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Omni-channel retailing is a novel form that combines online, offline, and mobile channels to provide consumers with a seamless shopping experience. Nevertheless, the implementation of omni-channel retailing necessitates effective logistics support. Hence, the quality of logistics services is critical for omni-channel retailing services. This research aims to investigate logistics service quality (LSQ) and its impact on consumer satisfaction by combining the decomposed SERVQUAL framework, Kano model and hierarchical regression analysis. A total of 460 valid responses were obtained. Building upon the SERVQUAL framework, this study presents a comprehensive framework for evaluating omni-channel retail logistics service quality. Using the Kano model, 11 logistics service quality attributes were categorised into three categories: must-be, one-dimensional, and attractive, based on their respective impact on satisfaction. The results of the hierarchical regression analysis confirm that the attributes belonging to the must-be category exert the most significant influence on satisfaction. The findings add to theoretical studies of omni-channel retailing LSQ and provide insights for omni-channel retailers and logistics service providers.

Upon lifting lockdown measures, pent-up consumer demand resulted in a phenomenon known as “revenge-buying” that was influenced by cognitive and habitual factors. This study collected 629 samples from Beijing consumers using straightforward online random sampling methods, ensuring the sample’s representativeness. Structural equation modeling was employed to investigate the impact of cognitive factors on revenge-buying after lockdowns and explain this phenomenon from the consumers’ habitual behavior perspective. Specifically, we developed a novel model that incorporates insights from the concept of attitudes and habits literature within the framework of the protection motivation theory to address this research question. The results reveal that habit significantly affects all cognitive factors, except response cost. Cognitive factors such as perceived severity, perceived vulnerability, response efficacy, self-efficacy, and response cost all play a role in shaping consumers’ attitudes and driving revenge-buying behavior. Furthermore, consumers’ habits contribute significantly to their likelihood of revenge-buying. This study provides empirical evidence for revenge-buying, and the proposed theoretical model offers a more complete understanding of the cognitive factors and habits that drive this behavior. These findings can help businesses to attract consumers, improve satisfaction, and better compete in the context of revenge-buying, ultimately benefiting both consumers and businesses involved in this behavior.

The rapid development of modern logistics and e-commerce highlights the importance of exploring various modes of transportation in the last-mile delivery (LMD) process. However, no comprehensive studies exist in the literature exploring all modes of LMD transportation, the changes in these transportation modes, and the commonalities between them. In this study, we address this gap by conducting a systematic review of 150 academic journal articles utilizing a combination of the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) content analysis and text mining analysis. Nine primary transportation methods (parcel lockers, autonomous drones, trucks, bicycles, crowd logistics, electric vehicles, tricycles, autonomous robots, and autonomous vehicles) are identified in this research. Additionally, we provide an analysis of the historical changes in these transportation modes in LMD. Using a bottom-up induction method, we identify the three major clusters of scholarly focus in the LMD literature: emphasis on value co-creation between consumers and logistics providers, practical delivery performance (path optimization or algorithms), and environmental friendliness. Further, we analyze the main themes under each cluster, leading to the identification of opportunities, challenges, and future research agendas. Our findings have implications for scholars, policymakers, and other stakeholders involved in LMD transportation modes.