Delegating decision-making to autonomous products: A value model emphasizing the role of well-being

Laurent Bertrandias, Ben Lowe, Orsolya Sadik-Rozsnyai and Manu Carricano

Technological Forecasting and Social Change, 2021, vol. 169, issue C

Abstract: Given the rapid growth of autonomous technologies it is important to understand how consumers attribute value to them. Such technologies require consumers to give up control to a machine by delegating decision-making power. To better understand value perceptions, and ultimately adoption, this paper proposes a conceptual model that explains the value attributed to autonomous cars as an archetypal consumer autonomous technology. The model is developed from literature around the theme of autonomy and two qualitative studies, which identify consumers’ perceived individual benefits (Freeing Time, Overcoming Human Weaknesses, Outperforming Human Capacities), risks (Loss of Competencies, Security and Privacy risk, Performance risk) and their proximal antecedents (Perceived Expertise, Attitude toward the Delegated Task, Previous Engagement in Delegation). The model is tested on a national sample of French drivers using a quantitative methodology and Structural Equation Modelling (SEM). This research contributes to literature on technological forecasting of autonomous technologies by developing and testing a conceptual model, which includes salient predictors of perceived value and highlights the mediating role of improvement in subjective well-being that consumers anticipate from adoption. The model can be used by managers to predict how users are likely to react to their products and communications about them.

Keywords: Perceived value; Autonomous cars; Autonomous products; Well-being; Consumers (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:169:y:2021:i:c:s004016252100278x

DOI: 10.1016/j.techfore.2021.120846

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