TY - CONF
T1 - Creating Business Value from Early Generative AI Adoption: A Contingency and Configuration Approach
AU - Chen, Yuyuan
AU - Zhang, Yueyue
AU - Wang, Furong
AU - Zhang, Cheng
PY - 2025
Y1 - 2025
N2 - This study contributes to the emerging literature on the adoption of generative AI, focusing on creating business value through a contingency and configuration approach. Our research examines the synergistic interaction of data sources and business scenarios in enhancing generative AI’s value in terms of cost efficiency and innovation. We analyze diverse types of data sources (inside-out, spanning, and outside-in data), and various business scenarios (inside-out, spanning, and outside-in scenarios). Utilizing both contingency and configuration perspectives, we explore how these elements individually and collectively drive firm performance. The contingency approach examines the effects of pairing different data and scenario types on cost efficiency and innovation, while the configuration approach identifies optimal combinations of data input types and application scenarios. Our findings underscore the significance of scenario breadth and the strategic application of outside-in data to maximize generative AI’s benefits. Ultimately, this research reveals key pathways for firms to harness generative AI’s potential, offering deep insights into enhancing efficiency and innovation in its early adoption stages.
AB - This study contributes to the emerging literature on the adoption of generative AI, focusing on creating business value through a contingency and configuration approach. Our research examines the synergistic interaction of data sources and business scenarios in enhancing generative AI’s value in terms of cost efficiency and innovation. We analyze diverse types of data sources (inside-out, spanning, and outside-in data), and various business scenarios (inside-out, spanning, and outside-in scenarios). Utilizing both contingency and configuration perspectives, we explore how these elements individually and collectively drive firm performance. The contingency approach examines the effects of pairing different data and scenario types on cost efficiency and innovation, while the configuration approach identifies optimal combinations of data input types and application scenarios. Our findings underscore the significance of scenario breadth and the strategic application of outside-in data to maximize generative AI’s benefits. Ultimately, this research reveals key pathways for firms to harness generative AI’s potential, offering deep insights into enhancing efficiency and innovation in its early adoption stages.
M3 - Abstract
ER -