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Community question answering (CQA) platforms play a significant role in knowledge dissemination and information retrieval. Expert recommendation can assist users by helping them find valuable answers efficiently. Existing works mainly use content and user behavioural features for expert recommendation, and fail to effectively leverage the correlation across multi-dimensional features.

To address the above issue, this work proposes a multi-dimensional feature fusion-based method for expert recommendation, aiming to integrate features of question–answerer pairs from three dimensions, including network features, content features and user behaviour features. Specifically, network features are extracted by first learning user and tag representations using network representation learning methods and then calculating questioner–answerer similarities and answerer–tag similarities. Secondly, content features are extracted from textual contents of questions and answerer generated contents using text representation models. Thirdly, user behaviour features are extracted from user actions observed in CQA platforms, such as following and likes. Finally, given a question–answerer pair, the three dimensional features are fused and used to predict the probability of the candidate expert answering the given question.

The proposed method is evaluated on a data set collected from a publicly available CQA platform. Results show that the proposed method is effective compared with baseline methods. Ablation study shows that network features is the most important dimensional features among all three dimensional features.

This work identifies three dimensional features for expert recommendation in CQA platforms and conducts a comprehensive investigation into the importance of features for the performance of expert recommendation. The results suggest that network features are the most important features among three-dimensional features, which indicates that the performance of expert recommendation in CQA platforms is likely to get improved by further mining network features using advanced techniques, such as graph neural networks. One broader implication is that it is always important to include multi-dimensional features for expert recommendation and conduct systematic investigation to identify the most important features for finding directions for improvement.

This work proposes three-dimensional features given that existing works mostly focus on one or two-dimensional features and demonstrate the effectiveness of the newly proposed features.

Mentor secure-base support, characterized as mentor availability, noninterference and encouragement of growth, has important implications for newcomer socialization. Drawing on attachment theory, this paper aims to examine the relationship between mentor secure-base support and newcomers' workplace courage.

Data were collected at three time points with a new police officer sample (n = 124). A cross-lagged panel design was used to test the hypotheses.

Mentor secure-base support is causally precedent to newcomers' workplace courage, whereas the reverse relationship from workplace courage to mentor secure-base support was not held.

To help newcomers integrate into their organization and enhance their workplace courage, organizations should actively promote and foster mentoring relationships in which mentors can provide a secure base for mentees.

The authors' findings support that newcomers' workplace courage can be cultivated by mentor secure-base support. It provides insight for organizations to explore workplace courage development for newcomers.

This paper develops an instrument of organizational agility. The instrument is utilized to assess the extent to which Ras Al Khaimah government agencies have adopted agility and to examine its impact on the achievement of strategic outcomes and employee satisfaction.

The dimensions of agility are determined using factor analysis. The reliability of the dimensions is tested based on the Cronbach alpha coefficient, while the predictive validity of the instrument is assessed using correlation and multiple linear regression analysis. The extent to which Ras Al Khaimah government agencies adopted the dimensions of agility is assessed using one-sided T-test, and the difference between the levels of adoption of the dimensions is determined using one-way ANOVA. The relationships between agility the dependent variables of achieving strategic outcomes and employee satisfaction are assessed using multiple linear regression.

The paper determined two valid and reliable dimensions of organizational agility, namely leadership and strategic sensitivity and resource fluidity. Culture, a third reliable dimension found through factor analysis was found to influence agility indirectly. Government agencies have adopted the two dimensions that are found to increase the achievement of strategic outcomes and employee satisfaction.

This paper provides a valid and reliable measure for assessing organizational agility. This measure includes both enablers and capabilities. It adds to the limited empirical research on agility, particularly in the Arab world. The paper focused on local government agencies and its findings may not be applicable in other sectors.

The measure can serve as an effective agility self-assessment tool for organizations, enabling them to identify areas for improvement and specific practices they need to adopt to enhance their agility. This, in turn, allows them to become more responsive to changes, achieve strategic outcomes and improve employee satisfaction.

This paper has important research and practical implications. It provides a valid and reliable measure of organizational agility with both enablers and capabilities. This measure can help organizations become agile and achieve higher strategic outcomes and employee satisfaction.

Master information delivery plan (MIDP) is a key requirement for building information modelling (BIM) execution plan (BEP) that enlists all information deliverables in BIM-based project, containing information about what would be prepared, when, by who, as well as the procedures and protocols to be used. In a well-conceived BEP, the MIDP facilitates collaboration among stakeholders. However, current approaches to generating MIDP are manual, making it tedious, error-prone and inconsistent, thereby limiting some expected benefits of BIM implementation. The purpose of this study is to automate the MIDP and demonstrate a collaborative BIM system that overcomes the problems associated with the traditional approach.

A BIM cloud-based system (named Auto-BIMApp) involving naming that automated MIDP generation is presented. A participatory action research methodology involving academia and industry stakeholders is followed to design and validate the Auto-BIMApp.

A mixed-method experiment is conducted to compare the proposed automated generation of MIDP using Auto-BIMApp with the traditional practice of using spreadsheets. The quantitative results show over 500% increased work efficiency, with improved and error-free collaboration among team members through Auto-BIMApp. Moreover, the responses from the participants using Auto-BIMApp during the experiment shows positive feedback in term of ease of use and automated functionalities of the Auto-BIMApp.

The replacement of traditional practices to a complete automated collaborative system for the generation of MIDP, with substantial productivity improvement, brings novelty to the present research. The Auto-BIMApp involve multidimensional information, multiple platforms, multiple types and levels of users, and generates three different representations of MIDP.

Coronavirus disease 2019 (COVID-19) has made telecommuting widely valued, but different individuals have different degrees of acceptance of telecommuting. This article aims to identify suitable individuals for telework and to clarify which types of workers are suitable for what level of telework, set scientific, reasonable hybrid work ratios and processes and measure their suitability.

First, two working scenarios of different risk levels were established, and the theory of planned behavior (TPB) was used to introduce latent variables, constructing a multi-indicator multi-causal model (MIMIC) to identify suitable individuals, and second, constructing an integrated choice and latent variable (ICLV) model of the working method to determine the suitability of different types of people for telework by calculating their selection probabilities.

It is possible to clearly distinguish between two types of suitable individuals for telework or traditional work. Their behavior is significantly influenced by the work environment, which is influenced by variables such as age, income, attitude, perceived behavioral control, work–family balance and personnel exposure level. In low-risk scenarios, the influencing factors of the behavioral model for both types of people are relatively consistent, while in high-risk scenarios, significant differences arise. Furthermore, the suitability of telework for the telework-suitable group is less affected by the pandemic, while the suitability for the non-suitable group is greatly affected.

This study contributes to previous literature by: (1) determining the suitability of different population types for telework by calculating the probability of selection, (2) dividing telework and traditional populations into two categories, identifying the differences in factors that affect telework under different epidemic risks and (3) considering the impact of changes in the work scenario on the suitability of telework for employees and classifying the population based on the suitability of telework in order to avoid the potential negative impact of telework.