انتشار فناوری اطلاعات و ارتباطات داخل سازمانی: مفاهیم برای فروشندگان و خریداران صنعتی

Information and communication technologies (ICTs) like CRM, ERP and Intranet are considered important for creating competitive advantage. Despite their rapid deployment rates, only a few studies mainly from the information technology (IT) and engineering literature have been devoted in uncovering the factors that influence the diffusion of new information technologies within an organization. Similarly, empirical studies regarding the impact of ICT diffusion on organizations are strikingly limited. In an attempt to fill this research void, the present study examines the implementation of ICT tools within marketing-related and non-marketing-related functions. By testing a number of hypotheses using structural equation modeling, the authors conclude that the antecedents and consequences of ICT diffusion in these functions vary. Their findings provide the foundation for a more thorough examination of both intraorganizational diffusion of ICT tools as well as their impact on organizations.

In today's highly competitive markets the use of information and communication technologies (ICTs) by organizations (e.g., LAN, WAN, ERP, CRM) appears to be an imperative for reducing the uncertainties surrounding production and administrative processes (Dewett & Jones, 2001) and, consequently, for sustaining competitive advantage (McKee, Varadarajan, & Pride, 1989). Despite the importance attached to ICT, only few studies mainly from the information technology (IT) and engineering literature have been devoted in uncovering the factors that influence the implementation of such innovations within an organization, once these innovations have been put in place 3 (see, inter alia, Brancheau and Wetherbe, 1990, Cats-Baril and Jelassi, 1994, Cooper and Zmud, 1990, Grover and Goslar, 1993, Klein et al., 2001, Lai and Mahapatra, 1997 and Zmud, 1982, for a meta-analysis of implementation research within the field of MIS). Surprisingly enough, in the marketing literature only two studies, one conceptual ( Kim & Srivastava, 1998), and one empirical ( Pae, Kim, Han, & Yip, 2002) have explored the factors affecting intraorganizational diffusion of innovations. In this respect, a thorough understanding of the effective implementation of ICT becomes a relentless necessity. In business-to-business markets, intraorganizational ICT diffusion constitutes a very important issue, as quite often the buying organization initiates a cooperation with a seller by purchasing an innovation in small quantities to avoid the technological risks related to the new provider's products and services (Bettman 1973). Risks like uncertainty in quality, incompatibility with current systems and vendor-related switching costs may lead to increased levels of loyalty to certain suppliers (Dick and Basu, 1994, Puto et al., 1985 and Weiss and Heide, 1993). In this sense, from a supplier's perspective, an innovation can only be considered a success when it is accepted and integrated into the adopting organization and the target users exhibit commitment by continuing to use the product over a period of time (Bhattacherjee, 1998). Hence, an understanding of the factors contributing to the successful intraorganizational diffusion of information and communication products is critical for the selling organization, as it can better exploit the market potential for its innovations and sustain long term customer relationships. Moreover, given the significant investment costs required for deploying new ICT tools, the adopting organization should identify the enabling conditions for enhancing diffusion, and therefore, make a full utilization of the ICT capabilities. Equally important is the examination of the consequences of accepting or rejecting information technologies within the organization (Rogers, 1995), both because of the high investments involved (Finance CustomWire, 2005, Grover et al., 1998 and Tyson, 2005) as well as the high failure rate of ICT implementation (Street, 2004). For example, it is projected that U.S. companies will spend US$27.8 billion in 2005 for CRM implementations (CRM News, 2001), while the failure rate of most Sales Force Automation (SFA) implementations has been reported to be as high as 55–80% (Galvin, 2002 and Speier and Venkatesh, 2002). IT acceptance, which was the main dependent variable in past work, should become a predictor of a dependent variable, namely the impact of IT acceptance (Igbaria and Tan, 1997). Research that investigates the impact of ICT tools (e.g., Intranet) on organizational performance may provide additional knowledge about how to maximize the potential benefits of these tools (Eder & Igbaria, 2001). Paradoxically, and in spite of the provocative and sanguine speculations, which replenish the practitioner press, the relationship between ICT diffusion and organizational effectiveness remains largely unsubstantiated. One notable exception within the marketing literature is the work of Pae et al. (2002). Furthermore, past studies have treated intraorganizational diffusion as a unidimensional construct that reflects the degree to which an innovation is used by an organization as a whole (Kim and Srivastava, 1998 and Meyers et al., 1999). However, innovation use may vary between individuals and between groups within an organization (Klein & Sorra, 1996). More specifically, it has been argued that the benefits of information systems- and subsequently of ICTs-are intertwined with the needs of the user (Franz & Robey, 1986). Hence, different users (e.g., marketers vs. accountants) desire distinctive benefits from computer use (Good & Stone, 2000). Similarly, Kallman and O'Neil (1993) argue that the variety of functional differences (e.g., marketing and human resources) suggests that the implementation of computer technology must be viewed within the context of specific users, while Aydin and Rice (1991) postulate that both occupational and departmental social worlds are important predictors of individual reactions to medical information systems. Despite the acknowledged departmental differences within the same organization, the studies regarding innovation implementation, let alone ICT, are extremely limited. In particular, Kamath, Mansour-Cole, and Apana (1993) found empirical support for the different objectives that guide innovation adoption decisions between manufacturing and marketing functions. Speier and Brown (1997) have found significant differences across marketing, sales and financial operations in end-user computing support and control. More recently, Van Everdingen and Wierenga (2002) have found differences in the effects of various factors on the adoption of Euro across different departments (i.e., finance, purchasing, and sales), and thus, emphasized the very need for an intra-firm analysis of adoption and diffusion of innovations. Based on the above arguments, the purpose of this study is to take a function-wise perspective in examining ICT diffusion by investigating the antecedents and consequences of ICT diffusion, defined as the extent of ICT use, within marketing-related and non-marketing-related functions. For the purposes of the present study, marketing-related functions include marketing, sales, customer service/support and product development (Cespedes, 1995 and Cespedes, 1996), while non-marketing-related functions include purchasing/supplies, warehousing and finance/accounting (Ruekert & Walker, 1987). 2. Theoretical background and hypotheses development As depicted in Fig. 1, central to our conceptual model are the constructs of marketing and non-marketing-related ICT diffusion. We define marketing-related ICT diffusion as the extent of ICT usage within the functions of marketing, sales, customer service/support and product development, while non-marketing-related ICT diffusion is defined as the extent of ICT usage within the finance, warehousing, and purchasing/supplies functions. It is important to stress out that both marketing-related and non-marketing-related ICT diffusion refer to the extent of usage of the same ICT tools, but within different functions. These variables are linked to four ICT characteristics (i.e., relative advantage, compatibility, cost and security), two organizational characteristics (i.e., formalization and commitment to change), and two market characteristics (i.e., demand uncertainty and intensity of competition). Furthermore, organizational consequences of ICT diffusion in the marketing-related and non-marketing-related function are captured in terms of marketing, financial and communication/informational effectiveness. We define effectiveness as the organization's realization of the intended benefits of a given innovation (Klein et al., 2001, p. 816). In particular, these benefits involve improvements in marketing activities (e.g., improvements in market share), financial activities (e.g., operational costs reduction) and communication/informational activities Intraorganizational ICT diffusion antecedents and consequences were selected by following a three-step approach. Firstly, a thorough review of the innovation adoption literature was undertaken. Secondly, we extended our literature review by focusing on intraorganizational diffusion studies. Given the considerable lack of relevant studies in the marketing literature, the main source of investigation was the IS/IT literature. Thirdly, the selected variables were tested for relevance and were refined based on personal interviews with IT managers.

Overall, the results offer strong support for the hypothesized model relationships (Table 2 and Fig. 1). In particular, relative advantage has a positive effect on both the marketing-related (γ = .33, p < .05), as well as the non-marketing related ICT diffusion (γ = .15, p < .05), providing support for hypotheses 1a and b. Moreover, compatibility is not found to be an antecedent of marketing-related ICT diffusion; thus hypothesis 2a is not supported by the data. By contrast, the higher the compatibility within existing systems, the higher the ICT diffusion in non-marketing-related functions (γ = .12, p < .05), thereby supporting hypothesis 2b. Furthermore, hypothesis 3a, which posits that cost has a positive impact on the marketing-related ICT diffusion, is not supported. By contrast, hypothesis 3b, which posits that cost has no impact on ICT diffusion in non-marketing-related functions, is supported. In addition, hypothesis 4a is supported, as the higher the security, the higher the marketing-related ICT diffusion (γ = .12, p < .05). However, we fail to find any statistically significant relationship between security and non-marketing-related ICT diffusion; thus, hypothesis 4b is not supported. Formalization is significantly and positively associated with the marketing-related and the non-marketing-related ICT diffusion as suggested in hypotheses 5a (γ = .14, p < .05) and 5b (γ = .21, p < .05), respectively. Hypothesis 6a, which posits that commitment to change is positively associated with marketing-related ICT diffusion, is supported (γ = .20, p < .05), and so does hypothesis 6b, which postulates that commitment to change has no impact on the non-marketing-related ICT diffusion. Hypothesis 7a, which suggests that demand uncertainty has no impact on marketing-related ICT diffusion, is supported. Similarly, the negative association of demand uncertainty with non-marketing-related ICT diffusion is statistically significant (γ = − .12, p < .05), thus supporting hypothesis 7b. Hypothesis 8a that suggests a positive association between intensity of competition and marketing-related ICT diffusion is not supported. However, as suggested in hypothesis 8b, intensity of competition has no impact on the non-marketing-related ICT diffusion. Furthermore, marketing-related ICT diffusion is positively associated with marketing effectiveness (β = .23, p < .05) and communication/informational effectiveness (β = .17, p < .05), while non-marketing-related ICT diffusion is positively associated with financial effectiveness (β = .09, p < .05). Thus, hypotheses 9a and 9b are both supported. Finally, although not initially hypothesized, the marketing-related ICT diffusion is also positively associated with financial effectiveness (β = .17, p < .05), while relative advantage is positively associated with all three types of effectiveness, namely marketing (γ = .36, p < .05), financial (γ = .26, p < .05) and communication/informational (γ = .35, p < .05).