Knowledge that Transforms

Grocery retailers are well-positioned to help address the shortage of EV charging infrastructure but seek insight on the implications for store performance. They lack clarity on the revenue impact of EV charging availability on their parking lots, the underlying mechanisms, and the conditions under which the effect is more pronounced. Drawing on a dataset of over 300 stores of a leading German grocery chain and utilizing a staggered Difference-in-Differences quasi-experimental design, the authors show that EV charging availability increases revenues by 3.4%. Market expansion alone cannot fully explain the effect as the authors find significant evidence for market stealing. They further find that the increase is due to more transactions, not to changes in basket size. The largest increase materializes during off-peak weekend hours. They identify several attitudinal (perceived impulsiveness, storability) and behavioral (penetration, purchase frequency) category characteristics that explain where the revenue increase comes from and provide insights into the role of various store (surface-to-parking ratio, management type) and market (e.g., local competition, apartment-to-house ratio, neighborhood wealth) characteristics. Guidance for store managers interested in increasing the benefits of having EV charging on their premises and chain managers supervising the roll-out throughout their store network is provided.

Product performance claims play an important role in shaping consumers’ purchase decisions. Through six preregistered studies, including one field study, the authors demonstrate that consumers’ preferences for point versus range performance estimates in common comparative purchase settings depend on whether their attention is directed toward the content of the claim or the manner of its presentation. At baseline, consumers focus on what information is communicated and prefer point over range estimates because point estimates convey greater certainty about the expected performance outcome. When consumers’ persuasion knowledge is activated, however, their attention shifts toward how information is communicated. In this mode, the format of the claim, rather than the stated performance figures, becomes relatively more informative: range estimates signal brand transparency by acknowledging the natural variability of real-world performance, enhancing perceived trustworthiness and reversing consumer preference. These effects are attenuated when products are evaluated in isolation rather than compared side by side. Collectively, these findings inform both theory and practice by revealing when and why consumers prefer point versus range estimates of product performance.

People encounter and evaluate temporal information every day, for example when evaluating product delivery terms or choosing between online courses. Using data from an online course aggregator platform and nine pre-registered experiments, we show that describing time with a larger and a smaller unit (e.g., “by 8 PM on June 30”, “2 hours and 59 minutes”) rather than with a larger unit alone (e.g., “by the end of day on June 30”, “3 hours”) increases perceived duration. We refer to this phenomenon as the added-unit effect. The added-unit effect emerges because added smaller units change people’s mental representation of time, making them more likely to construe time as a collection of more numerous, smaller units. We document the effect across multiple unit combinations (e.g., dates + hours, hours + minutes, minutes + seconds) and show its implications for online course interest, evaluations of product efficacy, temporal goal setting, and time–money trade-offs. This research contributes to a more nuanced understanding of how number and unit information shape people’s judgments and adds to extant work on time perception.

Traditional experiments often rely on a few, stylized stimuli, which can limit realism and undermine generalizability beyond the sampled stimuli—known as the stimulus-sampling problem. To address this challenge, this paper introduces BRIDGE, a novel analytical method that enables the use of many unaltered real-world descriptions as experimental stimuli. Leveraging foundational generative AI embeddings, BRIDGE develops (1) structured, low-dimensional, and interpretable representations of focal constructs and (2) statistical controls for non-focal nuisance variations, facilitating causal inference. Extensive Monte Carlo simulations, two coffee certification experiments, and a large-scale choice experiment (plus its validation study) show that BRIDGE recovers true parameters even when textual stimuli contain unobserved nuisance variations, and can effectively account for different sources of confounding. In the choice experiment, 1,000 participants evaluated approximately 50,000 unique product descriptions randomly sampled from a corpus of nearly 120,000. Results show that entirely incidental initial products can shape participants’ subsequent preferences. By incorporating many unaltered product texts into experiments, BRIDGE enhances the realism, generalizability, and practical relevance of consumer research in information-rich environments. A detailed researcher’s guide and Python package bridge are provided.

Artificial intelligence (AI) increasingly assists frontline employees (FLEs) in nonroutine tasks; however, its implications remain uncertain. This study examines how thinking AI, i.e., a machine learning-based tool that derives recommendations from historical patterns, affects external outcomes (market offerings and satisfaction with FLEs) and internal outcomes (FLE job autonomy and satisfaction with the responses received). Quasi-experimental evidence in banking reveals that AI-assisted FLEs adopt more conservative behaviors, resulting in decreases in mortgage approvals, default payments, and customer satisfaction with FLEs. Textual analysis and a survey provide first evidence consistent with AI-generated solutions appearing more rigid and less open to alternative possibilities, in turn reducing FLE satisfaction. A preregistered experiment tests this evidence, showing that AI-generated responses are perceived as less flexible, which in turn reduces their satisfaction with the response received and, primarily through this indirect pathway, constrains their perceived job autonomy, regardless of perceived message helpfulness. This study advances the marketing literature by showing that the role of AI at the frontline may not be inherently supportive for nonroutine tasks. AI should be carefully aligned with the characteristics of such tasks so that marketers can harness the potential of AI assistance while mitigating unintended effects on employees and customers alike.

The authors examine the role of store-branded, “private label” products in determining retailers’ profits. Exploiting a novel setting in which a patent expiration concided with private-label entries, they estimate a structural model of demand and supply-side bargaining to identify primary determinants of retailer gains from private labels. They find private-label entry causes a significant and heterogeneous impact on retailer profits. Utilizing unique data on the identity of private-label manufacturers, they show a stronger retailer bargaining position (disagreement payoffs) vis à vis private-label supplier leads to a larger retailer gain. These gains arise both through improved direct profits from the private label, and indirectly through a better bargaining outcome on national-brand products. These results highlight the importance of supply-side arrangements in determining private-label profits, and the private label’s role as bargaining leverage against national brands. The authors also quantify how the retailer's “brand” equity affects the profitability of its private label.

Lead calling is a vital aspect of B2B prospecting, yet how salesperson speech influences lead conversion remains understudied. Drawing on Symbolic Interactionism theory and grounded analysis, we develop a framework that conceptualizes salesperson speech as comprising linguistic acts (legitimizing, piquing, nudging) and paralinguistic acts (intensity, speech rate, pitch). We further theorize how these communicative acts influence lead receptivity, a speaking turn-level indicator of a lead’s openness to the sales message, and how lead receptivity shapes conversation-level conversion. Using data from 783 lead calling conversations involving 11,566 speaking turns, we test the proposed framework. Results show that a 1 SD increase in lead receptivity raises conversion likelihood by 13.44%. We also show that the effectiveness of communicative acts depends on whether and when salespeople deploy them during conversations. For instance, more specific (compared to less specific) nudges increase lead receptivity by 11.82% earlier in the conversation but reduce it by 81.40% later in the conversation. Similarly, speaking faster (vs. slower) has a neutral effect on lead receptivity earlier in the conversation, compared with 8.26% reduction later in the conversation. These findings offer new insights into the lead conversion process and provide actionable guidance to improve lead calling effectiveness.

One component of a brand's identity is the brand slogan, and an important desideratum for the slogan is that it is memorable. We develop predictive models of how well a slogan is remembered and to what extent it cues its brand. To do so, we identify proposed memorability factors through a systematic literature review and operationalize them using a combination of natural language processing, human ratings, and constructs derived from text embeddings. We evaluate the predictive accuracy of models that use these factors and text embeddings on recognition and cued brand recall judgments for existing slogans. We use our models to assess the extent to which the literature is missing textual characteristics important for predicting memorability. Our models provide a tool to help marketers select between slogans, which we demonstrate by predicting the memorability of candidate slogans for new brands.

Modern marketing increasingly requires managers to deploy new content at scale, often with limited opportunity for prior testing. As a result, decisions about what to launch become strategic managerial choices under uncertainty rather than purely creative exercises. While generative AI makes the creation of new content fast and highly scalable, it simultaneously expands the set of options managers must evaluate, making reliable content selection increasingly difficult. We develop a framework for causal prediction that enables managers to evaluate and deploy novel marketing content generated by AI. The framework uses pretrained large language models to represent previously deployed content and learn how its features causally relate to outcomes. Using a rejection-sampling procedure, the framework screens new content proposed by generative AI to avoid extrapolation beyond what historical data can reliably support. In a large-scale email marketing application (3.3 million observations across 34 campaigns), the framework improves out-of-sample prediction and real-world deployment performance relative to standard approaches, enabling outcome-guided generation of higher-performing AI-generated content. The framework establishes a threshold based on how closely new content resembles past campaigns, separating cases where causal prediction is reliable from cases where direct experimentation is warranted. The framework has important implications for marketing decision making in a rapidly evolving environment where generative AI is transforming content creation and deployment.

Consumers often behave opportunistically, taking more than what fair marketplace exchange warrants (e.g., taking excess samples, returning used items). While each individual transgression may be minor, cumulatively they can undermine firm profitability. Thus, addressing consumer opportunism is an important managerial concern. We identify a novel antecedent of consumer opportunism: consumers’ acceptance of societal hierarchy (i.e., power distance belief [PDB]), including firms’ higher position in it than consumers. Nine studies (plus two supplementary studies) employing archival, correlational, and experimental data provide converging evidence that, in marketplace interactions, this belief evokes a need to feel clever (i.e., to feel smart and knowledgeable vis-à-vis firms), which, in turn, promotes opportunistic behaviors. These behaviors exploit firm policy loopholes, allowing consumers to feel clever without explicitly defying firm authority. Higher-status consumers, who may be accustomed to a position of advantage and consequently less deferential, experience this need more, leading to greater opportunism. Reminders of surveillance reduce opportunism, but brand relationship reminders, paradoxically, license it. This research thus offers novel theoretical insights into consumer opportunism along with substantive managerial implications.