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Underlying Factors of Switching Intention to Unmanned Stores: A PPM Framework

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This study empirically analyzes the major factors influencing consumers’ switching intention toward unmanned stores, based on the Push-Pull-Mooring (PPM) framework. The findings reveal that the perceived task complexity of self-checkout systems negatively affects the intention to switch, whereas the perceived efficiency and perceived privacy offered by unmanned stores have significant positive effects. Moreover, switching intention was found to be a strong predictor of actual usage behavior. These results highlight that consumers’ perception of efficiency and psychological comfort are critical values in the unmanned retail environment. Accordingly, securing user-friendly interfaces and robust privacy protection measures can serve as key competitive advantages in the design and operation of unmanned stores. By applying the PPM model to this context, the study makes both theoretical and practical contributions. However, limitations exist in terms of sample generalizability, the range of variables included, and the inability to assess automated checkout systems such as Amazon Go. Future research is recommended to explore diverse consumer groups and retail environments, and to examine additional factors such as switching costs or user habits to provide a more comprehensive understanding of consumer behavior in unmanned retail settings.

Abstract

This study empirically analyzes the major factors influencing consumers’ switching intention toward unmanned stores, based on the Push-Pull-Mooring (PPM) framework. The findings reveal that the perceived task complexity of self-checkout systems negatively affects the intention to switch, whereas the perceived efficiency and perceived privacy offered by unmanned stores have significant positive effects. Moreover, switching intention was found to be a strong predictor of actual usage behavior. These results highlight that consumers’ perception of efficiency and psychological comfort are critical values in the unmanned retail environment. Accordingly, securing user-friendly interfaces and robust privacy protection measures can serve as key competitive advantages in the design and operation of unmanned stores. By applying the PPM model to this context, the study makes both theoretical and practical contributions. However, limitations exist in terms of sample generalizability, the range of variables included, and the inability to assess automated checkout systems such as Amazon Go. Future research is recommended to explore diverse consumer groups and retail environments, and to examine additional factors such as switching costs or user habits to provide a more comprehensive understanding of consumer behavior in unmanned retail settings.

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