Forecasting Information Sharing Strategies in Competitive Smart Connected Platforms

This study delves into forecasting information sharing strategies within competitive smart connected platforms, aiming to enhance market performance. Forecasting information sharing involves software vendors analyzing user data to provide accurate market forecasts and optimization advice to hardware platforms, leveraging insights from user interactions that hardware platforms cannot directly observe. Hardware platforms can adjust their commissions to accommodate varying levels of information sharing. Through the analysis of a Stackelberg game model involving two hardware platforms and software vendors, optimal decisions regarding hardware and software prices, as well as commissions, are determined. The study investigates three competitive strategies: no forecasting information sharing for both platforms (NN), no forecasting information sharing for one platform and forecasting information sharing for the other platform (SN/NS), and forecasting information sharing for both platforms (SS). Results indicate that software vendors’ strategies vary based on price sensitivity, with low sensitivity favoring partial sharing, moderate sensitivity favoring complete sharing, and high sensitivity favoring no sharing. Conversely, hardware platforms consistently prefer complete information sharing. Additionally, factors such as software-hardware complementarity, competition intensity, hardware price sensitivity, and market forecasting accuracy influence vendors’ willingness to share information. These insights offer valuable guidance for stakeholders in competitive smart connected platforms, enriching their understanding of optimal information sharing strategies and contributing to both theory and practice in market competitiveness analysis.