3.2 Gen AI Explained

This study explores the potential of Large Language Models (LLMs) in predictive ad evaluation by emulating specific demographic and psychographic profiles. Utilizing a Profile-Based Instruction Framework and a post-test-only control group design, the LLM was instructed to assume predefined profiles while evaluating ad copies based on relevance, engagement, and clarity. Comparative analysis between LLM evaluations and human participants representing the same profiles scrutinized the alignment and accuracy of LLM responses. The findings reveal a promising avenue for employing LLMs in preliminary ad evaluations, potentially expediting the market research process and providing early-stage insights into ad effectiveness across diverse consumer segments. The study highlights the symbiotic potential between AI and market research, suggesting that LLMs can offer valuable initial insights, thereby fostering a more agile, insightful, and consumer-centric approach to ad evaluation. This research contributes to the evolving discourse on AI-driven market research methodologies.

African American heritage tourism encompasses a rich tapestry of historical sites, cultural landmarks, and significant events that highlight the enduring legacy of African American contributions to American society. We incorporated Generative AI to create an outline as a guide to examine the evolution of African American heritage tourism, its socio-cultural impact, and its potential for promoting economic development and social awareness. Through a comprehensive review of literature, mass media, and empirical research, this study provides insights into the importance of preserving and promoting specific African American heritage sites, the challenges faced by heritage tourism initiatives, and strategies for growth, development, and community involvement. We apply Butler's (1980) Tourism Area Life Cycle (TALC) as theory for this study. To assess the potential for success, we propose validating, via a mixed methods approach, the insights created by Generative AI by interviewing and surveying tourists for African American heritage tourism. 

The growing popularity of Generative AI is responsible for its extensive acceptance in several study disciplines. Given the recent changes in legislation concerning the disclosure of genAI content, it is essential to investigate the impact of such disclosure on consumer behaviour. Our study explores the effects of disclosure on the intention to visit a tourist destination. First, we conducted a content analysis by extracting 1001 posts from Instagram, of which 501 were genAI content and 500 were human-created content. Second, we experiment with disclosure as an independent variable and visit intention as a dependent variable. We also check the mediating role of perceived information quality. Our findings indicate that the engagement for genAI content is lower when compared to that of human-created content. Also, the intention to visit is lower for posts with disclosure of genAI content. 

The digital age has significantly transformed consumer behavior, mainly driven by advancements in artificial intelligence (AI). As AI technologies evolve, their integration into consumer-facing applications has reshaped how individuals interact with information, products, and services. A critical factor influencing this transformation is epistemic curiosity. The desire for knowledge and understanding drives epistemic curiosity. This paper explores the interplay between epistemic curiosity and AI and how this relationship impacts consumer behavior. The relationship between epistemic curiosity and artificial intelligence (AI) is intricate and presents challenges and ethical considerations.

Artificial intelligence (AI) is a major topic for many businesses. Some firms are aggressively pursuing AI while others are only exploring AI (Matzelle, 2024). Research has focused largely on how large organizations are pursuing and utilizing AI (Babina et al., 2024). The purpose of this research study is to explore the acceptance, application, and implementation of AI in small to medium firms. The theoretical framework for this study is technology acceptance model. A survey of both qualitative and quantitative questions was administered to small and medium size businesses across a variety of industries. Topics on survey include: (1) adoption of AI, (2) implementation process of AI, (3) applications of AI, (4) benefits of AI, (5) challenges and concerns of AI, (6) prospects and strategy of AI, and (7) feedback and reflections on AI. Overall, this study seeks to explore if and how small to medium size businesses are using AI.