Journal of Retailing and Consumer Services最新文献

Unmanned stores are characterized by the addition of new technical attributes to existing retail services to replace human staff. This study examines consumer preferences for store- and technology-related attributes across both goods and services retail settings. Using a sample of 600 participants, we conducted a choice experiment and analyzed multinomial logit models to estimate consumer preferences for each attribute and the levels of preferences. The results indicate that not only technology-related attributes but also store-related attributes influence consumer preferences for unmanned stores. Furthermore, we found that consumer preference declines as stores adopt more advanced technologies. The effects of these attributes are largely consistent across both settings, except in the case of payment-related technologies. The findings provide practical insights on optimally managing store attributes and adopting retail technologies, taking into account consumer perceptions associated with technological integration in unmanned stores.

Despite the rise in luxury organizations’ efforts to embrace the circular economy and offer more sustainable options to consumers, research on luxury goods in the circular economy remains limited and primarily focused on “closing loop” strategies––mainly second-hand and rental business models that challenge the traditional concept of luxury. By contrast, there is a complete paucity of research on luxury product repairs that represent a “slowing loop” strategy that aims to prolong the lifecycle of the luxury good. To investigate this phenomenon, we employed a methodology involving 40 interviews with luxury consumers in the United Arab Emirates (UAE), a significant luxury goods market. The findings distinguish between different types of repairs (i.e., luxury brands, luxury department stores, or independent services) and reveal three key barriers to repairs (i.e., the time frame required for the repairs, perceived inconvenience, and lack of experience/awareness). The results further point to three rational (e.g., investment value) and three emotional (e.g., inheritance value) drivers of repairs, as well as positive (e.g., pleasure or pride) and negative (e.g., compromised brand relationship) outcomes. This study contributes to the emerging literature on luxury in a circular economy by specifically examining luxury product repair context and the outcomes of luxury repairs by demonstrating that these outcomes can be either positive or negative.

The proliferation of mobile stock trading has introduced various apps with distinct features, emphasizing the need to understand users' evaluations after adopting the service. This study explores the determinants of retail investors’ satisfaction with mobile stock trading services by employing an advanced textual analysis of customer reviews for four leading trading applications. We utilized Bidirectional Encoder Representations from Transformers (BERT) based Topic modeling (BERTopic modeling) to identify key topics within customer reviews and used the results as input for generative AI to discern the theme and sentiment of each topic. Based on Service Quality (SERVQUAL) theory, topics are categorized into key quality dimensions: functionality, usability, information quality, customer service, and system quality. Regression models were employed to assess the impact of the quality dimensions on investor satisfaction, revealing positive feedback on usability, information quality, and service quality as primary enhancers of satisfaction. In contrast, negative feedback on service quality, system quality, and functionality was identified as the primary inhibitor of satisfaction. This study explores how the influence of each quality dimension varies among different types of brokers (full-service vs. online-only brokerages). Finally, we propose a visualization tool called Topic Rating Impact and Frequency Analysis (TRIFA), which is designed to categorize topics based on their frequency of occurrence and impact on satisfaction. This tool aids in identifying the strengths and areas for improvement in services by effectively visualizing the results of text review analysis. This research not only deepens our understanding of the quality dimensions of mobile financial services but also offers valuable insights for service providers by suggesting predictive models that could help increase customer retention.

To address the challenges posed by the emergence of live streaming (LS) channels and mitigate competition among different distribution channels, manufacturers are increasingly adopting quality-differentiated distribution strategies. In this study, we develop a model involving a manufacturer and an influencer to investigate the following two potential strategies: the HL strategy (where high-quality products are marketed via the LS channel while low-quality products are marketed via the traditional online channel) and the LH strategy (where low-quality products are marketed via the LS channel while high-quality products are marketed via the traditional online channel). Moreover, we categorize products into cost-driven and demand-driven types to explore the factors that influence a manufacturer's strategic choice of different types of products. Our key insights are as follows. First, manufacturers should focus on the following two factors: product type and influencer ability. When products belong to cost-driven category and the influencer possesses high ability, implementing the LH strategy can lead to a mutually beneficial outcome for both parties. Furthermore, when the influencer's ability is low, achieving a win‒win situation for both parties becomes unattainable, regardless of the type of product. Second, the pricing of low-quality products may not necessarily be lower than that of high-quality products. When products belong to demand-driven category and the influencer's ability is high, manufacturers can set a higher price point for low-quality products when selling them through the traditional online channel. Third, the influencer exhibits varying preferences for product quality when selling different types of products. When products belong to demand-driven category, the influencer prefers selling high-quality products. Conversely, for cost-driven products, the influencer favors low-quality products. If there is a mismatch between the manufacturer's strategy and the influencer's preference, then the influencer tends to minimize its losses by reducing its LS effort. Fourth, from the perspective of consumer surplus and social welfare, our analysis reveals that the HL strategy yields a higher level of consumer surplus and contributes more to social welfare for demand-driven products; but for cost-driven products, the LH strategy emerges as the more favorable choice.

In the current landscape, smartwatches have gained popularity as wearable devices thanks to their fitness tracking and health monitoring capabilities. However, the abundance of features and options has made it challenging to select the right alternative. In this regard, we propose a text analytics-based product recommender system that leverages online reviews as peers' recommendations and creates a shortlist of available alternatives based on existing users’ perceptions. It uses a pre-trained transformer-based aspect-level sentiment analysis algorithm, InstructABSA, to quantify consumer sentiments expressed in textual reviews, which are analysed using the integrated House of Quality (HoQ) and Preference Ranking Organisation Method for Enrichment Evaluation-II (PROMETHEE-II) to construct a relative performance index for the selected manufacturers. The proposed framework may assist potential customers in making well-informed purchase decisions and help manufacturers understand their relative position in the market. It also helps customers compare the alternatives concerning selected features and associated consumer perceptions. In addition, manufacturers may use it to discover their perceived strengths and weaknesses. The proposed framework is tested on a review dataset pertaining to 12 smartwatch manufacturers, and their relative ranks are proposed.