Problem Definition: Data integrity is fundamental to effective quality management, as firms rely on accurate and reliable production information to detect process deviations, diagnose problems, and prevent defective products from reaching customers. Yet there remains limited understanding of how failures in data integrity translate into downstream product quality outcomes. We examine this question in the pharmaceutical manufacturing context by investigating how data integrity violations identified during regulatory inspections affect subsequent product recall hazard. Methodology/Results: We compile a dataset that connects FDA inspection records with pharmaceutical recall occurrences. Using the Cox Proportional-Hazards model, we find that products associated with data integrity violations face a 13.9% higher recall risk than their counterparts. Notably, data integrity violations in quality-related domains significantly predict highseverity recalls, while data integrity violations related to non-quality functions more likely correspond to lower-severity recall events. We also show that alternative information, reflected in generic status and withinfacility product similarity, could attenuate these effects along, respectively, the quality/high-severity and non-quality/low-severity pathways. Managerial Implications: Our findings highlight data integrity as an important dimension of operational reliability in pharmaceutical manufacturing rather than merely a compliance concern. For managers, the results suggest that responses to data integrity failures should account for both where within the quality system information integrity breaks down and what alternative information remains available to assess the underlying quality state. This perspective can help firms prioritize remediation efforts and strengthen the reliability of the information infrastructure underlying quality control.

Smart technologies play one important role in improving quality of life and fostering sustainable development. This study investigates the factors influencing smart thermostat adoption, with a specific focus on the different policy measures that encourage such adoption. Moreover, it explores how these policies potentially narrow the adoption gap between urban and rural regions. Through the logistic regression, we identified a positive relationship between the implementation of policies and the promotion of smart thermostat adoption, with the tax rebate policy being the most effective in mitigating urban-rural adoption disparities. These findings contribute to the broader discourse on sustainable development, technology diffusion, and equitable socioeconomic progress.