GENERATIVE AI USAGE AND ENTREPRENEURIAL DECISION READINESS: THE ROLE OF DECISION QUALITY AND AI LITERACY
Description
This dataset examines the relationships among Generative AI Usage Intensity, Data-Driven Decision Quality, Entrepreneurial Decision Readiness, and AI Literacy among university students in Indonesia. Drawing on Human–AI Collaboration Theory and Information Processing Theory, the study investigates how the intensity of generative AI usage contributes to entrepreneurial decision readiness both directly and indirectly through data-driven decision quality. The study also explores the moderating role of AI literacy in strengthening the effectiveness of AI-assisted decision-making. A quantitative cross-sectional survey design was employed, with data collected from 150 business students who had prior experience using generative AI tools such as ChatGPT, Gemini, Copilot, and similar platforms. Responses were measured using a five-point Likert scale and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The questionnaire included indicators related to generative AI usage intensity, data-driven decision quality, entrepreneurial decision readiness, and AI literacy. The dataset contains the raw survey data, measurement items, and PLS-SEM analysis outputs, including algorithm results and bootstrapping results. The analysis provides empirical evidence that generative AI usage enhances entrepreneurial decision readiness both directly and through improved decision quality, while AI literacy strengthens the positive effect of AI usage on data-driven decision-making. These data may be useful for researchers interested in artificial intelligence, entrepreneurship education, technology adoption, digital decision-making, and human–AI collaboration.
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Steps to reproduce
Data were collected through an online survey distributed to business students from various higher education institutions in Indonesia. Respondents were required to have prior experience using generative AI tools such as ChatGPT, Gemini, Copilot, or similar platforms. After data collection, incomplete responses, duplicate submissions, and invalid entries were removed. A total of 150 valid responses were retained for analysis. The dataset was coded and imported into SmartPLS 4. The measurement model was evaluated using indicator reliability, composite reliability, convergent validity (AVE), and discriminant validity. The structural model was assessed through bootstrapping procedures to examine direct effects, mediation effects, and moderation effects among Generative AI Usage Intensity, Data-Driven Decision Quality, Entrepreneurial Decision Readiness, and AI Literacy.
Institutions
- Binus UniversityJakarta, Jakarta