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A question of interest is whether online social networks are effective in promoting behavioral changes and weight loss. The purpose of this paper is to examine the contagion effect of an online buddy network on individuals’ self-monitoring behavior.

This study collects data from an online weight-loss community and constructs an online buddy network. This study compares the effects of the network structure of the buddy network and the actor attributes when predicting self-monitoring performance by employing the auto-logistic actor attribute models.

This study confirms the contagion effect on weigh-in behavior in the online buddy network. The contagion effect is significantly predictive when controlling for actor attribute and other network structure effects.

There is limited evidence that one’s weight-related behavior can be affected by online social contacts. This study contributes to the literature on peer influence on health by examining the contagion effect on weight-related behavior between online buddies. The findings can assist in designing peer-based interventions to harness influence from online social contacts for weight loss.

Whether automatically generated summaries of health social media can assist users in appropriately managing their diseases and ensuring better communication with health professionals becomes an important issue. This paper aims to develop a novel deep learning-based summarization approach for obtaining the most informative summaries from online patient reviews accurately and effectively.

This paper proposes a framework to generate summaries that integrates a domain-specific pre-trained embedding model and a deep neural extractive summary approach by considering content features, text sentiment, review influence and readability features. Representative health-related summaries were identified, and user judgements were analysed.

Experimental results on the three real-world health forum data sets indicate that awarding sentences without incorporating all the adopted features leads to declining summarization performance. The proposed summarizer significantly outperformed the comparison baseline. User judgement through the questionnaire provides realistic and concrete evidence of crucial features that remarkably influence patient forum review summaries.

This study contributes to health analytics and management literature by exploring users’ expressions and opinions through the health deep learning summarization model. The research also developed an innovative mindset to design summarization weighting methods from user-created content on health topics.

Gamification elements have been increasingly used in online weight-loss communities to help users lose weight. The purpose of this paper is to systemically examine whether and how social interactions influence users’ continued participation in the context of online weight-loss competitions (OWCs).

This study empirically investigated sustained involvement in OWCs using a Cox proportional hazards model. Additionally, the research utilized a text-mining technique to identify various types of social support and explored their roles in sustaining participation behavior in OWCs.

Community response both within and outside OWCs positively influence users’ continued participation in OWCs. Moreover, whereas emotional support and companionship received within OWCs have a greater impact on users’ continued participation than informational support received within OWCs, informational support received outside OWCs has a greater impact on users’ continued participation than emotional support and companionship received outside OWCs.

This paper highlights users’ social needs in OWC engagement and provides empirical evidence on how different types and sources of social support influence continued participation behavior in OWCs. The research additionally provides management implications for online health community service providers.

Online medical repositories provide a platform for users to share information and dynamically access abundant electronic health data. It is important to determine whether case report information can assist the general public in appropriately managing their diseases. Therefore, this paper aims to introduce a novel deep learning-based method that allows non-professionals to make inquiries using ordinary vocabulary, retrieving the most relevant case reports for accurate and effective health information.

The dataset of case reports was collected from both the patient-generated research network and the digital medical journal repository. To enhance the accuracy of obtaining relevant case reports, the authors propose a retrieval approach that combines BERT and BiLSTM methods. The authors identified representative health-related case reports and analyzed the retrieval performance, as well as user judgments.

This study aims to provide the necessary functionalities to deliver relevant health case reports based on input from ordinary terms. The proposed framework includes features for health management, user feedback acquisition and ranking by weights to obtain the most pertinent case reports.

This study contributes to health information systems by analyzing patients' experiences and treatments with the case report retrieval model. The results of this study can provide immense benefit to the general public who intend to find treatment decisions and experiences from relevant case reports.

On the basis of the approach-avoidance motivation theory, this study aims to examine the role of information confusion in influencing consumer switching intention among social commerce platforms, with the mediating effect of emotional exhaustion and the moderating role of social overload.

This study applied a multi-method quantitative approach including a survey and two experiments. Data were obtained from consumers on popular social commerce platforms in China. The survey's sample size was 327 respondents, whereas a total of 1,621 consumers participated in the two experiments.

Findings from the survey reveal that information confusion affects switching intention directly and indirectly via emotional exhaustion. Moreover, social overload moderates the emotional exhaustion–switching intention relationship and the indirect impact of information confusion on switching intention. Results of the two experiments further confirm the relationships found in the survey.

This study develops and validates a mediation and moderation model which expectedly serves as a framework to better explain consumer switching intention on social commerce platforms. The study also offers fresh insights into consumer switching intention in the unique context of social commerce in an emerging market (i.e. China), which has been largely ignored in the prior literature.

Whether automatically generated summaries of health social media can aid users in managing their diseases appropriately is an important question. The purpose of this paper is to introduce a novel text summarization approach for acquiring the most informative summaries from online patient posts accurately and effectively.

The data set regarding diabetes and HIV posts was, respectively, collected from two online disease forums. The proposed summarizer is based on the graph-based method to generate summaries by considering social network features, text sentiment and sentence features. Representative health-related summaries were identified and summarization performance as well as user judgments were analyzed.

The findings show that awarding sentences without using all the incorporating features decreases summarization performance compared with the classic summarization method and comparison approaches. The proposed summarizer significantly outperformed the comparison baseline.

This study contributes to the literature on health knowledge management by analyzing patients’ experiences and opinions through the health summarization model. The research additionally develops a new mindset to design abstractive summarization weighting schemes from the health user-generated content.

The purpose of this paper is to propose and demonstrate a new additive manufacturing approach that breaks the layer-based point scanning limitations to increase fabrication speed, obtain better surface finish, achieve material flexibility and reduce equipment costs.

The freeform additive manufacturing approach conceptually views a 3D article as an assembly of freeform elements distributed spatially following a flexible 3D assembly structure, which conforms to the surface of the article and physically builds the article by sequentially forming the freeform elements by a vari-directional vari-dimensional capable material deposition mechanism. Vari-directional building along tangential directions of part surface gives surface smoothness. Vari-dimensional deposition maximizes material output to increase build rate wherever allowed and minimizes deposition sizes for resolution whenever needed.

Process steps based on geometric and data processing considerations were described. Dispensing and forming of basic vari-directional and vari-dimensional freeform elements and basic operations of joining them were developed using thermoplastics. Forming of 3D articles at build rates of 2-5 times the fused deposition modeling (FDM) rate was demonstrated and improvement over ten times was shown to be feasible. FDM compatible operations using 0.7 mm wire depositions from a variable exit-dispensing unit were demonstrated. Preliminary tests of a surface finishing process showed a result of 0.8-1.9 um Ra. Initial results of dispensing wax, tin alloy and steel were also shown.

This is the first time that both vari-directional and vari-dimensional material depositions are combined in a new freeform building method, which has potential impact on the FDM and other additive manufacturing methods.

This paper aims to examine the relation between the cost of debt and the adoption of eXtensible Business Reporting Language (XBRL).

The financial data are obtained from the Compustat database. Regression analysis is used to examine the research hypotheses.

The authors find that both voluntary and mandatory adoption of XBRL lead to a lower cost of debt for firms, with weak evidence that this reduction is greater for the former than the latter.

The findings support the policy of the USA Securities and Exchange Commission (SEC), and thus this paper recommends that adoption of XBRL should be mandatory for all public firms.

The findings encourage top managers to develop their firms’ XBRL systems.

The results support the SEC’s policy of mandatory XBRL adoption, as it can lead to greater financial reporting transparency and mitigate information asymmetry between management and bondholders.

Drawing on the perspective of trust and individual differences, this study developed and tested a model to identify the effect of leaders' demonstrated humility on employees' feedback-seeking processes. Specifically, it examined the role of feedback orientation and employees' trust in supervisors in the way humble leadership affects employees' feedback-seeking behavior (FSB).

The moderated mediation model was tested using matched surveys from 175 Chinese employees and their direct supervisors from various organizations.

The results revealed that humble leadership could predict employees' FSB through supervisor trust. Moreover, feedback orientation not only moderated the relationship between supervisor trust and employees' FSB but also moderated the indirect effect of humble leadership on employees' FSB via supervisor trust.

These findings can provide guidance to corporate organizations on how to motivate employees to actively seek feedback to improve their job performance.