اثرات انتقال نفوذ تجارت الکترونیک : چشم انداز تجزیه و تحلیل شبکه ای از ساختار صنعتی

This study presents a quantitative method for investigating the diffusion of e-commerce adoption using social network analysis methodologies. The contagion effects on innovation diffusion are examined by two different social network models: the cohesion model, which is based on diffusion by direct communication, and the structural equivalence model, which is based on diffusion by similarity of network position. This study then empirically examines a sample of e-commerce diffusion taken from the Taiwan's industrial structure in 2001. The analytical results show that e-commerce diffusion among firms in Taiwan exhibits both contagion effects, but that the mimetic behavior is predicted better by network position than by interactions with others.

The accelerated growth of Internet-based commerce and the significant attention paid to it in the media has led to increasing interest among businesses in electronic commerce (e-commerce). Managers are told that the use of e-commerce is already reshaping supplier and customer relationships, streamlining business processes and even, in extreme cases, restructuring whole industries [1]. Meanwhile, the rapid growth of e-commerce attention and adoption has led to many studies on e-commerce-related research. However, most studies have focused on the internal factors affecting the adoption of e-commerce, and comparatively little attention has been given to the mechanisms of e-commerce diffusion among firms within an economy. There is a need to understand the mechanisms that characterize the diffusion of e-commerce adoption, to identify the main driving force of firms' intention to adopt e-commerce, and to gather empirical evidence in cases and practices of e-commerce diffusion. The above research needs can be implemented by investigating e-commerce diffusion within an economy by using social network analysis, which has a well developed set of models for systematically examining the contagion effects of innovation diffusion within a social structure. The measurements and models of social network analysis, although primarily developed for studying sociology, are highly appropriate for applications to explore the mechanisms of innovation diffusion [2], [3] and [4]. There are two classes of network models, the cohesion model and the structural equivalence model, to examine the contagion effects of two actors that make them socially proximate, so that adoption of an innovation by one actor would trigger adoption by the other. This study will introduce appropriate social network analysis measurements and models to reveal that the social network analysis can be a useful methodology for studying the mechanisms of e-commerce diffusion within an economy, and test them by examining a sample of e-commerce diffusion taken from the Taiwan's industrial structure. Taiwan has a vibrant manufacturing sector, and is aggressively promoting information technology and e-commerce [5], making it is a good sample for the purposes of this study. This study aims to investigate the diffusion of e-commerce adoption by firms in Taiwan, using social network analysis to explain the diffusion mechanisms. This investigation examines the contagion effects on diffusion with two social network models, contagion by cohesion, which is based on diffusion by direct communication, and contagion by structural equivalence, which is based on diffusion by similarity of network position. This study tests and compares the ability of these competing models to explain the mechanisms of e-commerce diffusion among different sectors in Taiwan. The rest of this paper is organized as follows. Section 2 reviews pertinent literature on adoption of e-commerce by organizations, and the theories concerning innovation diffusion and contagion effects to construct a framework for research on the mechanisms of e-commerce diffusion within an economy. Section 3 then proposes the research hypotheses in relation to the test and comparison of the different contagion effects. Next, Section 4 introduces the measurement and models of social network analysis used to investigate the mechanisms of e-commerce diffusion. Section 5 empirically examines a sample of e-commerce diffusion taken from the Taiwan's industrial structure to test the research hypotheses, and discusses the managerial implications. Conclusions are finally drawn in Section 6.

As the importance of e-commerce increases, so do the need to understand the mechanisms characterizing the diffusion of e-commerce adoption, to identify the main driving force of firms' decisions to adopt e-commerce, and to collect empirical evidence on cases and practices of e-commerce diffusion. This study provides a case-specific illustration of these investigations with the help of methodologies derived from social network analysis. Two different social network models, the cohesion model and the structural equivalence model, exist to examine the contagion effects of two actors that make them social proximate so that one actor's adoption of an innovation can be expected to trigger the other actor's adoption. The contagion effect by cohesion model, which is based on diffusion by direct contact and communication, measures the influence of adoption of e-commerce by supply-chain partners on a firm that is considering adopting e-commerce. Conversely, the contagion effect by structural equivalence model, which is based on diffusion by similarity of network position, measures the imitation between competitors as a result of conformity to prevalent norms within structurally similar sectors. Social network analysis is highly applicable to studying e-commerce diffusion from the contagion perspective, since each firm's decision to adopt e-commerce is affected by the adoption and non-adoption of other firms within an economic system. This study employed the two contagion effects to examine the mechanisms of e-commerce diffusion, and empirically tested and compared the two models by studying a sample of e-commerce diffusion taken from Taiwan's industrial structure. Social network analysis has been successfully applied in studying the contagion effects of e-commerce diffusion to demonstrate the usefulness of the proposed methodologies and to find the contagion effect that most accurately predicts mimetic behavior. The empirical results show that both contagion effects, cohesion and structural equivalence, simultaneously exist in the context of e-commerce diffusion among firms in Taiwan during the period studied. A particular firm's decision to adopt e-commerce depends on adoption by its supply-chain partners, including suppliers and consumers, and its competitors. However, the phase of technology adoption life cycle of e-commerce in Taiwan was at the early stage in 2001, making the mechanism of the structural equivalence model more effective that that of the cohesion model. Adoption of e-commerce by firms derives more from adoption by their competitors as a mimetic process for pursuing competitive advantage than from adoption by their supply-chain partners as an assimilative process for seeking identification with each other. The limitations of this study should be acknowledged to identify directions for future research. The data examined herein relate to only one year, so the contagion effects on innovation diffusion in different phases of the technology adoption life cycle would be hard to determine. Future research is required to collect data for a particular innovation in different phases of the technology adoption life cycle to enable a comparison analysis to be conducted over time. Second, the measure of analysis in this study is the extent to which the firms in a certain sector adopt e-commerce. However, the entity of making a decision to adopt e-commerce is the individual firm. The level of e-commerce adoption in a certain sector is proxied as an aggregate result of the decisions made by the constituent firms in this sector. Future research could use the individual firm as the unit of analysis to examine the contagion effects of e-commerce diffusion. In addition, this study only examined the three external factors affecting adoption of e-commerce by firms, namely the influences from competitors, customers, and suppliers, based on the literature reviewed by Daniel and Grimshaw [1], but did not study the fourth factor concerning the internal operations of firms. Behavior and opinions of ego are not only determined by behavior and opinions of others (interaction effects), but also by reaction to various other constraints and opportunities granted by the ego's conditions (local effects) [45]. Such a process is typically modeled in sociology as an autocorrelation model1. Owing to the lack of the local effects, this study points out the need for future research to examine the autocorrelation model of e-commerce diffusion to consider both interaction effects and local effects simultaneously.