Marketing

Generative Artificial Intelligence (GenAI) tools based on Large Language Models (LLMs) are quickly reshaping how researchers conduct surveys and experiments. From reviewing the literature and designing instruments, to administering studies, coding data, and interpreting results, these tools offer substantial opportunities to improve research productivity and advance methodology. Yet with this potential comes a critical challenge: researchers often use these systems without fully understanding how they work.

Language plays a crucial role in marketing, influencing outcomes such as consumer engagement and decision-making. Although prior research has extensively analyzed the relationship between linguistic features and business outcomes, most approaches have been descriptive or predictive, limiting their value for crafting more effective content. Understanding the causal effects of specific linguistic features is essential but challenging because, in real-world settings, the focal textual feature often changes simultaneously with other confounding factors.

This paper addresses a simple theoretical question of high substantive relevance: What makes a consumption experience special in a consumer’s mind?

We aim to stimulate discussion on how innovation research within marketing can use a better world (BW) perspective to help innovation become a driver of positive change in the world. In this "Challenging the Boundaries" series paper, we hope to provide purposeful research opportunities for scholars seeking to bridge innovation research with the BW movement. We frame our discussion with four areas of innovation research in marketing that are particularly relevant to BW objectives.

One of the most crucial aspects and significant levers that gaming companies possess in designing digital games is setting the level of difficulty, which essentially regulates the user’s ability to progress within the game. This aspect is particularly significant in free-to-play (F2P) games, where the paid version often aims to enhance the player’s experience and to facilitate faster progression.

Language is a uniquely human trait at the core of human interactions. The language people use often reflects their personality, intentions and state of mind. With the integration of the Internet and social media into everyday life, much of human communication is documented as written text. These online forms of communication (for example, blogs, reviews, social media posts and emails) provide a window into human behaviour and therefore present abundant research opportunities for behavioural science.

With unprecedented access to consumer information, firms are increasingly interested in designing highly effective data-driven targeting policies based on detailed consumer data. The current standard for implementing such policies involves the “test-then-learn” approach, where randomized experiments are used to estimate the differential impact of marketing interventions on various customers. However, this method fails to incorporate the firm’s ultimate business objectives, leading to inefficient experimentation and suboptimal targeting strategies.

One of the most significant levers available to gaming companies in designing digital games is setting the level of difficulty, which essentially regulates the user’s ability to progress within the game. This aspect is particularly significant in free-to-play (F2P) games, where the paid version often aims to enhance the player’s experience and to facilitate faster progression. In this paper, we leverage a large randomized control trial to assess the effect of dynamically adjusting game difficulty on players’ behavior and game monetization in the context of a popular F2P mobile game.