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Using a multilevel model, this study examined how paternalistic leadership behaviors, including authoritarianism, morality and benevolence, influence followers' performance.

A sample of 556 leader–follower dyads from 66 groups in a manufacturing firm in China was collected for analysis. Descriptive statistics and multi-level regression analyses were used to analyze the data.

The results indicated that group efficacy mediates the relationship between authoritarian leadership and followers' performance and that self-efficacy mediates the relationship between benevolent leadership and followers' performance. In addition, the positive relationship between self-efficacy and followers' performance is weaker when followers exhibit higher levels of group efficacy.

The data were collected in a manufacturing firm in China, it is difficult to generalize the results to other settings.

Managers should use their abilities and skills to interpret which paternalistic leadership styles their followers prefer, so as to improve their performance.

This study developed a multilevel model to examine the mediating processes of group efficacy and self-efficacy in the effect of PL behaviors, including authoritarianism, benevolence and morality, on followers' performance.

This study aims to shed light on the relationship between gender diversity and group performance by considering the moderating role of relative cultural distance. Drawing from the categorization–elaboration model (CEM), the authors hypothesize that gender-diverse collaborative learning groups perform better when a low level of relative cultural distance in country-level individualism–collectivism or power distance exists among group members.

To test this hypothesis, the authors conducted a study on 539 undergraduate students organized into 94 groups. The assessment of group performance was based on scores given by external raters.

The authors found that relative cultural distance significantly moderated the gender diversity–group performance relationship such that gender diversity was positively related to group performance when the collaborative learning group included members who similarly valued individualism–collectivism or power distance (i.e. relative cultural distance was low) and was negatively related to group performance when the collaborative learning group comprised members who differently valued individualism–collectivism or power distance (i.e. relative cultural distance was high).

This study contributes to understanding when gender diversity is positively associated with group performance by expanding the range of previously examined diversity dimensions to include relative cultural distance in country-level individualism–collectivism and power distance.

Online collaboration in today's world is a topic of genuine interest to Internet researchers. The purpose of this paper is to explore the role of group knowledge heterogeneity (GKH) in open collaboration performance using the mediating mechanisms of group cognition (GC) and interaction to understand the determinants of the success of online open collaboration platforms.

Study findings are based on partial least squares structural equation modeling (PLS-SEM), the formal mediation test and moderating effect analysis from Wikipedia's 160 online open collaborative groups.

For online knowledge heterogeneous groups, open collaboration performance is mediated by both GC and collaborative interaction (COL). The mediating role of GC is weak, while the mediating role of COL is strengthened when knowledge complexity (KC) is higher. By dividing group interaction into COL and communicative interaction (COM), the authors also observed that COL is effective for online open collaboration, whereas COM is limited.

These findings suggest that for more heterogeneous large groups, group interaction would explain more variance in performance than GC, offering an in-depth understanding of the relationship between group heterogeneity and open collaboration performance, answering what determines the success of online open collaboration platforms as well as explaining the inconsistency in prior findings. In addition, this study expands the application of Interactive Team Cognition (ITC) theory to the online open collaboration context.

Age is one of the critical factors used by many organizations to determine employees' performance. It is being considered in the retirement process of construction professionals. However, age as a critical factor is not considered in the recruitment of a specific set of workers in the construction industry. Therefore, this study investigated the significant relationship between the ages of tradespeople and their performance in construction projects. The study also explored the age at which performance begins to decline and proposes strategies that can be used to sustain their effectiveness before the official retirement age.

Mixed methods research designs were adopted in the study. To be precise, physical observations, interviews and questionnaires were the instruments used for data collection in the mixed methods research design.

The outcomes of the study revealed that the age groups of tradespeople in the Nigerian construction industry are 16–30 (group one), 31–45 (group two) and 46–58 (group three) respectively. Group three is the prevalent age group. It was also discovered that performance begins to decline at 53 years. The age-performance decline of tradespeople in Nigerian construction projects can be delayed through certain strategies such as regular strength training exercises and an adequate nutritional lifestyle.

The study enables construction managers to have an adequate understanding of the negative influence of old age on the performance of tradespeople in construction projects. This will enable construction firm managers to recruit from the age range of 16–52 and stop retiring employees within this age bracket, consequently curbing the skills gap which is prevalent in the study context and the global construction industry. The study is limited to tradespeople performance in construction firms in Abuja, Nigeria where there are several ongoing projects on a daily basis.

The study enables project managers to estimate the number of tradespeople required for a particular task and consequently save the aged tradespeople from health risks associated with excessive workloads.

This paper is the first of its kind to be conducted in the study context, to establish the specific age at which performance begins to decline among construction tradespeople.

Relationship management evolves with dynamic and complex environments of megaprojects. However, studies on the longitudinal measurement of relationship management performance for each stakeholder in dynamic and complex project environments are lacking. The purpose of this research is to propose an NK-network evolution model to evaluate stakeholder performance on relationship management in the development of megaprojects.

The model input includes the stakeholder-associated issues and stakeholders' relational strategies, the co-effects of which determine the internal effects of relationship management in megaprojects. The model processing simulates the stakeholder performance of relationship management under the dynamic and complex nature of megaprojects. The NK model shows the dynamic stakeholder interactions on relationship management, whereas the network model presents the complex stakeholder structures of the relationships between stakeholders and relevant issues. The model output is the evolution graph to reveal the weak stakeholder performance on relationship management in the timeline of the project duration.

The research finding reveals that all stakeholders experience the plunge of stakeholder performance of relationship management at the decision-making moment of the planning stage. Construction, environmental and pressure groups may experience the hardship of relationship management at the start of the construction stage. The government is likely to suffer difficulties in relationship management in the late construction stage. Local industry groups would face challenges in relationship management in the middle of the construction stage and handover stage.

The research provides a useful approach to measuring weak moments of relationship management for each stakeholder in various project phases, considering the dynamic and complex environments of megaprojects. The proposed model extends the current knowledge body on how to make project stakeholder analysis by modelling dynamic and complex environments of megaprojects, with bridging the knowledge domains of evolution modeling techniques and network methods.

The author proposed a mobile learning model of pervasive animated games which allows college students to learn via games accessed through a smartphone. It can develop the process of field observation and self-reflection to enhance learning effectiveness, and the motivation, and attitude of students towards learning.

The author proposed a model for teaching via pervasive animated games. The author used SPSS software and Pearson's correlation coefficients to explore different mobile learning strategies and their relationship with learning attitudes and achievement. Participants were vocational technology college students, who each experienced animated games in individual and group learning settings.

The results found that the learning performance of students in the individual learning group was better than that of the group learning group. A higher level of digital experience was associated with better learning performance, and a more positive attitude towards using mobile phones was associated with better learning performance.

The learning method still has its limitations, the learner's digital information level, learning mode, learning attitudes will have an impact on the student playing teaching pervasive animation games. Therefore, improving student information level is one of the important topics of teaching pervasive animation games and mobile learning.

The author proposed a mobile learning strategy based on pervasive animated games. The result in the strategy of mobile learning shows that the level of students' digital experience and the overall design of animated games are important criteria for successful implementation.

Creating suitable learning conditions in university classrooms continues to be a topical issue in higher education. The above means that a continued search for innovative teaching and learning strategies in universities remains a priority issue. The study therefore investigated conditions that facilitate the effective implementation of cooperative learning (CL) as well as how CL influences the academic performance of students in universities in Zimbabwe.

The research adopted a quantitative research approach located in the descriptive research design. A sample of 341 undergraduate students selected using a simple random sampling strategy from a sample frame of 701 undergraduate students participated in the study. The study first used a pre-test and later, a post-test to determine the initial and end of semester (EOS) academic performance levels of students, respectively, on a Calculus 1 course. A self-constructed structured questionnaire was also used for data collection on the views of students towards the use of CL with particular reference to the 5 dimensions of positive interdependence (PDI), promotive interaction (PI), individual and group accountability (IGA), social and interpersonal skills (SS) and group processing (GP). The questionnaire was validated using confirmatory factor analysis. Structural equation modelling was used to test proposed relationships. A total of 341 questionnaires were administered through an email survey. Of the total, 149 completed questionnaires were received from the students, giving a return rate of 43.7%.

The results showed that positive interdependence, promotive interaction, social and interpersonal skills, individual and group accountability as well as group processing were important antecedents to the effective implementation of CL in universities. The results further showed that CL plays a significant role in improving the academic performance of university students; hence, academic performance was viewed as a significant consequence of CL.

This is the first study on the application of CL to enhance the academic performance of university students in the context of Zimbabwe. As a result, caution should be exercised when generalising the results as more studies to either confirm or disconfirm these results in the context of Zimbabwe may still be required.

These results have implications on policy and practice with regards to active teaching strategies in universities. With regards to practice, the results demonstrated that the use of group activities that allow students to share knowledge and support each other while the teacher plays a facilitating role is important for enhancing students' academic performance. With regards to policy, the results showed that universities can enhance the academic performance of students if policies that promote student-centered teaching and learning approaches to ensure that teaching in universities is more student-led than lecturer-led are developed.

While many studies have been conducted in other contexts, to establish the influence of CL on the academic performance of university students, there is no known study on the influence of CL that has been conducted in the context of Zimbabwean universities. The results of this study therefore are an eye opener on the role of CL in enhancing the academic performance of students in Zimbabwean universities.

This paper aims to present the development of a holistic campus facility management (CFM) performance assessment framework that incorporates a fuzzy logic approach and integrates a comprehensive set of key factors for successful management of campus facilities. The devised framework aims to cater to the needs of campus facilities management firms and departments for the purpose of gauging and assessing their performance across different management domains. Through this approach, facility management organizations can detect potential areas of enhancement and adopt preemptive steps to evade issues, foster progress and ensure success.

After a comprehensive analysis of the literature, conducting in-depth interviews with industry experts and employing the Delphi technique in two rounds, a total of 45 indicators critical to CFM success were identified and subsequently sorted into seven distinct groups. Through an online questionnaire, 402 subject-matter experts proficiently assessed the significance of the critical success indicators and their groups. A fuzzy logic framework was developed to evaluate and quantify a firm's compliance with the critical success indicators and groups of indicators. The framework was subsequently weighted using computations of the relative importance index (RII) based on the responses received from the questionnaire participants. The initial section of the framework involved a comprehensive analysis of the firm's performance vis-à-vis the indicators, while the latter part sought to evaluate the impact of the indicators groups on the overall firm's performance.

The utilization of fuzzy logic has uncovered the significant effects each effective CFM key indicator on indicators groups, as well as the distinct effects of each CFM indicators group on the overall performance of CFM. The results reveal that financial management, communications management, sustainability and environment management and workforce management are the most impactful indicators groups on the CFM performance. This suggests that it is imperative for management to allocate increased attention to these specific areas.

This study contributes to the advancement of current knowledge by revealing vital indicators of effective CFM and utilizing them to construct a thorough fuzzy logic framework that can assist in evaluating the effectiveness of CFM firms worldwide. This has the potential to provide crucial assistance to facility management organizations, facility managers and policymakers in their quest for informed decision-making.

This study aims to demonstrate the importance of organizational networks in organizational performance is relatively rich; less understood are processes in organizational networking that entrepreneurs and organizations use in making sense of rapidly changing contexts for organizational performance.

This study conducts an exploratory organizational-level narrative analysis into firms’ experiences in two major emerging markets (EMs), namely, Russia and India – to identify organizational networking processes in the midst of institutional upheavals. The study is based on in-depth case studies of firms in EMs sourced from interview data from senior management and consolidated with secondary data.

The authors find that initially firms rely on informal networks (including blat/svyazi and jaan-pehchaan/jan-pehchan) and later formal (in the form of bureaucratic followed by proprietary) networks to make sense of the changes and uncertainties in turbulent environments. The authors also demonstrate the cyclical nature of strategic sensemaking in the process of developing organizational networks for performance.

The study has a number of theoretical and practical contributions. First, it extends the well-established business networking construct to a more inclusive organizational networking construct. Second, it demonstrates that sensemaking is dependent on interorganizational networking from the outset and throughout the growth of an organization in turbulent markets – from informal to formal bureaucratic and proprietary networks. Finally, this study is unique in documenting the entire process of sensemaking from scanning to performance as well as successfully demonstrating the cyclical nature of sensemaking.

For ranking aggregation in crowdsourcing task, the key issue is how to select the optimal working group with a given number of workers to optimize the performance of their aggregation. Performance prediction for ranking aggregation can solve this issue effectively. However, the performance prediction effect for ranking aggregation varies greatly due to the different influencing factors selected. Although questions on why and how data fusion methods perform well have been thoroughly discussed in the past, there is a lack of insight about how to select influencing factors to predict the performance and how much can be improved of.

In this paper, performance prediction of multivariable linear regression based on the optimal influencing factors for ranking aggregation in crowdsourcing task is studied. An influencing factor optimization selection method based on stepwise regression (IFOS-SR) is proposed to screen the optimal influencing factors. A working group selection model based on the optimal influencing factors is built to select the optimal working group with a given number of workers.

The proposed approach can identify the optimal influencing factors of ranking aggregation, predict the aggregation performance more accurately than the state-of-the-art methods and select the optimal working group with a given number of workers.

To find out under which condition data fusion method may lead to performance improvement for ranking aggregation in crowdsourcing task, the optimal influencing factors are identified by the IFOS-SR method. This paper presents an analysis of the behavior of the linear combination method and the CombSUM method based on the optimal influencing factors, and optimizes the task assignment with a given number of workers by the optimal working group selection method.