International Journal of Consumer Studies最新文献

Research has shown mismatches between the gifts that givers give and the gifts that recipients want. However, these mismatches are mostly observed in decisions regarding new products. With the rapid growth of the resale market, an important yet overlooked question is whether givers send used items as often as receivers expect. This study investigates how givers and receivers choose between used gifts that match receivers' tastes and new gifts that are less taste matching. We demonstrate a giver–receiver mismatch: benchmarked against receiver willingness, givers over-avoid sending used gifts. This occurs because givers are more sensitive to the brand-newness norm and less sensitive to taste match than receivers are. This mismatch is mitigated when gifts are exchanged on casual occasions, between close friends, when used gifts are of poor quality, or when the purpose of gifting is to encourage resource utilization. Our study contributes to the gift-giving literature by demonstrating an understudied giver–receiver mismatch and a novel gift-giving norm.

After integrating into urban life, do rural migrants experience social exclusion and alter their consumption habits because of how they live now? The authors applied a grounded theory approach with semi-structured in-depth interviews and field notes from direct interaction to answer this question. The study explores how rural-to-urban migrants use consumerism to cope with their feelings of exclusion. We interacted with migrants who are first-generation cohorts moving from rural to urban areas in search of exposure and opportunity. The study identified two consumer orientations. Sensitive orientation: anchored in performative adaptation through social legibility, community membership, and social camouflage. It reflects emotional responses to social pressure. In contrast, receptive orientation, characterized by reflective adaptation through self-plasticity and intentional resistance, captures openness to adopt or resist new behaviors to fit into the new place. According to the findings, rural migrants face challenges in assimilating through consumption, as these activities shape their behaviors in symbolic or purposeful ways, depending on how they expect to adapt within the dominant culture. These insights could enable practitioners and academic scholars to understand better consumer realities and the shades of consumption under exclusionary pressure.

The present study offers insights into the evolution of the Experience Economy (EE) theory over the past 25 years (1998–2023), identifying the conceptual and intellectual structure of academic outputs, as well as emerging and potential themes. Using a sample of articles from Scopus and Web of Science (WoS), 548 records were analyzed through a co-word analysis with SciMAT, establishing four periods: “1998–2008,” “2009–2013,” “2014–2018,” and “2019–2023.” Focusing on the most recent period (n = 246), a bibliographic coupling analysis was performed using Bibliometrix. A content analysis was then conducted to gain deeper insights into scenarios where EE is currently being applied. The findings reveal that the original model has expanded by exploring new contexts, consumption modes, and incorporating diverse theoretical frameworks, while also underscoring how the original components of the EE model remain a robust foundation for analyzing emerging phenomena, particularly in the context of digital transformation and personalized customer experiences. This research provides insights into the future trajectory of the EE, emphasizing transformative and life-changing experiences as key drivers of business innovation.

Consumer algorithmic trust represents a research topic of both theoretical and practical significance. Existing research has identified two key phenomena within the domain of consumer algorithmic trust: algorithm aversion and algorithm appreciation. However, the majority of prior studies have primarily focused on one of these two phenomena. The present research demonstrates that consumers exhibit both algorithm aversion and algorithm appreciation across various unstructured tasks. Utilizing a text analysis from Douyin (the Chinese TikTok) and three experimental studies, it is demonstrated that consumers have higher trust in algorithm agents compared with human agents in lower-degree unstructured tasks, with perceived accuracy mediating this effect. Conversely, higher trust in human agents compared with algorithm agents is observed in higher-degree unstructured tasks, with perceived flexibility mediating this effect. This study contributes to the extant literature on consumer algorithmic trust and offers actionable insights for the development of artificial intelligence systems.

Robo-advisors are algorithm-driven platforms that automate financial planning and investment management, offering cost-effective and accessible services with minimal human interaction. Despite their rapid adoption, particularly among retail investors, scholarly research remains fragmented. This study conducts a systematic review of 71 peer-reviewed articles using the SPAR-4-SLR protocol and organises the analysis through the theories, contexts, characteristics, methodologies (TCCM) framework. Findings demonstrate that prominent contributions are published in finance and marketing journals, with the United States and United Kingdom leading global research productivity. Robo-advisor adoption is primarily driven by attitude and social influence, while behavioural intention, adoption, and continued use are mediated by financial literacy, user perception, emotion, and moderated by trust, demographic characteristics, and income-to-investment ratios. This research identifies underexplored theoretical perspectives, including commitment-trust theory, critical theory of technology and the affect infusion model, particularly from psychological and marketing domains. It further calls for methodological diversification, advocating for quantitative, qualitative and mixed-method approaches. Emerging research opportunities include novel outcome variables such as consumer vulnerability, resilience and adaptability, especially in response to technological disruption and data breaches. This review advances theoretical development and offers practical insights to support sustained engagement with robo-advisory services in the post-adoption phase.

Over the years, it has been observed that discussions on research methods in academic writing often oscillate between two extremes: either abstract philosophical debates or purely technical instructions. Many early career researchers—and sometimes even experienced scholars—struggle with the simple question: Which method should be used? The answer, however, is rarely straightforward. It depends not only on the convenience or the disciplinary habit of a researcher but also on the nature of the research question, the timing of the inquiry, the context in which it is situated, the units of analysis involved, and the resources available for its execution. Textbooks have contributed richly to our understanding, yet they frequently overwhelm rather than provide clarity. In this context, we provide a comparative overview of important research methods using the 5WH framework (What, Why, When, Where, Who, and How), based on years of experience as an Editor. We provide a synthesis of regression-based methods, experimental methods, multivariate methods, qualitative methods, mixed methods, configurational and comparative methods (QCA family), big data and computational methods, AI-driven methods and techniques, and finally, literature review methods.

The integration of smart technologies into everyday life has raised significant questions about their role in fostering sustainability and shaping consumer behavior. This study presents a comprehensive systematic literature review to explore the intersection of smart technologies, sustainability, and consumerism, motivated by increasing academic fragmentation across these domains. We employ a systematic review of 192 articles that combines the Theory–Context–Characteristics–Methodology (TCCM) framework with Probabilistic Topic Modeling (PTM). This dual method enables a synthesis of theoretical, contextual, and empirical developments in the field. The findings reveal dominant themes, evolving research trends, and underexplored areas, particularly in consumer-centric adoption and sustainability-centric outcomes. This contributes to the literature by offering two conceptual frameworks that integrate insights across sectors and highlight behavioral and sectoral dynamics in technology adoption. The study identifies theoretical blind spots, practical implications across industries, and proposes an agenda for future research in the era of digital and sustainable transformation.

Firms increasingly use digital content as a valuable tool to persuade consumers. Consequently, research on digital content has begun to explore which content elements help firms achieve positive outcomes. As the study of digital content continues to evolve, this systematic literature review aims to identify research gaps, thoroughly examine the state of the art, and provide researchers and managers with a comprehensive future agenda. To achieve this, we use the theory–characteristics–context–methods protocol, incorporating an analysis of 85 research articles. The findings classify digital content elements employed by firms into six broad categories—visual, auditory, linguistic, symbolic, social, and message—and identify their influence on various outcomes, including engagement, purchase intention and sales, brand-related outcomes, and consumers' content perceptions and attitudes. Additionally, the findings identify significant research gaps and propose several areas for further investigation. The article serves as a comprehensive review of digital content, addressing the fragmented nature of the field and proposing a comprehensive conceptual framework.