Identifying Innovativeness Among Users of Wireless Features and Services.
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2001-11-15
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Research and case studies have emphasized the role Early Adopters play (as gatekeepers and disseminators) in the diffusion of new technologies and innovations (Rogers & Cartano, 1962; Robertson, 1971; Rogers, 1983; Flynn & Goldsmith 1993). Marketers have acknowledge the need to identify, profile, utilize and potentially influence Early Adopters to successfully launch a new product or service (Robinson, 1988). This research assists with identifying the salient characteristics of Early Adopters (those with a high predisposition towards innovativeness) of wireless phone features and services. The findings provide a framework for future identification of Early Adopters within the wireless market, as well as, contributing to the study of Early Adopters in general.The research utilized a random sample of 3,045 wireless phone users within an eleven state calling area. Each user completed a detailed survey regarding telecommunication needs, usage and attitudes. Basic demographics, psychographics and lifestyle measures were also collected from each user. An adapted cross-sectional measure of innovativeness (Midgely & Dowling 1978) based on prior wireless phone features and services adoption behavior, was used to identify a predisposition towards innovativeness, with regard to wireless features and services. Past research has identified the cross-sectional innovativeness measure as viable surrogate of adoption behavior (Stanton, 1999). A strong relationship was observed between traditional time-of-adoption measures and the cross-sectional innovativeness measures, within this analysis.The cross-sectional innovativeness measure was utilized as the dependent measure for the analysis. Two dependent variables were constructed from the cross-sectional innovativeness measure. The first dependent variable was a continuous variable, employing the cross-sectional innovativeness measure as a ratio scale variable. The variable provided a distribution from 0 (low innovativeness) to 20 (high innovativeness). The second dependent variable was a categorical variable that divided the cross-sectional innovativeness measure into to three distinct categories of adoption, similar to the adoption categories identified by Rogers (1983) in his definitive book Diffusion of Innovations. Similarly, the categories were labeled as Early Adopters, Middle Majority and Later Adopters.Three sets of independent variables were developed, based on past research findings concerning Early Adopters of the technology market and available measures found in the study questionnaire. The first set of independent variables, Demographics (Model 1), included the following predictor variables: age, marital status, education, household income, children in the household and home ownership. The second set of independent variables, Psychographics (Model 2), included self-perceived attitudes towards opinion leadership and risk taking/venturesomeness. The third set of independent variables combined both of the Demographics and Psychographics variables into one comprehensive set.In general the results of the study support many of the hypothesis posed. Few exceptions were noted. Early Adopters of the wireless market tended to be younger, more likely to be single, college educated, with higher household incomes, with fewer children, less likely to be owners of their primary residence, more likely to perceive themselves as opinion leaders, as well as having positive attitudes towards risk taking/venturesomeness. Analysis of the independent models, utilizing regression coefficients and uniqueness indices for the continuous dependent variable, and the proportional chance criteria and Press's Q for the categorical dependent variable, pointed to the Demographics and Psychographics (Model 3) as the strongest model (followed by the Demographics (Model 1)). Age and income variables (followed by Opinion Leadership) provided the greatest contribution to the explanatory power of the independent models.The findings aid with present and future diffusion of wireless technologies, including wireless e-mail, wireless internet access and M-commerce, as well as other categories such as telecom and the technology market in general. The analysis marks the first step in a process of identifying, profiling, utilizing and potentially influencing Early Adopters of the wireless technology market.
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Master of
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Psychology
