The prologue to innovation adoption has brought about a number of theoretical frameworks proposed to investigate the major factors affecting adoption of new innovation and technologies. As a result a lot of explanatory variables have been identified to measure and predict how those factors are affecting adoption of new innovation and technologies. Nevertheless, most of these theoretical foundations emphasised on innovation characteristics to predict and explain the acceptance of innovations by individuals. They hardly considered the external and organization readiness factors in undermining technology adoption.
Moreover, the innovation adoption discourses have unable to clearly distinguish the painstaking differences of organizations and individual attributes in adoption of innovations. For new innovations like e-marketing, few studies have been conducted to comprehensively examine the determinant aspects of e-marketing adoption and implementation in tourism businesses. Thus, it looks hard to find comprehensive models to empirically and theoretically test the determinant factors affecting new technology adoption in tourism organizations.
Whatever argument persists to exist, unlike new technological innovation, which occurs at a given incident, technology adoption and implementation is gradual and slow process. New innovation and technology adoption requires an integration of the changing technologies with new marketing strategies for each organization (Hall & Khan, 2003).
On the other hand there are a number of accepted theoretical frameworks that have been used by researchers to investigate the adoption and diffusion of new technologies by the business community. These frameworks provide fundamental foundations for business leaders, researchers and academic community to gain better understanding of the applications and potentials of new technologies. Moreover, there is a growing desire to predict and explain the major factors affecting ICT and internet adoption in tourism organizations. These desires have motivated the recent research on ICT and internet adoption and its successful application in e-marketing context.
But e-marketing is still a relatively new concept particularly for tourism businesses. Therefore, there is an increasing motivation to have deep understanding of e-marketing problems as well as opportunities for tourism, and how these technologies can be used to carry out the marketing processes in a more effective way than traditional practices (El-Gohary, 2012).On the other hand adoption and diffusion of technology in tourism organizations is complex and demonstrates unique characteristics calling for distinctive approaches in investigating technology adoption behaviour( Wang & Qualls,2007).
Thus, prior to the development of conceptual model for this research, it is essential to understand how technology adoption theories were conceptualized regardless of organizations and individuals. In this case at its early phases, technological adoption theories have been predominantly developed to investigate individual behavioural intention to accept new technologies.
But organizations have different decision making processes from individual and thus the factors influencing businesses adoption decisions may be different. For example, organizational adoption decision may be affected by the characteristics of the organization such as size, level of IT knowledge of employees and others that are unique to the organization. Identifying why and how organizational adoption of technological innovations occurs is fundamental for ensuring successful adoption.
So by merely studying individual behavioural acceptance of new technology, it is impossible to make a conclusion about organizational technology acceptance. Hence, this section will provide a review of Innovation Diffusion Theory (Rogers, 1995), Technology Acceptance Model (TAM)(Davis, 1989), Unified Theory of Acceptance of Use of Technology(UTAUT)(Venkatesh, Morris, Davis & Davis,2003) and Perceived E-Readiness Model (PERM) (Molla and Licker, 2005) to develop comprehensive conceptual model applicable to investigate e-marketing adoption and implementation in tourism business.
Diffusion of Innovation Theory (DOI)
Diffusion of innovation theory was initially proposed to examine the inconsistency of agricultural technology adoption in U.S.A. However, in its later days DOI has been widely applied in information system, information technology, marketing, business, consumer behaviour, education, agriculture, health, communication, and anthropology and sociology studies. Rogers (1995) introduced DOI to explain the rate and stages of diffusion of innovation.
Rogers (2003) conceptualized diffusion as “the process in which an innovation is communicated through certain channels over time among the members of a social system (p. 5).” He contended that many diffusion studies focused on the innovation itself: the “idea, practice or object that is perceived as new by an individual or other unit of adoption” (p. 12). He strongly criticised the belittling of stages and process of diffusion of innovation. Rogers recognized the huddleness and interrelatedness of innovations its possibility in confusing diffusion scholars to overlook interdependency and cluster of new ideas.
DOI was built on the assumption that innovation diffusion within the social system is a process and cannot takes place immediately rather require sequential actions over time. The theory assumes that new innovation and adoption passes through certain pragmatic stages, i.e, Innovation–decision processes. Innovation-decision process is seen as persuasive process of new innovation in which an individual or an organization evaluates a new idea and decides whether or not to incorporate it into their operations (Rogers, 2003). Rogers postulated five stages in which each stage consists of series of different actions and decisions.
Rogers’s innovation-decision process starts with the cognition of the information about new innovation and its functions. Consequent to information cognition, adoption of new innovations requires the psychological engagement and development of affections and feelings towards new innovation. This tends to make adoption decision with less uncertainty. The innovation-decision process is further extends to the implementation of the new innovation which likely to be followed by confirmation either to accept or reject (Ekdale et al., 2015).
Rogers noted that innovation diffusion began with the exposure of individuals or other decision making unit about new innovations and its functions. By acquiring knowledge, individuals or other decision making unit would like to understand the cause-effect relationship of the new innovation’s capacity to solve problems (Rogers, 2003). For Rogers and other succeeding scholars exposure to new innovation and ideas may not lead to adoption and implementation rather these new innovations should persuade individuals or other decision making unit about its perceived characteristics in terms of relative advantage, compatibility, complexity, triability and observability. Thus, innovation perceived advantageous, compatible, ease for use, traiable and observable likely to be adopted (Rogers, 2003).
It is further argued that adoption decision is made based on the subjective evaluation of new innovations and its pursuant functions. Those innovations accepted with less uncertainty would likely to be implemented and chosen for confirmation. Finally at the confirmation stage, the individual or other decision making unit seeks information to reinforce the already taken innovation adoption decision and to reduce dissonance. Discontinuation may occur at this stage if performance problems appear or if a better idea supersedes.
However, Rogers’s innovation- decision process seems to lack the empirically testability and transiency of specific stage. In addition, individuals passing through the different stages may or may not recognize when one stage ends and the other begins (Rogers, 2003). In spite this Rogers’s model provides an informative framework to study technological innovations and adoptions in organization including e-marketing adoption.