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Winter 2009 Journal of Computer Information Systems 13

FACTORS AFFECTING THE ADOPTION LEVELOF C-COMMERCE: AN EMPIRICAL STUDY

ALAIN YEE-LOONG CHONG KENG-BOON OOI INTI International University College Jalan University Putra Nilai, Negeri Sembilan Perak, Malaysia BINSHAN LIN MURALI RAMAN Louisiana State University in Shreveport Multimedia University Shreveport, LA Selangor Darul Ehsan, Malaysia

Received: October 15, 2008 Revised: January 7, 2009 Accepted: January 27, 2009

ABSTRACT

The major objective of this paper is to examine the determinants of collaborative commerce (c-commerce) adoption with special emphasis on Electrical and Electronic organizations in Malaysia. Original research using a self-administered questionnaire was distributed to 400 Malaysian organizations. Out of the 400 ques-tionnaires posted, 109 usable questionnaires were returned, yielding a response rate of 27.25%. Data were analysed by using correlation and multiple regression analysis. External environment, organization readiness and informa-tion sharing culture were found to be significant in affect-ing organ izations decision to adopt c-commerce. Informationsharing culture factor was found to have the strongest influ-ence on the adoption of c-commerce, followed by organiza-tion readiness and external environment. Contrary to other tech-nology adoption studies, this research found that innovationattributes have no significant influence on the adoption ofc-commerce. In terms of theoretical contributions, this study has ex-tended previous researches conducted in western countries and provides great potential by advancing the understanding be-tween the association of adoption factors and c-commerce adoption level. This research show that adoption studies could move beyond studying the factors based on traditional adoption models. Organizations planning to adopt c-commerce would also be able to applied strategies based on the findings from this research. KEYWORDS: Collaborative Commerce, Technology Adop-tion, Collaborative Supply Chain

INTRODUCTION

Supply Chain Management (SCM) is defined as the systemic, strategic coordination of the traditional business functions within a particular company and across businesses within supply chain, for the purposes of improving the long-term performances of the individual companies and the supply chain as a whole [8]. Traditionally, the supply chain faces common challenges such as the forecasting of demand and supply of products resulting in the inability to meet demand for certain products while having oversized and expensive inventories for other products [49]. However, many of the problems found along the supply chain

could be solved via strategic SCM through the implementation of Information Technology (IT) [15]. Recent literature on IT and SCM focus on IT and especially Internet tools that support collaborative SCM known as Collaborative Commerce (c-commerce) [8], [20]. C-commerce is defined as a set of electronically-enabled collaborative inter-actions between an organization, its suppliers, trading partners, customers and employees, and also leverages the internet to create and maintain an interactive business community of employees, trading partners, suppliers and customers [13]. Unlike e-commerce, c-commerce covers exchanges of information and ideas between trading organizations and within the organizations, and allows them to collaboratively design, develop, build and manage products through their life cycle. It enables companies to automate information flows within a multi-channel distribution network. An effective and efficient supply chain will increase the competitiveness and the survival of organizations [44]. As such, the implementation of an effective SCM via IT technologies such as c-commerce tools will enable the industry to gain and maintain its competitive advantage. Most existing literature on the adoption issues on c-commerce technologies or collaborative supply chain were mainly conducted in western countries such as the United States and United Kingdom. Previous technology adoption literatures have studied the factors affecting the implementation of technologies based might not be able to fully provide the reasons for c-commerce adoption as c-commerce have different characteristics with existing information systems (i.e.c-commerce implementation requires the sharing of information between the trading partners). Therefore in order to bridge the gap and provide companies with practical assistance of adopting c-commerce, this research will examine the adoption factors that influence the implementation of c-commerce among Malaysian E&E companies. The paper proceeds as follows: In the next section, the theories laid down in the literatures of IT adoption theories and the link between E&E organizations and c-commerce adoption are reviewed. In the following section, the hypotheses development and development of conceptual framework are presented. The next section provides information concerning the data used in the study, including descriptive information on the sample drawn out of Malaysian E&E companies as population. Finally, the results are discussed followed by research limitations, conclusions and implications, and recommendations for future research.

14 Journal of Computer Information Systems Winter 2009

LITERATURE REVIEWAND HYPOTHESIS DEVELOPMENT

C-commerce adoption

Existing technology adoption literatures on EDI provided strong theoretical foundation for the studies of c-commerce and adoption as these two technologies involve IT support for relationships among established business partners [6]. A review on previous empirical studies on adoption of EDI technologies have shown that most of the studies on EDI adoption derived from theories such as Innovation Diffusion Theory [41] and Technology-organization-environment model [18], [49]. The dependent variable in this study used the adoption level of the c-commerce tools. The adoption level was modified from assimilation level used in existing literatures [11], [12], [28]. The c-commerce tools were modified from [3] which identified the e-Collaboration tools studied and applied to the supply chain of telecommunication industry. The c-commerce tools are listed and defined in Table I.

Factors affecting the adoption of collaborative commerce technologies in Supply Chain

Based on existing studies on using Innovation Diffusion Theory [40] and Technology-organization-environment model [18], [49], the three main variable categories that can influence the adoption of IT innovations in organization can be summarized as environmental characteristics, innovation characteristics, and organization characteristics [19]. In Innovation Diffusion Theory, an organization’s decision to implement an IT technology could be influenced by the innovation’s characteristics such as its relative advantage, compatibility, complexity, trialability, and observability. The Technology-organization-environment model

on the other hand, also evaluated an organization’s decision to implement an innovation based on 1) technological (i.e. the technologies relevant to the organization) 2) organizational attributes (i.e. company size, resources available, quality of human resources) and 3) environmental factors (i.e. business environment of the organization). Although literatures on IT adoptions were based on the models mentioned above, for IT technologies with unique attributes, the adoption factors might be different from findings based on these models [33] [47]. C-commerce is different from existing IT technologies due to the fact that c-commerce requires the co-adoption of more than one organization, and there is a need for a change in organization mindset in terms of sharing information. As such, in order to understand what will encourage the implementation ofc-commerce, there is a need to develop a new technology adoption model. According to [39], although models such as Innovation Diffusion Model and Technology-organization-environment have served as a theoretical basis to many adoption studies, the model “needs to be enriched when the innovation relate to complex technologies with an interorganizational locus of impact, for which adoption decisions are linked (e.g. when imposed by business partners) and when the innovation are adopted by organizations” [39, p. 412] This study thus focused on innovation characteristics [28], [33], [40], [43], external environment characteristics [28], [18], organizational readiness characteristics [18], [27], [28], [35], [36], [43] and an additional new factor in adoption study called information sharing culture characteristics [1], [2], [16], [41].

Innovation Factor

One of the most frequently studied factors in IT adoption studies is the Innovation factor. Shah Alam [42] found that

TABLE I. Definitions of c-commerce tools (Source: Adapted from [3])

C-Commerce tools Definitions

Direct procurement tools Direct procurement tools that will forward purchase orders (POs) to pre-qualified suppliers.

Replenishment tools The tool will drive an ordering system from the shop. When materials are needed on the pro-duction line, an order will be placed through the replenishment system.

Projected Shortages tool This tool will scan the buyer’s production plan to project expected material shortages. The tool will also provide real-time information to manufacturing and supply management units.

Delivery and tracking tool This tool will generate a payment and a delivery request automatically when a product goes from suppliers to its customers. It can also collect shipping information from third party logis-tics provides.

Design tool Enables the use of interactive engineering drawing and storage of CAD designs by all the key stakeholders.

Supply chain planning Exchanges the forecast information provided by both the buyer and supplier.Forecasting tool

Capacity planning tool Determines the amount of capacity required to produce.

Business strategy tool Collects and shares the actions that need to be taken to support the objectives and mission of the supply chain.

Rosetta Net standards Standard that is based on XML and defines message guidelines, business processes interface and implementation frameworks for interactions between companies in the supply chain.

E-Hub, E-Marketplace, E-Exchanges Internet platform where firms register as sellers or buyers to communicate and conduct busi-ness over the Internet.


there is a significant relationship between innovation factor and adoption of e-commerce in Malaysian E&E companies. Innovation characteristics are also studied as c-commerce is different from many existing technologies such as EDI ande-commerce, and thus might give different results from previous studies. For example, Lin [24] in his study of innovation characteristics on e-business implementation success, suggested that e-business may have features which are new when compared to previous generations of technology innovations, thus the effects of innovation characteristics deserved attention that have not been fully understood in the e-business adoption [24]. E-business when compared to traditional information systems is “complex, emerging technologies that can provide a wide range of functionality ranging from developing online business processes to facilitating cooperation with customers and business partners” [24, p. 62]. For this research, the variables that are included under Innovation construct include relative advantage, compatibility, and complexity. Relative advantage is the degree to whichc-commerce is perceived as being better than its precursor.The organizations which want to adopt c-commerce wouldhave seen that the technology has direct financial and opera-tional benefits over previous ways of performing the same tasksin the supply chain. Compatibility assesses the compatibility of the c-commerce with the existing IT infrastructure and work procedure needs of the company. Complexity is the degree to which c-commerce is perceived as relatively difficult to under-stand and use by the organization. Accordingly, we hypothesizethat:

H1: There is a positive and significant relationship between innovation and the adoption level of c-commerce.

External Environment

From the review of existing literature, external environmental constructs have been widely studied and found to be significant in many IT adoption/diffusion studies [6],[18],[9],[21],[28]. In the existing literature, the majority of the studies were conducted using EDI technologies where the pressure to adopt the technology usually come from one or more dominant firm. However, in the current business environment of the E&E industry, the competition is more of an industrial group versus another industrial group [28]. As such, the perceived pressure on the company to adopt c-commerce in the supply chain would be felt from the entire industry. Another attribute in the environmental factor used in this study is the expectations of market trends of the c-commerce technology [8], [28]. This study focused on the E&E industry which has characteristics that is different from other industries. The impact from the increased in processing power of semiconductors for example, means there is an intense pressure on the organizations to reduce cost. The competitive pressure and the expectations in the market trends of c-commerce in the industry can force companies into adopting c-commerce technologies in SCM to gain competitive advantages over their rivals. Given the existing literature, we hypothesize that

H2: There is a positive and significant relationship between external environment and the adoption level of c-commerce.

Organization readiness

The organization readiness construct is used to assess whether the organization has the necessary attributes that ensure the overall readiness towards adopting c-commerce in the supply chain. The top management support is an important factor in determining whether the company can successfully adopt the innovation. Result from [27] also showed that top management support is able to differentiate between adopters and non-adopters of e-commerce. Organization readiness is also important from a resource point of view. The resources include both from a financial as well as technical standoff [28]. Financial feasibility and technical feasibility of the organization include conducting cost-benefit analysis, forecasting total cash expenditures, and estimating the indirect impact of the c-commerce (product costs, process re-engineering efforts, etc.) [28]. Technical feasibility include assessing skill sets of the IS staff, identifying infrastructure enhancements necessary to accommodate the new technology, and evaluating and prioritizing which shared business processes should be automated [28]. As the implementation of c-commerce is a long term project for companies as they have to continually increase their level collaboration, and continually increase the number of suppliers or buyers in the collaboration. C-commerce is also a technology that requires an organization to plan, commit and execute according to requirements established with trading partners. As such, it requires evaluating top management’s support, financial and technical feasibility [18]. Project champion appointment and characteristics is also included in organization readiness attribute. This attribute will look at whether the organization has appointed a project champion and the experience and IT background of the project champion. Organization attributes such as top management support [36], [48], Feasibility [18], [28] have been considered in previous research and found to be significant. Therefore we hypothesize that:

H3: There is a positive and significant relationship between organization readiness and the adoption level of c-commerce.

Information Sharing Culture

The information sharing construct is used to assess whether the organization has the necessary information sharing attributes for the overall readiness towards adopting c-commerce in the supply chain. Sharing of information is important as the success of a firm’s SCM would depend upon the accuracy and velocity of the information which every business partner provides [23] [52]. In [1] the authors called for a collaborative culture in order to ensure the successful implementation of collaborative supply chain. In this study, Information Sharing culture is created because past adoption models might not be able to fully explain all the factors due to c-commerce’s needs for companies to change their mindsets on information sharing. As such, Information Sharing Culture construct is created to determine if the organization has the attributes to share information which is necessary for the successful implementation of c-commerce. The information sharing culture attributes include trust, information distribution and information interpretation. Trust has been increasingly an important factor in success of IT technologies such as e-commerce [38]. Trust has been studied in relation to EDI and E-Business adoption and it is an important


factor in explaining interorganizational relationship [41], [45]. When business partners collaborate in their supply chains, an organization that trusts its partners is more likely to reach consensus in terms of achievable benefits by c-commerce [41]. Existing studies on SCM have mentioned that the success of SCM is built on a foundation of trust [22]. Trust is able to contribute to the long-term stability of an organization and is conveyed through faith, reliance, belief or confidence in the business partner and is viewed as a willingness to forego opportunistic behaviour [46]. Trust is thus the belief that the supply chain partner will act in a consistent manner and do what he/she says he/she will do. Due to trust, supply chain partners will be more willing to share information about future plans and designs, competitive forces, and R&D. The same can be said of c-commerce as when business partners want to adopt c-commerce in their supply chain collaboration, an organization that trusts its partners is more likely to reach consensus in terms of achievable benefits by c-commerce [34] [41]. With the implementation of c-commerce, information will be exchanged. Information raises the issues such as in what format should the information be sent, for what purposes, how they can be processed etc. Two variables used in [17] are applied to information sharing culture. They are information distribution and information interpretation. Information distribution refers to the process by which an organization shares information among its units and members, thereby promoting learning and producing new knowledge or understanding. Greater distribution of information will lead to more information sharing, which in turn leads to greater organizational learning [17]. Thus companies that have been using Information systems/Internet technologies to distribute information are more likely to have an information sharing culture in place. They are more likely to adopt c-commerce technologies with their supply chain partners to share information. In order for information to be shared, the information must be interpreted. Information interpretation is the “process by which distributed information is given one or more commonly understood meanings. Sense-making or the formation of meaning is called procedural knowledge [10]. The aim of many IOS standards such as RosettaNet helps interpret information for the supply chain members. As mentioned earlier, RosettaNet standards enable companies to communicate electronically with other companies using the same e-business standards. By defining the technical terms as well as the business processes that are used, the risks of different supply chain partners interpreting the information differently is reduced. In order for information sharing to occur between companies and their partners, methods of interpreting information through using IOS standards, as well as willingness to set up mechanisms for interpretation of information to be shared through a consistent field terminology and consistent business definitions should be considered. Based on the variables discussed for information sharing construct, we hypothesize that:

H4: There is a positive and significant relationship between information sharing and the adoption level of c-commerce.

RESEARCH FRAMEWORK

Based on the above literature review, a research framework is developed to examine the relationship between adoption factors

and c-commerce adoption. The link between adoption factors and c-commerce adoption are shown in Figure 1. The adoption factors in this research framework are independent variables and c-commerce adoption is a dependent variable respectively. This research is situated in the context of limited adoption of c-commerce and the evidence of opportunities for competitive advantages gained through their application for a collaboration supply chain. The model suggests that the greater the extent to which these adoption factors are present; the higher will be the adoption of c-commerce in the Malaysian E&E organizations. The purpose of this study is thus to answer the following research questions.

RQ (1): What are the determinants towards the adoption of c-commerce within the Malaysian E&E companies?

RQ (2): Which adoption factors have greater association with c-commerce adoption within the Malaysian E&E companies?

Innovation Attributes• Relative advantage• Compatibility• Complexity

Environmental• Expectations of market trends• Competitive pressure

Information Sharing Culture• Trust• Information Distribution• Information Interpretation

Organization readiness• Top management support• Feasibility• Project champion characteristics

C - C o m m e r c e Adoption Level

FIGURE 1. Model of the adoption factors of c-commerce in Malaysian E&E organizations

METHODOLOGYBackground

A survey instrument was developed for the testing of the hypothesis developed using data gathered from a review of current literature combined with in-depth case studies with two major E&E companies in Malaysia together with ten of their suppliers. The focus of these case studies was to refine the variables developed from the literature review, gain a greater understanding of the most important issues with regard to


c-commerce adoption and non adoption, as well as improving the viability of the questionnaire.

Sampling and Data Collection

The target population of this study were E&E companies in Malaysia. The target population of this study is E&E companies in Malaysia. Samples were randomly drawn from the database of Federation of Malaysian Manufacturer (FMM) 2007 listed members of E&E manufacturing companies in Malaysia. As the FMM directory consists of manufacturing companies of various sizes based on their revenue and employee size as well as having companies which are local, joint venture and MNCs, it is viewed as a valid representation of the entire Malaysian E&E organizations in Malaysia. The unit of study for this research was the organizations. The survey was administered to 400 managers and executives fromthe purchasing or IT departments of the FMM listed organizations. The mail survey was the main form of data collection. There were 120 responses received, indicating an estimated response rate of 30 percent. However, only 109 of the questionnaires were usable.

Variable Measurement

Independent Variables

The four independent variables used in this study (i.e. innovation, environmental, organization and information sharing culture) consist of 11 items. A total of 38 questions captured the four adoption factors under investigation. Responses to the items were made on a 5-point Likert Scale ranging from 1 = strongly disagree to 5 = strongly agree.

Dependent Variable: C-commerce adoption Level

The adoption level was measured using 5 items modified from the use of assimilation level from [11], [13] [28]. The adoption level here is measured using the items from whether the organization has 1 = deployed, 2 = committed, 3 = shown an interest, 4 = aware and 5 = unaware of the c-commerce tools given. The c-commerce tools used in this research were modified based on the recommendations of senior executives from two major E&E companies in Malaysia. The 10 c-commerce tools used in this study were direct procurement tools, replenishment tools, projected shortages tools, delivery and tracking tools, design tools, Supply chain Planning and Forecasting tool, Capacity planning tool, RosettaNet standards, E-Hub, and Business Strategy tool. All the adoption level questions on the c-commerce tools added together bring the total points to 50. The mean was calculated giving a maximum mean score of 5 which signify a high adoption level of C-Commerce tools while a minimum mean score of 1 signify low adoption level C-Commerce tools. The reliability coefficient for the scale was 0.94.

DATA ANALYSIS

Profile of organizations

There were total of 109 respondents. 53 (48.6%) are local

organization, 38 (34.9%) are MNCs, and 18 (16.5) of them are joint venture organizations. Most of these companies have been operating for more than 10 years with 84 (77.1%) of them operating in Malaysia for more than 10 years. In terms of organization size, 57 (52.3%) of these organizations have more than 150 employees, 30 (27.5%) of them have between 51 and 150 employees, and 22 (20.2%) of them have 50 or less employees.

Normality Test of c-commerce Adoption Level

As the data sets was larger than 50, Kolmogorov-Smirnov’s test is applied to test for data normality among the inde-pendent variables [14]. The Kolmogorov-Smirnov statistic with Lilliefors significant level for testing normality is ap-plied to test the normality of c-commerce adoption level.The significant level for the Kolmogorov-Smirnov test shows a P value of 0.20 which is more than 0.05, hence normality isassumed.

Scale reliability and Factor Analysis

The reliability of the questionnaire was tested according to Cronbach Alpha measurements [4]. The reliability coefficients (Alpha) of each element of adoption factors for c-commerce were as follows: Innovation (0.9268); Environmental (0.9357); Information Sharing Culture (0.9267); Organization Readiness (0.9679). The reliability coefficients of all the five adoption factors for c-commerce were above 0.70, which concurs with the suggestion made by [30]. Factor analysis was conducted to evaluate construct validity. Although the survey used in this research is adapted from previous studies, parts of the survey included new items. Furthermore, the adapted survey has not been applied in the context of Malaysian E&E organizations. Therefore, exploratory factor analysis is applied to this research. Principle component analysis is applied to determine how and to what extent the items are linked to their underlying factors [51]. Principle component analysis is able to identify whether the selected items cluster on one or more than one factor. Factor loadings are applied to present these relations. According to [14], factor loadings greater than 0.50 is considered to be very significant. Thus a factor loading of 0.50 was used as the cut off point. Latent root criterion was applied to determine if the items are loading into one factor. Factors which have eigenvalues values which are more than 1 are considered significant while those that have eigenvalues of less than 1 are considered insignificant and therefore were disregard. From Table II, all items had high factor loadings of greater than 0.50 for factor 1. When items in a scale loaded on more than one factor, varimax rotation was applied. The factoranalysis showed that except for innovation, items in 5 ofthe 6 scales formed a single factor. For Innovation scale,two factors emerged based on the rule that the eigenvaluesare grater than 1 [14]. Based on the principal componentanalysis, only factors with eigenvalues that are more than 1 are considered as significant while eigenvalues less than 1 is considered as not significant and thus are disregarded.


TABLE II. Exploratory Factor Analysis Results Scales Factor Eigenva Factor number lues Loadings

Item 1 Item 2 Item 3 Item 4 Item 5 Item 6 Item 7 Item 8 Item 9 Item 10 Item 11 Item 12 % of variance

Innovation 1 8.201 .859 .857 .855 .851 .845 .836 .834 .824 .823 .812 .709 .723 68.34

2 1.088 -.309 -.175 .255 -.301 -.319 -.181 .389 .434 -.135 .470 .146 -.268 9.06

Organization 1 7.175 .938 .929 .921 .920 .917 .884 .873 .831 .815 79.73

External environment 1 3.981 .923 .920 .894 .871 .851 79.16

Information Sharing Culture 1 4.857 .900 .894 .889 .875 .842 .823 .816 .805 .803 .794 0.784 0.714 60.71

c-commerce adoption level 1 7.175 .938 .929 .921 .920 .917 .884 .873 .831 .815 79.73

Table III lists the rotated factor matrix for scale 1 (Innovation). Based on the Table III, Items 1 to 7 forms a factor which can be constituted as “ease of implementation”. The other items in scale 1 formed another factor which can be constituted as “relative advantage”.

be divided into two factors if they can be supported by literature. The contents of items in the innovation factor were carefully examined and it was clear that the aim of using relative advantage information was part of the innovation’s attributes. Furthermore, as relative advantage has been studied under innovation factors in most existing literature, and relative advantage is indeed an attribute of the innovation factors, the construct of innovation was not divided into two constructs [51].

Correlation analysis: Relationships between the Variables

The correlation matrix in Table IV indicated that the adoption factors were positively and strongly correlated with c-commerce adoption level. There was a significant positive relationship between environmental factor and c-commerce adoption level (r = 0.791, p < 0.01), organization readiness factor and c-commerce adoption level (r = 0.820, p < 0.01), information sharing culture and c-commerce adoption level (r = 0.807, p < 0.01) and innovation factor and c-commerce adoption (r = 0.725, p < 0.01). The correlation coefficients between the independent variables were less than 0.9, indicating that the data was not affected by a multicollinearity problem [14]. These correlations were also further evidence of validity and reliability of measurement scales used in this research. The results indicated that the most important adoption factor affecting c-commerce adoption level was organization readiness (i.e. with the highest scores of correlation), which proved that where organization readiness was perceived as a dominant adoption factor, improvements in c-commerce adoption level was significant.

Multiple Regression Analysis

Multiple regressions were employed to test the hypothesis. Multiple regression analysis according to [14] is applied to analyze the relationship between a single dependent variable and several independent variables. As such, multiple regression analysis was selected and viewed as the best method in this study. The summary of results analysis is depicted in Table V. The Durbin-Watson of 1.822 falls between the acceptable range (1.5 c<cD<2.5) indicating no autocorrelation prob-

TABLE III.Factor Matrix for Scale 1 (Innovation)

Component 1 2

Item 1 .849 .336

Item 2 .846 .318

Item 3 .838 .336

Item 4 .759 .435

Item 5 .747 .416

Item 6 .720 .276

Item 7 .707 .442

Item 8 .300 .889

Item 9 .332 .869

Item 10 .369 .843

Item 11 .474 .756

Item 12 .497 .631

However, although two factors emerged for Scale 1 (Inno-vation), this result was obtained based on the rule of eigen-values greater than 1. As [25] stated, results obtained from sta-tistical data analysis should be interpreted with caution as theremay be temptation to overstate statistical findings. The interpre-tation should be based on the total view of research content as well as the sampling frame. As cited in [51], given that the eigenvalues for Factor 1 is only 1.088 as shown in Table II, it is just slightly greater than 1 and it is possible that the construct does not need to


lem in the data. Therefore, it indicated that the error term was independent. Variance-inflation factor (VIF) and Tolerance were applied to test for multicollinearity among the independent variables. The multicollinearity statistics showed that the tolerance indicator for Innovation, Environmental, Information Sharing Culture and Organization readiness are all greater than 0.1, and Variation Inflation Factors (VIF) were all lesser than 10. This result indicates no multicollinearity problem has occurred according to [29], [31]. The F-statistics produced (F = 66.199) which was significance at 1 percent level (Sig. F < 0.01), thus confirming the fitness for the model. This indicated that there was a statistically significant relationship between the adoption factors in the model andc-commerce adoption level. The coefficient of determination, R2 was 70.7 percent. This expressed that the adoption factors could significantly accounted for 70.7 percent in c-commerce adoption level among Malaysian E&E companies. The results showed that external environment (b = 0.248, p = 0.039), information sharing culture (b = 0.354, p = 0.002) and organization readiness (b = 0.344, p = 0.021) are positively associated with the adoption of c-commerce. It can be argued that these three factors were all directly involved in the adop-tion level of c-commerce. Based on Table III, the most impor-tant c-commerce adoption factors were information sharingculture, followed by organization readiness and external envi-ronment. Hence the hypotheses H2, H3 and H4 were supported. Innovation factor however, was found not to be significantly associated with c-commerce adoption. Therefore H1 was not supported.

DISCUSSION

The results of this study revealed that information sharing culture is perceived as a dominant factor in the adoption ofc-commerce. Unlike existing studies on technology adoption, c-commerce which is implemented to support a collaborative supply chain requires the sharing of information. A major part of collaborative supply chain is the openness of the supply chain members with regards to the sharing of information, objectives, issues and problems [1]. The result shows that Information sharing culture has the most significant in the adoption of c-commerce. As such, organizations need to adopt the culture of sharing information before they can fully implement c-commerce. Organization readiness is also an important determinant in whether organizations adopt c-commerce which is supported by existing studies [19], [28], [36], [48]. For c-commerce to implement a collaborative supply chain there is a need to have top management support. When the organization has the support of top management with both the financial and technical resources available to the organization, the chances of adopting c-commerce will be higher. The aspect of appointing a project champion and looking at the project’s champion experience has sometimes being overlooked in adoption studies. This study also shows that organizations which are committed to adopting c-commerce will have a higher adoption level if they appoint a project champion with computer background and has been involved in similar projects before. In addition, external environment also has a significant and positive relationship with c-commerce adoption. Companies

TABLE IV. Correlations between adoption factors and c-commerce adoption level

Information C-Commerce Environmental Sharing Culture Organization Innovation Adoption level

Environmental 1 .820 (**) .889 (**) .825 (**) .791 (**)

InformationSharing Culture .820 (**) 1 .877 (**) .812 (**) .807 (**)

Organization .889 (**) .877 (**) 1 .862 (**) .820 (**)

Innovation .825 (**) .812 (**) .862 (**) 1 .725 (**)

C-Commerceadoption level .791 (**) .807 (**) .820 (**) .725 (**) 1

** Correlation is significant at the 0.01 level (2-tailed).

TABLE V. Regression analysis of adoption factors on c-commerce adoption level

Independent variable Beta t C-Commerce adoption level significance Results Tolerance VIF

(Constant) -15.181 .000

Innovation -.064 -.590 .557 Rejected .231 4.323

External Environment .248 2.093 .039 Accepted .193 5.181

InformationSharing Culture .354 3.156 .002 Accepted .215 4.641

Organization Readiness .344 2.345 .021 Accepted .126 7.942

Overall model F = 66.199, p < 0.01, R2 = 0.718, Adjusted R2 = 0.707; Durbin-Watson test 1:822


with higher adoption of c-commerce have been pressured by their business partners and the industry’s demands into adoptingc-commerce. Many of these companies also expect c-commerce to be pervasively adopted in industry. In the long term, with the continued business pressure and the business trends moving towards implementation of c-commerce for a collaborative supply chain, more and more E&E organizations will adopt c-commerce. The expectation of market trend is viewed as an important external environment factor, it is important the Malaysian E&E industry established the industry standards with regards to the implementation of c-commerce. The significant finding for expectation of market trend is consistent with prior studies[28]. [28] is particularly important as they studied on IOS standards, which is very closely related to c-commerce since both c-commerce and IOS standards need to be agreed upon by majority of companies in the industry before it can be pervasively adopted. For many companies, once they are committed toc-commerce, they cannot afford the time or the cost to reconfigure their backend applications. The Malaysian government has committed to establishing xML and Interorganizational Standards (IOS) such as RosettaNet as the industry standards for Malaysian E&E [7]. Innovation factor however, was found to be an insignificant determinant of the c-commerce adoption. This is a significant finding as it contradicted with findings from existing technology adoption studies. Attributes such as relative advantage, com-patibility, complexity has been found to be significant determinants of adoption in prior research by researchers such as [40] and [49]. It also contradicted with recent study in e-commerce adoption among Malaysian E&E companies [42]. However, the finding from this research is not without precedence as prior adoption studies on EDI from [36] have found a lack of significance in Innovation attributes such as relative advantage. Similarly, [28] also found that innovation attributes such as relative advantage, compatibility and complexity were not significant. Thus in certain situations, other factors are more important in distinguishing the c-commerce adoption level. Other factors such as information sharing culture, organization readiness and external environ-ment are found to be strongly associated with the adoption ofc-commerce. Furthermore, it also shows that organizations have understood c-commerce technologies better. Innovation factor such as its relative advantage, compatibility and complexity are no longer significant factor in determining their adoption ofc-commerce.

CONCLUSIONS AND IMPLICATIONS

The study revealed that adoption factors such as information sharing culture, organization readiness and external environment are positively related to c-commerce adoption level. The findings made a contribution in terms of creating an understanding of what influence the adoption of c-commerce, which is essential in the implementation of collaborative supply chain. The implication of this study can be divided into two categories: theoretical contributions and practical contributions. Existing literatures have shown that the collaborative supply chains are moving towards the direction of collaborative supply chain through the use of c-commerce. In terms of theoretical contributions, firstly, this study has extended previous researches conducted in western countries and provides great potential by advancing the understanding between the association of adoption factors and c-commerce adoption level. This research has also

shows that adoption studies can move beyond studying the factors based on traditional models such as Innovation diffusion model which is focused on the Innovation factors. Secondly, many existing adoption studies have studied on older Internet technologies for supply chain such as EDI and E-commerce, but there has not been an empirical study on the adoption of c-commerce in supply chain. As c-commerce requires co-adoption of more than one trading partners, this study has added an information sharing construct consisting of trust, information interpretation and information distribution. Although trust has been studied in existing interorganizational relationships studies, existing studies did not include information interpretation and information distribution. Thirdly, this study has also added two new c-commerce tools to studies in [4], which are RosettaNet standards and E-Hub.As this study was conducted in Malaysia’s E&E industry, thec-commerce tools are slightly different from [3] after being verified by Malaysian E&E executives. Future studies attempting to study on c-commerce tools can include the tools used in this study. Regarding practical contributions, organizations which would like to adopt c-commerce or increase the level of adoption will be able to apply strategies and make managerial decisions based on the findings from this research. In order to implement c-commerce or Collaborative supply chain, the commitment is needed from more than one member of the supply chain. Likewise, efforts in promoting a collaborative supply chain in the industry require the implementation of c-commerce from the industry. Organizations that would like to adopt the technology will require changing their mindsets in terms of sharing of information. Furthermore, organizations should also be ready and commit the necessary technical and financial resources to the implementation ofc-commerce. As financial and technical feasibility seems to be an important influence on whether organizations adoptc-commerce, a company with better financial or technical resources might consider providing more of such assistance to their trading partners. In the long run, the financial cost of assistance could be covered by having a more efficient supply chain. Organizations with less resource will also realize the benefits of adoptingc-commerce in their supply chain which in the long run will enable them to compete with their bigger rivals. Organizations have also adopted c-commerce due to pressure from the E&E industry in general, and pressure from trading partners in particular. The industry as a whole therefore needs to continuously promote the use of c-commerce and made award of competitive advantages which could be gained through the implementation of c-commerce. Technologies such as EDI suffered from lack of adoption due to its lack of standards. However, as this study shows, organizations are willing to share information if there is an information interpretation process that takes place. This study also showed that when organizations have existing information distribution systems using IT, they are more likely to adoptc-commerce. Therefore before an organization plans to adoptc-commerce with its partners, they might consider communicating or distributing information using IT technologies such as video conferencing and emails. In the long rum, organizations are more likely to develop an information sharing culture through the use of IT technologies if they have been distributing information using IT. Organizations should also focused on building trusts with their supply chain partners if they plan to implement c-commerce.


Therefore the process of implementing c-commerce can be a gradual one whereby organizations start with some c-commerce tools, build up trust before including more c-commerce tools in their supply chain. Lastly, an organization should consider appointing a project champion with past technology implementation experience as well as computing background in to be successful in adopting c-commerce.

LIMITATIONS AND FUTURERESEARCH DIRECTIONS

This research has been conducted in Malaysia and further research should be carried out to investigate whether the results from this research will be consistent with findings from different countries in various industries. Future studies can focus on conducting a multi-country comparison to test the influence of moderating factors such as the culture from the countries. Future studies can also be conducted to test if national culture will have an impact in the information sharing culture among organizations. As with many adoption model, there is a risk that additional significant factors have not been included in the framework. Additional variables such as partners’ power which has been conducted in existing studies in e-commerce adoption can be further examined in future study [37]. It would also be useful to conduct a follow up studies to find out the financial, operation and relationship benefits of implementing c-commerce. This research only discussed c-commerce adoption as it happened at a specific time within a specific environment and at an early stage of adoptions. Many adopters are likely to be early adopters. Therefore, future research based on time series analysis can be carried out to trace the changes in the adoption factors that affect the adoption of c-commerce and compared it with the current model.

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