29. Equal-opportunity of platform V26. Reducing product inventory V30. Reducing human mistakes V27. Reducing transaction cost V31. Integrated industry and procurement V28. Collaborative opportunity V32. Transaction service fees

Please cite this article as: Yu-Hui Tao et al., Unmet adoption expectation as the key to e-marketplace failure: A case of Taiwan's steel industry, Industrial Marketing Management (2006), doi:10.1016/j.indmarman.2006.08.004


ARTICLE IN PRESS Y.-H. Tao et al. / Industrial Marketing Management xx (2006) xxx–xxx Table 2 Statistics of respondents versus non-respondents Category

Statistics

Employee size

Respondent Non-respondent Capital Respondent (million US$) Non-respondent

Table 4 Equivalent sample test for respondents vs. non-respondents on industry type

Number Average Standard Standard of deviation error of company the average 41 50 46 50

65.5 60.1 8.78 6.63

51.0 46.5 19.23 20.60

Statistics

Number of companies

Percentage (%)

Rolled

Respondent Non-respondent Respondent Non-respondent Respondent Non-respondent Respondent Non-respondent

16 10 12 15 15 16 4 9

34

Sheered Others

Pearson χ2 Likelihood ratio Linear-by-linear association N of valid cases

8.0 6.6 2.67 2.91

Industry type

Tube/framed

5

Value

d.f.

Asymp. sig. (2-sided)

3.584 3.644 2.572 97

3 3 1

.310 ⁎ .303 .109

Sum of squares Between groups Within groups Total

Mean square

F

Sig.

2.655

d.f. 1

2.655

2.616

.109 ⁎⁎

96.437 99.093

95 96

1.015

⁎ p b 0.1. ⁎⁎ p N 0.1.

20 25.5 30 31.9

respondent companies and the 47 respondent companies were conducted based on the employee size, the capital, and the industry type as shown in Tables 2, 3 and 4. As shown in the first half of Table 2, the average employee sizes were 65.5 and 60.1 while the average capitals were 8.78 versus 6.63 (million US$) for the respondent sample and the non-respondent sample, respectively. Since the Levene tests support equal variance assumption (employee size: F = 0.812 p = 0.37; capital: F = 0.09, p = 0.765) as shown in Table 3, we adopted the corresponding t values under the equal variance assumption. The null hypothesis assumes that there is no difference between the means of either the employee size or the capital from the respondents and the non-respondents. As seen in Table 3, the differences of means were 5.4 (persons) and 2.15 (million US$) while the standard errors of the difference were 10.2 and 4.08, respectively. Consequently, the t-tests (employee size: t = 0.527, p = 0.599; capital: t = 0.528 and p = 0.599) indicated that there was no significant difference between the respondent and the non-respondent samples. For the industry type, the second half of Table 2 lists the occurrences of respondents and non-respondents by steel types of rolled, tube/ frame, sheared, and others. Because industry type is a nominal scale, the chi-square test was used to inspect the independence of responding decision on industry type from the respondents and the non-respondents. As seen in Table 4, the result (Pearson χ2 = 3.584, d.f. = 3, p = 0.310) did not reject the indifference hypothesis. Accordingly, the above bias analyses on employee size, capital and industry type all suggested that the respondent sample was a good representative of the targeted steel enterprises in this study.

32 8.6 18

questionnaires returned. After removing four invalid questionnaires, the valid response rate was calculated as 15%. Although this response rate seemed to be low, it was comparable enough to other Taiwan's studies on the manufacturing sector, such as 14.6% (Huarng & Chen, 2002), 13.1% (Chen & Wei, 2002), 13% (Liang & Hung, 1997), and 16.5% (Shang & Marlow, 2005) on large manufacturing enterprises, 12.6% (Tao, Ho, & Yeh, 2001) on medium enterprises, and 16.3% (Kao, Lee, & Kuo, 1997) and 13% (Carr, Leung, & Sheu, 2000) on all sizes. The main causes of this low response rate in Taiwan's manufacturing survey studies may be attributed to the dramatic increase of higher education institutions from less than 30 to over 170 since the 90s, very limited sources of company directories such as Common Well magazine, China Credit Information Services, and the Ministry of Economic Affairs, and a large portion of Taiwan's business category is in the manufacturing sector. Combining these factors with the fact that Taiwan's manufacturing enterprises, especially the SMEs and medium-sized enterprises lack adequate professionals and skills to satisfactorily respond to the overflown survey studies, the low response rate has been noted to be a common situation in Taiwan's survey studies on the manufacturing sector. Although this low response rate is common in Taiwan's SME surveys, a further analysis was conducted to assess whether the responding sample was biased by comparing to a random sample of 50 companies who did not respond from the base population. The comparison between the 50 non-

Table 3 Equivalent sample test for respondents vs. non-respondents on employee size and capital Category

Statistics

Levene test for equal variance F test

Sig. t level

T-value

d.f.

Sig. level(two-tailed)

Average difference

Std. error of difference

Employee size

E. V. assumption Un-E.V. assumption E. V. assumption Un-E.V. assumption

0.812

0.37

0.09

0.765

0.527 0.522 0.528 0.529

89 82.018 94 93.978

0.599 ⁎ 0.603 0.599 ⁎ 0.598

5.4 5.4 2.15 2.15

10.2 10.3 4.08 4.07

Capital

T-test for equivalent averages

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Further investigation was conducted with a set of telephone call back. As indicated in Table 5, the records show that 72 telephone follow-ups only resulted in 18 actual returns (25%), and 45 (62.4%) did not respond due to closing of business, business reorganization, unavailability of proper contact personnel, and failure to return the call as agreed upon. There were only 9 (12.5%) who did not feel relevant and many of them had the same reasons as the other 45, but they added their disappointment to the e-marketplaces. According to the supporting analyses, we concluded that the non-respondents did not indicate significant systematic bias in relation to the e-marketplaces. 4.2.2. Sample profile As seen in Table 6, the steel company can be classified into manufacturing (40/85.1%), trading (4/8.5%) or both (3/6.4%). In other words, only 14.9% of steel companies are in trading business, and most of them are in pure manufacturing business. Among the four ranges of employee size which included 0–20, 21–50, 51–120, and 121–200, nearly one-third of the respondents fell into the category of 51–120 people. The average employee size was 65.5 and the median was 53, which were relatively low compared to the ceiling of 200 according to Taiwan's SME definition. The average capital of these companies is US $8.78 million, and the median is US$6.4 million. Compared to Taiwan's SME manufacturing standard of US$2.6 million, only 14 of the respondents showed lower than the standard and most of the respondents were far above, which fits the profile of a typical medium-sized enterprise as described in Sections 1 and 4.1. Among the various exporting status, most steel enterprises fell under the categories below 50% of their business. Moreover, 37% did not export at all, which indicates a domestic orientation among Taiwan's steel medium-size enterprises. This attribute of low exporting percentage matches the profile by MIRDC (2001a). Among the respondents, 28 (59.6%) are not members of either steel e-marketplaces as shown in Table 6, which is a fairly large percentage of e-marketplace dropout rate. 4.2.3. User satisfaction As indicated in Table 7, among the 19 current members who responded to the satisfaction part of the questionnaire, most were negative (within the range of 3 and 3.5) in their average scores toward each of the eight satisfaction dimensions Table 5 Questionnaire follow-up analysis Item

Statistics

Returned Not returned

Number of companies 18

Ratio 25%

Empty number or closed Too busy or no proper person to fill out the questionnaire Organization downsized Not fulfilling the promise Not relevant to the company

8 9

11.1% 12.5%

5 23 9

6.9% 31.9% 12.5%

Sub-total

54

75%

Total

72

100%

Table 6 Profile of the respondents Variable

Option

Count

Percentage

Company type

Manufacturing Trading Manufacturing + trading 0~20 21~50 51~120 121~200 b0.93 [0.93, 3.53] [3.53, 8.32] N8.32 0% 1~50% 51~100% Neither Universal exchange Professional steel net Both

40 4 3 10 10 13 8 11 13 11 11 14 22 2 28 8 6 5

85.1% 8.5% 6.4% 24% 24% 32% 20% 24% 28% 24% 24% 37% 58% 5% 59.6% 17% 12.8% 10.6%

Employee sizes

Capital (million US$)

Exporting percentage

e-Marketplace membership

(S2–S9). Moreover, the highest ranked dimension, “Industry information”, only receives an extremely modest score of 2.79 (between fairly and medium satisfied), which indicates an overall dissatisfaction to the functionalities of existing emarketplaces. Since most current services provided by the two e-marketplace sites are related to “industry information” which is indeed one demand of the steel industry (MIRDC, 2001a), it is not difficult to understand why it receives the highest and the only positive satisfaction score. Nevertheless, this modest satisfaction level on “industry information” dimension alone does not compensate for all other seven dimensions by the 3.16 average score on the overall (S1) satisfaction level. As stated in Section 1, there are more critical demands that motivated these steel medium-sized enterprises to participate in these e-marketplaces. Therefore, we further inspected the correlations between the eight single and the overall satisfaction factors by the multiple regression analysis. Results from both a full model and a stepwise model are shown in Table 8. Although the R2 of the full model (.677) was higher than the stepwise model (.573), only the stepwise model achieved a significant level at 0.001. In the stepwise model, only “Auction” factor (S4) has a significant beta coefficient with regard to the overall satisfaction level (S1). Accordingly, the overall satisfaction level will significantly increase if the e-marketplaces can provide a better auction service, which may help the mediumsized steel enterprises obtain lower-price products and consume overstock products as described in MIRDC (2001a). This result again sustains our point that the two e-marketplaces simply could not satisfy the members with desirable functionalities. However, since only nearly 60% of the variance is explained by this stepwise model (R2 = 573), a further analysis on discovering other factors that affect users' satisfaction is needed. 4.3. Formal analyses The formal analyses, as described in the third step of the research method in Section 3, included the analysis of adoption

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Table 7 Satisfaction analysis of e-marketplace Dimension

Statistics

S1 S2 S3 S4 S5 S6 S7 S8 S9 a

Overall Industry information Catalog management Auction Transaction services Logistics Back-office integration Supply chain management Value-added services

Frequency of satisfaction level

Aggregation

(1) Very

(2) Fairly

(3) Medium

(4) Un-

(5) Very un-

Average a

Rank

0 0 0 0 0 0 0 0 0

5 7 4 2 3 2 0 0 2

6 9 10 8 11 9 13 12 10

8 3 4 8 4 6 4 6 6

0 0 1 1 1 2 2 1 1

3.16 2.79 3.11 3.42 3.16 3.42 3.42 3.42 3.32

3 1 2 6 3 6 6 6 5

1 for very satisfactory and 5 for unsatisfactory.

factors in Section 4.3.1 and adoption decision analysis in Section 4.3.2. 4.3.1. Analysis of adoption factors Kaiser-Meyer-Olkin (KMO) test for sampling adequacy and Bartlett's test for sphericity were used to first examine the suitability of selected adoption variables for the factor analysis (Bryman, 1989). Using SPSS statistical software, calculations show that KMO has a 0.696 value which is larger than the suggested minimum value of 0.5 for adequacy, and Bartlett's test also demonstrated a very good sphericity (χ2 = 1063.992, d.f. = 496, p b 0.000), which indicates that the 32 variables are suitable for the following factor analysis. Before applying the factor analysis, a concern on the case-tovariable ratio needed to be addressed first. The general rule of thumb is that at least a 5:1 ratio of case to variable is recommended when applying the factor analysis, as seen in Hair et al. (1998) and Gorsuch (1983). However, there have been debates regarding this issue of case-to-variable ratio, and a recent article by Roberts, Gibson, Rainer, and Fields (2001) sheds some light to our situation. Roberts et al. claim that the rule of thumb to exploratory studies could be loosened based on the evidences of existing references. They first enumerated 18 samples of top IS journal articles with exploratory factor analysis below 5:1 ratio, such as Abdul-Gader and Kozar (1995) from Management Information Systems Quarterly, and Essex, Magel, and Masteller (1998) from Journal of Management Information Systems. Among their supporting arguments are “Cattell (1988) clearly Table 8 Multiple regression analysis of satisfaction factors Items

Full model beta coefficient

S2 S3 S4 S5 S6 S7 S8 S9 R2 F test P-value

0.131 0.008 0.414 0.045 0.259 − 0.255 0.319 0.059 0.677 2.616 0.078

⁎⁎⁎ p N 0.001.

Stepwise beta coefficient

0.757 ⁎⁎⁎

indicates that factor analysis can be performed below that 2:1 ratio” and “Baggaley (1982) states the ratio need only to be a 1:1 ratio to obtain proper results.“ Under the circumstance that over one hundred more questionnaires are needed for satisfying the 5:1 ratio (i.e. one third of the population) is not feasible for Taiwan's steel manufacturing enterprises. Therefore, we still applied the factor analysis with a lower but adequate case-tovariable ratio as supported by Roberts et al. (2001), and added this as a limitation to our conclusion. To be safe, principle component factor analysis was adopted, which, according to Mcardle (1990) is more practical than common factor analysis in situations like low case-to-variable ratio. In our principle component factor analysis, only those extracted factors with eigenvalues bigger than one were selected. This demonstrates the convergent validity of the selected factors. In the end, a total of nine factors emerged which together explains 77.6% of the total variance. Varimax with Kaiser normalization was applied to ensure that the extracted factors were distinct from each other. As seen in Table 9, most factor loadings are greater than 0.5 and this is considered “practically” significant while the remaining five, V10, V13, V14, V16 and V20, also have “more important” loadings greater than 0.4 (Hair et al., 1998). Therefore, the discriminant validity is thus demonstrated. Also as seen in Table 9, the nine major factors are identified and named as Internal Management Efficiency, Market Competitive Advantage, Security Mechanism, Barrier of Table 9 Adoption variables and factors with loadings Factors

Adoption variables with loadings

Internal Management Efficiency Market Competitive Advantage Business Resources

V2 (0.788), V4 (0.735), V5 (0.617), V3 (0.602), V30 (0.598), V1 (0.579) V25 (0.779), V26 (0.753), V27 (0.743), V28 (0.73)

Trading Opportunities Security Mechanism

0.573 22.829 0.000 ⁎⁎⁎

Barrier of Electronic Transactions e-Marketplace Trend Paying Fees Authentic Documents

V9 (0.727), V31 (0.617), V8 (0.599), V24 (0.594), V14 (0.411) V11 (0.83),V10 (0.452) V29 (0.716), V17 (0.671), V21 (0.615), V12 (0.555), V16 (0.488) V22 (0.808), V18 (0.736), V23. (0.582), V20. (0.476) V6 (0.811), V19 (0.531) V7 (0.796),V32 (0.778) V15 (0.86), V13 (0.412)

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Table 10 Averages and ranks of adoption factors

Table 12 Classification matrix of the logistic regression

Adoption factors

Average ⁎

Rank

Predicted observed

Non-adopters

Adopters

% Correct

Barrier of Electronic Transactions Authentic Documents Security Mechanism Business Resources e-Marketplace Trend Trading Opportunities Market Competitive Advantage Paying Fees Internal Management Efficiency

1.91 2.07 2.08 2.35 2.39 2.58 2.59 2.73 2.95

1 2 3 4 5 6 7 8 9

Non-adopters Adopters Overall

21 8

7 11

75% 57.89% 68.09%

⁎ 1 for very important and 5 for very unimportant.

Electronic Transaction, Business Resource, e-Marketplace Trend, Paying Fees, Trading Opportunity and Authentic Document. The average scores of adoption factors were calculated and then ranked as seen in Table 10. Overall speaking, these nine adoption factors fall into the level of importance with average scores smaller than 3. By examining the top three adoption factors, i.e., “Barrier of Electronic Transactions”, “Authentic Documents” and “Security Mechanism”, we know that the medium-sized steel enterprises particularly cared for safety and secured transactions. On the other hand, the bottom three factors reveal that the medium-sized steel enterprises are deemed low on basic management concerns, including cost (Paying Fees), market share (Competitive Advantage), and management performance (Internal Management Efficiency). The above observations imply that medium-sized steel enterprises had a high priority in adopting e-marketplaces for the opportunity of generating actual transactions instead of regular business values, such as cost-saving or gaining competitive advantage. This finding is similar to what Eng (2004) concluded in his survey of U.K.'s retailer section that e-marketplace supply chain applications are more concerned on automating transaction-based activities and procurement-related processes rather than on strategic supply chain activities. Also, this finding matches with what the users' desire in order to be satisfied, i.e., auction (S4), transaction services (S5) and Logistics (S6) as shown in Table 7.

4.3.2. Adoption decision analysis Binary logistic regression analysis was used to test whether the nine factors significantly affected the adoption of the two steel e-marketplaces by their current membership status. The Hosmer and Lemeshow goodness of fit test (χ2 = 8.9452, d.f. = 7, significance = 0.2566) as seen in Table 11 demonstrates that this logistic regression model is not significantly different from a perfect model that correctly classifies between the members and the non-members. Also, the discriminating power is assessed by the classification matrix as shown in Table 12, which achieves an acceptable overall classification accuracy of 68%. Nevertheless, the result of logistic regression is not significant at all. As can be seen in Table 11, Wald statistics was used to test the significance of the regression coefficients, which are all insignificantly different from zeros. Meanwhile, the − 2 Log likelihood (52.386) is large while both Cox and Snell R2 and Negelkerka R2 are low (b 0.3). Accordingly, the binary logistic regression analysis does not support the idea that the current e-marketplace membership status is significantly affected by the extracted adoption factors. This test result supports our view that Taiwan's e-marketplaces failed to meet the expectation of the medium-sized steel enterprises. Viewing from a different perspective, Taiwan's steel emarketplaces, like those of Hong Kong's and Singapore's, are more information-oriented, but Taiwan's medium-sized steel enterprises expect a strong transaction-oriented e-marketplace environment just like the ones in the U.S. Therefore, after a few years of free membership on either e-marketplace, many medium-sized steel enterprises decreased their participation and lowered their enthusiasm. All these complications added up to a situation where previously perceived adoption factors were inconclusive and unable to predict the current adoption of an emarketplace for Taiwan's medium-sized steel enterprises. 5. Conclusion and implications

Table 11 Results of binary logistic regression a

5.1. Limitations

Factors

Coefficient Wald statistics Significance

Internal Management Efficiency Market Competitive Advantage Security Mechanism Barrier of Electronic Transactions Business Resources e-Marketplace Trend Paying Fees Trading Opportunities Authentic Documents Constant

− 0.316 − 0.007 − 0.781 − 0.564 − 0.473 0.051 0.128 − 0.581 0.368 − 0.546

0.796 0 3.786 2.124 1.257 0.017 0.127 2.352 0.962 2.335

0.372 0.984 0.052 0.145 0.262 0.897 0.722 0.125 0.327 0.126

a − 2 Log likelihood = 52.368; Cox and Snell R2 = .210, Nagelkerke R2 = .283 Goodness of Fit = 8.9452 (d.f. = 7), significance = 0.2566.

As part of careful investigation, there are three research limitations identified in the conduct of this study. First of all, because the case-to-variables ratio was only around 1.5:1 (47:32), lower than the 5:1 ratio of the general rule of thumb, the theoretically-sound common factor analysis could not be comfortably applied. Therefore, an alternative principle component factor analysis was adopted to accomplish this study. Secondly, since the initial stage of this research is exploratory in nature, some of the failure factors that have been identified in the literature may have escaped our attention. Of all these, trust (Bhattacherjee, 2002; Gefen, 2000; Jarvenpaa, Tractinsky, &

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Vitale, 2000; Yousafzai, Pallister, Foxall, & Gordon, 2003), privacy (Udo, 2001; Wang, Lee, & Wang, 1998), and more complete coverage on security factors (Furnell & Karweni, 1999; Kolsaker & Payne, 2002) were most notable. Finally, the eight satisfaction factors only accounted for 60% of the overall variances, which means that there are other hidden factors affecting the overall level of satisfaction. 5.2. Conclusions We summarize the results of the research objectives listed in Section 2. First, based on the survey, nine latent adoption factors of the Taiwan's steel e-marketplaces were identified from 32 items. These are 1) Internal Management Efficiency, 2) Market Competitive Advantage, 3) Security Mechanism, 4) Barrier of Electronic Transaction, 5) Business Resource, 6) e-Marketplace Trend, 7) Paying Fees, 8) Trading Opportunity, and 9) Authentic Document. From the top three and bottom three adoption factors, we found that medium-sized steel enterprises desired an emarketplace that generates secured and safe transactions, but they did not care much about the conventional management concerns in cost-saving or management efficiency. Second, a binary logistic regression analysis indicated that these originally perceived adoption factors did not significantly reflect the current adoption status of the sample steel enterprises, thus supporting our observation that the current e-marketplaces failed to meet the members' expectations which led to a high dropout rate of 59.6%. Third, according to the user satisfaction analyses, only “Industry Information” index gained a modest satisfaction and all other seven indexes were somewhat unsatisfied. However, “Auction Service” was the only index identified to significantly affect the overall satisfaction. This mismatch of the satisfaction also supported our assumption that the e-marketplace providers had not done the correct means to meet the customers' needs. Analysis of the telephone follow-ups also revealed that steel medium-sized enterprises are not enthusiastically concerned about e-marketplace issues as they used to. Hence, one of the marketing challenges is to rekindle their enthusiasm on steel emarketplace by focusing on these original adoption factors. The highest priority of auction service is a good starting point to win back their satisfaction level. 5.3. Implication Extending the second limitation in Section 5.1, only the security issue came out in the focus group interview which was intended to uncover the very original adoption factors. However, security control significantly impacts on a customer's trust in ecommerce (Gefen, 2000; Jarvenpaa, Tractinsky, & Vitale, 2000; Suh & Han, 2003), and privacy protection is one component constituting the trust construct (Suh & Han, 2003) or influence trust (Culnan & Armstrong, 1999). Furthermore, both Bauer, Grether, and Leach (2002) and Luo (2002) emphasized that customer satisfaction is meant to build up customer trust over the Internet since a higher level of uncertainty and risk exists (Grabner-Kräuter & Kaluscha, 2003). Because trust, privacy

9

and security are interrelated, it implies that these critical factors may need to be explicitly included in the developmental policies of Taiwan's steel e-marketplace, in addition to fulfilling the member's adoption factors for higher satisfaction levels. Since security has been investigated in this study, we must point out that security-control requirements are more toward the technical aspects in five categories, including authentication, nonrepudiation, confidentiality, privacy protection and data integrity (Suh & Han, 2003). A brief discussion on the development regarding privacy and trust follows. Privacy affects aspects such as the obtaining, distribution or the non-authorized use of personal information (Wang, Lee, & Wang, 1998). Advances in information technologies give enterprises a great power in manipulating personal information, therefore procedural fairness (Culnan & Armstrong, 1999) becomes a concern to consumers and the solution relies on the collaboration between the government, the industries and the individuals (Kruck, Gottovi, Moghadami, Broom, & Forcht, 2002). On the other hand, technical solutions for privacy protection are also being formed. These include the technical specification, “Platform for privacy preferences project” that was drafted in the World Wide Web (W3C) Consortium which was a collaboration among the W3C member associations and the European Union communities (Moghe, 2003) for a wider implementation of privacy protection. Trust is most frequently cited as “the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control that other party” (Mayer, Davis, & Schoorman, 1995). Examples of diverse trust studies include the study conducted by Luo (2002) who proposed a framework based on relationship marketing and social exchange theory to increase customer's trust of e-commerce and decrease privacy concerns. In addition, Gefen and Straub (2004) validated a fourdimensional scale of trust in the context of e-Products and e-Services. Lee and Turban (2001) developed a theoretical model for investigating the four main antecedent influences on consumer trust in Internet shopping. Recognizing the lack of consensus about the meaning of trust in different disciplines, Grabner-Kräuter and Kaluscha (2003) facilitated a multi-level and multi-dimensional analysis of research problems related to trust in e-commerce. Consequently, developing a trust mechanism in an EC environment will be a great challenge for emarketplace providers. Based on the implication and corresponding brief explanations, we know that for an e-marketplace to succeed, good developmental policies and strategies on security, privacy and trust are as critical as satisfying customers' desirable adoption factors. Acknowledgements The authors would like to thank the anonymous referees for their valuable suggestions in improving the readability of this paper.

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Tao, Y., Ho, I., & Yeh, R. (2001). Building a user-based model for web executive learning systems— A study of Taiwan's medium manufacturing companies. Computers & Education, 36(4), 317−332. Tsang, S. -W. (2002, November 12). Electronic marketplaces: Three times of growth three years later. China Times Daily News. Udo, G. (2001). Privacy and security concerns as major barriers for e-commerce: A Survey study. Information Management & Computer Security, 9(4), 165−174. Wang, H., Lee, M., & Wang, C. (1998). Consumer privacy concerns about internet marketing. Communications of the ACM, 41, 63−70. Weller, T. C. (2000). B to B eCommerce— The risk of eMarketplace. Legg Mason Equity Research, 8. Yasin, M., Correia, E., & Lisboa, J. (2004). The profitability of customertargeted quality improvement efforts: An empirical examination. The TQM Magazine, 16(1), 45−49. Yousafzai, S., Pallister, Y., Foxall, J. G., & Gordon, R. (2003). A proposed model of e-trust for electronic banking. Technovation, 23(11), 847−860. Yuan, J. -K. (2001). An Empirical Study: Factors Affecting Taiwan's Leading Manufacturers in Adopting eMarketplace, unpublished Master Thesis, National Chiao-Tung University, Taiwan, R.O.C. Yu-Hui Tao is an associate professor of Information Management in National University of Kaohsiung, Taiwan, ROC. He received his Ph.D. degree in Industrial and Systems Engineering from The Ohio State University in 1995. His current research interests include electronic business, management information systems, and Internet applications. Dr. Tao has published various journals such as Computers in Industry, International Journal of Information Management, Internet Research, Intentional Journal of Electronic Business Management, Computers and Education, Intelligent Data Analysis and IIE Transactions. Chia-Ping Chen is an assistant professor in the business administration department at National Chung Cheng University, Taiwan, ROC. He holds an MBA and a Ph.D. in business administration from National Sun Yat-sen University, Taiwan, ROC. His current research interests include organizational learning, system dynamics, and electronic business. Chia-Ren Chang is a project manager in industrial information and planning section at the Metal Industries Research and Development Centre, Taiwan, ROC. He holds an MBA in business administration from I-Shou University, Taiwan, ROC.

Please cite this article as: Yu-Hui Tao et al., Unmet adoption expectation as the key to e-marketplace failure: A case of Taiwan's steel industry, Industrial Marketing Management (2006), doi:10.1016/j.indmarman.2006.08.004

10.1.1.118.1953  
10.1.1.118.1953  

Pleasecitethisarticleas:Yu-HuiTaoetal.,Unmetadoptionexpectationasthekeytoe-marketplacefailure:AcaseofTaiwan'ssteelindustry,Industrial Market...

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