A Review of Approaches to EC-enabled IOS Adoption Studies

Sherah Kurnia and Robert B. JohnstonDepartment of Information Systems, The University of Melbourne, Australia

Email: SherahK@unimelb.edu.au and R.Johnston@dis.unimelb.edu.au

AbstractThe importance of inter-organizational system (IOS) has been increasingly recognized by organizations. However, IOS adoption has proved to be difficult and, at this stage, why this is so is not fully uncovered. Based on a previously published empirical study, the authors argue that the ”factor“ approach, which has been widely used to study technology adoption, is not generally appropriate to study IOS adoption. To further examine this claim, this paper reviews the literature in order to identify the current state of IOS adoption studies. For this purpose, the factor and processual approaches are defined and used to bring order to the existing studies. This review finds that, until recently the majority of IOS studies have used the factor approach and that they have yielded inconsistent results. It also demonstrates that the use of the processual approach is just being recognized and a research gap exists for this kind of study.

1. IntroductionInter-organizational systems (IOS) are defined by

Cash and Konsynski [1] as automated information systems shared by two or more companies. Many organizations are now engaged in the adoption of IOS, particularly those enabled by electronic commerce (EC), in order to improve efficiency of their supply chains or industry. This is crucial for organizations in this era of globalisation, since the level of competition is increased and consumers are becoming more demanding [2, 3]. Inter-organizational systems have the potential to create competitive advantage for organizations through reduced costs, improved internal efficiency, and improved inter-organizational efficiency. Cost reductions and improvements in efficiency can in turn be achieved through better coordination, higher visibility of demand, and faster communication [4-7].

The adoption of IOS, however, has proved to be considerably more difficult than technology adoption within an organization, because such systems span organizational boundaries [8-11]. Unlike organizational innovations, IOS involve multiple decision makers within and across organizations within supply chains or the entire industry. In addition, they require collaboration and the concerted actions of the participants of a supply chain or industry, which normally consists of different entities such as trading partners, regulators, industry associations, and third parties. These entities often have different and conflicting objectives and interests [11-13] and, therefore, complex interactions between a focal organization attempting to adopt a particular IOS and other external entities normally occur before adoption can take place. Furthermore, adoption is mediated by the capability of trading partners and other industry wide conditions, such as the existence of industry standards.

The fact that the adoption of IOS involves significant changes in organizations’ culture, structure, business practices, trading relationships, power relationships and other relationships [14, 15] further complicates adoption by organizations in various industries [10, 13].

Based on our previous studies on the adoption of Efficient Consumer Response in Australia [[16, 17]], we argue on theoretical grounds that, given the characteristics of IOS outlined above, the processual approach should be more appropriate than the factor approach to IOS adoption studies. Partly to test this claim and partly to bring some order to the proliferating literature on IOS adoption, in this paper we review and classify the previous IOS adoption studies according to their adherence to these two contrasting approaches to theorising adoption. We find that until recently the vast majority of IOS studies have used the factor approach and that they have yielded highly inconsistent results, as would be expected from the mismatch of this approach to IOS context. We also find that the case for using the processual approach to IOS adoption study is just being recognized and a research gap exists for this kind of study.

The method of this paper is to first demonstrate the limitations of the factor approach to studying IOS adoption and how the processual approach can overcome these limitations. Existing studies of IOS adoption are then reviewed and classified according to their adherence to one or other of the approaches. Finally, a discussion of the implications of the study findings for IOS adoption research is presented.

2. Limitations of the factor approach to IOS adoption

Many previous studies of diffusion and adoption of technology in general [18-22] use the factor approach. This approach assumes that innovation adoption is affected by the characteristics of the adopters, the change agents, the technology or / and the environment (also known as factors) which are identified at a single epoch. These factors can be classified into three groups: the nature of the technology, the capability of the organizations, and the nature of the external environment, and have essentially a one-way impact on the adoption. The innovation is considered to be static since it cannot be modified by potential adopters and the adoption process is treated as simple and linear. The inclusion of the context and content of change is not emphasized. Adoption is often studied outside the context of change by employing positivist and quantitative techniques such as surveys, although qualitative studies can also be employed (see, for example, [18, 23]). In addition, the type and scale of the change are confined to a small unit of analysis, such as

individual adopters, which in the IOS case would be individual organizations. A typical model, explaining the adoption of IOS using this approach is depicted in Figure 1, where arrows indicate strong causal connection.

This approach is well suited to studies of IOS adoption by small industry players, since such organizations are normally more “passive” than large enterprises in adoption of technologies. Their actions are usually determined by the nature of the technologies, their own

capabilities, and external forces, such as pressure from other more influential trading partners [18]. At the other extreme, however, very influential companies may construct or modify technological visions for the industry and define the organizational capabilities that they require. By virtue of their influence they define what constitutes “adoption”. In this case, the direction of arrows a and b in Figure 1 would be reversed.

Figure 1. A typical IOS adoption model with the factor approach

Thus, if typical companies are to be considered, they are actually neither totally victims of their environment, nor in total control of their environment. They exercise an influence over part of their environment by virtue of their interactions with other organizations that make up the industry of which they are part. This necessitates the inclusion of the inter-organizational environment in IOS adoption study. The inter-organizational environment consists of supply chains, trading partners, standards organizations, industry bodies, transport companies, trade organizations, software providers, and so on. These organizations are in turn linked by a set of relations (transactional, political, normative, communicative, economic, corporate), which constitute the industry structure [11]. Through the interactions of a company and its inter-organizational environment, the organization’s capability to adopt a technology and indeed the nature of the technology itself can be altered over time. Therefore, in order to study adoption of IOS more comprehensively, the unit of analysis has to be extended beyond the organizational level and the time scale needs to be extended beyond a single epoch. This requires a different approach which is outlined in the next section.

3. The processual approach to IOS adoptionStudies that involve a large unit of analysis such as the

entire supply chain or the industry and over an extended period of time can only be accomplished through the processual approach [24].

Figure 2 illustrates the conceptual changes leading from the factor approach (Figure 2a) to the processual approach when the inter-organizational environment is included as part of the unit of analysis in IOS adoption studies. First, the unit of analysis is extended to the focal organization and its inter-organizational environment (supply chain and the entire industry), as shown in Figure 2b. Adopting this large unit of analysis then leads to a reconsideration of what factors are external (Figure 2c).

Some factors identified as “external” using the factor approach are now recognized as part of internal industry interactions, within this larger unit of analysis. Finally,

the interactions within the inter-organizational environment lead to a changed view of the causal links between actions of organizations, inter-organizational environment, nature of the technology, and capability of the organizations (Figure 2d). Now, not only are actions of an organization mediated by the nature of the technology factors, its capability factors, and environmental factors, but these factors are themselves altered by mutual interactions of the focal firm with its

inter-organizational environment. These interactions cannot be explained by the causal links of the factor model alone.

Figure 2. Change of conceptual framework from the factor approach to the processual approach as the inter-organizational environment is recognized [16]

The two-way causality indicated in Figure 2d above really stands for repeated interaction over time and space. This interaction is more explicitly represented in Figure 3 below. The space dimension captures the influence of other organizations within the industry upon the focal organization. It shows that the actions of the focal organizations are enabled by the nature of the technology and their capability but through interactions with their inter-organizational environment, organizations can alter these two factors and the structure of their environment over time and space [17].

For example, at first (time t=1), the nature of the technology and the capability of organization A mediate

its action. However, through interactions with its inter-organizational environment, a sufficiently powerful organization A will be able to alter the nature of the technology to suite its needs, possibly to modify its internal capability, as well as to modify the structure of the inter-organizational environment (at time t=2). The structure of the inter-organizational environment consists of a set of relationships, such as power, economic, communication, and so on, which link members of the supply chain or industry [25]. External factors beyond the control of organizations will always have an essentially one-way impact on their actions over time and space. What is produced at time t=2 will then mediate

the actions of organization A at time t=3, and again, through the interactions with its inter-organizational environment, the nature of the technology, its capability

and the structure of the inter-organizational environment can be altered.

Figure 3. A processual model of IOS adoption

Company B in Figure 2 and other organizations within the industry (which are not shown in the figure because of the difficulties in drawings) will go through similar processes as described above. The processes continue throughout the course of the adoption process until a state of equilibrium is reached, in which the actions and capability of organizations and the nature of the technology are consistent with the emergent modified structure of the industry. At this stage, changes proposed by the IOS are routinized within the industry. Only when this happens through complex and dynamic interactions over time and space, can the “adoption” take place at the firm and industry level. This outcome, however, cannot be fully predicted, as it emerges from the interactions of organizations through intended and unintended actions over time and space. Thus, employing this approach to understanding IOS adoption challenges the traditional use of the term “adoption”, which is now viewed as a

dynamic emergent process without a well-defined end (depicted by dotted arrows in Figure 3).

The new processual model provides a richer and broader picture of IOS adoption as it incorporates extra influences arising from complex and dynamic interactions between organizations and change processes occurring in the process of adoption. Since the two-way interactions between variables proposed in this model are difficult to analyze with statistical methods or other positivist scientific approaches, the model suggests the use of more in-depth interpretive research methods, such as case studies or action research to ensure the inclusion of the context and content of change. Such methods allow the researcher to document mutual influences of actions of various organizations over time.

Table 1 summarizes the differences in properties between the factor and the processual approaches (see also [24, 26, 27]).

Unlike the factor approach, the processual approach posits that the adoption of technology is a complex process and that the outcomes emerge from interactions between the adopters, environment and the technology, whose characteristics may evolve over time. Due to the complex view of the adoption process, it can only be

studied within the context in which it occurs. Innovation is considered to be subject to re-invention and there is a

multi-directional notion of causality between the various factors associated with the technology, the adopters and the inter-organizational environment, as illustrated by Figures 2d and 3. The existence of these two-way causal links has been empirically demonstrated for the case of adoption of Efficient Consumer Response in Australia

reported elsewhere [16, 17].

Table 1. Basic properties of the factor and the processual approaches

4. IOS adoption studies employing the factor approach

A large number of previous studies of IOS adoption employ the factor approach, which has been widely used in technology adoption studies in the last 15 years [28]. Such studies are cross-sectional, employing a survey method or a case study to assess various factors affecting adoption, including the characteristics of IOS, the organizations, and the external environment. Generally, they make use of factors identified from the organizational innovation adoption literature rooted in the Diffusion of Innovation theory, assess the relevance of these factors to a particular IOS under study, and in some cases, identify additional factors. The unit of analysis used in such studies is individual organizations.

Table 2 summarizes a number of factors that have been addressed in many previous studies of IOS adoption. These studies normally only examine a few of the factors shown in the table. For examples, [29-32] investigated the influence of organizational and environmental factors on IOS adoption, while other studies ([8, 33-35]) assessed the impact of technological and environmental factors. Furthermore, some studies only considered one of the three types of factors: Daugherty et al. [36], for example, only assessed organizational factors affecting the adoption of EDI.

While other studies may address all types of factors, they do not assess all the individual factors depicted in the table. Within the technological factors (the nature of the technology), some studies ([23, 37-39]) addressed the relative advantage, complexity and compatibility, while others, ([34, 35, 40]), only examined the perceived risks or costs.

Similarly, there are various emphases given to the organizational factors. A number of studies place great emphasis on communication-related factors within an organization and consider the role of change agents (for examples, [30, 32, 39, 45]), while some ([23, 31, 37, 44]) stress the structure of organizations such as size, centralisation, and formalisation. Thus, these studies provide partial insights into the understanding of IOS adoption.

In addition to the above problem, many inconsistencies exist between the findings of these previous studies. For example, the relative advantage of the technology was discovered to have a positive impact on IOS adoption by ([8, 18, 23, 32, 34, 37-39, 41, 42]). Other studies ([29, 35, 43, 46]), however, indicated that relative advantage does not have a significant relationship with adoption. Similarly, the complexity of the technology was found by [34, 39] to be a significant factor inhibiting adoption, but other studies ([23, 32, 38]) did not indicate the importance of this factor. There are

also inconsistencies in the findings of the previous studies regarding the effect of other technological factors (shown in the table) upon adoption. Similar problems were also discovered when dealing with organizational factors, particularly the impact of the size of organization on adoption ([23, 31, 36, 40, 43, 44]).

A number of studies have discovered the importance for IOS adoption of environmental factors, such as the level of competitiveness among companies within an industry and the industry concentration [8, 18, 35, 42, 43]. Industry concentration is related to the number of key players (companies with large portion of shares) within an industry. The fewer the number of key players within an industry, the higher the industry concentration, and, therefore, the more likely these key players will support and assist other smaller trading partners in IOS adoption. Industry concentration is also related to the power issue. In a highly concentrated industry, key players have more power than other small trading partners. These more powerful organizations can

therefore pressure small trading partners to adopt a particular IOS. Saunders and Hart [35] conducted a survey to examine the impact of power on EDI adoption, in addition to other factors such as trust, EDI benefits and costs. However, no evidence was obtained to support the hypothesized relationships between EDI adoption and power and between adoption and trust.

In short, the above analysis demonstrates the inadequacy of the factor approach to capture a complete picture of IOS adoption, because it tends to ignore the aspects of the context and content of change in IOS adoption. All these studies use individual firms as the unit of analysis and consider all inter-organizational influences and relationships as environmental factors that are beyond firms’ control and, thus, simplify the complexity of decision making in the IOS adoption context. As a result, inconsistencies in the findings were encountered and, in some cases, the expected evidence was not obtained from the empirical studies.

Table 2. A summary of factors influencing IOS adoption identified in various studies

5. IOS adoption studies employing the processual approach

IOS adoption studies employing approaches other than the factor approach tend to address some but not all the properties of the processual approach set out above in Table 1. It is not always immediately apparent whether a particular study has employed the processual approach,

since, in some cases, some properties of the factor approach are also addressed. A dozen examples of studies that can be best viewed as at least partly processual, are summarized in Table 3. An assessment of which of the processual properties were addressed by each study, based on a four-point scale: nil (-), low (L), medium (M) and high (H), is presented below to examine the emphasis each study placed on each property.

Table 3. As assessment of the existing IOS studies and the level of emphasis on the properties of the processual approach

Few of the studies shown in Table 3 address many of the processual properties. Only the work of Damsgaard and Lyytinen [47] displays all the characteristics of the processual approach, although the multi-directional notion of causality between various factors and adoption outcomes is not explicitly addressed. Using field studies, this work examines EDI adoption in Finland by considering three levels of analysis: micro, meso and macro levels. At the micro level, specific features of EDI were considered: characteristics of the innovation, including its nature and capability to demonstrate path dependencies, type of innovation decision, communication channels and the structure of the social system in which EDI is diffused. At the meso level, the inter-organizational nature of EDI is considered by

examining the interactions between focal organizations and external institutions (such as supply chain intermediaries, trade and industry associations, multi national corporations and telecommunication service provider), their mutual dependencies and their power relationships. At the macro level, the role of the institutional (including national and inter-governmental) regimes that mediate EDI diffusion is considered.

From this study, the authors identified five patterns of diffusion of EDI in Finland. They concluded that EDI diffusion is a complex interplay of organizational, industry and institutional factors, which cannot be simply explained by linear relationships between adoption and various factors. The features of the technology should not be viewed as static, since they may change over time as

the structure of the environment changes. Therefore, the study of EDI diffusion requires a breadth and depth of analysis that can only be achieved through interpretive studies. This necessitates the inclusion of the organizational, industry and institutional levels to understand the complex mixture of different factors located at different levels of the analysis.

Johnston and Gregor [25] also recognized that IOS adoption can only be completely studied through a deep understanding of the possibilities and nature of coordinated activities at the industry level. They formulated a preliminary theory of industry-level activity to explain the adoption and diffusion of supply chain electronic commerce technologies. Three levels of analysis, which are consistent with those proposed by Damsgaard and Lyytinen [47] were suggested: the individual industry units (organizations), the industry group (immediate environment) and the remote environment. Their theory purports that the trajectory of the actions of the organizations within the industry is a complex interplay of interactions between individual organizations and their immediate and remote environment, and, therefore, the adoption outcome is an emergent, rather than a planned phenomenon. While most of the other properties of the processual approach are addressed in the theory with a high level of emphasis for each property, it does not consider a specific type of innovation and overlooks the fact that innovation is subject to re-invention. The need for an interpretive research method in IOS adoption studies is implicitly addressed by acknowledging a requirement for a deep understanding of complex IOS adoption processes. This theory was further elaborated later with a number of case studies in [48] and four hypotheses were formulated: that behavioural change involved IOS adoption is incremental; that planned change at the industry level is more likely to occur when there are changes in the remote environment; that the likelihood of IOS adoption at the industry level is dependent on the industry structure; and that the attributes of change agents can facilitate IOS development.

Similarly, Gregor and Johnston [11] acknowledged the need for a multiple level of analysis and the importance of context in exploring interactions between individual organizations and the industry. Employing a case study, they examined the adoption of EDI in the beef industry at two levels of analysis: the industry and the enterprise within the industry. The findings indicate that the properties of the industry are related to those of the entities at the organizational level and that the activities of entities at both levels affect each other through complex interactions. The authors also acknowledge the interactive process of the structure of the environment (the industry) and the focal organizations in EDI adoption, which is in line with the structuration theory. However, the study does not consider the role of the

technology in the adoption process and does not address the fact that the technology can be re-invented over time.

In another study, Gregor and Jones [49] applied Rogers' theory to understand the development and adoption of communication technology based on electronic commerce in the Australian beef industry. They assessed the social system, the innovation's characteristics, the time element of the innovation diffusion process and the communication channels. Action research with the Blue Gum Beef producer group was conducted from November 1996 to December 1997. This study indicates the importance of the knowledge of industry bodies' initiatives, government policies and economic forces in understanding the diffusion process. The innovation process was found to be very complex for the members of the social system and unanticipated consequences were observed. Although this study considers the characteristics of the technology, it does not give sufficient attention to the scope of the change proposed by the technology and, therefore, other parties external to the organization, which may be affected by and affect the adoption process within the focal organization, are not considered. Moreover, the multi-directional notion of causality and the fact that technology can be altered are not addressed.

Other studies shown in Table 3 address some of the properties of the processual approach, but to a lesser extent than those discussed above. Some studies employ the Stage Model theory to address the time element of the adoption process [50-52] or the Critical Mass theory to emphasize the importance of the level of penetration of the technology in the social system [46]. Most of these studies, however, do not address the emergent causal agency aspect of IOS adoption, the multi-directional notion of causality and the possibility of the technology re-invention. Furthermore, some studies do not view innovation adoption as a complex process although the authors acknowledge the importance of the context and content of change to a certain extent ([23, 45, 50-53]). Thus, it appears that there are still relatively very few studies of IOS adoption employing the processual approach.

6. Discussion and conclusionsBased on our previously published empirical studies,

we have presented a theoretical argument suggesting that the factor approach to technology adoption study is inadequate in the context of IOS. This is because this approach ignores the inter-organizational interactions between parties involved in adoption, the time element of the adoption process, the importance of multi-level unit of analysis, and the context and the content of change introduced by the IOS, which are all important in IOS adoption. To further test this claim, in this paper, we have analysed the literature in order to examine the

insights that have been obtained thus far in IOS adoption. The two competing approaches to theorising adoption have been precisely defined so that the degree of conformance of previous IOS adoption studies to these two approaches can be assessed, and are used in this study to bring order to the rapidly expanding literature of IOS adoption.

Our findings demonstrate that the vast majority of IOS adoption studies have employed the factor approach uncritically, simply because this approach has been widely used in intra-organizational technology adoption studies. However, there exist inconsistencies and disagreement in the findings of IOS adoption studies using this approach, which is as expected if, as we have argued, this research approach is largely inappropriate to the IOS context. We argue that these problems were not caused by wrong analyses, but because the aspects of the context and content of change that do matter in IOS adoption were ignored. The use of the processual approach, on the other hand, has appeared gradually in more recent studies, but many of these studies still have not made use of all aspects of the approach. It appears that researchers have started to recognize, some explicitly, the problems of employing the factor approach to IOS adoption studies due to the differences between IOS and organizational innovation. Nevertheless, it seems that there is still a large opportunity for future research of the processual kind, because such studies are still currently very limited.

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