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Most educational institutions worldwide have shifted to online teaching and learning approaches to mitigate risks imposed by the COVID-19 pandemic. This causes several issues, particularly in delivering the construction management (CM) courses which require site visits, interpreting technical drawings and developing 3D building models. This paper aims to identify the key strategies for online learning and teaching adopted during the COVID-19 pandemic and to investigate the implications for construction management education.

The research approach is twofold. First, the study presents a systematic literature review (SLR) through a synthesis of the existing literature to identify the key strategies and lessons learned about online education during the COVID-19 pandemic in tertiary programs. It also discusses their implications in the context of the construction management (CM) sector in particular. Secondly, the authors shared their hands-on experience as construction management course facilitators – using the autoethnography approach – during the COVID-19 crisis.

In addition to identifying the key strategies such as online course delivery and assessments, the paper critically discusses the barriers to online learning and teaching, including (1) the technological and infrastructure barriers; (2) required online teaching skills and competencies; (3) issues surrounding mental health and wellbeing; (4) lack of consistency in the online delivery of various courses in a given program (5) difficulties around students' engagement and (6) the course characteristics and requirements.

The study offers some implications and recommendations not only for educational institutions and staff but also for vendors of online course delivery software. To prepare educational institutions for future online course delivery, the paper proposes several strategies. These include developing a set of guidelines for online course delivery, incorporating online teaching training modules into the recruitment process for academic staff, applying agile and resilience teaching and learning methods, wellbeing and mental health support and continuously improving course features to adapt to the online environment.

This study attempts to explore the linkages between reliable big and cloud data analytics capabilities (RB&CDACs) and the comparative advantage (CA) that applies in the manufacturing sector in the countries located in North Africa (NA). These are considered developing countries through generating green product innovation (GPI) and using green process innovations (GPrLs) in their processes and functions as mediating factors, as well as the moderating role of data-driven competitive sustainability (DDCS).

To achieve the aim of this study, 346 useable surveys out of 1,601 were analyzed, and valid responses were retrieved for analysis, representing a 21.6% response rate by applying the quantitative methodology for collecting primary data. Convergent validity and discriminant validity tests were applied to structural equation modeling (SEM) in the CB-covariance-based structural equation modeling (SEM) program, and the data reliability was confirmed. Additionally, a multivariate analysis technique was used via CB-SEM, as hypothesized relationships were evaluated through confirmatory factor analysis (CFA), and then the hypotheses were tested through a structural model. Further, a bootstrapping technique was used to analyze the data. We included GPI and GPrI as mediating factors, while using DDCS as a moderated factor.

The empirical findings indicated that the proposed moderated-mediation model was accepted due to the relationships between the constructs being statistically significant. Further, the findings showed that there is a significant positive effect in the relationship between reliable BCDA capabilities and CAs as well as a mediating effect of GPI and GPrI, which is supported by the proposed formulated hypothesis. Additionally, the findings confirmed that there is a moderating effect represented by data-driven competitive advantage suitability between GPI, GPrI and CA.

One of the main limitations of this study is that an applied cross-sectional study provides a snapshot at a given moment in time. Furthermore, it used only one type of methodological approach (i.e. quantitative) rather than using mixed methods to reach more accurate data.

This study developed a theoretical model that is obtained from reliable BCDA capabilities, CA, DDCS, green innovation and GPrI. Thus, this piece of work bridges the existing research gap in the literature by testing the moderated-mediation model with a focus on the manufacturing sector that benefits from big data analytics capabilities to improve levels of GPI and competitive advantage. Finally, this study is considered a road map and gaudiness for the importance of applying these factors, which offers new valuable information and findings for managers, practitioners and decision-makers in the manufacturing sector in the NA region.

Recognising the as-built state of construction elements is crucial for construction progress monitoring. Construction scholars have used computer vision-based algorithms to automate this process. Robust object recognition from indoor site images has been inhibited by technical challenges related to indoor objects, lighting conditions and camera positioning. Compared with traditional machine learning algorithms, one-stage detector deep learning (DL) algorithms can prioritise the inference speed, enable real-time accurate object detection and classification. This study aims to present a DL-based approach to facilitate the as-built state recognition of indoor construction works.

The one-stage DL-based approach was built upon YOLO version 4 (YOLOv4) algorithm using transfer learning with few hyperparameters customised and trained in the Google Colab virtual machine. The process of framing, insulation and drywall installation of indoor partitions was selected as the as-built scenario. For training, images were captured from two indoor sites with publicly available online images.

The DL model reported a best-trained weight with a mean average precision of 92% and an average loss of 0.83. Compared to previous studies, the automation level of this study is high due to the use of fixed time-lapse cameras for data collection and zero manual intervention from the pre-processing algorithms to enhance visual quality of indoor images.

This study extends the application of DL models for recognising as-built state of indoor construction works upon providing training images. Presenting a workflow on training DL models in a virtual machine platform by reducing the computational complexities associated with DL models is also materialised.

Going beyond the traditional approach of formal and informal institutions as antecedents of entrepreneurship (directly) and development (indirectly), this paper seeks to explore knowledge institutions as a necessary input for entrepreneurship and the development of societies.

Institutional economics lenses are utilized to observe other factors (e.g. the number of R&D staff and researchers from the public sector) that involve laws and socialization processes, which at the same time create knowledge useful for entrepreneurs and society. These ideas are tested through a sample of 281 observations from 17 autonomous communities and two autonomous cities in Spain. The information coming from the Global Entrepreneurship Monitor (GEM), Ministry of Economics, Industry, and Competitiveness, and INE (Instituto Nacional de Estadística), was analyzed through 3SLS, which is useful for a simultaneous equation strategy.

Knowledge institutions such as the number of R&D staff and researchers from the public sector are found positively associated with entrepreneurship, which is a factor directly and positively linked to economic development across Spanish regions.

The findings help the operationalization of other institutions considered in institutional economics theory and its application to entrepreneurship research. Moreover, the results bring new insights into the knowledge spillover theory of entrepreneurship in the public sector, in which the institutional analysis is implicit.

This study, rooted in affordance-actualization theory and communication theory, aims to critically examine how ChatGPT influences users’ transition from new adopters to loyal advocates within the context of travel decision-making. It incorporates constructs including communication quality, personalization, anthropomorphism, cognitive and emotional trust (ET), loyalty and intention to adopt into a comprehensive model.

This study used quantitative methods to analyze data from 477 respondents, collected online through a self-administered questionnaire by Embrain, a leading market research company in South Korea. Lavaan package within R studio was used for evaluating the measurement model through confirmatory factor analysis and using structural equation modeling to examine the proposed hypotheses.

The findings reveal a pivotal need for enhancing ChatGPT’s communication quality, particularly in terms of accuracy, currency and understandability. Personalization emerges as a key driver for cognitive trust, while anthropomorphism significantly impacts ET. Interestingly, the study unveils that in the context of travel recommendations, users’ trust in ChatGPT predominantly operates at the cognitive level, significantly impacting loyalty and subsequent adoption intentions.

The findings of this research provide valuable insights for improving Generative AI (GenAI) technology and management practices in travel recommendations.

As one of the few empirical research papers in the burgeoning field of GenAI, this study proposes a highly explanatory model for the process from affordance to actualization in the context of using ChatGPT for travel recommendations.

The purpose of this study was to explore and critique the benefits and disbenefits that transport orientated development (TOD) brings to neighbourhoods in proximity to public transport hubs.

This is an exploratory study that may also be described as a rapid review that aims to provide coverage of the available literature in a systematic process that is simplified to produce information in a timely manner. Due to the relatively small number of available studies from peer-reviewed sources, the variety of methods and data used and the constrained time available for this study, the study did not immediately lend itself to a more thorough systematic literature review.

The literature shows the discourse on TOD upholds its promise to create a high-density mixed-use walkable neighbourhood supported by transport infrastructure, increasing accessibility, minimising vehicle dependency, reducing traffic congestion, moderating urban sprawl and reducing pollution. There are few articles on the negative aspects of TOD, particularly concerning social exclusion, crime, sustainability and concerns about gentrification of neighbourhoods.

The study did not immediately lend itself to a more thorough systematic literature review due to the relatively small number of available studies, the variety of methods and data used and the constrained time available for this study.

This study allows social investigators, policymakers and developers understand the benefits and disbenefits of TOD including policy implications regarding potential criminogenic factors.

The purpose of the study is to perform a scientific mapping and detect the evolution pattern of two emerging fields, organizational capabilities and sustainable supply chain management (SSCM), to detect and visualize the existing conceptual domains and identify less-explored areas.

This study uses a methodological combination involving systematic literature review and bibliometric analysis. The methodology was implemented in the following order: definition and selection of the material using an electronic database, descriptive analysis of the material, category selection using bibliographic coupling analysis by VOSviewer (clusterization), material evaluation and content analysis.

The research results clarify the intellectual structure within the academic field. The authors’ identified three main clusters: (1) sustainable capabilities and practices in supply chain management (SCM), (2) green SCM and performance and (3) information technology and innovation. The findings reveal that there is a rich field to be explored, especially regarding issues involving sustainable technological capabilities, sustainable initiatives and key resource development.

This study facilitates researchers’ and practitioners’ understanding and their ability to map the different paths and evolution of SSCM and organizational capabilities. It can encourage managers and policymakers alike to conceive new approaches to engage in the adoption of SSCM.

This work employs a singular approach to identify the intellectual knowledge and topics related to the implementation of SSCM by adopting the theoretical approach of sustainable organizational capacity. It contributes to the debate on distinguishing specific sustainable organizational capabilities from traditional capabilities.

This study applies the modified brand avoidance model to examine factors that influence sustainable fashion avoidance behaviour among millennial shoppers in South Africa.

A positivistic approach and a web-based online survey were employed to collect cross-sectional data from 423 millennial fashion shoppers. Standard multiple regression analysis was used to test proposed hypotheses.

Unmet expectations, materialism and symbolic incongruence emerged as major predictors of millennials' intention to avoid sustainable fashion. Sustainable fashion avoidance intention was found to have a positive effect on sustainable fashion avoidance behaviour.

This study relied on self-reported data collected from millennial shoppers. Future studies may improve the generalizability of this study's results by conducting a comparative study with other cohorts such as baby boomers and Generation X who espouse different shopping values. Future studies may benefit from the use of longitudinal data in order to understand how millennial shoppers relate to sustainable fashion as it evolves.

The results of this study suggest the importance of developing value propositions that align sustainable fashion with cultural, personality and symbolic cues valued by millennial shoppers. Consumer education on the benefits of sustainable fashion is recommended as a long-term behavioural change strategy.

The purchase behaviour of sustainable fashion should be encouraged as it enhances environmental sustainability including safeguarding the livelihoods of future generations.

This study contributes to literature on sustainable fashion avoidance behaviour. This is one of the pioneering studies to empirically examine the influence of unmet expectations, symbolic incongruence and ideological incompatibility in the context of an emerging market, such as South Africa.

This study explores the impact of difficult coworkers on employees' turnover intention. Additionally, this study investigates the roles of employees' attitude toward difficult coworkers, perceived organizational support and affective commitment in the relationship between difficult coworkers and turnover intention.

Based on the stimulus-organism-response theory, a theoretical model was established that linked difficult coworkers to employees' attitude toward the, then to turnover intention directly and indirectly through perceived organizational support and affective commitment. The model was validated using responses from 343 Chinese employees in Macao's banking industry.

Results of the partial least squares-structural equation modeling (PLS-SEM) showed that difficult coworkers significantly influenced employees' attitude toward them. Employees' attitude toward difficult coworkers had a small and significant effect on turnover intention while perceived organizational support and affective commitment mediated the relationship between attitude toward difficult coworkers and turnover intention.

The study is the first empirical study to employ the stimulus-organism-response theory to characterize the impact of difficult coworkers on turnover intention. Fortunately, perceived organizational support and affective commitment were able to lessen the impact of difficult coworkers on turnover intention.

The main aim of the present study is to assess the moderating effect of joint audit (JA) on the relationship between audit committee effectiveness (ACEFF) and audit quality (AQ) in Egypt.

The sample included 61 non-financial corporations listed on the Egyptian Exchange from 2016 through 2020. The results are estimated using panel data analysis with fixed-effect models.

The findings exhibit that audit committee (AC) independence, ACEFF; and audit firm size negatively affect AQ. Conversely, the influence of AC meetings on AQ is positive and significant. The findings also reveal that JA moderates the relation between the ACEFF and AQ.

The study offers theoretical contributions to corporate governance mechanisms, JA; and AQ by using data from listed firms in Egypt. The study is the first one that examines the moderating role of JA on ACEFF and AQ.

The study has practical implications for investors, board members, practitioners, academicians; and policymakers. Moreover, the study contributes using a composite measure for the ACEFF score.

The findings, supported by agency, resource dependence; and signaling theories, contribute to a better understanding of the relationship between ACEFF, AQ; and JA. The evidence about JA is still unknown in developing countries. Further, revisiting AQ with different measures, particularly accounting conservatism, has not been a subject of prior studies.