Habits and technology fit: a study of technology acceptance

The purpose of this research is to study Habit-Technology Fit (HTF) as an approach to capture a dynamic mix of habits which are perceived as salient from the respondent’s perspective, and the effect of including it in the Unified Theory of Acceptance and Use of Technology (UTAUT) model framework. The HTF construct and its measurement were developed in order to capture multiple non-predetermined habits. All measures were semantically validated with Q-Sorting exercises, and their reliability was statistically confirmed. A cross-sectional online survey was distributed using the Respondent-Driven Sampling technique, reaching a sample of 251 adults from 25 countries, who are ‘Software-as-a-Service’ users in public clouds and understand English. Seven hedonic and utilitarian pieces of technology were included in the study. Data was analyzed using covariance based Structural Equation Modeling and triangulated with Partial Least Squares. Unified Theory of Acceptance and Use of Technology (UTAUT) was tested alone, compared with an extension of the model that included HTF, and to a post-hoc modified model. All models were assessed with and without the original moderators of UTAUT. Results revealed a positive and significant relationship between Habit-technology fit and behavioral intention, with a stronger effect in older and more experienced individuals. When UTAUT was extended by HTF the variance explained increased from 52 to 58 percent, and the model fit was slightly reduced. However, the probability for the original and extended models to fit other samples of the same population was less than 0.001. The results also showed that HTF’s effect upon intention overlaps with the union of performance expectancy (PE) and effort expectancy’s (EE) effect, but HTF provides an additional margin of the variance explained. This suggested that UTAUT model could be simplified by using HTF in substitution of PE and EE without missing their explanatory power. The post-hoc modified model substituted PE and EE with HTF. It achieved the best model fit without moderators, and acceptable probability for theory tests (x²/_df = 1.54, p =.10, RMSEA = .033 and PCLOSE = .822). Despite the reduction of variables, the post-hoc model still explained 46 percent of the variance. Besides testing previous theory, this research makes an original contribution to knowledge with the conceptualization and development of the Habit-Technology Fit, parsimonious model that was presented. These contributions addressed a gap in the literature of habit and technology acceptance by incorporating multiple non-predetermined habits. HTF measurement has the limitations of self-report instruments. Generalizability might be limited because conventional probabilistic sampling techniques were not feasible for a ubiquitous population. Future research is needed testing the measurement and post-hoc modified model in different settings, in longitudinal designs and using diverse samples; the HTF model is proposed as a parsimonious alternative model for acceptance of technology.