International Journal of Research in Marketing最新文献

Knowledge of a customer’s willingness to pay (WTP) at early stages of product development is key to the success of innovations. However, since innovative products do not exist yet, only the hypothetical WTP can be surveyed, inducing a measurement bias. Unfortunately, little is known about the factors that induce this bias and how it differs depending on the method utilized in measuring WTP. We address this gap by focusing on direct methods to survey hypothetical WTP. Based on anchoring theory and the corresponding psychological mechanisms for open questions as well as closed questions, we conducted two experiments, each comprised of a survey and a field study. The experiments differ regarding the product category and the product’s degree of innovativeness. Our results show that open questions are less accurate in estimating real WTP than closed questions. Further, our research offers insights into moderating factors that influence the efficacy of open and closed questions. For example, for customers with a very high product category knowledge, open questions are applicable, while closed questions result in higher accuracy when accounting for the customers’ cognitive abilities.

Since the release of ChatGPT, heated discussions have focused on the acceptable uses of generative artificial intelligence (GenAI) in education, science, and business practices. A salient question in these debates pertains to perceptions of the extent to which creators contribute to the co-produced output. As the current research establishes, the answer to this question depends on the evaluation target. Nine studies (seven preregistered, total N = 4498) document that people evaluate their own contributions to co-produced outputs with ChatGPT as higher than those of others. This systematic self–other difference stems from differential inferences regarding types of GenAI usage behavior: People think that they predominantly use GenAI for inspiration, but others use it to outsource work. These self–other differences in turn have direct ramifications for GenAI acceptability perceptions, such that usage is considered more acceptable for the self than for others. The authors discuss the implications of these findings for science, education, and marketing.

Firms can rely on various data protection methods to comply with the General Data Protection Regulation’s (GDPR) anonymization directive. We develop a privacy attack to estimate customers’ privacy risk and find that data protection methods commonly used in practice do not offer a reliable guarantee of privacy protection. We therefore develop a framework that describes the use of deep learning to generate synthetic data that are both (differentially) private, and useful for marketing analysts. Empirically, we apply our framework to two privacy-sensitive marketing applications in which an analyst is faced with everyday managerial practices. In contrast to GDPR’s directive to minimize data collection, we show that customers’ privacy risk can be reduced by blending into a large crowd: a “Where’s Waldo” effect. Our framework provides a data protection method with a formal privacy guarantee and allows analysts to quantify, control, and communicate privacy risk levels with stakeholders, draw meaningful insights, and share data even under privacy regulations.

Detailed feedback on exercises helps learners become proficient but is time-consuming for educators and, thus, hardly scalable. This manuscript evaluates how well Generative Artificial Intelligence (AI) provides automated feedback on complex multimodal exercises requiring coding, statistics, and economic reasoning. Besides providing this technology through an easily accessible web application, this article evaluates the technology’s performance by comparing the quantitative feedback (i.e., points achieved) from Generative AI models with human expert feedback for 4,349 solutions to marketing analytics exercises. The results show that automated feedback produced by Generative AI (GPT-4) provides almost unbiased evaluations while correlating highly with (r = 0.94) and deviating only 6 % from human evaluations. GPT-4 performs best among seven Generative AI models, albeit at the highest cost. Comparing the models’ performance with costs shows that GPT-4, Mistral Large, Claude 3 Opus, and Gemini 1.0 Pro dominate three other Generative AI models (Claude 3 Sonnet, GPT-3.5, and Gemini 1.5 Pro). Expert assessment of the qualitative feedback (i.e., the AI’s textual response) indicates that it is mostly correct, sufficient, and appropriate for learners. A survey of marketing analytics learners shows that they highly recommend the app and its Generative AI feedback. An advantage of the app is its subject-agnosticism—it does not require any subject- or exercise-specific training. Thus, it is immediately usable for new exercises in marketing analytics and other subjects.

A systematic review of sustainable consumer behaviors in five prominent consumer research journals revealed that green behaviors with greater potential for climate mitigation (e.g., plant-based consumption) have not been broadly studied, indicating promising opportunities for future research. In an exploratory survey, we conceptually replicate this finding using a sample of consumer researchers with a general interest in studying higher-potential behaviors. We explore potential explanations, including researchers' focus on construct-to-construct mapping, preference for behaviors they personally experience or find easy to implement, lack of clear incentives to study higher-potential behaviors, and inadequate understanding of mitigation potential. To help shift consumer researchers’ focus on higher-potential behaviors, we offer concrete recommendations, such as proactively considering mitigation potential both as authors and reviewers, and utilizing phenomenon-to-construct mapping for enhancing theoretical contributions. In sum, this research will help interested consumer researchers to provide more relevant answers to the urgent challenge of climate change mitigation.

This research examines whether and why disclosing information to AI as opposed to humans influences an important brand-related outcome—consumers’ trust in brands. Results from two pilot studies and nine controlled experiments (n = 2,887) show that consumers trust brands less when they disclose information to AI as opposed to humans. The effect is driven by consumers’ inference that AI shares information with a larger audience, which increases consumers’ sense of exploitation. This, in turn, decreases their trust in brands. In line with our theorizing, the effect is stronger among consumers who are relatively more concerned about the privacy of their data. Furthermore, the negative consequences for brands can be mitigated when (1) customers are informed that the confidentiality of their information is protected, (2) AI is anthropomorphized, and (3) the disclosed information is relatively less relevant.

Developments in digital technologies have extended the abilities of marketers to collect, process, and share consumer data to optimize personalized messages across media in real time, a strategy known as synced advertising. Previous research has found promising effects related to synced advertising. At the same time, consumer knowledge appears to be low, and informing consumers could increase their critical attitudes towards synced ads. Our eye-tracking lab study (N = 163) showed that informing consumers on synced advertising helps them to understand and increase their knowledge about this new marketing strategy. Moreover, this strategy increases recall of the product mentioned on TV as well as perceived surveillance. Finally, we found that all participants closed the synced ad with an average of 6.5 s and fixated on it for an average of 1.3 s. This study contributes to the growing literature on synced advertising by empirically investigating the impact of consumer knowledge on the tensions and opportunities of this new marketing strategy.