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This study aims to develop a comprehensive measurement that consists of the most significant indicators of team effectiveness. Based on the input-mediator-outcome model, this measurement assesses two main aspects related to team effectiveness: team processes and emergent states (TPES).

The scale was developed and validated following three major steps. First, items were generated through literature reviews and expert reviews. Second, a pretest was conducted to refine the scale among 755 Chinese employees recruited on a survey service platform. Finally, in the evaluation section, 3,950 samples were used for scale validation, including a predictive validity test examining the relationship between TPES and team outcomes among 3,550 participants in paired samples (team members and leaders), from 381 work teams from several private companies in IT industries in China.

The TPES scale measures the team members’ interactions and the team status, consists of five dimensions: goal and role, collaboration, innovation, motivation and cohesiveness, showing acceptable reliability, validity and model fit. The five dimensions show significant positive effects on team performance and members’ satisfaction.

This novel measure of team process and emergent state tailored to IT companies provides a way to comprehensively diagnose a team’s strengths and/or weaknesses, thus suggesting directions for team development.

Free knowledge sharing in the online health community has been widely documented. However, whether free knowledge sharing can help physicians accumulate popularity and further the accumulated popularity can help physicians attract patients remain unclear. To unveil these gaps, this study aims to examine how physicians' popularity are affected by their free knowledge sharing, how the relationship between free knowledge sharing and popularity is moderated by professional capital, and how the popularity finally impacts patients' attraction.

The authors collect a panel dataset from Hepatitis B within an online health community platform with 10,888 observations from April 2020 to August 2020. The authors develop a model that integrates free knowledge sharing, popularity, professional capital, and patients' attraction. The hierarchical regression model is used to for examining the impact of free knowledge sharing on physicians' popularity and further investigating the impact of popularity on patients' attraction.

The authors find that the quantity of articles acted as the heuristic cue and the quality of articles acted as the systematic cue have positive effect on physicians' popularity, and this effect is strengthened by physicians' professional capital. Furthermore, physicians' popularity positively influences their patients' attraction.

This study reveals the aggregation of physicians' popularity and patients' attraction within online health communities and provides practical implications for managers in online health communities.

Drawing upon social information processing theory, this study investigates the correlation between self-serving leadership and employee expediency. It also explores the mediating effect of self-interest motivation and the moderating effect of trait mindfulness.

A total of 147 part-time MBA students were enlisted to participate in a scenario experiment (Study 1), and 291 valid employee questionnaires were collected through a multiple-time point survey (Study 2). SPSS 23.0, MPLUS 8.0 and PROCESS programs were used to analyze the data and test the hypotheses.

Study 1 illustrated a positive correlation between self-serving leadership and employee expediency. It also identified self-interest motivation as a mediating factor in the correlation between self-serving leadership and expediency. Study 2 replicated the results obtained in Study 1 and expanded upon them by demonstrating that trait mindfulness moderates the association between self-serving leadership and self-interest motivation. Additionally, trait mindfulness moderates the indirect effect of self-serving leadership on expediency.

This research argues that organizations should take steps to prevent self-serving leadership in order to reduce employee expediency. Furthermore, it is advisable to provide ethics training to employees who exhibit high trait mindfulness, as they show increased sensitivity to self-serving leadership and are more likely to engage in unethical behavior.

This study expands the existing research on the ethical outcomes of self-serving leadership and contributes to a deeper understanding of the negative aspects of trait mindfulness.

This study aims to comprehensively examine sustainable mutual funds (SMFs) research by conducting a systematic literature review and bibliometric analysis of articles spanning 33 years from 1991 to 2023. This review seeks to uncover the principal contributors and the structural framework of knowledge within the realm of business, finance and management research concerning SMFs.

Following the “Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR)” methodology, the author selected 597 documents for the analysis and collected the bibliographic information from the Scopus database. The author uses RStudio and VOSviewer software to address five research questions.

The findings indicate a notable expansion in research concerning SMFs within high-quality journals over the last 33 years. The review illuminates the principal contributors in SMFs research by using performance analysis based on journal, article, author, country and institution criteria. By using science mapping techniques, the author identifies five prevailing themes and outlines future research prospects in the domain of SMFs.

This review paper can serve as a roadmap for future researchers, aiding them in discerning the trending research topics within this domain.

To the best of the author’s knowledge, this is the first study that comprehensively provides an overview of different variants, diverse strands and research hotspots of SMFs literature. The study offers insight into the evolution of SMFs, showcasing their progression from a segmented market to a prominently specialized domain in the contemporary landscape.

This study aims to explore the impact of cultural practices on consumers’ buying behavior in sales promotional activities toward the fashion clothing brands based on the Theory of Black-Box Model under the conditions of COVID-19 at Pakistan.

The study has collected data through questionnaire-based survey from 600 consumers of fashion clothing brands using convenience sampling technique in Pakistan and analyzed through PLS-SEM to find results.

The results confirmed that cultural practices and sales promotional activities have positive significant relationship with the consumers’ buying behavior during the celebration of events, also under the conditions of COVID-19. The study also found that ethnocentrism and xenocentrism are positively associated with cultural practices while social factors, physical factors, product innovation and marketing innovation with sales promotional activities.

The outcomes provide interesting insights about consumers’ assessment toward the sale’s promotional activities and cultural practices of fashion clothing brands during the celebration of events and guide the marketing practitioners to develop the customers edifying and environment-oriented business strategies to boost up the buying behavior in crisis situations.

Although prior research has widely investigated the cultural practices, sales promotion and consumers’ buying behavior in various settings, but to the best of the authors’ knowledge, this is the first study in these domains.

This study aims to investigate the influences of Al₂O₃ mass fraction on the corrosive wear and electrochemical behaviors of FeAl–xAl₂O₃ coatings.

FeAl–xAl₂O₃ coatings were prepared on S355 steel by laser cladding to improve its corrosive wear and electrochemical properties.

The average coefficients of friction and wear rates of FeAl–xAl₂O₃ coatings are decreased with the Al₂O₃ mass fraction, and the Al₂O₃ plays a positive role in the corrosion wear resistance. Moreover, the charge transfer resistance of FeAl–xAl₂O₃ coatings is increased with the Al₂O₃ mass fraction, showing the FeAl–15%Al₂O₃ coating has the best corrosion resistance. The findings show the corrosion resistance of FeAl–15%Al₂O₃ coating is the highest among the three kinds of coatings.

Al₂O₃ was first added into FeAl coatings to further improve its corrosive wear and electrochemical properties by laser cladding.

This paper aims to present an unmarked method including entire two-dimensional (2D) and three-dimensional (3D) methods to recover absolute 3D humanoid robot poses from multiview images.

The method consists of two separate steps: estimating the 2D poses in multiview images and recovering the 3D poses from the multiview 2D heatmaps. The 2D one is conducted by High-Resolution Net with Epipolar (HRNet-Epipolar), and the Conditional Random Fields Humanoid Robot Pictorial Structure Model (CRF Robot Model) is proposed to recover 3D poses.

The performance of the algorithm is validated by experiments developed on data sets captured by four RGB cameras in Qualisys system. It illustrates that the algorithm has higher Mean Per Joint Position Error than Direct Linear Transformation and Recursive Pictorial Structure Model algorithms when estimating 14 joints of the humanoid robot.

A new unmarked method is proposed for 3D humanoid robot pose estimation. Experimental results show enhanced absolute accuracy, which holds important theoretical significance and application value for humanoid robot pose estimation and motion performance testing.

Over recent years, public health threats, economic losses and crises of confidence resulting from adverse events along the global food chains have pressured food traceability systems to operate more efficiently. Based on that, this paper aims to expand the body of knowledge on the connection between Industry 4.0 technologies and food chain traceability and how they have been integrated.

Before conducting a systematic literature review (SLR), we consulted academic experts in traceability and Industry 4.0 technologies to define the leading 4.0 technologies adopted. We then developed protocols and criteria for article selection. Based on the developed codebook, we performed a content analysis, refining it through discussions with experienced researchers.

This paper identified seven Industry 4.0 technologies applied in conjunction with support and device systems. These combinations hold promise for generating value for food chain traceability systems. Value for the food supply chain originates from five sources: trust, process improvement, data processing, security, sustainability and regulation. Besides, three barriers to implementing such traceability technologies were identified: resources, lack of structure, stakeholders and values.

While this study focused specifically on the food chain, the technology combinations, values, barriers and categories identified herein can inform analyses for other chains, e.g. the pharmaceutical chain. Moreover, due to the recent growth of this topic, some existing values and barriers may not have been fully explored and the technology combinations may vary slightly.

The results of this study provide food chain professionals with a comprehensive understanding of the values and corresponding barriers associated with employing Industry 4.0 traceability technologies. This knowledge can be leveraged to develop effective strategies and implement improvements in traceability, thereby benefiting society and addressing identified barriers.

This study expands the literature on using Industry 4.0 technologies for traceability in food chains, providing valuable directions to build safer, more efficient, transparent and potentially more sustainable food chains. Additionally, we provide promising avenues for future research.

This study aims to determine how cognitive diversity at the workplace influences team creativity. In this regard, the authors examined knowledge sharing and team-focused inclusion through which team members’ cognitive diversity was expected to elevate their positive work outcomes.

A quantitative method is used to accumulate the data. The authors surveyed workers and their respective managers at a single China-based food company. The supervisors rated the outcome variables (creativity and team effectiveness) regarding their employees, whereas employees were asked to rate the cognitive diversity, inclusion and knowledge sharing within the workgroup. The final valid sample size (n = 391) consisted of 137 workgroups with an adequate response rate (62.3%).

Cognitive diversity is related to team effectiveness but not creativity. The research found that cognitive diversity can increase creativity only through enhanced inclusion and knowledge sharing. Inclusion, likewise, explained the impact of cognitive diversity on effectiveness.

The originality of the current research lies in its contemporary exploration of inclusion and cognitive diversity and their pathways to team creativity and effectiveness. The social capital theory was applied to explain the proposed relationships.

The aim of the current study is to recommend and compare the estimates of finite element model (FEM), analytical model, and artificial neural networks (ANN) model for capturing the LCC of FCSC members. A database comprising 325 FCSC columns was constructed from previous studies to propose FEM and ANN models while the analytical model was proposed based on a database of 712 samples and encasing mechanics of steel tube and FRP wraps. The concrete damage plastic model was used for concrete along with bilinear and linear elastic models for steel tube and FRP wraps, respectively. Analytical and ANN models effectively considered the lateral encasing mechanism of FCSC columns for accurate predictions.

The study aimed to compare the prediction accuracy of finite element (FEM), analytical, and artificial neural network (ANN) models for the load-carrying capacity (LCC) of fiber reinforced polymer (FRP)-encased concrete-filled steel tube (CFST) compression members (FCSC). A database of 325 FCSC columns was developed for FEM and ANN models, while the analytical model was based on 712 samples, utilizing encasing mechanics of steel tube and FRP wraps. FEM used a concrete damage plastic model, bilinear steel tube, and linear elastic FRP models. Statistical accuracy was evaluated using MAE, MAPE, R², RMSE, and a 20-index across all models.

Based on the experimental database, the FEM presented the accuracies in the form of statistical parameters MAE = 223.76, MAPE = 285.32, R² = 0.94, RMSE = 210.43 and a20-index = 0.83. The analytical model showed the statistics of MAE = 427.229, MAPE = 283.649, R2 = 0.8149, RMSE = 275.428 and a20-index = 0.73 while ANN models portrayed the predictions with MAE = 195, MAPE = 229.67, R² = 0.981, RMSE = 174 and a20-index = 0.89 for the LCC of FCSC columns.

Although various investigations have already been performed on the prediction of the load-carrying capacity (LCC) of fiber reinforced polymer (FRP)-encased concrete-filled steel tube (CFST) compression members (FCSC) using small and noisy data, none of them compared the accuracy of prediction of different modeling techniques based on a refined large database.