Search results

The online healthcare platform (OHP) has become an essential element of the healthcare system, representing a technological shift in the job responsibilities of medical professionals. Drawing on a technology-based job demands–resources (JD-R) model, this study aims to examine how the technological characteristics of OHP affect doctors’ OHP use psychology and behavior.

This empirical study was based on a survey conducted among 423 doctors with OHP use experience. The proposed model underwent assessment through partial least squares structural equation modeling (PLS-SEM) to reveal the effects of technology-based job demands (i.e. technology-based work overload and technology-based work monitoring) and resources (i.e. perceived usefulness, facilitating conditions and IT mindfulness) on doctors’ OHP fatigue and continuance use intention.

Results suggest that technology-based work monitoring, perceived usefulness and facilitation conditions have significant impacts on doctors’ psychological and behavioral responses to using OHP, whereas technology-based work overload and IT mindfulness have a single impact on continuance use intention and fatigue of OHP.

It assists doctors, healthcare administrators, policymakers and technology developers in understanding OHPs’ technological characteristics, enabling them to harness its benefits and mitigate potential challenges. Additionally, given the self-reported cross-sectional data from China, future studies can improve generalizability and adopt experimental methods or longitudinal designs with objective data.

It extends the research on OHP by employing a technology-based JD-R model to explore work attributes and dual effects associated with OHP’s technological characteristics. It also enriches existing research by examining the role of OHP’s technological characteristics in doctors’ psychological and behavioral responses.

This paper aims to develop a comprehensive model on intra- and interpersonal emotion regulation (ER) in hospitality and tourism (H&T) service encounters.

A critical review and reflection of ER research from multiple disciplines was conducted. Methodologies appropriate for investigating ER were also reviewed.

A comprehensive framework was proposed to outline key influential factors, processes and consequences of intra- and interpersonal ER in service encounters in the H&T industry. Methodologies integrating advanced tools were suggested to measure complex and dynamic emotion generation and regulation processes in social interactions from a multimodal perspective.

The researchers developed a comprehensive conceptual model on both intra- and interpersonal ER based on a critical review of the most recent psychological research on ER. Various theoretical and methodological considerations are discussed, offering H&T scholars a solid starting point to explore dynamic emotion generation and regulation processes in complex social settings. Moreover, the model provides future directions for the expansion of ER theories, which have been mostly developed and tested based on laboratory research.

The proposed model addresses two critical issues identified in emotion research in the H&T field: the lack of a dynamic perspective and the neglect of the social nature of emotions. Moreover, the model provides a roadmap for future research.

This paper aims to identify the optimal forming angle for the selective laser melting (SLM) process and evaluate the mechanical properties of the SLM-formed GH3536 alloy in the aero-engine field.

Forming the samples with optimized parameters and analyzing the microstructure and properties of the block samples in different forming angles with scanning electron microscope, XRD, etc. so as to analyze and reveal the laws and mechanism of the block samples in different forming angles by SLM.

There are few cracks on the construction surface of SLM formed samples, and the microstructure shows columnar subgrains and cellular subgrains. The segregation of metal elements was not observed in the microstructure. The pattern shows strong texture strength on the (111) crystal plane. In the sample, the tensile strength of 60° sample is the highest, the plasticity of 90° forming sample is the best, the comprehensive property of 45° sample is the best and the fracture mode is plastic fracture. The comprehensive performance of the part is the best under the forming angle of 45°. To ensure the part size, performance and support structure processing, additional dimensions are added to the part structure.

In this paper, how to make samples with different forming angles is described. Combined with the standard of forged GH3536 alloy, the microstructure and properties of the samples are analyzed, and the optimal forming angle is obtained.

The online users’ characteristic information can provide decision support for policy-designing and construction of public strategies. Hence, this paper aims to conduct online public opinion mining on the recovery policy stimulating the economies stroked by COVID-19 epidemic. Also, sentimental analysis is performed to uncover the posters’ emotion towards the target policy.

This paper adopts bidirectional encoder representations from transformers (BERT) as classifier in classification tasks, including misinformation detection, subject analysis and sentimental analysis. Meanwhile, latent Dirichlet allocation method and sentiment formulations are implemented in topic modelling and sentiment analysis.

The experimental results indicate that public opinion is mainly non-negative to the target policy. The positive emotions mainly focus on the benefits that the recovery policy might bring to stimulate economy. On the other hand, some negative opinions concerned about the shortcomings and inconvenience of the target policy.

The authors figured out the key factors focused by the public opinion on the target recovery policy. Also, the authors indicated pros and cons of the recovery policy by analysing the emotion and the corresponding topics of the public opinion on social media. The findings of the paper can be generalized in other countries theoretically to help them design recovery policy against COVID-19.

The purpose of this paper is to synthesize and evaluate research on hospitality and tourism education in the past ten years (2005-2014) and to suggest directions for future inquiries.

From 13 hospitality and tourism journals, 644 full-length articles were reviewed. A multi-stage process was used to code and analyze each article by two analysts independently to ensure objectivity and accuracy. Two more researchers were involved in discussion to resolve differences in coding.

The analysis resulted in five distinctive meta-themes, grounded within 30 sub-themes. Observations are made in terms of teaching and learning, student development, curricula and programs, education environment and faculty development. Areas requiring further scholarly attention under each theme were identified.

This review provides an important reflection of the scholarly activities over the past decade on hospitality and tourism education, summarizes the current knowledge on various relevant concepts and offers avenues for future education research.

This review provides a one-stop information source for education and industry practitioners engaged in human capital, professional and executive development practices.

Operating under the dynamic industry and changing higher education environment, it is timely to conduct a comprehensive evaluation of recent education research to assess whether these activities address the challenges faced.

The rapid development of the Internet has led to an increasingly significant role for E-commerce business. This study examines how the green supply chain (GSC) operates on the E-commerce online channel (resell mode and agency mode) and the traditional offline channel with information sharing under demand uncertainty.

This study builds a multistage game model that considers the manufacturer selling green products through different channels. On the traditional offline channel, the competing retailers decide whether to share demand signals. Regarding the resale mode of E-commerce online channel, just E-tailer 1 determines whether to share information and decides the retail price. In the agency mode, the manufacturer decides the retail price directly, and E-tailer 2 sets the platform rate.

This study reveals that information accuracy is conducive to information value and profits on both channels. Interestingly, the platform fee rate in agency mode will inhibit the effect of a positive demand signal. Information sharing will cause double marginal effects, and price competition behavior will mitigate such effects. Additionally, when the platform fee rate is low, the manufacturer will select the E-commerce online channel for operation, but the retailers' profit is the highest in the traditional channel.

This research explores the interplay between different channel structures and information sharing in a GSC, considering price competition and demand uncertainty. Besides, we also considered what behaviors and factors will amplify or transfer the effect of double marginalization.

With the development and application of mobile internet access, social media represented by Weibo, WeChat, etc. has become the main channel for information release and sharing. High-impact users in social networks are key factors stimulating the large-scale propagation of information within social networks. User influence is usually related to the user’s attention rate, activity level, and message content. The paper aims to discuss these issues.

In this paper, the authors focused on Sina Weibo users, centered on users’ behavior and interactive information, and formulated a weighted interactive information network model, then present a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc., the model incorporated the time dimension, through the calculation of users’ attribute influence and interactive influence, to comprehensively measure the user influence of Sina Weibo users.

Compared with other models, the model reflected the dynamics and timeliness of the user influence in a more accurate way. Extensive experiments are conducted on the real-world data set, and the results validate the performance of the approach, and demonstrate the effectiveness of the dynamics and timeliness. Due to the similarity in platform architecture and user behavior between Sina Weibo and Twitter, the calculation model is also applicable to Twitter.

This paper presents a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc.

The purpose of this paper is to investigate alliance partner diversity (APD) as a driving force that potentially enhances firms’ innovation generation (IG) in interfirm open alliance contexts. The authors propose that APD enhances IG but that the effects depend on both alliance network position and the double-edged external knowledge search strategy. Building on the knowledge-based view and social capital theory, the authors formally model how external knowledge search strategies can lead to productive or destructive acquisitions of external knowledge in interfirm open alliance networks. The authors theorize that when an individual firm adopts a central position in a complex interfirm open alliance network, its propensity toward beneficial IG depends on its knowledge search strategy (i.e. its breadth and depth) due to the joint influence of network position and knowledge search strategy on innovation.

Using an original large-scale survey of high-tech firms, this study shows that the relationship between partner diversity and IG is contingent on a firm’s network position and knowledge search strategy. The authors also offer an original analysis of how knowledge search strategy (i.e. its breadth and depth) in network centrality (NC) affects the efficacy of knowledge acquisition in interfirm open alliance networks. Empirically, the authors provide an original contribution to the open innovation literature by integrating social capital and knowledge-based theory to rigorously measure firm IG.

Overall, our findings suggest that the knowledge search strategy imparts a double-edged effect that may promote or interfere with external knowledge in IG in the context of the diversity of alliance partners.

The work has important limitations, such as its analysis of a single industry in the empirical models. Therefore, further studies should consider multiple industries that may provide useful insights into innovation decisions.

External knowledge search is valuable, particularly in the high-tech industry, as external knowledge acquisition generates innovation output. This study serves to raise managers’ awareness of various approaches to external knowledge searches and highlights the importance of network position in knowledge acquisition from interfirm open alliance collaborations.

This paper is the first to investigate the double-edged effect of knowledge search on interfirm open alliance networks. It also contributes to the theoretical and practical literature on interfirm open alliance networks by reflecting on external knowledge search and underlying network centrality and APD factors.

This paper aims to solve the problem of information overload and reduce search costs. It proposes a social e-commerce online reputation formation model and community state-introduced model. A system dynamics trend simulation has been run to capture the relationship among the sellers, buyers, social e-commerce platforms and external environment to obtain an online reputation.

Empirical research relating to social e-commerce reputation has been used to confirm the influencing factors in social e-commerce, and a conceptual framework is developed for social e-commerce reputation formation. Thereafter, a trend simulation is generated to classify the relationship among the factors based on system dynamics. Also, the improved algorithm for community detection and a state-introduced model based on a Markov network are proposed to achieve better network partition for better online reputation management.

The empirical model captures the interaction effect of social e-commerce reputation and the state-introduced model to guide community public opinion and improve the efficiency of social e-commerce reputation formation. This helps minimize searching cost thereby improving social e-commerce reputation construction and management.

There is no appropriate online reputation system to be constructed to test the relationship proposed in the study for a field experiment. Also, deeper investigation for the nodes’ attributes in social networks should be made in future research. Besides, researchers are advised to explore measurement for the reputation of a given seller by using social media data as from Twitter or micro blogs.

Investigations that study online reputation in the social e-commerce are limited. The empirical research figured out the factors which can influence the formation of online reputation in social e-commerce. An SD model was proposed to explain the factors interaction and trend simulation was run. Also, a state-introduced model was proposed to highlight the effect of nodes’ attributes on communities’ detection to give a deeper investigation for the online reputation management.