عوامل تعیین کننده مختلف در زمان های مختلف: تصویب B2B یک نوآوری رادیکال

This research aims to empirically determine which factors best explain business to business adoption of a radical, high-tech innovation early in the diffusion process. Early lifecycle data collection provides insights about the differences in determinants of adoption at different times in the product diffusion process. The results indicate that differences do exist between the determinants of early adoption, intent to adopt later, and unawareness of the innovation. The influencers of earliest adopters appear to be innovation-focused: the perceived benefits of the innovation as well as the strength of the producer network positively relate to early adoption; early adopters also tend to perceive the technology in the innovation as less different than previous technology than do those who intend to adopt later. The influence of a champion within the adopting firm, the ability of the firm to sense and respond to new technology, and the depth of technology knowledge within the adopting firm are significant influencers across multiple stages of diffusion, showing that firm-internal traits are particularly important influencers of adoption. Laggard firms are missing the critical firm traits that lead to information gathering and understanding of innovations. In addition to contributing to adoption research theory and methodology, this research has implications for innovation-marketing and innovation-adopting firms.

Firms face a variety of challenges when deciding whether or not to adopt a new product or service. Whether the innovation is new to the world or simply new to the firm, the firm faces a unique multi-phase, multi-person, multi-department and multi-objective purchasing process (Johnston & Lewin, 1996). The process becomes increasingly complex when the innovation incorporates high technology that is radically different from the predecessor. High-tech industries typically exhibit a rapid pace of technological change and a wide array of alternatives (Weiss & Heide, 1993), which makes acquiring knowledge particularly challenging for resource-constrained businesses. A key question for producers of highly innovative products or services is who to target early in the product lifecycle. How does a producer know which potential customers are most likely to be among the first to adopt a radically new product or service? Do firm attributes, actions, or other factors cause a firm to be more or less likely to buy early in the product lifecycle? Little research exists on the relationship between innovator-type or innovation-type and adoption. The extant adoption literature also lacks focus on factors that influence adopters at different stages of the diffusion process (Waarts, van Everdingen & van Hillegersberg, 2002). The early timing of this study in the diffusion curve addresses the issue of pro-innovation bias (Rogers, 2003) and captures data from potential adopters as the diffusion is occurring, which creates the possibility to empirically assess differences in the characteristics and perceptions of early adopting firms as compared to firms yet to make the adoption decision. Previous studies of radical innovation judge the degree of innovation from the producer or expert point of view (e.g. Dewar and Dutton, 1986, Kleinschmidt and Cooper, 1991, Moon, 2010 and Veryzer, 1998). While an innovation may be radical from the producer's point of view, the perception among potential adopters of the innovation is relevant in the context of analyzing adoption (Robertson & Gatignon, 1986). This research considers degree of innovation from the potential adopter perspective.

The results provide empirical evidence that differences do exist between the determinants of early adoption, intent to adopt later, and unawareness of the innovation. The influence of a champion, the ability to sense and respond to new technology and the depth of technology knowledge within the adopting firm are significant across multiple stages of diffusion, showing that firm-internal traits are particularly important influencers of adoption. Five of the variables hypothesized to influence adoption do not appear to have any significant direct effects and one variable performed differently than expected. The five variables are knowledge dissemination, innovation-related experience, depth of search, perception of supplier marketing, and future size of networks. Knowledge dissemination may be a required underlying element of the technology opportunism trait, but does not appear to drive innovation adoption. As earlier work shows, knowledge management systems can create an overwhelming level of information that users cannot leverage KPMG (2000). The lack of influence of innovation-related experience is surprising, as the same measure proved significant in previous studies (Frels et al., 2003 and Weiss and Heide, 1993). This result could reflect a key challenge of radical innovation: the underlying change in core technology makes past experience irrelevant. Neither of the two formative variables, depth of search and supplier marketing, has a significant impact on adoption. This outcome could indicate an issue with the use of or approach to implementing the formative scales. The role of supplier marketing has theoretical support, without empirical test in the adoption literature. Depth of search and supplier marketing could play moderating roles or the radicalness of the innovation could moderate, making the retrieval of new information more important. Further exploration of the potential moderating role of these variables is warranted. Lastly, the integrated networks construct presents two surprises. First, the future size of networks is the only concept within the integrated networks construct that is not significant in any of the models. This result could relate to study timing; the future size of networks may not influence innovators and early adopters, having already committed to a provider. The size of future networks may not yet concern those with intent to adopt. Second, the strength of the complements network negatively relates to intent to adopt (−.015). Previous research provides evidence that compatibility becomes less of a concern for later adopters (Waarts et al., 2002). Several of the findings are useful to managers. Understanding more about the characteristics of early adopter firms enables others to emulate those characteristics. This study highlights the importance of empowering employees to create processes to seek new innovations and champion their adoption. Networking with producers and users of an innovation is important for businesses. Firm characteristics such as the number of technology-savvy employees and the technology opportunism trait are also important. These elements relate to investing in and empowering IT employees. Such an investment may be a challenge for smaller businesses, which often rely on outsourced IT personnel to fix existing systems or help with basic upgrades. For innovation producers, the findings provide insights into how best to maximize market penetration early in the lifecycle of a radical innovation. Suppliers can impact adoption rates by targeting a champion and arming them with the knowledge to influence others in their organization. The early timing of this research addressed the issue of pro-innovation bias and captured data from potential adopters as the diffusion was occurring. Such a methodology usefully highlights differences in the characteristics and perceptions of early adopting firms as compared to firms yet to make the adoption decision. These findings contribute to theory-building about the changing predictors of adoption throughout the diffusion process. Two of the findings are of particular note. First, the impact of a champion appears to overpower other marketing activities in support of the innovation. The product champion role therefore warrants further theory development and testing. Second, explicitly considering the customer point of view furthers the theory of innovation radicalness. The research shows that the perception of radicalness may change over time, and relates in turn to a firm's knowledge and acceptance of an innovation.