Offline retailers increasingly use location-based coupons to target consumers in their vicinity in real-time. The rationale for the use of location-based coupons is that geographic proximity increases the relevance for consumers and, thus, the effectiveness of these campaigns. Two key interface-specific aspects of location-based coupon applications that influence their effectiveness are 1) the provision of distance information and 2) the distance-based ranking of coupons. The aim of this paper is to study and quantify the impact of these two key aspects of interface design on the effectiveness of location-based coupons. We conduct a randomized field experiment with 399,913 observations, including 3,499 different coupon promotions offered by 3,930 different stores located in 2,392 ZIP code areas in a large Western European country. Our results show that the most effective interface design for location-based coupons is based on a distance-based ranking. Furthermore, we find significant differences in the impact of distance and display rank based on the interface design and the actual geographic location of users. Our results thus contribute to the understanding of consumers’ behavioral responses to location-based advertising and provide important implications for the interface-design of location-based advertising applications.

While data breaches are a frequent and universal phenomenon, the characteristics and dimensions of data breaches are unexplored. In this novel exploratory research, we apply machine learning (ML) and text analytics to a comprehensive collection of data breach litigation cases to extract insights from the narratives contained within these cases. Our analysis shows stakeholders (e.g., litigants) are concerned about major topics related to identity theft, hacker, negligence, FCRA (Fair Credit Reporting Act), cybersecurity, insurance, phone device, TCPA (Telephone Consumer Protection Act), credit card, merchant, privacy, and others. The topics fall into four major clusters: “phone scams”, “cybersecurity”, “identity theft”, and “business data breach”. By utilizing ML, text analytics, and descriptive data visualizations, our study serves as a foundational piece for comprehensively analyzing large textual datasets. The findings hold significant implications for both researchers and practitioners in cybersecurity, especially those grappling with the challenges of data breaches.

Firms have two distinct options when delivering content to consumers’ mobile devices: mobile push and mobile pull. Mobile push delivers firm-initiated (ad) content directly to consumers, while mobile pull requires consumers to initiate requests for (ad) content. This study tests the impact of mobile push and mobile pull on consumers’ coupon redemption behavior in a large-scale randomized field experiment in a geo-conquesting setting, targeting customers located around competitor retail stores with mobile coupons to drive them to stores of the focal retailer. The results show that mobile push increases coupon redemption rates by 6.0%, with substantial heterogeneity based on app-specific use experience and store density: App-specific use experience negatively moderates the effect of mobile push delivery on redemptions, likely because both usage experience and push notifications reduce app-specific search costs, thereby acting as substitutes for one another. In areas with higher store density, the positive effect of mobile push delivery on the redemption likelihood is greater, suggesting that push notifications can highlight the focal coupon among alternative store choices, thereby reducing consumer switching costs. These findings have important implications for retailers and brands in creating competitive mobile targeting campaigns that effectively leverage both mobile push and pull delivery mechanisms.

As climate change, environmental, social, and governance (ESG), along with sustainability, become increasingly crucial for businesses and society, there is a noticeable scarcity of information and transparency regarding corporate practices. Often, government agency enforcement actions lead to litigation and are ultimately resolved by court decisions. Moreover, in instances when there is perceived inadequacy in government enforcement, citizens frequently turn to the courts for preventive judgments against businesses or agencies. In an effort to shed light on the multifaceted aspects of climate change, we adopted a novel, exploratory approach to analyze climate change-related litigation cases. Utilizing a blend of machine learning-based text analytics, we have extracted key insights from individual case narratives. Our analysis encompassed over four hundred cases from the Westlaw database through various keyword searches. The emergent topics from our case dataset revolved around four critical environmental themes: forest, land, water, and air emissions. Our findings provide insight into the nature and dimensions of climate change and also carry significant policy implications, laying the groundwork for future research in this domain.

A natural experiment reveals that an ad ban reduces sales of a grocery retailer by 6% but does not affect overall market size.

This exploratory applied study examines the nature and dimensions of cryptocurrency, namely bitcoin, a peer-to-peer network for facilitating digital barter. As the most widely used cryptocurrency, bitcoin has carved itself a niche market while also promoting the use of other cryptocurrencies. Through descriptive analysis and a visual analytic approach, the study highlights key characteristics and dimensions of bitcoin. The study helps understand the nature and extent of bitcoin use, assisting policymakers to shape and regulate the cryptocurrency marketplace in this contemporary volatile environment.