Influencing factors of personal privacy protection in open government data: an interpretive structural modeling approach

Purpose The purpose of this paper is to build a comprehensive structural model to reveal the interrelationships of factors influencing personal privacy protection in open government data (OGD) and evaluate the varying degrees of influence. Design/methodology/approach Through an extensive literature review and expert consultation, we identify 17 factors influencing personal privacy protection in OGD. We use interpretive structural modeling (ISM) and a matrix of cross-impact multiplications applied to classification (MICMAC) analysis to build a hierarchical model and classify these factors into four clusters. Findings Our results indicate that privacy protection legislation, data security legislation, privacy regulation, judicial remedies and privacy protection technology play a crucial role in ensuring personal privacy protection in OGD, as they exert a significant influence on other factors. System security, data collection, data usage and privacy intentions lead directly to personal privacy protection in OGD. Originality/value This study (1) contributes to existing research on personal privacy protection in OGD by revealing the hierarchical organization of the determinants of personal privacy protection; (2) provides logical consistency in the ISM-based model for personal privacy protection in OGD by grouping identified factors into dependent and independent categories and (3) extends the applicability of the integrated ISM and MICMAC approaches to the phenomenon of personal privacy protection in OGD.