Delighting customers: Evaluating service quality and customer satisfaction of self-checkout users in sports retail
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DOIhttp://dx.doi.org/10.21511/im.20(3).2024.08
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Article InfoVolume 20 2024, Issue #3, pp. 97-109
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Digitalization has transformed dynamics across all fields, and technology has completely changed the customer experience. One prominently utilized technology in offline retail is self-checkout services. The present study intends to investigate the attributes that influence people to use self-checkout services and assess their impact on service quality and customer satisfaction. Drawn from Dabholkar’s attribute-based model, the study employs a positivist approach to test the conceptual framework. After the preliminary survey of 330 respondents, it identified ninety-nine consumers who had used the self-service check-out facility. The data collected were analyzed using a multi-variate technique – Partial Least Squares Structural Equation Modeling (PLS-SEM) – owing to the small sample size requirement. All independent variables taken for the study positively affect the service quality. Customer perception of control, ease of use, reliability, enjoyment, speed, adventure, and openness positively affect service quality. It was noted that ease of use, with a variance value of 2.451, and openness to experience, with a variance value of 2.437, show the importance of determining independent variables with service quality. The study findings reported that service quality is primarily influenced by ease of use, enjoyment, and openness to experience. It underlines that some retail customers will likely feel frustrated rather than enjoy the self-service technology, perceiving it as less reliable. The study suggests incorporating openness to experience and adventure shopping in retail outlets that can enhance consumer satisfaction and loyalty. Adopting an immersive and interactive shopping experience will ultimately improve the perception of service quality and customer happiness.
- Keywords
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JEL Classification (Paper profile tab)M31, L81, O33
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References45
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Tables8
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Figures2
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- Figure 1. Conceptual model
- Figure 2. Structural model Assessment-Output derived from Smart PLS-4 software
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- Table 1. Respondents’ profile
- Table 2. Assessment of measurement model
- Table 3. Heterotrait-Monotrait (HTMT)
- Table 4. Collinearity statistics
- Table 5. R2 and Q2 values
- Table 6. Model fit
- Table 7. Path analysis
- Table A1. Operationalization of the constructs
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