Online health communities (OHCs) are platforms that help health consumers to communicate with each other and obtain social support for better healthcare outcomes. However, it is…
Abstract
Purpose
Online health communities (OHCs) are platforms that help health consumers to communicate with each other and obtain social support for better healthcare outcomes. However, it is usually difficult for community members to efficiently find appropriate peers for social support exchange due to the tremendous volume of users and their generated content. Most of the existing user recommendation systems fail to effectively utilize the rich social information in social media, which can lead to unsatisfactory recommendation performance. The purpose of this study is to propose a novel user recommendation method for OHCs to fill this research gap.
Design/methodology/approach
This study proposed a user recommendation method that utilized the adapted matrix factorization (MF) model. The implicit user behavior networks and the user influence relationship (UIR) network were constructed using the various social information found in OHCs, including user-generated content (UGC), user profiles and user interaction records. An experiment was conducted to evaluate the effectiveness of the proposed approach based on a dataset collected from a famous online health community.
Findings
The experimental results demonstrated that the proposed method outperformed all baseline models in user recommendation using the collected dataset. The incorporation of social information from OHCs can significantly improve the performance of the proposed recommender system.
Practical implications
This study can help users build valuable social connections efficiently, enhance communication among community members, and potentially contribute to the sustainable prosperity of OHCs.
Originality/value
This study introduces the construction of the UIR network in OHCs by integrating various social information. The conventional MF model is adapted by integrating the constructed UIR network for user recommendation.
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Huiying Gao, Shan Lu and Xiaojin Kou
The purpose of this study is to identify medical service quality factors that patients care about and establish a medical service quality evaluation index system by analyzing…
Abstract
Purpose
The purpose of this study is to identify medical service quality factors that patients care about and establish a medical service quality evaluation index system by analyzing online reviews of medical and healthcare service platforms in combination with a questionnaire survey.
Design/methodology/approach
This study adopts a combination of review mining and questionnaire surveys. The latent Dirichlet allocation (LDA) model was used to mine hospital reviews on the medical and healthcare service platform to obtain the medical service quality factors that patients pay attention to, and then the questionnaire was administered to obtain the relative importance of these factors to patients' perception of service quality. Finally, the index system was established.
Findings
The medical service quality factors patients care about include medical skills and ethics, registration service, operation effect, consulting communication, drug therapy, diagnosis process and medical equipment.
Research limitations/implications
The identification of medical service quality factors provides a reference for medical institutions to improve their medical service quality.
Originality/value
This study uses online review mining to obtain medical service quality factors from the perspective of patients, which is different from previous methods of obtaining factors from relevant literature or expert judgments; then, based on the mining results, a medical service quality evaluation index system is established by using questionnaire data.
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Recommending suitable content for users of online health communities (OHCs) is critical for overcoming information overload problem and facilitate medical decision making, but…
Abstract
Purpose
Recommending suitable content for users of online health communities (OHCs) is critical for overcoming information overload problem and facilitate medical decision making, but remains not fully investigated. This study aims to provide a content recommendation approach to automatically match valuable health-related information for OHC members.
Design/methodology/approach
A framework of health-related content recommendation was proposed by leveraging rich social information in online communities. The authors constructed user influence relationship (UIR) utilizing users' interaction records, user profiles and user-generated content. The initial user rating matrix and the user post matching matrix were then created by analyzing text content of posts. Finally, the user rating matrix and the recommended content were generated for community members. Datasets were collected from an OHC to evaluate the effectiveness of the proposed approach.
Findings
The experimental results revealed that the proposed method statistically outperformed baseline models in content recommendation for users of OHCs.
Research limitations/implications
The incorporation of social information can significantly enhance the performance of content recommendation in OHCs. The user post matching degree based on text analysis can improve the effectiveness of recommendation.
Practical implications
This study potentially contributes to the social support exchange and medical decision making of community members and the sustainable prosperity of OHCs.
Originality/value
This study proposes a novel social content recommendation method for online health consumers based on UIRs by leveraging social information in OHCs. The results indicate the significance of social information in content recommendation of healthcare social media.
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In view of the difficulty in determining the key parameters d in the Corten-Dolan model, based on the introduction of small loads, damage degrees and stress states to the…
Abstract
Purpose
In view of the difficulty in determining the key parameters d in the Corten-Dolan model, based on the introduction of small loads, damage degrees and stress states to the Corten-Dolan model and the existing improved model, the sequential effects of the adjacent two-stage load were further considered.
Design/methodology/approach
Two improved Corten-Dolan models were established on the basis of modifying the parameter d by two different methods, namely, increasing stress ratio coefficient as well as considering the effects of loading sequence and damage degree as independent influencing factors respectively. According to the test data of the welded joints of common materials (standard 45 steel), alloy materials (standard 16Mn steel) and Q235B steel, the validity and feasibility of the above two improved models for fatigue life prediction were verified.
Findings
Results show that, compared with the traditional Miner model and the existing Corten-Dolan improved model, the two improved models have higher prediction accuracy in the fatigue life prediction of welding materials whether under two-stage load or multi-stage load.
Originality/value
Because the mathematical expressions of the models are relatively simple and need no multi-layer iterative calculation, it is convenient to predict the fatigue life of welded structure in practical engineering.
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Zhijun Yan, Roberta Bernardi, Nina Huang and Younghoon Chang
Yankun Zhou, Xiaoqiang Zhi, Huiying Wu and Yongqing Li
This paper aims to examine the role of the Chinese People’s Political Consultative Conference (CPPCC), a political advisory body in China, in addressing environmental challenges.
Abstract
Purpose
This paper aims to examine the role of the Chinese People’s Political Consultative Conference (CPPCC), a political advisory body in China, in addressing environmental challenges.
Design/methodology/approach
This study uses 457 CPPCC environmental proposals across 160 cities for the period of 2013 to 2015 and a mediation effect model to examine the effect of CPPCC environmental proposals on environmental quality.
Findings
This study shows that CPPCC environmental proposals improve environmental quality; and the relationship between CPPCC environmental proposals and environmental quality is partially mediated by enforcement of environmental laws and regulations only although the proposals positively influence both law enforcement and environmental public budget expenditures.
Research limitations/implications
Future research may examine how the interaction between the government and other important stakeholders such as non-governmental organizations can help improve environmental quality. In addition, future research may examine whether other policy tools such as pollution tax and fees, environmental subsidies, and emissions trading can play a role in dealing with environmental issues.
Practical implications
This study provides evidence that supports CPPCC members to take an even more active role in public governance by engaging with both the government and the public.
Social implications
The CPPCC’s participation in public governance helps the government respond to critical issues more effectively. The government should pay close attention to CPPCC proposals when making public policies. Furthermore, the government probably needs to review its policies in relation to environmental expenditures.
Originality/value
This study is the first to examine the role of the CPPCC, a political advisory body, in addressing environmental challenges through functioning as a bridge between government and the public, whereas the extant literature has predominantly focused on the role of government, market and the public.
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Zhaoping Duan, Zhihua Ding, Yupeng Mou, Xueling Deng and Huiying Zhang
The residential sector is a principal contributor to global energy consumption, underscoring the critical importance of promoting green housing initiatives to mitigate energy use…
Abstract
Purpose
The residential sector is a principal contributor to global energy consumption, underscoring the critical importance of promoting green housing initiatives to mitigate energy use and environmental degradation. The prevalence of uncertainty in the natural environment, exemplified by phenomena like extreme weather events, highlights the urgent need for adaptive strategies and sustainable practices to mitigate the impact on human communities and ecosystems. Against this backdrop, this paper presents a theoretical framework examining the influence of natural environmental uncertainty on consumers' willingness to purchase green housing.
Design/methodology/approach
Through three experiments, this study modeled the mechanism by which the natural environment uncertainty affects consumers' willingness to purchase green housing, and then verified the mediating effect of the threat of ontological security and the moderating effect of the degree of consumers' natural connectedness.
Findings
This paper concludes (1) natural environmental uncertainty exerts a significant positive impact on the willingness to purchase green housing, with the threat to ontological security serving as a pivotal mediating variable; (2) the degree of natural connectedness significantly moderates the effect of ontological security threats on the purchasing intent for green housing.
Originality/value
This research contributes to the marketing literature by offering a novel perspective on the impact of natural environmental uncertainty on consumer behavior, augmenting the body of knowledge concerning the determinants of green housing purchase intentions, and provides new ideas for marketers.
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Jie Sun, Xi Yu Leung, Huiying Zhang and Kim Williams
This study aims to examine how COVID-related corporate social responsibility (CSR) activities affect future Generation Z employees’ intention to join the hotel industry through…
Abstract
Purpose
This study aims to examine how COVID-related corporate social responsibility (CSR) activities affect future Generation Z employees’ intention to join the hotel industry through experimental designs.
Design/methodology/approach
Based on signaling theory, construal level theory and value theory, this study established an integrated research framework to explain the mechanism of CSR communication. The proposed study conducted three online experiments on a total of 463 participants. ANCOVA test and PROCESS macro were performed to analyze the data for main, mediation and moderation effects.
Findings
The results of this study indicate that in-kind donation is more efficacious in improving Generation Z’s job pursuit intention, as compared to cause-related marketing (CRM). CSR messages framed in a “how” mindset are favored by Generation Z members who are either unemployed or eager to change their current job. The findings also confirm the effect of brand warmth as a mediator and other-regarding personal value as a moderator.
Research limitations/implications
The present study contributes to the limited knowledge on CSR initiatives by addressing the research gap of future employees and examining CSR as a response to COVID-19. The findings also provide hotel executives actionable implications to plan and communicate future CSR programs, especially during times of crisis.
Originality/value
This study is one of the first studies to address Generation Z employees and to investigate the role of CSR initiatives on future hotel workers.
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Hong Luo and Huiying Qiao
A new round of technological revolution is impacting various aspects of society. However, the importance of technology adoption in fostering firm innovation is underexplored…
Abstract
Purpose
A new round of technological revolution is impacting various aspects of society. However, the importance of technology adoption in fostering firm innovation is underexplored. Therefore, this study aims to investigate whether robot adoption affects technological innovation and how human capital plays a role in this relationship in the era of circular economy.
Design/methodology/approach
Based on the robot adoption data from the International Federation of Robotics (IFR) and panel data of China's listed manufacturing firms from 2011 to 2020, this study uses regression models to test the impact of industrial robots on firm innovation and the mediating role of human capital.
Findings
The results demonstrate that the adoption of industrial robots can significantly promote high-quality innovation. Specifically, a one-unit increase in the number of robots per 100 employees is associated with a 13.52% increase in the number of invention patent applications in the following year. The mechanism tests show that industrial robots drive firm innovation by accumulating more highly educated workers and allocating more workers to R&D jobs. The findings are more significant for firms in industries with low market concentration, in labor-intensive industries and in regions with a shortage of high-end talent.
Research limitations/implications
Due to data limitations, the sample of this study is limited to listed manufacturing firms, so the impact of industrial robots on promoting innovation may be underestimated. In addition, this study cannot observe the dynamic process of human capital management by firms after adopting robots.
Practical implications
The Chinese government should continue to promote the intelligent upgrading of the manufacturing industry and facilitate the promotion of robots in innovation. This implication can also be applied to developing countries that hope to learn from China's experience. In addition, this study emphasizes the role of human capital in the innovation-promoting process of robots. This highlights the importance of firms to strengthen employee education and training.
Social implications
The adoption of industrial robots has profoundly influenced the production and lifestyle of human society. This study finds that the adoption of robots contributes to firm innovation, which helps people gain a deeper understanding of the positive impacts brought about by industrial intelligence.
Originality/value
By exploring the impact of industrial robots on firm innovation, this study offers crucial evidence at the firm level to comprehend the economic implications of robot adoption based on circular economy and human perspectives. Moreover, this study reveals that human capital is an important factor in how industrial robots affect firm innovation, providing an important complement to previous studies.