In cause-related marketing (CRM) programs, the fit between the cause and brand is an important factor influencing consumer perceptions and behavior. However, the literature demonstrates that there is disagreement regarding the effect of cause–brand fit on consumer responses with varying corporate reputation. This study aims to examine the influence of cause–brand fit on consumer attitudes, attributed company motives, and the moderating role of corporate reputation. With a two (fit: high/low) by three (reputation: low/medium/high) experimental study, we reveal that consumers hold positive attitudes toward companies that engage in CRM campaigns. The effect of cause–brand fit on consumer-attributed company motives is moderated by corporate reputation. For low-reputation companies, a high cause–brand fit CRM campaign resulted in consumers attributing more negative motives to companies than low-fit campaigns. The opposite was true for medium-reputation companies. Meanwhile, high-reputation companies with a high cause–brand fit elicit greater value-driven attributed motives from consumers than other motives. Recommendations for implementing CRM programs and for future research are discussed.

This study mapped personality based on the newly proposed extraction method from consumers’ textual data and revealed the relevance (attention) and polarity (affection) of words associated with a specific personality trait. Furthermore, we illustrate how unique words are used to predict a consumer’s behavior associated with certain personality traits. In this study, we employed the scales of the Kaggle MBTI Personality dataset to examine the methodology’s effectiveness, extract the personality traits from the textual data into features, and map them into the traits/dimensions of the existing scale. Based on the results obtained in this study, we assert that using the TF-IDF algorithm is a good way to generate a custom dictionary. Furthermore, sentiment scoring with an AI-empowered machine learning algorithm provides useful data to filter and validate more coherent words to understand and, thus, communicate a particular aspect of personality. Finally, we proposed that four situations involving the interaction between attention (frequency) and affection (sentiment) allow us to better understand the consumer and how to use the feature words in terms of the interaction between attention (TF-IDF score) and affection (sentiment score).