Learning organizational ambidexterity : a joint-variance synthesis of exploration-exploitation modes on performance

Purpose: The purpose of this paper is to reexamine exploration-exploitation’s reciprocality in organizational ambidexterity (OA) research. OA figures prominently in a variety of organization science phenomena. Introduced as a two-stage model for innovation, theory specifies reciprocal reinforcement between the OA processes of exploration (eR) and exploitation (eT). In this study, the authors argue that previous analyses of OA necessarily neglect this reciprocality in favor of conceptualizations that conform to common statistical techniques. Design/Methodology/approach: The authors propose joint-variance (JV) as a soluble estimator of exploration–exploitation (eR-eT) reciprocality. An updated systematic literature synthesis yielded K = 50 studies (53 independent samples, N = 11,743) for further testing. Findings: Three primary findings are as follows: JV reduced negative confounding, explaining 45 per cent of between-study variance. JV quantified the positive confounding in separate meta-analytic estimates of eR and eT on performance because of double-counting (37.6 per cent), and substantive application of JV to hypothesis testing supported OA theoretical predictions. Research limitations/implications: The authors discuss practical consideration for eR-eT reciprocality, as well as theoretical contributions for cohering the OA empirical literature. Practical implications: The authors discuss design limitations and JV measurement extensions for the future. Social implications: Learning in OA literature has been neglected or underestimated. Originality/value: Because reciprocality is theorized, yet absent in current models, existing results represent confounded or biased evidence of the OA’s effect on firm performance. Subsequently, the authors propose JV as a soluble estimator of eR-eT learning modes

An Exploratory Study of Patient Falls

Debate continues between the contribution of education level and clinical expertise in the nursing practice environment. Research suggests a link between Baccalaureate of Science in Nursing (BSN) nurses and positive patient outcomes such as lower mortality, decreased falls, and fewer medication errors. Purpose: To examine if there a negative correlation between patient falls and the level of nurse education at an urban hospital located in Midwest Illinois during the years 2010-2014? Methods: A retrospective crosssectional cohort analysis was conducted using data from the National Database of Nursing Quality Indicators (NDNQI) from the years 2010-2014. Sample: Inpatients aged ≥ 18 years who experienced a unintentional sudden descent, with or without injury that resulted in the patient striking the floor or object and occurred on inpatient nursing units. Results: The regression model was constructed with annual patient falls as the dependent variable and formal education and a log transformed variable for percentage of certified nurses as the independent variables. The model overall is a good fit, F (2,22) = 9.014, p = .001, adj. R2 = .40. Conclusion: Annual patient falls will decrease by increasing the number of nurses with baccalaureate degrees and/or certifications from a professional nursing board-governing body

AI Governance for Businesses

Artificial Intelligence (AI) governance regulates the exercise of authority and control over the management of AI. It aims at leveraging AI through effective use of data and minimization of AI-related cost and risk. While topics such as AI governance and AI ethics are thoroughly discussed on a theoretical, philosophical, societal and regulatory level, there is limited work on AI governance targeted to companies and corporations. This work views AI products as systems, where key functionality is delivered by machine learning (ML) models leveraging (training) data. We derive a conceptual framework by synthesizing literature on AI and related fields such as ML. Our framework decomposes AI governance into governance of data, (ML) models and (AI) systems along four dimensions. It relates to existing IT and data governance frameworks and practices. It can be adopted by practitioners and academics alike. For practitioners the synthesis of mainly research papers, but also practitioner publications and publications of regulatory bodies provides a valuable starting point to implement AI governance, while for academics the paper highlights a number of areas of AI governance that deserve more attention

Analytics and Intelligence for Smart Manufacturing

Digital transformation is one of the main aspects emerged by the current 4.0 revolution. It embraces the integration between the digital and physical environment,including the application of modelling and simulation techniques, visualization, and data analytics in order to manage the overall product life cycle