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The prevalence of mobile usage data has provided unprecedented insights into customer hyper-context information and brings ample opportunities for practitioners to design more pertinent marketing strategies and timely targeted campaigns. Granular unstructured mobile data also stimulate new research frontiers. This paper integrates the traditional marketing mix model to develop a framework of personalized mobile marketing strategies. The framework incorporates personalization into the center of mobile product, mobile place, mobile price, mobile promotion, and mobile prediction. Extant studies in mobile marketing are reviewed under the proposed framework, and promising topics about personalized mobile marketing are discussed for future research.

Firms are exploiting artificial intelligence (AI) coaches to provide training to sales agents and improve their job skills. The authors present several caveats associated with such practices based on a series of randomized field experiments. Experiment 1 shows that the incremental benefit of the AI coach over human managers is heterogeneous across agents in an inverted-U shape: whereas middle-ranked agents improve their performance by the largest amount, both bottom- and top-ranked agents show limited incremental gains. This pattern is driven by a learning-based mechanism in which bottom-ranked agents encounter the most severe information overload problem with the AI versus human coach, while top-ranked agents hold the strongest aversion to the AI relative to a human coach. To alleviate the challenge faced by bottom-ranked agents, Experiment 2 redesigns the AI coach by restricting the training feedback level and shows a significant improvement in agent performance. Experiment 3 reveals that the AI–human coach assemblage outperforms either the AI or human coach alone. This assemblage can harness the hard data skills of the AI coach and soft interpersonal skills of human managers, solving both problems faced by bottom- and top-ranked agents. These findings offer novel insights into AI coaches for researchers and managers alike.

Consumers often abandon e-commerce carts, so companies are shifting their online advertising budgets to immediate e-commerce cart retargeting (ECR). They presume that early reminder ads, relative to late ones, generate more click-throughs and web revisits. The authors develop a conceptual framework of the double-edged effects of ECR ads and empirically support it with a multistudy, multisetting design. Study 1 involves two field experiments on over 40,500 customers who are randomized to either receive an ECR ad via email and app channels (treatment) or not receive it (control) across different hourly blocks after cart abandonment. The authors find that customers who received an early ECR ad within 30 minutes to one hour after cart abandonment are less likely to make a purchase compared with the control. These findings reveal a causal negative incremental impact of immediate retargeting. In other words, delivering ECR ads too early can engender worse purchase rates than without delivering them, thus wasting online advertising budgets. By contrast, a late ECR ad received one to three days after cart abandonment has a positive incremental impact on customer purchases. In Study 2, another field experiment on 23,900 customers not only replicates the double-edged impact of ECR ads delivered by mobile short message service but also explores cart characteristics that amplify both the negative impact of early ECR ads and positive impact of late ECR ads. These findings offer novel insights into customer responses to online retargeted ads for researchers and managers alike.

The sharing economy has radically reshaped marketing thought and practice, and research has yet to examine whether and how platform-level buyer protection insurance (PPI) affects buyers and sellers in this economy. The authors exploit a natural experiment involving an unexpected system glitch during a PPI launch and estimate difference-in-differences models using over 5.4 million data points from a food sharing platform. Results suggest that PPI significantly increases buyer spending and seller revenue, affirming the benefits of this platform-level insurance in the sharing economy. The authors also uncover multifaceted buyer-side and seller-side responses that enable such benefits. PPI increases buyer spending by boosting product orders and variety-seeking behavior. Furthermore, it enhances seller revenue by increasing customer retention and acquisition. This work contributes to the literature by (1) putting a spotlight on the topic of PPI, a platform governance policy that reduces consumer risks and improves the efficacy of sharing platforms; (2) accounting for how PPI alters buyer and seller behaviors on a platform; (3) addressing what types of buyers and sellers benefit more or less from PPI; and (4) offering guidance for managers to improve platform reputation, marketplace efficiency, and consumer welfare in the context of the sharing economy.

As consumers spend more time on their mobile devices, a focal retailer's natural approach is to target potential customers in close proximity to its own location. Yet focal (own) location targeting may cannibalize profits on inframarginal sales. This study demonstrates the effectiveness of competitive locational targeting, the practice of promoting to consumers near a competitor's location. The analysis is based on a randomized field experiment in which mobile promotions were sent to customers at three similar shopping areas (competitive, focal, and benchmark locations). The results show that competitive locational targeting can take advantage of heightened demand that a focal retailer would not otherwise capture. Competitive locational targeting produced increasing returns to promotional discount depth, whereas targeting the focal location produced decreasing returns to deep discounts, indicating saturation effects and profit cannibalization. These findings are important for marketers, who can use competitive locational targeting to generate incremental sales without cannibalizing profits. Although the experiment focuses on the effects of unilateral promotions, it represents an initial step in understanding the competitive implications of mobile marketing technologies.

This study posits that security analysts heed corporate social performance information and factor it into their recommendations to general investors. In particular, as corporate social performance is often uncertain and ambiguous to general investors, analysts may serve as the informational pathway connecting corporate social performance to firm stock returns. Thus, we argue that analyst recommendations mediate the relationship between corporate social performance and firm stock returns. On the basis of not only a qualitative study with literature searches and interviews of stock analysts but also a quantitative study with two longitudinal samples of large firms, we find support for these arguments. Our findings uncover an informationbased underlying mechanism for the link between corporate social performance and financial performance.

Consumer buzz in the form of user-generated reviews, recommendations, and blogs signals that consumer attitude and advocacy can influence firm value. Web traffic also affects brand awareness and customer acquisition, and is a predictor of the performance of a firm's stock in the market. The information systems and accounting literature have treated buzz and traffic separately in studying their relationships with firm performance. We consider the interactions between buzz and traffic as well as competitive effects that have been overlooked heretofore. To study the relationship between user-initiated Web activities and firm performance, we collected a unique data set with metrics for consumer buzz, Web traffic, and firm value. We employed a vector autoregression with exogenous variables model that captures the evolution and interdependence between the time series of dependent variables. This model enables us to examine a series of questions that have been raised but not fully explored to date, such as dynamic effects, interaction effects, and market competition effects. Our results support the dynamic relationships of buzz and traffic with firm value as well as the related mediation effects of buzz and traffic. They also reveal significant market competition effects, including effects of both a firm's own and its rivals' buzz and traffic. The findings also provide insights for e-commerce managers regarding Web site design, customer relation management, and how to best respond to competitors' strategic moves.

As the online channel is crucially important, traditional offline retail stores seek to induce their existing consumers to buy online with incentives (i.e., offline-to-online targeting). However, it is debatable whether such targeting is truly effective. While advocates argue that online shopping should complement a firm’s store channel, critics counter that doing so may result in cannibalization. Drawing on the channel interplay literature and considering customers’ travel costs, we examine whether and how inducing online shopping complements or cannibalizes a firm’s offline sales. Using a randomized field experiment on over 11,200 customers of a large department store, we provide causal evidence for both the complementarity and cannibalization effects of online and offline channels. Offline-to-online targeting engenders higher online purchases (as intended) than no targeting. The local average treatment effects models suggest that once induced to buy online, consumers who live near the retailer’s physical store tend to increase their offline spending and total sales by 47% (i.e., complementarity effects for nearby consumers). However, for consumers who live far away from the brick-and-mortar store, inducing them to buy online can backfire by reducing offline and total sales by approximately 5.7% for each additional kilometer of distance (i.e., cannibalization effects for distant consumers). Explorations of these mechanisms suggest that distant consumers who are induced to buy online may fail to return to shop in the offline store and purchase less experiential category products with a smaller basket size than other customers, thus leading to a negative net impact on the total sales. These findings alert managers to the dangers of improper targeting and investment in information technology and the importance of consumer heterogeneity for omnichannel commerce across online and offline channels.

Revealing new lncRNA-disease associations (LDAs) is necessary to decipher pathological mechanisms and find new clues of diagnosis and therapy for complex diseases. However, experimental methods for LDA identification need a significant amount of time and cost. Here, we introduce a novel deep learning-based method, LDA-CAMF, to infer LDA candidates. LDA-CAMF first designs a cross-attention mechanism to dynamically decode high-order interdependencies between lncRNAs and diseases, presents a multi-level feature fusion strategy to aggregate hierarchical node representations learned from different layers, and then fuse the original features and the optimized representations to enhance the model expressive ability, finally captures novel LDAs using XGBoost. In comparison with six state-of-the-art methods (SDLDA, LDNFSGB, IPCARF, LDASR, LDA-VGHB, and GEnDDn), LDA-CAMF computed the highest AUCs of 0.9632 and 0.9759, and the best AUPRs of 0.9369 and 0.9783 on lncRNADisease and MNDR under 5-fold cross validation, respectively. Under “cold-start” scenarios for lncRNAs and diseases, LDA-CAMF outperformed the above six baselines under most conditions. Five ablation studies further validated better predictive performance of LDA-CAMF. Visualization of LDP feature distributions also demonstrated the effectiveness of the proposed LDP feature learning strategy. Case studies elucidated that HNF1A-AS1 and BCYRN1 associated with prostate cancer, and HAR1A linked with diabetes. We forecast that LDA-CAMF assists in biomarker identification and mechanism investigation of complex diseases.

Targeted promotions based on individual purchase history can increase sales. However, the opportunity costs of targeting to optimize promoted product sales are poorly understood. A series of randomized field experiments with a large e-book platform shows that although targeted promotions increase promoted product sales and purchases of similar products, they can crowd out purchases of dissimilar products (i.e., e-books from nontargeted genres) by decreasing search activities of nontargeted goods on the same platform. The effects on total sales are heterogeneous, ranging from net decreases to insignificant drops, motivating a targeting exercise comparing strategies that optimize promoted product sales versus total sales. Targeting for promoted product sales tends to assign promotions to customers who purchased similar products, whereas targeting for total sales assigns promotions on the basis of other user characteristics. Targeting for promoted product sales generated incremental total sales that amounted to approximately 29% of the optimal incremental total sales when targeting for total sales (an opportunity cost of 71%). The optimal targeting exercise highlights how maximizing promotional lift can incur opportunity costs in terms of other forgone sales.