Cognitive and emotional resistance to innovations: concept and measurement

Felipe Zambaldi · Mateus Canniatti Ponchio

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Technology Adoption and User Behaviour Consumer Behavior and Marketing Influence Innovation Diffusion and Forecasting

Abstract

Purpose This paper aims to conceptualize two dimensions of active innovation resistance (AIR): cognitive active resistance and emotional active resistance. A scale to measure this construct is proposed and tested. Design/methodology/approach Three studies were conducted, with sample sizes of 195, 190 and 186, to test the discriminant, convergent, nomological and criterion validity of the proposed AIRc+e scale and to analyze its explanatory and predictive power. Data were gathered using the online platform of a US-based research company. Findings The authors provide evidence that AIR is a two-dimension construct comprising a cognitive and an emotional dimension. AIR was modeled as a third-order construct, comprising two second-order constructs, cognitive active resistance and emotional active resistance. The impact of adding an emotion dimension to active resistance was therefore assessed, and the results indicated that the explanatory and predictive power of the AIR measure improved as expected. Practical implications Consumers are most likely to resist innovations launched onto the marketplace, either prior to or after evaluating them. A better understanding of the reasons behind their resistance to innovation, as well as of its mechanisms, is of great importance in decreasing an innovation’s chances of failure. Originality/value This study proposes that incorporating emotion into the assessment of AIR will result in a deeper understanding of adoption and rejection behavior, expanding the current knowledge of consumer behavior in innovation-related, new product adoption and decisions.

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References

1981Evaluating Structural Equation Models with Unobservable Variables and Measurement Error 1989Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology 2000A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies 1998The partial least squares approach for structural equation modeling.1991Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation 2003A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research 1982Hedonic Consumption: Emerging Concepts, Methods and Propositions 2009How to Write Up and Report PLS Analyses

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