The findings of this study show retailers to how to strategically position smart service technologies in their stores based on customer-perceived smart service value.
Authors
Sanjit K. Roy, School of Business and Law, Edith Cowan University, 270 Joondalup Dr, Joondalup, 6027, WA, Australia
Gaganpreet Singh, Associate Professor, O.P. Jindal Global University, Sonipat, Haryana, India.
Linda D. Hollebeek, Department of Marketing Strategy & Innovation, Sunway University, Malaysia; Department of Marketing, Vilnius University, Lithuania; Department of Business Administration, Tallinn University of Technology, Estonia; Department of Business Administration, Umea University, Sweden; Department of Business Administration, Lund University, Sweden; Department of Marketing Management, University of Johannesburg, South Africa.
Saadia Shabnam, Curtin Business School, Curtin University, Perth, Australia.
Arnold Japutra, UWA Business School, The University of Western Australia.
Sebastian van Doorn, UWA Business School, The University of Western Australia.
Subhasis Ray, XLRI- Xavier School of Management, Jamshedpur, India.
Francesco Paolo Appio, Paris School of Business, France.
Summary
In-store smart technology is rapidly transforming service delivery and value creation in the retail sector. However, despite these advances, academic acumen of customers’ perceived value of their smart service interactions remains tenuous, exposing an important omission in extant literature. Addressing this gap, we conceptualize, operationalize, and validate smart service value (SSV) in the retailing context.
We first define SSV as the costs and benefits as perceived by customers of using in-store smart service applications. We then operationalize SSV and validate a third-order, reflective-formative construct by means of a scale development survey through Amazon MTurk (study 1; n = 326). To further validate the proposed SSV scale, we subsequently tested our conceptual model using a survey querying a hypothetical retail setting through an Australian panel provider (study 2; n = 298), which was analyzed by using PLS path modeling.
Specifically, we explore SSV’s effect on customer engagement and trust, which are in turn envisaged to impact customers’ quality of life. The results reveal a significant mediating effect of affective customer engagement/trust in the association of SSV and customer-perceived quality of life, highlighting the pertinence of customers’ emotional (vs. cognitive) SSV assessments. Our findings are aimed at helping retailers to strategically position smart service technologies in their stores based on customer-perceived SSV.
Published in: Technovation
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