Explaining factors affecting technological change adoption : A survival analysis of an information system implementation

Purpose – The purpose of this paper is to explore how drivers differentially speed up the change process adoption in the perspective of a technological change. More specifically, the paper aims to answer the following question: “Which factors impact the technological change adoption speed of an information system?” Based on an empirical study, our results identify three factors that have a direct influence on the speed of technological change adoption. Design/methodology/approach – Using the Unified Theory of Acceptance and Use of Technology model as a point of departure, the paper analyzes the impact of eight variables grouped in four categories: the perceived attributes of change (performance expectancy and effort expectancy), social influence (peer influence and supervisor influence), facilitating conditions (initial training and helpdesk) and individual characteristics (receptivity to change and self-efficacy). To evaluate which factors accelerate or inhibit change adoption, the paper uses a statistical model of survival analysis. Findings – Based on a 15-month longitudinal study of a workflow system implementation in a telecommunications firm, the results highlight that performance expectancy, supervisor influence and self-efficacy have a direct influence on the speed of technological change adoption. Research limitations/implications – As a case study, the research findings may only be valid in the particular organization in which it is developed. Indeed, the organizational culture, the company's internal rules, and the history of the organization are factors which significantly influence the speed of change. Practical implications – The results may help project leaders to be aware of the elements that must be dealt with effectively if a change process is to succeed within the allotted time. Originality/value – The statistical model of survival analysis allows analyzing change adoption from a dynamic perspective. This statistical approach is quite new and complementary with most of the studies which are qualitative in the field.