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Recent United Nations Climate Change Conferences recognise extreme climate change of heatwaves, floods and droughts as threatening risks to the resilience and success of public–private partnership (PPP) infrastructure projects. Such conferences together with available project reports and empirical studies recommend project managers and practitioners to adopt smart technologies and develop robust measures to tackle climate risk exposure. Comparatively, artificial intelligence (AI) risk management tools are better to mitigate climate risk, but it has been inadequately explored in the PPP sector. Thus, this study aims to explore the tools and roles of AI in climate risk management of PPP infrastructure projects.

Systematically, this study compiles and analyses 36 peer-reviewed journal articles sourced from Scopus, Web of Science, Google Scholar and PubMed.

The results demonstrate deep learning, building information modelling, robotic automations, remote sensors and fuzzy logic as major key AI-based risk models (tools) for PPP infrastructures. The roles of AI in climate risk management of PPPs include risk detection, analysis, controls and prediction.

For researchers, the findings provide relevant guide for further investigations into AI and climate risks within the PPP research domain.

This article highlights the AI tools in mitigating climate crisis in PPP infrastructure management.

This article provides strong arguments for the utilisation of AI in understanding and managing numerous challenges related to climate change in PPP infrastructure projects.

This study aims to examine the research trends in fashion industry during the coronavirus disease 2019 (COVID-19) pandemic. Besides, it also provides an overview on the new marketing and operational strategies, and reveals the corresponding business challenges of a footwear enterprise in Hong Kong during the COVID-19 pandemic.

A comprehensive literature review is first conducted to identify the research trends in fashion industry during the COVID-19 pandemic. A qualitative exploratory case study is then used to illustrate how a footwear enterprise has coped with the COVID-19 pandemic.

The case study has showed that omni-channel retailing, collaboration with e-tailers, quick response system and mixed production strategy are adopted in the targeted case during the COVID-19 pandemic. Besides, the targeted case has also faced the challenges in the areas of sales, customer relationship management, and demand forecasting and inventory planning during the COVID-19 pandemic.

This study provides managerial insights on the real practices used to deal with the COVID-19 pandemic and proposes various academic future research directions in fashion industry based on the real-world observations.

While there was heightened awareness on the technologies of the fourth industrial revolution (IR 4.0) prior to COVID-19, studies have shown that the adoption of these advanced technologies (e.g. Big Data, robotics, Internet of Things, etc.) continues to remain low across global industries. This qualitative study sought to explore the reasons for the low rate of adoption of these technologies and appropriate measures to enhance their adoption at hotels, through the lens of hotel executives.

Based on interpretivist's ideals, this study follows a case study design and adopts a qualitative method of enquiry. The heterogenous purposive sampling technique was employed to gather data for the study, using semi-structured interviews.

Grounded on the technology-organisation-environment (TOE) framework, the thematic analysis revealed technology, organisation and environment-related reasons for the low rate of IR 4.0 technologies adoption at hotels in Malaysia. Also, the study uncovered some interesting measures that will enhance the adoption of these advanced technologies at hotels.

This study unearths technology, organisation and environment-related reasons for low adoption, and measures to enhance the adoption of IR 4.0 technologies in hotels. This study also enlightens hotel owners and technology providers about practical issues that will ensure the successful adoption of such technologies to enhance hotel business. In line with SDG 9, this study also seeks to promote sustainable innovation in the hospitality industry. Practical and theoretical implications have also been discussed in this study.

The purpose of this study is to review the application of social media for knowledge sharing (KS) in higher education. KS is the most crucial component in knowledge management. Higher education institutions (HEIs) are the epitome of knowledge creation and acquisition. With the advancement in technology and the embracement of social media, knowledge should be shared more freely and easily.

Using a bibliometric analysis, this study applies bibliographic coupling and co-word analysis to analyze the present and future trends on KS using social media in HEIs. 455 journal publications and 21,181 cited references were retrieved from Web of Science (WoS) database.

Findings show that most themes are categorized towards academics and students. Themes related to academics are the use of social media for expertise sharing and KS's impact on university-industry networking. In contrast, themes related to students revolved around the impacts of social media and academic performance.

Implications towards major social media practices on KS are discussed.

This study provides a novel, state-of-the-art bibliometric review of knowledge sharing via social media in the higher education context.

One of the most decisive objectives of human resource management (HRM), essential for business performance, is to attract, maintain and engage a qualified workforce. This study aims to identify and prioritize the antecedents of employer branding, which are considered extremely important by potential applicants when they choose to join a particular employer.

The authors have adopted a popular multicriteria decision-making technique fuzzy analytical hierarchy process, to prioritize the identified antecedents. The modified Delphi techniques with the 22 experts have been conducted to validate the identified antecedents. Structured questionnaires were discussed, and their reliability was assessed using the expert’s opinion.

Results of this study indicate that potential applicants consider career advancement opportunities as the most important enabler that persuades them to join a particular employer. Future employees are also motivated to join if they are offered attractive compensation packages, which are distinct from competitors. Furthermore, corporate social responsibility is evolving as an antecedent of employer branding as potential candidates are attracted to employers who are socially responsible.

Employer branding can serve as a strategic HRM technique for any business seeking to attract, recruit, retain and involve outstanding employees, as the success of the organization is determined by how its image is built, communicated and promoted. This study provides insights for HR managers and practitioners who can think of developing an effective employer brand communication that offers a distinct and in-imitable image and reputation as an employer of choice.

This study is unique, as it offers meaningful visions to HR practitioners and experts for designing employer branding strategies for attracting potential applicants to join their organizations.

This study adopts the perspective of dynamic capabilities to investigate influencing factors and proposes improvement strategies of supply chain resilience of prefabricated construction.

The structural equation model (SEM) is used to identify and verify the relationship between factors influencing supply chain resilience of prefabricated construction from the perspective of dynamic capabilities. The system dynamics (SD) model is constructed to dynamically simulate the specific effects of different influencing factors.

Results indicate that: (1) An evaluation index system for supply chain resilience of prefabricated construction containing five first-level indicators and 36 second-level indicators is constructed; (2) Ability to anticipate, ability to respond, ability to adapt, ability to recover and ability to learn are positively correlated with the supply chain resilience of prefabricated construction and (3) ANT3 (information system), RES1 (quick response), ADA3 (buffer stock) and LEA4 (trust) are the most leading factors influencing supply chain resilience of prefabricated construction over time.

This study fulfills the need for an in-depth exploration of the various influencing factors on supply chain resilience of prefabricated construction from the perspective of dynamic capabilities. Furthermore, this study provides improvement strategies to enhance supply chain resilience of prefabricated construction in China.