Exploring factors influencing open government data value realization in China: A mixed design using grounded theory, system dynamics, and questionnaire survey

As digital economy strategies advance and the data market gradually matures, the pathways for realizing the value of China’s government open data have undergone significant changes, with numerous emerging influencing factors. Previous research has primarily relied on portal operational data and static efficiency assessment methods, making it challenging to identify the evolutionary mechanisms and dynamic relationships among these factors. To bridge this gap and inform policy-making and theoretical advancements, we employ a hybrid design that combines grounded theory and system dynamics. Utilizing 33 semi-structured interviews and 317 questionnaire responses (292 valid), we construct causal loops and a stock-flow structure. A 60-month simulation analysis examines factors influencing open government data (OGD) value realization, focusing on: (a) influencing factors and feedback positions; (b) the ranking of factor strengths and directionality. Our findings reveal that increased data demand rates sustainably elevate the long-run equilibrium level of government open data value realization, while higher data depreciation rates reduce this equilibrium. This research advances the dynamic theory of OGD value realization, broadens insights into key drivers and inhibitors, and provides methodological support for implementing strategies such as prioritizing high-demand data releases, optimizing APIs and data rights confirmation processes, and enhancing storage security by mitigating data depreciation. Our findings indicate that enhancing OGD value cannot be achieved solely by increased accessibility or platform capabilities. Instead, it requires examining multifaceted feedback loops and synergistic interactions to uncover specific value generation mechanisms and identify bottlenecks.