The study focuses on the development and validation of a generative AI model to select attractions and construct structured travel itineraries. Using three-stage systematic framework, the model synthesizes information on user preferences, attraction characteristics, time-of-day considerations, and contextual factors to generate coherent daily travel plans. Multiple real-world destination cases were used to evaluate the consistency, personalization quality, and logical structure of the generated itineraries. Preliminary findings indicate that generative AI can integrate diverse qualitative inputs and produce customized, context-aware itineraries that go beyond rule-based recommendation tools. The results demonstrate the feasibility of using generative AI as a decision-support layer in tourism planning.
The project aims to measure student perceptions regarding the improvement in learning outcomes in the Lean Manufacturing Systems and Concepts course by introducing an innovative assignment. By implementing an A3 problem-solving assignment for daily habits and tasks, students bridge theoretical knowledge with practical applications. The study evaluates student perceptions of how this method affects learning outcomes through surveys. Additionally, it measures student perceptions on how this new assignment can help them identify issues in their daily habits and improve upon them.
While AI technologies such as generative, predictive analytics, and automated communication systems are increasingly common, limited academic research has explored their use in customer facing, relationship driven sales roles. To address this gap, we are conducting semi-structured, in-depth practitioner interviews, seeking to better understand how sales professionals in the beverage industry integrate artificial intelligence (AI) into their daily work. We are almost finished interviewing 12-15 sales professionals in various roles including account managers, CEOs, sales directors, those in adjacent positions within the industry. Interviews explore how participants define AI, how they use it in practice, and the perceived impact on efficiency, decision-making, and performance. We also examine attitudes toward AI, including perceived benefits, challenges with integration, ethical concerns, and how sales professionals balance AI-generated insights with human intuition. Final results will contribute to research on current AI usage in sales and provide practical insights to help organizations integrate AI in ways that enhance human relational strengths in the sales process.
This study examines how momentary self-worth and gain vs. loss framing influence the amount of risk people take when making decisions for themselves. We ran a preliminary study, in which participants first recalled a memory that either boosts, lowers, or has a neutral effect on their sense of self-worth. Then, they chose between a riskless or risky option. This decision was presented in two versions that have equal expected outcomes across conditions, but where one version was framed in terms of gaining monetary value and the other was framed in terms of losing monetary value (Tversky &Kahneman, 1981). Across 372 participants, we found that people tended to chose the risky(riskless) option when the decision was presented as a potential loss(gain). We also found that lower momentary self-worth may lead to more cautious choices within loss framing. While this effect was not large, is provides early insight into how self-worth processes might shape risk sensitivity. We are currently running a new study, a more expanded version of what we have already distributed, that we hope shows similar and significant results.
