Adoption and implementation of technological innovations within long-term relationships

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Abstract

As businesses move toward long-term cooperative relationships, they face increasing needs to coordinate, especially with respect to the adoption of innovative technologies. Since effective adoption involves both adoption and implementation, both stages are critical. This study builds and tests models of adoption and implementation as a function of influence, dependence, and relational variables. Results of this study on electronic data interchange (EDI) adoption in hospital supply chains indicate social influence achieves higher adoption rates than either coercive or noncoercive influence efforts. In addition, communication and participative decision-making are critical implementation variables.

Introduction

Although often considered a natural extension of adoption, implementation does not follow automatically and additional research into successful implementation is necessary (Rogers, 1995). In addition, an understanding of potential adopters as active decision-makers, rather than as passive units, is required (Windsor, 1995). In organizations, adoption and implementation processes might be more complex due to the web of relationships surrounding the adopter, such that each independent stakeholder is potentially affected by technological changes (Hausman, 1996). Thus, promoting cooperative adoption among relational partners may be critical to successful adoption (Hakansson and Johanson, 1988).

Due to the complementary nature of cooperative adoption, the firm who desires to implement a particular innovation (the focal firm) may need to convince relational partners (recipient firms) to implement it as well. Extant literature is mute on the process most useful in encouraging this cooperation without damaging the partnership. Insights from relationship marketing do suggest that the type of influence exerted, as well as interorganizational variables, are drivers of other types of cooperation Brown and Pattinson, 1995, Dwyer and Gassenheimer, 1992.

Therefore, this study is directed towards answering the following questions: (1) what effects do influence efforts exerted by focal firms have on the technology adoption decisions of recipient firms?; (2) do these influence efforts affect the implementation of technological change?; and (3) to what extent do interorganizational variables, such as trust, communication, dependence, and participative decision-making between focal and recipient firms affect (a) adoption and (b) implementation of innovations?

Section snippets

Conceptual development

The context of this study is adoption of electronic data interchange (EDI) by hospitals. EDI is actually five related software programs designed to facilitate the ordering, tracking, and payment of goods across a channel. By electronically processing orders, EDI eliminates mistakes, shortens lead times, and speeds payment. Allegiance Healthcare, an early and strong proponent of EDI in this channel, has encountered difficulties in adoption of EDI by downstream channel partners over the past 20

Measures

Influence strategies were measured using modifications of the scales developed by Frazier and Rudy (1991). Hypotheses addressed only coercive and noncoercive influence, hence, a subset of the items comprising these constructs was used (see Boyle et al., 1992, Simpson and Mayo, 1997, Venkatesh et al., 1995 for support for this technique). Social influence was measured by modifying a scale for referent power originally developed by Brown et al. (1995). Participative decision-making was measured

Sample characteristics

MANOVA analysis supported the decision to combine data received from the two mailings, but exclusion of the pretest data. A total usable sample of 281 responses was received from the two mailings. Survey data show 70% of respondents were either facing adoption of EDI or had adopted EDI within the previous 2 years; suggesting recall adequacy. As shown in Table 1, almost 86% of respondents were actively involved in the adoption decision and all were involved in the implementation process. Thus,

Implications

The factors affecting adoption of technological innovations and those affecting implementation appear to be entirely different. Specifically, a number of interfirm relational variables affect the adoption stage, while implementation appears to require more coordination and input from various individuals. As expected, the correlation between adoption and implementation is not perfect (.5169). Findings offer empirical support for open interorganizational communication and participative

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