Journal of Retailing最新文献

Researchers are increasingly able to observe consumers’ behavior prior to a purchase, such as their navigation through a store or website and the products they consider. Such pre-purchase (or search) data can be valuable to researchers in a variety of ways: as an additional source of information to estimate consumer preferences, to understand how firms can influence the search process through marketing mix variables, and to analyze how limited information about products affects equilibrium market outcomes. We provide an overview of these three research areas with a particular emphasis on online and offline retailing.

Household panel data track purchases of a panel of households over time, while retail panel data track ̶ for a panel of retail stores ̶ brand or SKU sales at the store or retail-chain level over time. While most quantitative retailing research in the 1980s and 1990s was based on (household or retail) panel data, other data sets have since emerged shedding light on important new retailing concepts such as online sales, digital customer journeys, mobile marketing, and electronic word-of-mouth. Although to some retailing researchers panel data may have become a rather antiquated type of data, this article argues there are several trends to expect a panel-data revival or renaissance in retailing research. These include (1) an increasingly complete coverage of digital channels, (2) a wider acceptance of Empirics-First (EF) research, (3) an increased focus on Better-Marketing-for-a- Better-World topics, (4) a growing interest in pan-international research, (5) an increased focus on causality, (6) a growing recognition of the advantages of data fusion, and (7) more pressure to obtain research grants. As panel data have unique strengths that make them suitable for addressing these trends, there is considerable scope for a continuing important role for panel data in modern retailing research.

Web data collected via web scraping and application programming interfaces (APIs) has opened many new avenues for retail innovations and research opportunities. Yet, despite the abundance of online data on retailers, brands, products, and consumers, its use in retailing research remains limited. To spur the increased use of web data, we aim to achieve three goals. First, we review existing retailing applications using web data. Second, we demystify the use of web data by discussing its value in the context of existing retail data sets and to-be-constructed primary web datasets. Third, we provide a hands-on guide to help retailing researchers incorporate web data collection into their research routines. Our paper is accompanied by a mock-up digital retail store (music-to-scrape.org) that researchers and students can use to learn to collect web data using web scraping and APIs.

Globally, consumption of Fruit and Vegetables (F&V) remains below nutritional guidelines. With retailers accounting for a large portion of F&V sales, marketing can be key to increase F&V consumption at household level. However, a key challenge is the design of strategies that benefit retailers, e.g., improving loyalty, whilst promoting societal goals. This study evaluates a points-plus-cash loyalty program where participants received points by purchasing selected F&V, redeemable against a reward (plush toys in the shape of F&V). We estimate the impact of the program by comparing expenditures in several categories before, during, and after the promotional period, across two different years, and comparing consumers who redeemed a reward and those who did not. We use loyalty card data from a Croatian retailer, containing food expenditure in five categories for 268,359 consumers, over 27 weeks for 2 years. We find that the loyalty program increased F&V expenditures at the focal retailer during the promotional period. However, the increase was only for reward-redeemers, for whom the program increased expenditures in F&V as well as in other food categories. This effect persisted – at a declining rate – after the program stopped. Exposure only had a limited effect during the campaign, leading to a reduction in expenditure after the promotional period. Results indicate that a loyalty program promoting sales of F&V can create win-win benefits to both society and the retailer: it increases expenditures on healthy foods (F&V), while improving overall loyalty (i.e., expenditures) to the retailer amongst motivated consumers.

Private labels (PL), also known as store brands or private brands, account for hundreds of billions of dollars in consumer packaged goods sales every year. PLs build store loyalty, improve margins and have been a key factor in changing the balance of power between retailers and national brand (NB) manufacturers. Thus, retailers around the world have a stake in pushing their own store brands. Yet, while PLs enjoy great success in Western, and increasingly Central, Europe, their performance is much more muted in the world's largest market, the United States, and in emerging markets. Why is that the case? This thought piece provides a number of ideas, summarized in propositions that can be tested in future research.

Consumers residing in different areas have varying levels of access to local retail stores. In contrast, with the prevalence of mobile internet, consumers have gained widespread access to mobile shopping. This research examines the impact of local offline retail density on consumers’ mobile shopping app usage and heterogeneity across consumer segments and different types of shopping apps. We use a unique dataset of individual-level mobile app usage with real-time location information across 1622 counties in China and employ the control function approach to address the endogenous retail density. Overall, the findings show that local offline retail density has a negative effect on mobile shopping app usage frequency and duration, indicating that consumers in areas with lower offline retail density engage more in mobile shopping app usage. This negative effect is weaker for consumers with higher mobility. Also, we find that this negative effect is weaker for shopping apps selling more technically complex products such as electronics than for those selling less technically complex products such as clothing and cosmetics. These results offer managerial implications for online retailers to launch targeting strategies to enhance consumer engagement with their shopping apps.

Dynamic pricing has evolved with technology from earlier price negotiations. To maximize revenue and provide specialized shopping experiences, businesses today use algorithms and data analysis to adapt prices. We define dynamic pricing as price changes that are prompted by changes or differences in four key underlying market demand drivers: (1) People (i.e., individual consumers or consumer segments), (2) Product configurations, (3) Periods (i.e., time), and (4) Places (i.e., locations). The transition from static pricing (uniform prices) to dynamic pricing (changing prices) is evident from different examples, such as online retailers personalizing offers based on customer behavior, and algorithms using facial recognition for personalized pricing in physical stores.

Fueled by AI and machine learning algorithms, dynamic pricing is transforming industries from transportation to e-commerce, optimizing revenue and enhancing customer experiences. While it offers benefits like personalization, challenges include ethical concerns, cost of implementation, and customer dissatisfaction. Effective data organization and AI expertise are crucial, but potential pitfalls and regulatory oversight must also be considered. This paper examines the multidimensional application of dynamic pricing, highlights the adaptability and efficiency of dynamic pricing in forming profitable pricing strategies and maximizing revenue, and calls for continued research on the topic to balance revenue, customer satisfaction, and ethics.

The evolving landscape of physical retailing necessitates a reevaluation of personalization approaches to meet existing market conditions and the demands of modern customers. In this context, the authors explore the interplay between human-enabled and technology-enabled personalization, offering an examination of their characteristics, strengths, and weaknesses. The authors deconstruct the personalization process into three distinct stages, namely identification, implementation, and interaction, and discuss potential contingency factors of personalization in the physical retailing context. More so, the authors discuss the future of personalization in physical retailing along the three steps of the defined personalization process. In doing so, the authors lay out a research agenda that can serve as a valuable guide for future studies in this field.

This research presents a novel, conceptual perspective on distinctive opportunities available to retailers with a physical location to create reasons for customers to visit the physical store. A multi-method approach is used, where we combine the study of more than 25,000 online announcements of new approaches implemented in physical stores with 12 interviews with retail experts from around the globe. The result is an integrative framework with five key benefits and 14 subdimensions which retailers can use to create reasons for customers to visit their physical store. The five benefits are discovery, convenience, customization, community, and shoppertainment. In discussing the framework, the authors provide rich examples which illustrate different ways retailers can establish these benefits. This research also details the value and challenges of collaborating with partners when implementing new approaches to leverage such benefits. The paper concludes with a discussion of external and internal contingency factors that can impact the decision (of when) to implement (which) new approaches and offers a list of questions for further research.