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Abstract

  • Study investigates the impact of Generative AI (GenAI) on business decisions and performance.
  • An integrated framework is used, combining Decision Theory, Technology Acceptance Model (TAM), Information Processing Theory, Organizational Learning Theory, and Complexity Theory.
  • Findings suggest GenAI is a strategic asset for reshaping business decision-making.

Introduction

  • GenAI has transformed decision-making across strategic levels due to its ability to learn patterns and generate relevant outputs.
  • Evolution of AI includes branches like Machine Learning (ML), Deep Learning (DL), and Generative AI (GenAI), culminating in Large Language Models (LLMs).
  • ChatGPT's rise illustrates the rapid acceptance and concern surrounding AI in decision-making.

Research Questions

  • How does GenAI adoption impact business decision-making and performance?
  • What challenges exist in implementing GenAI, and how can they be overcome?

Research Gap

  • Limited empirical evaluations of GenAI's tangible outcomes in real-world business contexts.
  • Uncertainty regarding decision-making mechanisms and trust issues associated with GenAI.

Methodology

  • Quantitative approach using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine GenAI's influence.
  • Survey instrument developed around five theoretical perspectives with 60 valid responses collected.

Conceptual Framework

  • Framework integrates:
    • Decision Theory: role of GenAI in decision-making processes.
    • TAM: adoption attitudes toward GenAI.
    • Information Processing Theory: enhances data handling and insights.
    • Organizational Learning Theory: contribution to performance through learning.
    • Complexity Theory: managing data-intensive environments.

Data Analysis Methods

  • Measurement model assessed validity and reliability using various statistical methods.
  • Structural model tested hypotheses through path coefficients and statistical significance.

Findings

  • GenAI significantly improves decision accuracy (β = 0.506, p < 0.001) and is expected to be crucial for value generation.
  • Major barriers include trust, explainability, AI biases, and misinformation.

Practical Implications

  • Companies can incorporate GenAI to enhance decision-making; however, transparency and ethical considerations are vital.
  • It is essential to address integration challenges, data privacy, and training for effective adoption.

Conclusion

  • GenAI enhances decision-making speed and accuracy but faces challenges such as trust and ethical use.
  • Future research should further explore long-term impacts and scalability with varied sample sizes.