Managing employees and external partners effectively has been a primary concern for organizations and their managers. Many studies have investigated the effectiveness of organizational controls in a wide variety of contexts. Using organizational controls literature that discriminates among outcome, behaviour, and clan control, this study synthesizes the research on the effectiveness of these controls. In particular, the study examines 23,839 organizational controls–performance relationships from 120 independent samples, and tests several new hypotheses using advanced meta‐analytic methods. The results indicate that outcome, behaviour, and clan controls generally enhance performance, with each control having a distinct performance effect. Our analysis also demonstrates that controls function as complements to one another. This finding indicates that one form of control increases the effectiveness of other forms of control. We also examine the organizational controls–performance relationships across various contexts, and our results show that they vary according to the type of task. The paper concludes with a discussion on the theoretical and managerial implications of these findings.

Background Despite the high potential of big data, their applications in health care face many organizational, social, financial, and regulatory challenges. The societal dimensions of big data are underrepresented in much medical research. Little is known about integrating big data applications in the corporate routines of hospitals and other care providers. Equally little is understood about embedding big data applications in daily work practices and how they lead to actual improvements for health care actors, such as patients, care professionals, care providers, information technology companies, payers, and the society. Objective This planned study aims to provide an integrated analysis of big data applications, focusing on the interrelations among concrete big data experiments, organizational routines, and relevant systemic and societal dimensions. To understand the similarities and differences between interactions in various contexts, the study covers 12 big data pilot projects in eight European countries, each with its own health care system. Workshops will be held with stakeholders to discuss the findings, our recommendations, and the implementation. Dissemination is supported by visual representations developed to share the knowledge gained. Methods This study will utilize a mixed-methods approach that combines performance measurements, interviews, document analysis, and cocreation workshops. Analysis will be structured around the following four key dimensions: performance, embedding, legitimation, and value creation. Data and their interrelations across the dimensions will be synthesized per application and per country. Results The study was funded in August 2017. Data collection started in April 2018 and will continue until September 2021. The multidisciplinary focus of this study enables us to combine insights from several social sciences (health policy analysis, business administration, innovation studies, organization studies, ethics, and health services research) to advance a holistic understanding of big data value realization. The multinational character enables comparative analysis across the following eight European countries: Austria, France, Germany, Ireland, the Netherlands, Spain, Sweden, and the United Kingdom. Given that national and organizational contexts change over time, it will not be possible to isolate the factors and actors that explain the implementation of big data applications. The visual representations developed for dissemination purposes will help to reduce complexity and clarify the relations between the various dimensions. Conclusions This study will develop an integrated approach to big data applications that considers the interrelations among concrete big data experiments, organizational routines, and relevant systemic and societal dimensions. International Registered Report Identifier (IRRID) DERR1-10.2196/16779

Background The recent surge in clinical and nonclinical health-related data has been accompanied by a concomitant increase in personal health data (PHD) research across multiple disciplines such as medicine, computer science, and management. There is now a need to synthesize the dynamic knowledge of PHD in various disciplines to spot potential research hotspots. Objective The aim of this study was to reveal the knowledge evolutionary trends in PHD and detect potential research hotspots using bibliometric analysis. Methods We collected 8281 articles published between 2009 and 2018 from the Web of Science database. The knowledge evolution analysis (KEA) framework was used to analyze the evolution of PHD research. The KEA framework is a bibliometric approach that is based on 3 knowledge networks: reference co-citation, keyword co-occurrence, and discipline co-occurrence. Results The findings show that the focus of PHD research has evolved from medicine centric to technology centric to human centric since 2009. The most active PHD knowledge cluster is developing knowledge resources and allocating scarce resources. The field of computer science, especially the topic of artificial intelligence (AI), has been the focal point of recent empirical studies on PHD. Topics related to psychology and human factors (eg, attitude, satisfaction, education) are also receiving more attention. Conclusions Our analysis shows that PHD research has the potential to provide value-based health care in the future. All stakeholders should be educated about AI technology to promote value generation through PHD. Moreover, technology developers and health care institutions should consider human factors to facilitate the effective adoption of PHD-related technology. These findings indicate opportunities for interdisciplinary cooperation in several PHD research areas: (1) AI applications for PHD; (2) regulatory issues and governance of PHD; (3) education of all stakeholders about AI technology; and (4) value-based health care including “allocative value,” “technology value,” and “personalized value.”

No abstract

BACKGROUND Despite the high potential of big data, its applications in healthcare face manifold organizational, social, financial, and regulatory challenges. Big data embedment in the societal dimensions of healthcare systems is underrepresented in medical research. Little is known about integrating big data applications in the corporate routines of hospitals and other care providers. Equally little is understood about embedding big data applications in daily work practices and how they lead to actual improvements for healthcare actors, such as patients, care professionals, care providers, IT companies, payers and society. OBJECTIVE This planned study aims to provide an integrated analysis of big data applications, focusing on the interrelations between concrete big data experiments, organizational routines and relevant systemic and societal dimensions. To understand the similarities and differences between interactions in various contexts, the study covers 12 big data pilot projects in eight European countries, each with its own healthcare system. Workshops will be held with stakeholders to discuss the findings, our recommendations, and their implementation. Dissemination is supported by visual representations developed to share the knowledge gained METHODS This study will utilize a mixed-methods approach that combines performance measurements, interviews, document analysis and co-creation workshops. Analysis will be structured around four key dimensions: ‘performance’, ‘embedding’, ‘legitimation’ and ‘value creation’. Data and their interrelations across the dimensions will be synthesized per application and per country. RESULTS The multidisciplinary focus of this study enables us to combine insights from several social sciences (health policy analysis, business administration, innovation studies, organization studies, ethics and health services research) to advance a holistic understanding of big data value realization. The multinational character enables comparative analysis across eight European countries: Austria, France, Germany, Ireland, the Netherlands, Spain, Sweden, and the United Kingdom. Given that national and organizational contexts change over time, note that it will not be possible to isolate the factors and actors that explain the implementation of the big data applications. The visual representations developed for dissemination purposes will help to reduce complexity and clarify the relations between the various dimensions. CONCLUSIONS This study will develop an integrated approach to big data applications that considers the interrelations between concrete big data experiments, organizational routines and relevant systemic and societal dimensions. . CLINICALTRIAL This study is not a trial.