Knowledge that Transforms

As franchise systems expand, the clustering and resulting proximity of same-brand outlets often become contentious issues. The increased interactions among outlets may facilitate knowledge sharing, even while inducing intrabrand competition. Prior research has considered each possibility—knowledge sharing or intrabrand competition—in isolation, resulting in conflicting recommendations to the central question of whether multiple same-brand outlets should be close to or distant from one another. In this study, the authors take the perspective of the focal outlet and show that the opportunity to share knowledge afforded by clustering-based proximity may or may not be realized, depending on the motivation and ability of the proximal outlets to share knowledge, the focal outlet's ability to absorb knowledge, and the governance context. An analysis of more than 8,000 observations on the 988 outlets of a U.S.-based automotive service franchise system from 1977 to 2012, and corresponding outlet-level sales information from 2004 to 2012, provides support for the authors’ hypotheses.

Lateral exchange markets (LEMs) are sites of technologically intermediated exchange between actors occupying equivalent network positions. To develop an enriched understanding of these markets, the authors develop a more broad-based and differentiated understanding of peer-to-peer, sharing, and access-based markets. They focus on two key axes: the extent of (1) consociality and (2) platform intermediation. Drawing on these attributes, the authors theoretically deduce four ideal types—Forums, Enablers, Matchmakers, and Hubs. Each type provides value in a different way: Forums connect actors, Enablers equip actors, Matchmakers pair actors, and Hubs centralize exchange. Twenty organizational cases reveal insights into the failure, adaptation, and success of LEMs. Lateral exchange markets shift responsibility for personal and exchange security to relevant personal actors, to institutions, or to the governing algorithms of technology platforms. Extending the general proposition that sociality increasingly infuses market logics, the findings suggest a new frontier in which social resources and software platform algorithms interact as operand resources whose negative consequences (e.g., opportunism) require careful management through assurances and institutional arrangements matched to the type of LEM operation.

The authors examine an increasingly popular open innovation practice, “design crowdsourcing,” wherein firms seek external inputs in the form of functional design solutions for new product development from the “crowd.” They investigate conditions under which managers crowdsource design and determine whether such decisions subsequently boost product sales. The empirical analysis is guided by qualitative insights gathered from executive interviews. The authors use a novel data set from a pioneering crowdsourcing firm and find that three concept design characteristics—perceived usability, reliability, and technical complexity—are associated with the decision to crowdsource design. They use an instrumental variable method accounting for the endogenous nature of crowdsourcing decisions to understand when such a decision affects downstream sales. The authors find that design crowdsourcing is positively related to unit sales and that this effect is moderated by the idea quality of the initial product concept. Using a change-score analysis of consumer ratings, they find that design crowdsourcing enhances perceived reliability and usability. They discuss the strategic implications of involving the crowd, beyond ideation, in helping transform ideas into effective products.

Although research has examined the social media–shareholder value link, the role of consumer mindset metrics in this relationship remains unexplored. To this end, drawing on the elaboration likelihood model and accessibility/diagnosticity perspective, the authors hypothesize varying effects of owned and earned social media (OSM and ESM) on brand awareness, purchase intent, and customer satisfaction and link these consumer mindset metrics to shareholder value (abnormal returns and idiosyncratic risk). Analyzing daily data for 45 brands in 21 sectors using vector autoregression models, they find that brand fan following improves all three mindset metrics. ESM engagement volume affects brand awareness and purchase intent but not customer satisfaction, while ESM positive and negative valence have the largest effects on customer satisfaction. OSM increases brand awareness and customer satisfaction but not purchase intent, highlighting a nonlinear effect of OSM. Interestingly, OSM is more likely to increase purchase intent for high involvement utilitarian brands and for brands with higher reputation, implying that running a socially responsible business lends more credibility to OSM. Finally, purchase intent and customer satisfaction positively affect shareholder value.

Content platforms (e.g., newspapers, magazines) post several stories daily on their dedicated social media pages and promote some of them using targeted content advertising (TCA). Posting stories enables content platforms to grow their social media audiences and generate digital advertising revenue from the impressions channeled through social media posts’ link clicks. However, optimal scheduling of social media posts and TCA is formidable, requiring content platforms to determine what to post; when to post; and whether, when, and how much to spend on TCA to maximize profits. Social media managers lament this complexity, and academic literature offers little guidance. Consequently, the authors draw from literature on circadian rhythms in information processing capabilities to build a novel theoretical framework on social media content scheduling and explain how scheduling attributes (i.e., time of day, content type, and TCA) affect the link clicks metric. They test their hypotheses using a model estimated on 366 days of Facebook post data from a top 50 U.S. newspaper. Subsequently, they build an algorithm that allows social media managers to optimally plan social media content schedules and maximize gross profits. Applying the algorithm to a holdout sample, the authors demonstrate a potential increase in gross profits by at least 8%.

Practitioners increasingly employ relational price discounts by granting initial discounts to new customers with the goal of building sustainable relationships. However, extant research has provided mixed findings on the long-term effects of initial discounts on customer retention. The current research aims to reconcile this mixed evidence by exploring nonlinear effects of initial discounts on customer retention. Drawing on marketplace metacognition theory, the authors hypothesize that moderate initial discounts (5%–35%) have positive effects on customer retention, whereas low (<5%) and high (>35%) discounts have negative effects. Two large-scale field studies in an insurance company's car insurance branch and property insurance branch provide empirical support for the hypothesized patterns. An additional laboratory experiment tests the psychological mechanism underlying the nonlinear effects. When compared with low and high discounts, moderate initial discounts lead customers to form higher expectations of future relational benefits provided by the firm, as well as to lower their expectations of future discounts. Finally, this research offers customer lifetime value implications based on the depicted findings.

The increased penetration of mobile devices has a significant impact on customers’ online shopping behavior, with customers frequently switching between mobile and fixed devices on the path to purchase. By accounting for the attributes of the devices and the perceived risks related to each product category, the authors develop hypotheses regarding the relationship between device switching and conversion rates. They test the hypotheses by analyzing clickstream data from a large online retailer and apply propensity score matching to account for self-selection in device switching. They find that when customers switch from a more mobile device, such as a smartphone, to a less mobile device, such as a desktop, their conversion rate is significantly higher. This effect is larger when product category–related perceived risk is higher, when the product price is higher, and when the customer's experience with the product category and the online retailer is lower. The findings illustrate the importance of focusing on conversions across the combination of devices used by customers on their path to purchase. Focusing on the conversions on a single device in isolation, as is usually done in practice, significantly overestimates conversions attributed to fixed devices at the expense of those attributed to mobile devices.

The use of celebrity endorsements varies across countries; does their effectiveness similarly vary across cultures? The authors propose that power distance beliefs (PDB), a cultural orientation related to the extent to which people expect and accept differences in power, moderate the effects of celebrity endorsements. A positive effect of celebrity endorsers on evaluations of advertising should be more potent with greater PDB; source expertise and trustworthiness likely underlie this effect. To test the hypotheses, the authors use moderated mediation analyses, with corrections for measurement error and endogeneity of the mediators (source expertise and trustworthiness). The results of three studies, using both manipulated and measured PDB for respondents in different countries and with a variety of endorsers, demonstrate that PDB determine the effectiveness of celebrity endorsements on attitudes toward the advertisement and the brand. In support of the moderated mediation model, perceptions of source expertise and trust mediate the effect of celebrity endorsements, conditional on PDB. The results hold for nondurables but do not generalize to durable products.