Journal of the Academy of Marketing Science最新文献

Integrating generative artificial intelligence (AI), particularly large multimodal models (LMMs) like ChatGPT, into the research process offers significant opportunities for marketing scholars. This manuscript provides a field guide into the potential advantages and possible limitations of using LMMs in different stages of consumer research, including idea generation, theory development, pretesting and pilot testing, data collection for experimental designs, data analysis, and reporting. We illustrate LMMs’ capabilities by replicating the consumer research stages of 35 articles from five marketing journals using ChatGPT-4o. Our findings suggest that LMMs enhance the efficiency and effectiveness of consumer research, though their performance varies across stages. LMMs excel in developing theoretical frameworks and collecting data for experimental designs, offer moderate support for idea generation, pre-/pilot testing, and reporting but perform less effectively in data analysis (e.g., silicon sampling). This manuscript underscores generative AI’s potential in consumer research and calls for further exploration into ethical guidelines and best practices to ensure high-quality work.

There is tension among practitioners regarding whether to offer salesperson-level pricing discretion beyond discounts in B2B sales, with the literature providing mixed evidence. Using a multi-study approach, we explore the dynamics surrounding salesperson pricing discretion, addressing crucial gaps within the existing pricing discretion literature, especially concerning the contingent factors that can drive salesperson pricing discretion’s effects. Using 504,446 transactions, Study 1 offers an in-depth examination of the effects stemming from salesperson-exercised pricing discretion. Besides finding a non-linear relationship between salesperson-exercised pricing discretion and manufacturer performance, in contrast to the existing literature, which primarily considers market- and firm-level factors, the current paper identifies three pivotal salesperson-level variables that moderate the impact of exercised pricing discretion on manufacturer profitability. Study 2 extends our exploration with a field investigation focusing on pricing discretion's effects on customer-level outcomes, showing that discretion positively affects B2B customer satisfaction, purchase quantities, and downstream customer performance.

Adaptive selling represents a notable and influential concept in the marketing literature. Despite being discussed in scholarly research and managerial practice for over forty years and mixed findings about its impact, a comprehensive understanding of the construct of adaptive selling remains missing. To remedy this critical knowledge gap, we conduct a comprehensive review of 188 articles across twenty-seven journals. We combine three approaches in our survey of the literature, including a systematic review, a main path analysis, and a bibliographic analysis. Together, this three-prong review offers profound insights into the state of adaptive selling research by (1) mapping the key stages of evolution of research in this domain over the last four decades, (2) delineating various conceptualizations and operational measures of the construct of adaptive selling, and (3) advancing an integrated framework of the nomological network of adaptive selling. Based on these insights, we outline five major opportunities for future theoretical development and empirical research to move the domain forward.

Business-to-business (B2B) software-as-a-service (SaaS) providers increasingly bundle add-on services with their core service. The implications of such bundles for onboarding customers to the relationship remain unclear; in particular, the common practice of trying to maximize add-on bundling during the customer acquisition phase arguably might conflict with goals to achieve long-term retention of customers. The current study therefore focuses explicitly on the impact of add-on bundling on customer retention during the onboarding stage, using multiple methods. A theories-in-use exploration suggests that the positive effects of add-on bundling may not manifest in the initial relational stage of customer onboarding. A field study involving a B2B SaaS provider further reveals that bundling more add-on services can significantly decrease customer retention during the onboarding stage. Moving to leaner communication channels can aggravate such attrition. Finally, a cross-industry survey of B2B managers identifies complexity perceptions as the likely source of these detrimental effects of add-on bundling during the customer onboarding stage.

Customers process persuasive verbal messages through analytical or narrative routes. Extant marketing research offers limited findings regarding the relative effectiveness of different communication antecedents to these routes; neither does it sufficiently specify if and how communication modalities (written vs. audio) and product/service type (hedonic vs. utilitarian) moderate their impact. To address this gap, the current article presents the results of a multimethod investigation. With a meta-analysis, Study 1 establishes the differential effects of antecedents on analytical and narrative processing and the moderating roles of both modality and product/service type. Study 2 gathers the expectations of marketing professionals to provide a comparison with the meta-analytic findings, highlighting areas of misalignment and a relevant managerial question pertaining to the effects of blended analytical–narrative messages. Study 3 addresses this relevant question with an experimental approach. The combined results offer novel insights into verbal persuasion and suggest several directions for research.

Generative Artificial Intelligence (GenAI) is transforming marketing not only by automating tasks and augmenting human capabilities, but also by reshaping stakeholder roles and experiences. This commentary explores the implications of empowering diverse stakeholders, marketers, consumers, contributors, and marketing academics, across four dimensions: operational, creative, agentic, and normative empowerment. Stakeholder empowerment across these four dimensions can satisfy the basic human needs for competence, autonomy, and relatedness, helping GenAI to become a force for the greater good in society.

Data-driven decision-making is increasingly prevalent but can clash with managerial beliefs, risking biased decisions. A prime example is pricing strategy optimization, where traditional methods for estimating price elasticities of demand often lead to counter-intuitive results due to model misspecification and the reliance on single-point estimates. To address this, we propose utilizing structural vector-autoregressions (SVARs) to generate identified sets of elasticities, integrating managerial beliefs into the analysis to improve decision-making processes. Using weak restrictions about the directional effects of supply and demand shocks on sales and prices, and assumptions about the functioning of in-store promotions effectively sharpens the identified sets. Specifically, we analyze the demand for beer at a large scale for 1,953 stores in the US. For many stores (i.e., at least 40%), both recent endogeneity-robust single-equation methods and alternative identification strategies for SVARs used in marketing studies yield positive price elasticity estimates that oppose behavioral fundamentals. Hence, these are hardly informative for designing pricing strategies. Instead, the suggested approach to set identification yields elasticity estimates that are sufficiently precise to improve the design of retail pricing strategies and offer insights into customer’s distinct price sensitivities in grocery and drug stores. Overall, our approach emphasizes the importance of combining data-driven analysis with managerial insights for evidence-based decision-making.

Empirical studies of two-sided platform markets, like the video game console industry, typically rely on software and platform sales data, thereby overlooking today’s managerial focus on engagement. This present research leverages a unique dataset tracking the daily engagement of over 14,000 users of Microsoft’s Xbox One and Xbox Series video game platforms to remedy this gap. We investigate how software development and release characteristics affect consumers’ engagement with software titles and the platforms on which they release. Our analysis finds that releasing software on subscription services is the strongest determinant of engagement, overshadowing established determinants like software quality or exclusivity. While superstar software and exclusive titles generate engagement, their relative importance is smaller compared to sales-based findings, reported in prior literature. Instead, franchises, non-superstars, and multihomed software perform much better on engagement than on sales, especially when included in a subscription service. These findings have important industry implications.