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.

Knowledge withholding, which is defined as the likelihood that an individual devotes less than full effort to knowledge contribution, can be regarded as an emerging social deviance behavior for knowledge practice in online knowledge spaces. However, prior studies placed a great emphasis on proactive knowledge behaviors, such as knowledge sharing and contribution, but failed to consider the uniqueness of knowledge withholding. To capture the social‐deviant nature of knowledge withholding and to better understand how people deal with counterproductive knowledge behaviors, this study develops a research model based on the secondary control perspective. Empirical analyses were conducted using the data collected from an online knowledge space. The results indicate that both predictive control and vicarious control exert a positive influence on knowledge withholding. This study also incorporates knowledge‐withholding acceptability as a moderating variable of secondary control strategies. In particular, knowledge‐withholding acceptability enhances the impact of predictive control, whereas it weakens the effect of vicarious control on knowledge withholding. This study concludes with a discussion of the key findings, and the implications for both research and practice.

To advance the theoretical understanding on information adoption, this study tries to extend the information adoption model (IAM) in three ways. First, this study considers the relationship between source credibility and argument quality and the relationship between herding factors and information usefulness (i.e., bias effects). Second, this study proposes the interaction effects of source credibility and argument quality and the interaction effects of herding factors and information usefulness (i.e., synergistic effects). Third, this study explores the moderating role of an information characteristic – search versus experience information (i.e., information contingency effects). The proposed extended information adoption model (EIAM) is empirically tested through a 2 by 2 by 2 experiment in the social Q&A context, and the results confirm most of the hypotheses. Finally, theoretical contributions and practical implications are discussed.