Journal of Retailing and Consumer Services最新文献

This study presents an in-depth examination of customer satisfaction dynamics within the rapidly growing O2O food delivery (OFD) sector. Recognizing the exponential market growth and the gradual rise in consumer complaints, we seek to address the gaps in current academic research that tends to overlook the distinction between online and offline service experiences, as well as the psychological foundations of technology adoption. We propose a series of hypotheses grounded in a comprehensive theoretical framework that distinguishes between online and offline dimensions of consumer satisfaction and explores their interrelation by incorporating the technology acceptance model (TAM). We posit that, regarding online satisfaction, the quality of food information, the quality delivery tracking service, and user privacy protection functions from OFD applications hold positive significance for online customer satisfaction. Additionally, considering offline satisfaction, we assert that delivery quality and environment of the delivery service (i.e., delivery scape) contribute positively to offline customer satisfaction. We adopt trust transfer theory to hypothesize a positive relationship between online and offline satisfaction to comprehend a continuous flow in how consumer satisfaction develops. The paper also examines the pathway by which online satisfaction influences consumer loyalty through TAM. By investigating the dynamics regarding these relationships, this study contributes to offering valuable insights that could enhance OFD users’ satisfaction and their loyalty.

Consumer acceptance of social robots in retail and service settings is an emerging field in marketing research. Social Home Robots (e.g., Jibo, Cozmo, and Kuri) serving various groups in domestic settings show significant retail and consumer service potential. However, most current research focuses on social robots that serve older or disabled individuals in domestic settings, leaving a gap in understanding consumer acceptance of Social Home Robots, particularly across different cultures. From the Human-Robot Interaction perspective, this article examines consumers' intentions to use Social Home Robots through a sequential mixed-method approach. Our findings indicate that consumers' intentions to use these robots are primarily influenced by key factors in Human-Robot Interaction: belief in social robot benefits, concern about privacy risks, trust, the robots' overall anthropomorphic design, and consumers' self-construal tendency. Furthermore, trust mediates the relationships between belief in social support, privacy concerns, independent self-construal, and usage intention. This study explores consumer acceptance of social robots serving diverse groups rather than special groups in domestic settings, contributing to the literature on social/service robots. It also offers business guidelines for designing and marketing Social Home Robots in retail.

Despite the potential of near-expired food commercialization (NFC) to recover value from unsold food, skepticism persists due to concern about its impact on fresh food margins. This study introduces a game-theoretic model to explore the optimal strategy and price-setting timing for the fresh-food retailer considering NFC. We find that the optimal strategy transitions from no NFC to partial and then to full NFC as the order quantity increases. Specifically, under full NFC, the fresh-food retailer with strong bargaining power and high perceived quality benefits from adopting clearance-priority pricing, which prioritizes the pricing of the near-expired food. Conversely, the retailer with limited power and lower perceived quality prefers fresh-priority pricing, which sets the retail price of fresh food first. Under partial NFC, while clearance-priority pricing generally yields profits, the high disposal cost may compel even powerful fresh-food retailers to switch to fresh-priority pricing to mitigate the risks associated with negotiation failures. We further analyze extended models to confirm the robustness of our findings. Our research contributes to the literature and guides practice by addressing common misconceptions about NFC.

Consumer segmentation is vital for companies to customize their offerings effectively. Our study explores the application of Large Language Models (LLMs) in marketing research for consumer segmentation. We developed a workflow leveraging LLMs to perform clustering analysis based on consumer survey data, with a focus on text-based multiple-choice and open-ended questions. Firstly, we employed a LLMs model to embed text for clustering, demonstrating that LLMs enhance clustering accuracy over traditional models. Secondly, we created persona chatbots using LLMs, which achieved over 89% accuracy in simulating consumer preferences. Our findings underscore the potential of our LLMs framework in marketing research.

Supermarketization is transforming global food retailing, but research gaps around this transformation include the role of consumers and of crises in informing the supermarketization process, with implications for the resilience of retail structures. This study aims to apply a cultural-resource based theory of the customer and show that retailers need to think more broadly about the value they create for consumers and the meaning they engage in their interactions with consumers. We performed in-depth interviews with Lebanese consumers to understand whether and to what degree COVID-19, economic, and political crises have altered their food purchasing habits and perceptions, and the implications for the developing country context of supermarketization and retail modernization. Findings reveal that multiple factors influence the choice of food shopping destination ranging from those identified in mainstream retail theories (price, product assortment) to the individual-level activation of meaning and identity creation consistent with the cultural resource-based theory of the customer. Recent shifts in retail patronage patterns are linked to specific crisis impacts but do not uniformly favor modern or traditional retailers, suggesting ambiguous impacts on retailers’ resilience and the future trajectory of supermarketization in Lebanon.

Prior research suggests that marketing activities should follow consumers' shopping time habits, as it has been proven that consumers obtain higher satisfaction and exhibit greater revisit behavior when the timing of shopping follows their habits. This article calls into question the applicability of this strategy to the recommendation of new products. Through a randomized field experiment conducted in collaboration with a renowned fast-moving consumer goods company in China, we find that consumers targeted with recommendations following their shopping time habits exhibited heightened passive resistance, which is more pronounced among those with greater experience purchasing similar products. This study ventures further into the exploration of how the mechanisms of information acquisition and processing, inherent in shopping habits, exert influence on consumer passive resistance to innovation. The findings offer valuable insights for marketers leveraging shopping time habits in their activities or seeking to improve the success of a new product.

Artificial intelligence (AI) is increasingly applied to customer services, but failures inevitably occur from time to time. Empathy-based response through AI is a service recovery approach, but its effectiveness and boundary conditions remain unclear. In this study, we draw on the theory of system usage to investigate the conditions in which AI empathic responses can be an effective remedy, and explore the differences in the effectiveness of empathic responses from AI and human agents in service failures. The results of three scenario-based experiments reveal that the recovery of AI services through the use of high-empathy responses can increase users’ intentions to continue using the service. Our findings also suggest that the need for human interaction increases the impact of empathic responses on continued usage intention, and that this effect is reduced if the task is deemed urgent. We also find that the recovery effect from the empathic responses provided by both AI and human agents is similar. This article focuses on the issue of continued use when AI services fail, and we demonstrate the effectiveness of AI empathic responses in service recovery and reveal their boundary conditions. This provides insights into how to effectively use AI empathy in service recovery and provides a theoretical framework that companies can draw on when addressing AI service failures.

This study investigates the intricate relationship between consumer review quantity and conversion rates on online service-selling platforms. Leveraging data from a large platform, the research uncovers an inverted U-shaped relationship, where conversion rates initially improve with increasing review quantity but decline once a threshold is exceeded. The study also explores how store longevity and transaction volume moderates this relationship, revealing that established stores and those with higher transaction volumes can mitigate the negative effects of excessive reviews. These findings offer actionable insights for platforms and merchants on balancing review quantity to optimize conversion rates. Moreover, the conduction of this study also underscores the importance of reassessing established theories in the evolving landscape of e-commerce.

This study provides a comprehensive and theory-based framework of motives for choosing online or offline channels and analyzes the importance of such motives in different contexts. Based on in-depth interviews conducted in Germany and Poland, we identify 23 motives for channel use. A subsequent survey study with 4000 respondents from both countries reveals that most important motives relate to the search outcome, purchase outcome and utilitarian aspects of the shopping process, while hedonic motives, habits and ethical issues come second. Results further show that Polish respondents are more demanding and rate most motives as more important than German customers, except motives related to maintaining habits and supporting physical stores. We explain these results by different economic conditions and retail structures in Western and Eastern Europe. The results contribute towards understanding consumers’ retailer and channel choice and help retailers to focus on particular motives depending on market and product context.

Recent years have seen increased interest in applying robots to the retail sector. The present research aims to learn the criteria that make people more or less likely to use retail services assisted by robots. The study builds on the Technology Adoption Model (TAM) as an information system theory and presents a research model that considers how people perceive robots in their decisions. The model also investigates the impact of customers' social and cultural norms (SCN) and level of education towards customer adoption of robots (CAR). A survey from Saudi Arabian customers was used to compile the data. The PLS-SEM approach was applied in assessing customer acceptance of Robot-assisted retail technologies model and research proposed constructs. All indicators significantly impacted customer adoption, with perceived usefulness (PU) mediating between the two. Furthermore, education and SCN moderate the relationship between customer attitudes toward robots (ATR) and CAR. Retailers and governments can learn much from the study's findings about what motivates customers to use store robots. The findings strictly contribute to extending the current literature on merging TAM and consumer acceptance theories, practices, practitioners, and business decision-makers. In addition, to enhance the useful insights of synergizing customer acceptance of Robot-assisted retail technologies; we added theoretical, managerial, and social implications.