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Reducing employees’ knowledge hiding is vital for enterprise managers. The development of enterprise social media brings opportunities for them to manage employees’ knowledge-hiding behaviors. However, whether the use of enterprise social media inhibits or promotes knowledge hiding is still unclear. The purpose of this study is to explore how enterprise social media usage affect employees' knowledge hiding.

Based on the intrinsic and extrinsic motivation model, this paper proposes a research model to investigate the relationship between enterprise social media usage and knowledge hiding, using a structural equation modeling analysis of 288 employees’ data.

The results show that work-related public social media usage has an inhibiting effect on employees’ knowledge hiding, whereas the effect of work-related private social media usage on employees’ knowledge hiding is not significant; socially related public social media and private social media usage has a promoting effect on employees’ knowledge hiding; and job engagement acts in a positive moderating role between socially related private and public social media usage and evasive hiding.

First, this paper contributes to knowledge-hiding literature by revealing the relationship between enterprise social media usage and knowledge hiding. Second, this study contributes to motivation theory by clarifying how the use of enterprise social media with different motivations affects knowledge hiding. Third, this paper also contributes to knowledge-hiding literature by revealing a boundary condition, namely, job engagement.

Enterprises use social media for their daily work. The use of social media in the workplace is crucial for social connections, the growth and evolution of the enterprise, and it opens up new avenues for voice behavior. Employee voice involves the expression of ideas or opinions towards enterprise and is beneficial for employee work and enterprise development. Extant studies of voice behavior usually focus on the leadership and employee factors. However, the internal mechanism of voice behavior, especially the interrelationship between different kinds of social media use and voice behavior has not been well investigated. To fill that research gap, this study analyzes the internal mechanism of voice behavior, taking the effects of social media use and social capital into consideration.

Using structural equation model, this study collected data from employees using social media and analyzed the data using the software of Smartpls 3.0, SPSS and AMOS, in order to analyze the internal mechanism of voice behavior among employees.

Based on social capital theory, this study investigates the relationship between social media use, social capital and voice behavior, and provides some insights into the mechanism of voice behavior. The social media use, social capital and voice behavior are divided into several kinds in order to clarify the internal mechanism of voice behavior more comprehensively. The empirical results show that: (1) Social media use for both work and social-related purposes could positively affect employees’ promotive and prohibitive voice behaviors. (2) Social capital mediates the relationship between social media use and voice behavior. (3) In the process of social media use influencing employees’ voice behavior, employees of different genders and ages show significant differences in social capital and voice behavior.

This study explored the internal mechanism of voice behavior, which could help to elicit the relationship between social media use and voice behavior. By integrating the roles of social capital, individual differences, this study could uncover the deep internal mechanism of employee voice behavior more comprehensively, broadening social capital theory and enriching the researches of voice behavior among employees.

Peer instruction has been widely adopted as an instructional method in higher education. However, due to students' different preconceptions, the authors argued that peer instruction is not a panacea in international business education when students' prior knowledge extensively varies. The paper aims to discuss these issues.

In this experimental study, the authors focused on three conditions of an introductory statistics course: individual problem solving, peer instruction, and peer instruction with hints.

The authors have found students in peer instruction with hints class did not only outperform in the final exam, but also achieved the highest frequency of successful conceptual changes in comparison with their counterparts in the other two classes.

Providing instructional hints to improve the effectiveness of peer instruction may shed light on classroom instruction in higher education.

As for international business education, this was a unique exploration to capture students' conceptual changes using clickers. The authors believed this research paper will help the education practitioners to know their business students better.

This research aims to comprehensively investigate when and how cognitive conflict benefits team innovation in cross-functional project teams (CFPTs), by exploring the moderating role of knowledge leadership and dual mediation mechanisms of elaboration of task-related information/knowledge and affective conflict.

All hypotheses have been empirically tested by using structural equation model to analyze the quantitative data from a questionnaire survey covering 73 CFPTs in China.

Results indicate that knowledge leadership positively moderates the relationship between cognitive conflict and CFPT innovation. This moderating effect is directly or indirectly revealed by the dual mediating roles of task-related information/knowledge elaboration and affective conflict, which are two processes manifesting whether cognitive conflict can or cannot be incorporated into team innovation.

Despite the external validity of results limited by convenient sampling method, the findings offer implications for promoting CFPT innovation. This can be achieved by developing competent knowledge leadership into team sensegiver, dissent reconciler and facilitator to accentuate benefits of cognitive conflict in information/knowledge elaboration and attenuate the likelihood of escalating to affective conflict.

This study advances the understanding of why cognitive conflict has an equivocal effect on team innovation in the context of CFPT by originally revealing how leaders’ role in information/knowledge management acts as a contingency and suggesting the dual mediating mechanisms that reflect the contingent impact. Project-based teams or organizations, characterized by cognitive clashes, can enhance innovation performance by shaping the meaningfulness of information/knowledge activities triggered by cognitive conflict.

The purpose of this paper is to establish a more reasonable evaluation system and model for the development level of rural tourism, and provides a method for quantifying the development level of regional rural tourism.

This paper provides a method for evaluating the development level of rural tourism, constructs an evaluation index system according to the connotation of rural tourism, then calculates the index weight by entropy method, and makes a comprehensive evaluation by grey relational analysis. Taking the development of rural tourism in 11 cities in Jiangxi Province as the research object, the ranking results of 11 cities were obtained by using grey relational analysis.

The overall development level of rural tourism in Jiangxi Province is positive, but the spatial distribution is uneven, showing the characteristics of “low-level aggregation and high-level dispersion”. The barrier model diagnoses that the degree of financial input is the biggest constraint to the development level of rural tourism in Jiangxi Province.

This study puts forward an evaluation model based on entropy weight and grey relational analysis, which is an important supplement to the grey relational analysis method system and has a positive role in promoting the quantitative evaluation of regional rural tourism level.

The purpose of this paper is to solve new pricing issues faced by low-carbon companies in the Yellow River Basin, which is caused by the change of key pricing factors in the mixed appliance background of Big Data and blockchain, such as product quality and carbon-emission reduction CER level (hereafter, CER level).

We choose a low-carbon supply chain with a low-carbon manufacturer and a retailer as our research object. Then, we propose that using the ineffective effect of the CER level and the quality and safety level to reflect the relationships among the CER level, the quality and safety level and the market demand is more suitable in the new environment. Based on these, we revise the demand equation. Afterwards, by using Stackelberg game, four cost-sharing situations and their pricing rules are analyzed.

Results indicated that in the four cost-sharing situations, the change trends and the magnitudes of the best retail prices were not affected by the changes of the inputs of the demand information and the traceability services costs (hereafter, DITS costs), the proportion about retailer's DITS costs undertaken by the manufacturer, the ineffective effect coefficient of the CER level and the quality and safety level and the cost optimization coefficient. However, the cost-sharing situations could affect the change magnitudes of the best revenues.

This paper has two main contributions. First, this paper proposes a demand function that is more suitable for the mixed appliance background of Big Data and blockchain. Secondly, this paper improves the cost-sharing model and finds that demand information sharing and traceability service sharing have different impacts on key pricing factors of low-carbon product. In addition, this research provides a theoretical reference for low-carbon supply chain members to formulate pricing strategies in the new background.

This paper aims to improve the permeability and antifouling of polysulfone (PSF) ultrafiltration membranes, the PSF matrix was modified by incorporating sulfonated polysulfone (SPSF).

Systematic investigations were conducted on the synergistic effects of a pore-forming agent, coagulation bath temperature and SPSF doping in the casting solution on blended ultrafiltration membranes. The chemical composition of the membranes was analyzed using Fourier transform infrared spectroscopy. The morphology and surface roughness of the membranes were characterized using scanning electron microscopy and atomic force microscopy. The hydrophilicity of the membrane surface was analyzed using a contact angle meter. The permeability and antifouling properties of the blended membranes were also investigated through filtration experiments.

The results indicated that the blended ultrafiltration membranes demonstrated an optimal overall performance when PVP-K30 content was 5.0 Wt.%, coagulation bath temperature was 30°C and SPSF content was 2.4 Wt.%. In comparison to a pure PSF ultrafiltration membrane, there was a significant increase in pure water flux (390.7 L·m⁻²·h⁻¹) by 2.2 times, while bovine serum albumin retention slightly decreased to 93.8%. In addition, the flux recovery rate improved by 2.1 times (71.4%) compared to that of the original PSF ultrafiltration membrane.

The method provided a simple and practical solution for improving the antifouling and permeability of PSF ultrafiltration membranes.

SPSF was anticipated to serve as an excellent modification additive for the preparation of ultrafiltration membranes with superior properties.

With respect to the multiple-attribute decision-making problem with subjective preference for a certain attribute whose weight-value range have been given over other attributes whose weight values are unknown, a method based on the mean value of the grey number is proposed to analyse the decision-making problem. This method is used to choose a supply-chain partner under the condition that the decision makers have a preference for a certain attribute of various alternatives. The paper aims to discuss these issues.

First, the middle value of the preferred attribute’s weight-value range is supposed to be its weight value according to the content of the mean value of the grey number. Second, to reflect the decision maker’s subjective preference information, an improved optimisation model that requests the minimum deviation between the actual and expected numerical value of each attribute is constructed to assess the attributes’ weights. Third, the correlated degree and the correlation matrix, which are determined by the weight values of all attributes, are used to rank all the alternatives.

This paper provides a method for making a decision when decision makers have a preference for a certain attribute from an array of various alternatives, and the range of the certain attribute’s weight value is given but the weight value of the other attributes is unknown. When applied to supply-chain partner selection, this method proves feasible and effective.

This method is feasible and effective when applied to supply-chain partner selection, and can be applied to other kinds of decision-making problems. This means it has significant theoretical importance and extensive practical value.

Based on the mean value of the grey number, an optimisation model is built to determine the importance degree of each attribute, then the correlated degree of each alternative is combined to rank all the alternatives. This method can suit the decision makers’ subjective preference for a certain attribute well.

Assessing project criticality in a project portfolio (PP) is of great practical significance to improve robustness from damage. While project criticality assessment has increased diversity in approaches, the understanding of vulnerable project impacts is still limited. To promote a better understanding of assessing project criticality, a vulnerability measurement model is constructed.

First, integrating the tasks, projects and corresponding relationships among them, a project portfolio network (PPN) is constructed. Second, the project's vulnerability is measured by combining the topological structure and functional attributes. Third, project criticality is assessed by the vulnerability measurement results. Lastly, the proposed model is applied in a numerical example to illustrate its suitability and effectiveness.

For academia, this study provides a novel perspective on project vulnerability measurement and expands project criticality assessment tools. For practitioners, the straightforward model provides an effective tool for assessing project criticality and contributes to enhancing project portfolio management (PPM).

The impact of the task on the project is considered in this study. Topological structure and functional attributes are also integrated for measuring project vulnerability due to the impact of random attacks in an uncertain environment, providing a new perspective on the requirements of project criticality assessment and the measurement of project vulnerability.

This study aims to investigate the effect of ultrasound on interfacial microstructures and growth kinetics of intermetallic compounds (IMCs) at different temperatures.

To investigate the effect of ultrasound on IMCs growth quantitatively, the cross-sectional area of IMCs layers over a confirmed length was obtained for calculating the thickness of the IMCs layer.

The generation of dimensional difference in normal direction between Cu₆Sn₅ and its adjacent Cu₆Sn₅, formation of bossed Cu₆Sn₅ and non-interfacial Cu₆Sn₅ in ultrasonic solder joints made the interfacial Cu₆Sn₅ layer present a non-scallop-like morphology different from that of traditional solder joints. At 260°C and 290°C, the Cu₃Sn layer presented a wave-like shape. In contrast, at 320°C, the Cu₃Sn in ultrasonic solder joints consisted of non-interfacial Cu₃Sn and interfacial Cu₃Sn with a branch-like shape. The Cu₆Sn₅/Cu₃Sn boundary and Cu₃Sn/Cu interface presented a sawtooth-like shape under the effect of ultrasound. The predominant mechanism of ultrasonic-assisted growth of Cu₆Sn₅ growth at 260°C, 290°C and 320°C involved the grain boundary diffusion accompanied by grain coarsening. The Cu₃Sn growth was controlled by volume diffusion during the ultrasonic soldering process at 260°C and 290°C. The diffusion mechanism of Cu₃Sn growth transformed to grain boundary diffusion accompanied by grain coarsening when the ultrasonic soldering temperature was increased to 320°C.

The microstructural evolution and growth kinetics of IMCs in ultrasonically prepared ultrasonic solder joints at different temperatures have rarely been reported in previous studies. In this study, the effect of ultrasound on microstructural evolution and growth kinetics of IMCs was systematically investigated.