Why Traditional Technology Acceptance Models Won't Work for Future Information Technologies?

This paper illustrates why existing technology acceptance models are only of limited use for predicting and explaining the adoption of future information and communication technologies. It starts with a general overview over technology adoption processes, and presents several theories for the acceptance as well as adoption of traditional information technologies. This is followed by an overview over the recent developments in the area of information and communication technologies. Based on the arguments elaborated in these sections, it is shown why the factors used to predict adoption in existing systems, will not be sufficient for explaining the adoption of future information and communication technologies. Keywords—Technology Diffusion, Technology Acceptance Models, Ambient Intelligence, Ubiquitous and Pervasive Computing. I. DIFFUSION OF INNOVATIONS N ORDER to understand the importance of technological acceptance when introducing new technologies, it is important to be aware of the different steps of the diffusion process. Diffusion, in the context of innovation research, usually refers to “the process, by which an innovation is communicated through certain channels over time among the members of a social system” [66]. Research related to the diffusion of innovation has a quite long tradition, with first articles being published in the early 1940s (see, e.g., [68]). As new technologies are usually first used for commercial applications, the focus of this section will be on technology adoption in companies. In its easiest form, the diffusion process consists of two stages: adoption and implementation (see Figure 1). The adoption stage includes sub-stages of knowledge acquisition, persuasion and learning, and decision, leading to the actual adoption decision [61]. The process of innovation adoption can happen in different ways, which will be described later in more detail. Fig. 1. Different stages of the innovation diffusion process according to Prescott and Conger [61]. C. Röcker is with the Communication Sciences Department at RWTH Aachen University, Theaterplatz 14, 52056 Aachen, Germany (phone: +49 241 8025508; fax: +49 241-8022493; e-mail: [email protected]). Several factors directly influence the diffusion process. According to Prescott and Conger [61] those factors include the characteristics of the innovation, the social system and the communication channels, which are all interacting over time (see Figure 2). Fig. 2. Factors affecting the innovation diffusion process according to Prescott and Conger [61]. Within organizations, technology adoption mostly occurs in a two-step process (see, e.g., [43] or [45]). In the first step, the decision to adopt the technology is made on management level and is usually referred to as the ‘primary adoption’. The innovation decision is then followed by actual introduction of the technology to the work place, where it has to be adopted by users (secondary adoption). Fig. 3. Process of innovation adoption within organizations [30]. Depending of the adoption decision in the first and second stage, different types of innovation adoption are possible (see Table ). As shown in the lower left cell of the matrix, the primary adoption decision does not guarantee, that the innovation will actually be implemented or used by the targeted users [30]. TABLE I TAXONOMY OF TWO-STAGE INNOVATION ADOPTION TYPES ACCORDING TO GALLIVAN [30] Primary Adoption No Primary Adoption Secondary Adoption Authority-Based Innovation Adoption Bottom-Up Adoption No Secondary Adoption Adoption, but no Deployment Non-Adoption Carsten Röcker Why Traditional Technology Acceptance Models Won't Work for Future Information Technologies? I World Academy of Science, Engineering and Technology International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering Vol:4, No:5, 2010 490 International Scholarly and Scientific Research & Innovation 4(5) 2010 scholar.waset.org/1999.10/12313 In te rn at io na l S ci en ce I nd ex , I nf or m at io n an d C om m un ic at io n E ng in ee ri ng V ol :4 , N o: 5, 2 01 0 w as et .o rg /P ub lic at io n/ 12 31 3 The secondary adoption process can be influenced in different ways. Gallivan [30] distinguishes between three fundamentally different ways, how organizations can ensure secondary adoption: First, the organization can mandate that the innovation is directly adopted by all users. Second, the organization can provide the necessary infrastructure and support for users to adopt the innovation, while allowing it to diffuse voluntarily. Third, the organization can target specific pilot projects within the company, observe the processes and outcomes that unfold and decide, whether to implement the innovation more broadly later on. Saga and Zmud [69] explored the different stages of IT innovation. Based on their findings they developed a threestage model, including the steps of acceptance, routinization and infusion. According to this model, acceptance affects use, use affects routinization and routinization finally affects infusion [61]. Hence, it is of particular importance, that technological innovations are accepted in order to enable routinization and infusion in the second and third step. II. TECHNOLOGY ADOPTION THEORIES Predicting the adoption and use of information technology has been a key interest since the early days of information systems research [14]. The main goal of technology acceptance theory is, to explore the factors that influence the adoption and diffusion of new technologies throughout a social system [8]. Over the years, several independent theories for the acceptance as well as adoption of information technology have been developed. Most of these models apply to situations, in which individuals can voluntarily choose whether to adopt an innovation or not [30]. Adoption, in this context, refers to the process of choosing a specific innovation or technology (see, e.g., [18]). In the following sections the most prominent theories and models will be briefly outlined. A. Innovation Diffusion Theory One of the first theories was the Innovation Diffusion Theory developed by Rogers [66]. The Innovation Diffusion Theory sets forth a five-stage model describing the process, by which an innovation is communicated among the members of a social system [10]. Based on his observation of innovation diffusion over nearly three decades, Rogers [67] defined a developed specific innovation characteristics explaining the result of the diffusion process. Barnes and Huff [8] summarize the set of key characteristic as the following: Relative Advantage (the degree, to which the innovation is perceived as being better than the practice it supersedes) Compatibility (the extent, to which adopting the innovation is compatible with what people do) Complexity (the degree, to which an innovation is perceived as relatively difficult to understand and use) Trialability (the degree, to which an innovation may be experimented with on a limited basis, before making an adoption decision) Observability (the degree, to which the results of an innovation are visible to others) The Innovation Diffusion Theory served as the basis for a variety of other models in the area of technology adoption. One of the better known theories was developed by Moore and Benbasat [59][60]. Based on the original theory of Rogers [66], they developed a survey instrument using eight ‘belief scales’, which were specific tailored to the adoption and diffusion of information technology. B. Theory of Reasoned Action (TRA) Another model, dating back to the same time as the Innovation Diffusion Theory, is the Theory of Reasoned Action (TRA) developed by Ajzen and Fishbein (see, e.g., [29] or [6]). TRA is a social psychology model, describing the determinants of consciously intended behaviors [10]. The model focuses on predicting behavioral intention and actual behavior, based on behavioral beliefs and subjective norms [47]. In the context of technology adoption TRA postulates, that actual use of a specific technology is influenced by the user’s behavioral usage intention, which in turn depends on the user’s attitude towards the use of the technology as well as the subjective norms of using the technology predominant in the user’s social environment [8]. Fig. 4. Theory of Reasoned Action Model according to Mao and Palvia [47]. C. Theory of Planned Behavior (TPB) Similar to the Innovation Diffusion Theory, the original TRA model was extended by several authors. The most important extension is probably the Theory of Planned Behavior (see, e.g., [77] or [48]). The Theory of Planned Behavior (TPB) was developed by Ajzen and Madden [4][5] and is based on Ajzen’s previous work on TRA (see above). The TPB model extends the Theory of Reasoned Action by including the variable of ‘perceived behavioral control’, which measures a person’s perception of control over performing a given behavior [62]. As shown in Figure 5, TPB postulates that the intention to adopt a specific technology is determined by three factors: the user’s attitude, his subjective norms and the perceived behavioral control. Each factor, in turn, is generated by a number of beliefs and associated evaluations [10]. World Academy of Science, Engineering and Technology International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering Vol:4, No:5, 2010 491 International Scholarly and Scientific Research & Innovation 4(5) 2010 scholar.waset.org/1999.10/12313 In te rn at io na l S ci en ce I nd ex , I nf or m at io n an d C om m un ic at io n E ng in ee ri ng V ol :4 , N o: 5, 2 01 0 w as et .o rg /P ub lic at io n/ 12 31 3 Fig. 5. Theory of Planned Behavior according to Benham and Raymond [10]. The TPB model has been studied by a variety of authors (see, e.g., [15], [36], [40] or [77]), and proved to be reliable in predicting and explaining user behavior in several application areas. Based on the original TPB model, Taylor and Todd [76] extended the Theory of Planned Behavior and created the socalled decomposed TPB model. In their model they replaced behavioral, normative and control beliefs and their associated weights with new constructs specific to information systems, that are believed to be important in different usage situations