This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence Newcastle University ePrints-eprint.ncl.ac.uk Wang Y, Kung K, Byrd TA. Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations.

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A model of brand value co-creation by integrating its the antecedents of interactivity, social support, and relationship quality is proposed. Empirical data was collected from the brand pages of a social networking website in China. Structural equation modeling was adopted to analyze the data. The results demonstrate that interactivity, specifically, consumer-consumer interaction and consumer-seller interaction, positively affects social support, which in turn enhances consumers' intention to co-create brand value. The research contributes to the extant literature by providing an underlying understanding of how customers engage in brand value co-creation activities within social commerce context.

Consumers are empowered to exert influence on brands through social networking sites (SNSs), which make it possible for consumers to become active content creators in their relationship with firms. To further understand brand value co-creation, we use the socio-technical theory to build a model of brand co-creation with key antecedents−social commerce information sharing, social support, and relationship quality, with privacy concerns as a moderator. Through an empirical study, we found that social commerce information sharing, social support and relationship quality positively affect brand co-creation directly/indirectly and privacy concerns moderate the effects of social commerce information sharing on brand co-creation. This article contributes to the literature on the value co-creation paradigm and social commerce by: 1) developing the concept of brand co-creation in social commerce; 2) understanding how consumers engage in online brand co-creation activities; 3) arguing that privacy concerns may hamper the effects of brand co-creation. Our study provides an innovative approach to brand management practices in today's marketplace.

Drawing on the resource-based theory and dynamic capability view, this study examines the mechanisms by which business analytics capabilities (i.e. the effective use of data aggregation, analytics and data interpretation tools) in healthcare units, indirectly influence decision making effectiveness through the mediating role of knowledge absorptive capacity. Design/Methodology/Approach: Using a survey method, this study collected data from hospitals in Taiwan. Of the 155 responses received, three were incomplete, giving a 35.84% response rate, with 152 valid data points. Structural equation modelling (SEM) was used to test the hypotheses. Findings: This study conceptualizes, operationalizes and measures the business analytics (BA) capability as a multi-dimensional construct which is formed by capturing the functionalities of BA systems in health care, leading to the conclusion that healthcare units are likely to obtain valuable knowledge through utilizing the data interpretation tools effectively. The effective use of data analysis and interpretation tools in healthcare units indirectly influences decision making effectiveness: an impact that is mediated by absorptive capacity. Originality/Value: This study adds value to the literature by conceptualizing BA capabilities in healthcare and demonstrating how knowledge absorption matters when implementing BA in the decision making process. The mediating role of absorptive capacity not only provides a mechanism by which BA can contribute to decision making practices, but also offers a new solution to the puzzle of the IT productivity paradox in healthcare settings.

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