Structural factors

One of the key variables commonly found to be positively related to the adoption of any innovation is the size of a firm (Kennedy, 1983). Several conditions commonly encountered in small organizations, such as resource constraints and limited technical know-how, might account for differences observed based on firm size. In a network context, instead of firm size, we model the number of firms involved irrespective of the size of individual firms. Resource constraints, therefore, should have less impact on firm adoption or its ability to implement the innovation. However, large networks are likely to face additional constraints that negatively impact these abilities. For instance, large networks likely contain bureaucratic elements and political structures that impede change. Moreover as the number of firms increases, the potential for overlap between networks is likely to increase. These overlaps conceivably increase the difficulties associated with change. For instance, imagine a single firm involved in two networks; one of which desires to implement a new IOS program and the other does not. The firm is faced with having to maintain two systems; one utilized in each network. From a transaction cost perspective this situation is suboptimal, leading to redundancy and potential errors. As an example of potential errors imagine the firm operates a VMI system with suppliers in one network and a manual inventory system with suppliers in another network. It is easy to see how critical material purchases might be overlooked under the assumption that the supplier was electronically monitoring current inventory levels when, in fact, the material in question was to be obtained from a noon-VMI supplier. Or assume the VMI supplier shipped material based on their observations of current inventory. Meanwhile the material might have been manually ordered from a non-VMI vendor by an employee who neglected to manually update inventory records. Now

Angela Hausman, Wesley J. Johnston and Adesegun Oyedele imagine a situation where the firm is involved in more than two networks and the difficulties compound exponentially. In large networks, where firms are likely involved in overlapping networks, firms facing adoption decisions would have to weigh the potential operational and relational benefits of adoption against the complexity of maintaining multiple systems:

P1. Larger networks have a lower propensity to adopt an IOS innovation than smaller networks.

Using a similar argument, the amount of product variety a firm supports might be expected to affect cooperative adoption, however the direction of this impact is unclear. A positive relationship has been found between product variety and adoption of an IOS system in the context of e-commerce (e.g. Boeker and Huo, 1998). Boeker and Huo (1998), using an economies of scale argument, found companies offering a variety of products can enhance their economic development by using the internet to promote their products to prospective customers. Wang and Tsai (2002) similarly point out that a firm offering a wide variety of products may be able to reduce the cost of exchanging transaction information through the adoption of an IOS, such as e-technology. On this basis, an organization offering a variety of products will have a higher propensity to adopt an IOS innovation, such as e-commerce technology.

Unfortunately, the economies of scale provided by large product variety might be outweighed by increased transaction cost incurred when that variety concomitantly increases the number of network interrelationships. Since product variety and organizational size might be correlated variables, the same arguments made above with respect to size might also be valid with respect to product variety. Thus, it is difficult to propose the direction of the relationship between variety and adoption. In fact, an inverted U-shaped curve might be the most accurate reflection of the effect of variety on adoption. At relatively low and high levels of variety, adoption is negatively impacted, first due to an inability to capitalize on economies of scale, then to the size and overlap of networks required to provide for the variety. In the middle, variety does promote adoption due to favorable economies, without requiring networks that are too large or overlapping to function efficiently:

P2. Networked organizations offering a smaller amount of product variety or a very large amount of product variety will have a lower propensity to adopt an IOS innovation than organizations offering intermediate levels of product variety.

As with other types of organizational adoption involving complex technologies, technology readiness might affect IOS adoption. Technological readiness is a function of existing technological capabilities or the extent to which the firm currently uses innovative knowledge and skills (Dosi, 1991). This is based, in part, on the gap between existing technologies and proposed technologies. If this gap is large, implementing the technology involves a great deal of learning and increases the difficulty encountered in moving from one technology to a new one. For instance, the gap between two software versions is relatively small, while the gap between manual and automatic information processing is more substantial. In the first case, implementation might occur seamlessly while the second case will require retraining, possibly replacing existing workers. Empirical evidence

supports this relationship between adoption and technological readiness in EDI and IOS adoption (Dewar and Dutton, 1986).

Within a network, we can see where the same principles might function. Instead of organizational level technological readiness, however, the current means used to achieve inter-organizational collaboration might be a more appropriate assessment of the technological readiness of the network. Thus, using manual systems for collaboration represents a larger gap in implementing IOS than if the firms already have some level of automatic collaboration in place.

Readiness is enhanced if some members within a business network have previously adopted the innovation, a prospective adopter is more likely to adopt a similar innovation based on access to valuable information from this partner. (Frambach and Schillewaert 2002). In fact, as adoption of the innovation reaches critical mass within the network, there is increased pressure on previous non-adopters to adopt. For instance, non-adopters may lose their competitive advantage to competitive firms who have adopted the innovation (Abrahamson and Rosenkopf, 1993; Robertson and Gatignon, 1986). Further, the non-adopter may be ostracized from adopting network members, who find working with this firm is made more difficult by their inability to use IOS technology, and see their sales decline. Thus, we propose the following:

P3. The rate of adoption of IOS innovation by a network will be: (a) positively related to the degree of organizational readiness in member firms; and (b) positively related to the extent of their partners experience with the specific IOS innovations.

Perceptions of the value of the innovation by members of an organization's decision-making group influence the final decision to adopt a new innovation (Rogers, 1995; Tornatzky and Klein, 1982). Value is a reflection of both the costs and benefits of innovative technologies. Perceived benefit can be defined as the prospective adopters' belief in the probability that the new innovation will be beneficial to the organization. Not surprisingly, the more benefit a firm anticipates from an innovation, the more likely they are to adopt it (Mansfield, 1993). Among the benefits inherent in IOS adoption are strategic, operational, and opportunity ones (Sloane, 1994). Strategic benefits arise through improved customer satisfaction, cost efficiency, increased productivity, reduced manpower, and inventory control reduction (Sriram et al., 2000). Operational benefits are improvement of process activities, such as order entry, data accuracy, tracing shipment, better communication, paperwork reduction and quick response/access to information (Sriram et al., 2000). Opportunity benefits result from the organization's visibility among trading partners. In other words, as more companies adopt an EDI trading system, the companies using EDI increase their chances of securing new businesses from a wider choice of trading partners (Husein and Moreton, 1996).

These benefits appear to function in both a dyadic context, between partners, and a network context, across partners. In fact, network membership might further increase the benefits achieved through cooperative adoption. For instance, acquiescence to the focal firm's request to adopt an IOS sends a clear message about the willingness of the recipient firms to cooperate and demonstrates their internalization of

Angela Hausman, Wesley J. Johnston and Adesegun Oyedele relational norms, in addition to making the relationship more efficient (Hildebrand and Biemans, 2003). The net effect of cooperative adoption by the recipients is a strengthening of the relationship, encouraging deeper relationships, and a build-up of social capital that greases future demands of the recipients on the focal firm.

Of course, these benefits are counterbalanced by the costs involved in adoption of IOS (Wang and Tsai, 2002). For example, the deployment of an IOS innovation such as EDI or e-commerce involves considerable investment in hardware, software and skilled personnel (Howells and Wood, 1995). Aside from the initial set-up, other costs related to network maintenance of the IOS innovation should be considered (Howells and Wood, 1995). If the investment in the new innovation is perceived to be overwhelming cost-wise, the deployment of the new innovation will most likely happen at a slower pace or be squelched all together (Davies, 1979). The results of studies in the pharmaceutical industry support this contention (Howells and Wood, 1995). Based on these foregoing points, if the organization perceives the cost of deploying the IOS innovation as high, the propensity for adopting the IOS innovation will be low.

In addition to the normal costs associated with organizational adoption, networked firms potentially deal with a host of costs related to their network membership. One such cost comes from their membership in multiple networks. If not all the networks decide to adopt and IOS or they choose different IOS applications, the firm has the additional problem of reconciling these differences through utilization of different processes with firms from different networks. As mentioned earlier, this is unwieldy and potentially fraught with errors.

Another potential cost in cooperative adoption throughout the network is related to political issues across firms. For instance, a schism might develop, dividing firms in the network into two or more groups with different proposals for IOS adoption or opinions that adoption is not in the best interests of their firm. This situation can quickly become a political minefield where the firm cannot operate without aligning with one of the constituencies. Aligning with the losing contingency might have long-term costs for the firm. Similarly, the firm faces the possibility that the political process might weaken or destroy the network, reducing efficiency and profitability for all its members.

One of the key problems confronting the decision maker is the difficulty involved in assessing the true value of IOS innovation. Ahituv (1980) highlighted three major questions that the decision maker must consider when evaluating the value of any information related technological innovation. First, is the decision maker assesses the value from the perspective of the entire organization and the network. The second question is related to the type of value being considered by the decision maker. For example is the type of value to be assessed related to the perceived value by the user, by the network, or is it related to the calculated normative value of the innovation. The third question addresses the issue of when the value is assessed (in the near term or long term) and who will be responsible for performing the evaluation, the firm or other network partners. In network adoption, it is entirely likely that some firms will benefit more than others and some might see little benefit or might experience costs that exceed their benefit (Ahituv,

1980). The question of who determines the value thus becomes a critical one.

In addition to answering these complex questions, the evaluator of the innovation is also faced with some technical problems in terms of selecting the relevant attributes for evaluating information technology related innovation. Some of the problems include the measurement of each attribute and the relationship of each attribute to the innovation (Ahituv, 1980). Mukhopadhyay et al. (1995) study on evaluating the business value of EDI technology between Chrysler and its suppliers discussed measurement related problems, such as the measurement of IT outputs and aggregation issues. Aggregation problems denote the complexity that may arise from attempting to assess the effectiveness of all the IT applications within the firm together as one whole system, without consideration for the value added by each application as a stand alone unit.

Irani and Love's (2001), study on the evaluation of an MRPII system by leading UK manufacturing organization provides some useful insight about the different complexities involved in evaluating the value of an IS innovation. Their study finds that the use of traditional appraisal processes does not suffice in capturing the actual value of an IS innovation. As a consequence the adopting firm or the potential adopting firm may draw wrong conclusion about the actual value of the innovation. The use of more complex approaches that integrates human and other organizational factors were found to be significant in evaluating the value of IS investment:

P4. The rate of adoption of an IOS innovation by networked organizations will be positively related to the value the recipient firms anticipate from the innovation.

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