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Diffusion of Information Technology Innovations
Innovation difussion is a multidisciplinary field with contributions from sociologists, communication researchers, organizational researchers, IT researchers and many others (Kim & Galliers, 2004). The study of innovation diffusion is concerned with three fundamental research questions:
• What determines the pattern, and extent of diffusion of an innovation? (Fichman, 2000).
• What determines the likelihood of an organization to adopt and absorb innovations? (Fichman, 2000).
• What determines the likelihood of an organization to adopt and absorb a particular innovation? (Fichman, 2000).
Rogers (1995) classical model of diffusion greatly shaped the basic concepts, terminology, and scope of the field of innovation diffusion (Fichman, 2000).
Innovation studies conform to one of two general styles of research: adopter studies and diffusion modeling studies (Fichman, 2000). Adopter studies are basically concerned about understanding differences in adopter innovativeness. The usual approach is to survey organizations in some population of interest to capture data about:
• The characteristics of those organizations and their adoption context.
• The timing and/or extent of adoption of one or more innovations.
Diffusion modeling studies are primarily interested in what determines the rate, pattern and extent of technology diffusion (Kim & Galliers, 2004).
Factors Affecting Diffusion of IT Innovations
The factors affecting innovation can be classified into three broad groups:
• those belonging to the technologies and their diffusion context.
• those belonging to organizations and their adoption contexts.
• those belonging to the combination of technology and organization (Fichman, 2000).
The three groups are connected to the three fundamental research questions identified in the previous section. The pattern, and extent of diffusion of an innovation is affected most by technologies and their diffusion context. Organizations and their adoption environments affect what determines the likelihood of organizations to adopt and absorb innovations. Technology and organization determine the likelihood of an organization to adopt and absorb particular innovations (Fichman, 2000).
Research Model
The research questions is: What factors determine the likelihood of adoption of ecommerce in Nigerian banks? The research question is concerned with whether a bank is using ecommerce or not. Drawing from technological innovation literature, an integrated model of ecommerce adoption in Nigerian banks was developed (see figure 1). Each of the variables is discussed below.
Ecommerce Adoption
The dependent variable is adoption of ecommerce. In this study, adoption of ecommerce is defined as the use of computer networks, principally the internet, for sharing of business information; maintaining of business relationships; and conducting of business transactions (Turban et al., 2004 & Zwass, 2003). The likelihood of ecommerce adoption, was operationalized as a dichotomy: whether the business has or has not adopted ecommerce. A business is defined as having adopted ecommerce if it is achieved interactive ecommerce status. Molla and Licker (2005) identified a six-phase ecommerce status indicator, relevant to ecommerce in developing countries; they are: no ecommerce, connected ecommerce, static ecommerce, interactive ecommerce, transactive ecommerce, and integrated ecommerce.
Top Management Support
According to Tolbert and Zukar (1983) innovation of IT would be more likely if the political environment within an organization has norms favoring the change. Thus, adopting ecommerce will depend on whether support from top management is available. Top management support has been identified as crucial in the acquisition and diffusion of innovation (Orlikowski, 1993). Top management consists of individuals with power and authority to make strategic decisions; thus they can develop a clear-cut ecommerce vision and strategy while at the same time sending signals to different parts of the organization about the importance of ecommerce. Given the limited nature of organizational resources and the many competing projects, top management support ensures that an ecommerce innovation project will get the required resources and capabilities. There is a positive effect of leadership support on innovation adoption; Rai and Patnayakuni (1996) found that top management support had a positive effect on CASE tools adoption behavior in IS departments. Ecommerce can potentially influence the organization’s competitive position as well as its business relationships, therefore it is important that top management need to get involved in order to gain a good understanding of the issues surrounding ecommerce and mobilize organizational stakeholders (Epstein, 2004). Thus, we propose that (H1 indicates hypothesis number 1): H1 Top management support and commitment will be positively related to ecommerce adoption.
Organizational Competency
The availability of employees with competency for producing new ideas is important for ecommerce adoption (Mohr, 1969). Organizational competency refers to the availability of employees with adequate experience and exposure to information and communication technology and other skills (such as business strategy) that are needed to adequately staff ecommerce projects (Molla & Licker, 2005).
Chwelos et al (2001) stated that the level of management understanding of and support for using IT to achieve organizational objectives may influence the adoption of IT innovation. Thus, an understanding of ecommerce technologies and business models can facilitate the adoption of ecommerce.
Thus, we propose that: H2 A high level of competency from within the organization can have a positive impact on ecommerce adoption.
IT Capability
IT capability refers to the level of IT resources and personnel IT knowledge of an organization (Akbulut, 2002). Access to adequate equipment in the organization is a major determinant of the adoption of new technologies (Newcomer and Caudle, 1991).
Cohen & Levinthal (1990) state that an organization’s ability to appreciate an innovation, to assimilate it, and apply it to new ways is largely a result of the firms preexisting knowledge in areas relating to the intended innovation.
Adoption of ecommerce requires organizations to possess a set of IT-related skills and knowledge (Turban et al., 2004) such as telecommunication knowledge, IT security knowledge, and Internet application environment.
Thus, we propose that: H3 A high level of IT resources can positively impact ecommerce adoption
Innovation Characteristics
The likelihood and the rate of adoption of a given innovation depend on its characteristics as perceive by potential adopters. These characteristics include relative advantage, compatibility, complexity, trialability and observability (Rogers, 1995).
Perceived Benefits
Perceived benefits refer to the extent of managements recognition of the relative advantage of adopting ecommerce to the organization. Perceived benefits is an important factor in adoption of new innovations (lacovou et al., 1995; Rogers, 1995). Rogers (1995) defined Relative advantage as the extent to which an innovation is perceived as better than the idea it supersedes or its nearest alternative. Relative advantage can be measured in financial terms; however, social status, comfort, and satisfaction are important factors as well. The amount of objective advantage of an innovation have a great effect, what affects adoption of an innovation is whether the innovation is viewed as advantageous. The greater the perceived relative advantage of an innovation, the more rapid its rate of adoption will be (Rogers, 1995). This view is supported by lacovou et al. (1995); they found that perceived benefits have a positive effect on the likelihood of EDI adoption.
The higher the appreciation of the benefits of ecommerce by management the more likely they are to set aside organizational resources necessary to adopt and implement ecommerce.
Thus, we propose that: H4 A high level of perceived benefits will positively impact adoption of ecommerce.
Perceived Compatibility
Perceived compatibility refers to the degree to which an innovation is perceived as being consistent with existing needs, values, past experiences, and technological infrastructure of potential adopters (Rogers, 1995 & Rogers 1983). An innovation might be perceived as technically or financially superior in accomplishing a given task, but it may not be adopted, if a potential adopter views it as irrelevant to its needs (Rogers, 1995). If ecommerce is seen as compatible with the existing work practices, environments, and overall objective, organizations will be more likely to adopt it.
Thus, we propose that: H5 A high level of perceived compatibility will positively impact the adoption of ecommerce
Market e-readiness
Market e-readiness refers to “the assessment that an organization’s business partners such as customers and suppliers allow an electronic conduct of business” (Molla & Licker, 2005). For ecommerce to thrive sellers and buyers have to be willing to exchange goods and services for money online (Turban, 2004). Thus, an organization considering adoption may first examine the willingness of its existing customers and suppliers to do business online or the likelihood of generating new business online.
Thus, we propose that: H7 A high level of market e-readiness will positively impact adoption of ecommerce
Supporting Industries e-readiness
Supporting Industries e-readiness refers to “the assessment of presence, development, service level and cost structure of support-giving institutions such as telecommunications, financial, trust enablers and the IT industry, whose activities might affect the ecommerce initiative of businesses in developing countries” (Molla & Licker, 2005). Existence of adequate IT infrastructure is a necessary condition for the take-off of and development of ecommerce (Palacios, 2003); since organizations would rather concentrate on their core competencies, it is vital that there are other organizations whose main activity is provision of IT infrastructure and services.
Thus, we propose that: H8 The existence of supporting services for ecommerce would positively impact adoption of ecommerce
Government e-readiness
Government e-readiness refers to “the organizations’ assessment of the preparation of the nation state and its contributions to promote, support, facilitate and regulate ecommerce and its various requirements” (Molla & Licker, 2005). The government has a strong role in promoting and spreading the benefits of electronic commerce (Bandyo-padhay, 2002). The result of the research carried out in Slovenia showed that government’s activities played an important role in accelerating electronic commerce (Pucihar, 2006).
Governments can provide an enabling environment in which ecommerce can realize its full potential. They can help address the problems & challenges of awareness, infrastructure develop, local content creation depending on languages used & cultures prevailing in the local environment (Kamel, 2006).
Table of contents :
1 Introduction
1.1 Background
1.2 Overview of ecommerce in Nigeria
1.3 Problem Statement
1.4 Research Question and Objectives
2 Theoretical Framework
2.1 Background
2.2 Diffusion of Information Technology Innovations
2.3 Factors Affecting Diffusion of IT Innovations
2.4 Research Model
2.4.1 Ecommerce Adoption
2.4.2 Top Management Support
2.4.3 Organizational Competency
2.4.4 IT Capability
2.4.5 Innovation Characteristics
2.4.5.1 Perceived Benefits
2.4.5.2 Perceived Compatibility
2.4.5.3 Perceived Complexity
2.4.6 Market e-readiness
2.4.7 Supporting Industries e-readiness
2.4.8 Government e-readiness
3 Methodology
3.1 Research Method
3.2 Research Approach
3.3 Positivistic paradigm
3.4 Triangulation approach
3.5 Data collection and procedures
3.5.1 Questionnaire preparation
3.5.2 Survey questionnaires
3.5.3 Pre-Test and Pilot Test
3.5.4 Semi-structured interview
3.5.5 Document analysis
3.6 Population
3.7 Sample Population
3.8 Data Codification
3.9 Data Analysis
3.9.1 Discriminant Function Analysis
3.9.2 Independent Sample T-Test
4 Results
4.1 Statistical Analysis
4.2 Preliminary Discriminant Function Analysis
4.3 T-test of Mean Differences
5 Analysis
5.1 Top Management Support
5.2 IT Capability
5.3 Perceived Benefit
5.4 Perceived Compatibility
5.5 Perceived Complexity
5.6 Organizational Competence
5.7 Market e-readiness
5.8 Supporting Industries e-readiness
5.9 Government e-readiness
5.10 Rank of Ecommerce factors
6 Conclusion
7 End Discussion
7.1 Future Research
Appendix 1
References
