Journal of Retailing最新文献

Companies are increasingly expected to contribute, above and beyond a profit motive, to society. Leaders across industries have addressed the expectation by engaging in activism. While some upper echelons leaders initiate activism efforts in a way that strengthens their firms, others are struggling to do so without risking brand and business performance. This research addresses this important managerial challenge by conceptualizing activism through a risk paradigm. To do this, we first introduce and define a construct called Enterprise Activism. The construct comprises all forms of firm-initiated activism which can impact brand and business performance, such as brand activism, CEO activism, and corporate activism, regardless of who is making the decision (e.g., Chief Marketing Officer or store manager) or whether a portfolio brand or the corporate brand is involved (e.g., Harris Teeter or the corporate brand, Kroger). We then conceptualize the Enterprise Activism Risk Model, providing insight into how different types of actions can pose market share and positional advantage risks. Leveraging insight from C-level leaders, we identify ways that firm leaders can deploy the model to advance business practice. Finally, we provide a research agenda to develop new theory and evidence to help support and improve practice. The new insight provided herein provides a framework that can enable upper echelons leaders to evaluate, discuss, and navigate activism to minimize business risk.

This research examines online product reviewers and their influence on consumers who read and endorse those posted reviews. We examine three primary research questions. First, what kinds of products will reviewers review, and how do these choices influence consumers’ endorsements (i.e., user votes) of the reviews? Second, how does the volume of reviews that a reviewer writes affect consumers’ perceptions of how helpful the reviews are? Third, if reviewers are more positive (or critical), does that influence consumers’ endorsements? This research examines not only the static effects of these multifaceted relationships but also the dynamic changes in the effect magnitudes over time. Using the time-varying effect model (TVEM) with 11 years of Yelp review data, we find that a reviewer's influence on consumers tends to increase over time as the reviewer accumulates more experience in their review writing activities. However, we find two exceptions in the monotonically increasing pattern with Product Satisfaction and Reviewer Rating Positivity, demonstrating U-shaped and inverted ∩-shaped impacts over time, respectively. We explain such results with the “expertise trap” phenomenon. Thus, our research offers new insights to platform managers and product managers in governing influential reviewers.

Online retailing is still dominated by information asymmetries, as it often remains difficult for consumers to fully judge the quality of a product online. Reviews written by customers help to reduce this asymmetry. Helpful reviews have thus become an important tool to drive online sales. Beside textual information, reviews nowadays also often include images that can further help consumers to better judge products or services. While online retailers need to invest substantial resources in hosting and incentivizing review images, it remains unclear under which conditions review images drive (or reduce) review helpfulness and how review image content affects review helpfulness. We rely on a set of more than 97,000 reviews from Amazon to investigate the contingencies under which review images increase review helpfulness. Furthermore, we rely on more than 6,000 images in our data set to explore how review image content (i.e., image focus and context fit) drives review helpfulness. Our results show that online retailers should especially motivate consumers to include images in a review when the overall rating is extremely positive, when the reviewer has a high reputation, and when the review addresses a hedonic or experience product. Our image content analysis further shows that images help to increase helpfulness when they show the product in application. This effect is especially strong in the case of longer reviews.

Improving the healthiness of diets can be realized by replacing unhealthy with healthier product alternatives when shopping for groceries. For this strategy to be effective, shoppers need to consistently make healthier choices. However, shoppers may end up balancing the healthiness of their choices throughout the shopping trip, (partly) offsetting the benefits of a healthy product choice (e.g., low-fat milk) by an unhealthy subsequent choice (e.g., sugary cornflakes). Across two studies, one study with purchase data from a brick-and-mortar supermarket and one online experimental study, we empirically demonstrate that the relative healthiness of an initial product choice is indeed inversely related to the relative healthiness of the subsequent choice, regardless of the category of both products. That means: a relatively healthy choice is followed by a relatively unhealthier choice, and vice versa. Furthermore, the strength of this balancing effect differs depending on the nature of the product category; the dynamic effect is less pronounced when subsequently choosing within a vice (vs. virtue) product category. In the brick-and-mortar supermarket, the dynamics also become less pronounced as the shopping trip progresses. These findings contribute to literature on in-store decision-making and within-trip dynamics, and underscore the need for retailers to have a thorough understanding of these healthy shopping dynamics in order to effectively promote healthier baskets in support of the growing demand for healthy diets.

Eye-tracking offers many insights into shopping cognition by capturing proxies to attention patterns, visual processing, and decision sequences. While lab experiments using simulated shopping tasks are becoming more common in retailing research, field research using eye-tracking technology remains scarce despite higher ecological validity. This paper advocates expanding eye-tracking adoption into brick-and-mortar contexts and provides guidelines for researchers interested in conducting such retailing research. The authors discuss different data types that can be used to understand in-store shopping behaviours and argue why eye-tracking offers a compelling opportunity to decode consumer decisions in real-world complexity. Next, they review current literature, highlighting good examples of existing field studies and areas of opportunities for new research. Finally, the authors provide stepwise best-practice guidelines for executing impactful, triangulated field research across the retail landscape using eye-tracking. The paper presents a call to action for expanding eye-tracking research beyond the lab and into real-world retail aisles.

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.