Knowledge sharing motivations in online health communities: A comparative study of health professionals and normal users
TL;DR: Results show that reciprocity and altruism positively affect the knowledge sharing intention of both health professionals and normal users, and reputation and knowledge self-efficacy have a greater influence on theknowledge sharing intentions of health professionals than normal users.
About: This article is published in Computers in Human Behavior.The article was published on 2017-10-01. It has received 222 citations till now. The article focuses on the topics: Knowledge sharing & Online participation.
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TL;DR: The perceived health status differentially moderates the effects of privacy concerns and informational support on the PHI disclosure intention, which significantly influence personal health information (PHI) disclosure intention.
247Â citations
TL;DR: In this paper, the collective influences of cultural, hotel, and reviewer characteristics on online ratings in the hotel sector were investigated, and they empirically found a negative relationship between the reviewers' power distance and their online hotel ratings, thereby indicating that cultural factor plays a significant role in the customers' online rating behavior.
148Â citations
TL;DR: Perceived health risk and health self-efficacy significantly moderates the relationship between perceived risk and behavior intention, and this study highlights the integrated effects of social capital and risk perception attitude framework on health information-seeking intention.
145Â citations
TL;DR: Wang et al. as discussed by the authors explored the factors that affect the intention of sharing knowledge in health Q&A communities by integrating social capital and motivation theories, and empirically found that social capital positively affects intrinsic and extrinsic motivations, which then positively influence the intention for health professionals and normal users to share knowledge.
Abstract: Although health question-and-answer (Q&A) communities have become popular in recent years, only a few communities have successfully retained and motivated their members to share knowledge. The purpose of this paper is to focus on the ways by which social capital and motivation influence knowledge sharing intention from the perspectives of health professionals and normal users in health Q&A communities.,The developed theoretical model integrates individual motivation and social capital theories. On the basis of a sample comprising 363 members from health Q&A communities in China, the authors tested the hypotheses by using structural equation modeling.,This study empirically finds that social capital positively affects intrinsic and extrinsic motivations, which then positively influence the intention of health professionals and normal users to share knowledge. Motivations of members fully mediate the effects of social capital on knowledge sharing intention. Specifically, intrinsic motivation influences knowledge sharing intention more for health professionals than for normal users, whereas extrinsic motivation influences knowledge sharing intention more for normal users than for health professionals.,This study explores the factors that affect the intentions of sharing knowledge in health Q&A communities by integrating social capital and motivation theories. Individual motivations can then bridge social capital and knowledge sharing intention. The effects of the intrinsic and extrinsic motivations of two user types were further examined and compared. These findings can extend the understanding of the underlying drivers of intention to share knowledge in the context of e-health.
111Â citations
TL;DR: This paper combined the socio-technical approach and attachment theory to build a holistic framework to study the factors that affected addiction to short-form video apps and confirmed that social interaction anxiety and social isolation were positively related to interpersonal attachment and that personalization and entertainment were positive related to site attachment.
103Â citations
References
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TL;DR: In this paper, the statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined, and a drawback of the commonly applied chi square test, in additit...
Abstract: The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addit...
56,555Â citations
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01 Jan 1975TL;DR: In this article, the Mathematical Basis for Multiple Regression/Correlation and Identification of the Inverse Matrix Elements is presented. But it does not address the problem of missing data.
Abstract: Contents: Preface. Introduction. Bivariate Correlation and Regression. Multiple Regression/Correlation With Two or More Independent Variables. Data Visualization, Exploration, and Assumption Checking: Diagnosing and Solving Regression Problems I. Data-Analytic Strategies Using Multiple Regression/Correlation. Quantitative Scales, Curvilinear Relationships, and Transformations. Interactions Among Continuous Variables. Categorical or Nominal Independent Variables. Interactions With Categorical Variables. Outliers and Multicollinearity: Diagnosing and Solving Regression Problems II. Missing Data. Multiple Regression/Correlation and Causal Models. Alternative Regression Models: Logistic, Poisson Regression, and the Generalized Linear Model. Random Coefficient Regression and Multilevel Models. Longitudinal Regression Methods. Multiple Dependent Variables: Set Correlation. Appendices: The Mathematical Basis for Multiple Regression/Correlation and Identification of the Inverse Matrix Elements. Determination of the Inverse Matrix and Applications Thereof.
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TL;DR: This review revisits the classic definitions of intrinsic and extrinsic motivation in light of contemporary research and theory and discusses the relations of both classes of motives to basic human needs for autonomy, competence and relatedness.
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TL;DR: The article presents a running example which analyzes the same dataset via three very different statistical techniques and compares two classes of SEM: covariance-based SEM and partial-least-squaresbased SEM, and discusses linear regression models and guidelines as to when SEM techniques and when regression techniques should be used.
Abstract: The growing interest in Structured Equation Modeling (SEM) techniques and recognition of their importance in IS research suggests the need to compare and contrast different types of SEM techniques so that research designs can be selected appropriately. After assessing the extent to which these techniques are currently being used in IS research, the article presents a running example which analyzes the same dataset via three very different statistical techniques. It then compares two classes of SEM: covariance-based SEM and partial-least-squaresbased SEM. Finally, the article discusses linear regression models and offers guidelines as to when SEM techniques and when regression techniques should be used. The article concludes with heuristics and rule of thumb thresholds to guide practice, and a discussion of the extent to which practice is in accord with these guidelines.
5,688Â citations