Effect Of E-banking On Bank Profitability
[A STUDY OF GUARANTY TRUST BANK PLC ENUGU]

2.4 Theoretical Framework

2.4.1 Rogers’ Diffusion Theory

This ubiquitous innovator and early adopter concepts lie in diffusion theory, of which Everett Rogers is considered to be the founding father. The central assumption of the theory is that the spread of technology innovations follows a normal bell-shaped distribution pattern. In this pattern, the theory differentiates between five adopter segments, for which the theory holds to fix assumptions on their size, profiles and adoption determinants. According to Rogers (2003), innovativeness or the timing of one’s adoption decision is assumed to be determined by the subjective perception of a set of product features (relative advantage, complexity, compatibility, trialability and observability). Innovators and early adopters, for example, are assumed to have a higher perception of relative advantage than the majority segments and a lower complexity perception. The aggregation of adoption decisions for all individuals in a social system is assumed to result in a normal distributed diffusion pattern, in which innovators (2.5%), early adopters (13.5%), early majority (34%), late majority (34%) and laggards (16%) are distinguished.  Aggregated cumulatively, diffusion is reflected in an S-shaped penetration pattern (figure 1)

For each of the adaptor segments mentioned, diffusion theory also assumes typical demographic profiles. Innovators, for example, are assumed to have a typically male, younger, upscale, more cosmopolitan and less dogmatic profile (Parasuraman and Colby 2001; Green 2002). Laggards, on the other contrary, are assumed to be older, with a lower income, lacking curiosity, and socially more isolated (Weber and Evans 2002). Whether these profiles are now formulated for five (Rogers, 1995; Moore, 2006) three (Veryzer, 2003; De Marez et al., 2008) or two earlier and later adopter segments (Wei, 2001), all authors using diffusion theory as framework stick to the assumption of fixed relationship between the profiling variables and a person’s innovativeness. For the most recurrent profiling variables - socio-demographics- and the five adoption determinants.

According to diffusionism, technological innovation and social progress in a society are mainly determined by technology features, thus, the cashless policy is an indication of the adoption of technological features and innovation. But this approach has been criticized for its pro-innovation bias and ex-post locus (Li, 2004), its linearity in its assumptions on adoption decision processes (Tvede and Ohnemus, 2001), its lack of attention to the user and the innovation’s specific context of use (Robertson, 1984; Van de Wijngaert and Bouwman, 2009) and its lack of attention to non-users (Verdegem and Verhoest, 2009).

As a result of this wave of criticisms, Rogers, (1983) and others felt the pressure to improve their approach (e.g. by integrating post-adoption steps in the five step adoption decision process or by increasing emphasis on concepts as (‘re-invention’). Criticisms also caused the rise of new views such as the Social Shaping of Technology (SST) and domestication. In contrast to difffusionism, SST stresses the importance of the social context in technology change, instead of seeing the latter as an independent force. Domestication ten refers to the integration of technology in the daily patterns, structures and values of users (Silverstone and Haddon, 1996; Haddon 2006). Although some rely on a more social determinism (Bouwman et al, 2002), the domestication view should be seen from a mutual shaping perspective. One of the most compelling movements within SST is the Actor-Network Theory (ANT), which strongly rejects technological as well as social technological development (Callon et al., 1986; Latour, 1993). In addition, the Human-Computer Interaction (HCI) traditionally underwent some necessary changes during the last decade. Originally, focusing on computer engineering and human information processing, increasing emphasis has been put on the influence of culture, emotions and experience on technology design and development (Hasssenzhal and Tractinsky, 2006). This critical approach suggests that the adoption and use of technology are part of a more active process and they are context-dependent.

Despite the criticisms, and convergence of alternative research views, Rogers’ diffusion theory remains a central basis for much research effort in ICT innovation and adoption as its terminology (innovators, early adopters, laggards etc.) and assumptions (segment sizes, segment profiles, adoption determinants) still provide a popular framework in media and communication studies as well as in the domain of business, management and marketing. However, as the theory is a starting point for many works in various disciplines, a scattered use of the approach is observed. Demographic profile assumptions are used for marketing purposes to select and target different types of adopter segments (Daghfous et al. 1999; De Marez et al. 2008), econometric diffusion models have the normal diffusion pattern as underlying premise for forecasting purposes (Bass, 1969), whereas, social psychologists have used the determinant assumptions to develop innovativeness and personality scales (Assael, 2005; Goldsmith and Hofacker 1991