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This study investigated what are the effective leadership styles and practices that boost employees’ work outcomes during the COVID-19 pandemic from the perspective of facilities management professionals (FMPs).

Three predominant leadership styles (transformational, transactional contingent reward and disaster management) were operationalized into 38 leadership practices (X variables) and 8 work outcomes (Y variables). The explanatory sequential research design was adopted. Online questionnaire survey was first conducted on FMPs who managed facilities during the critical periods of COVID-19 pandemic in Singapore. In-depth interviews were then carried out with subject matter experts to elaborate on the quantitative findings.

During the pandemic, FMPs were significantly stressed at work, but also experienced significant job satisfaction and satisfaction with their leaders/supervisors. Statistical results revealed a range of leadership practices that are significantly correlated with FMPs’ work outcomes. One leadership practice is critical as it affects 4 of the 8 FMPs’ work outcomes - frequently acknowledging employees’ good performance during the pandemic.

The study explored 3 leadership styles. There are other styles like laissez faire and servant leadership that might also affect work outcomes.

Based on the findings, suggestions were provided to organizations that employ FMPs on how to improve their work outcomes during a crisis such as a pandemic.

The novelty is the discovery that in the context of a global disaster such as the COVID-19 pandemic, the most relevant leadership styles to boost employees’ work outcomes are transactional contingent reward and disaster management leadership. The study adds to knowledge by showing that not one leadership style is superior – all 3 styles are complementary, but distinct, forms of leadership that need to work in tandem to boost FMPs’ work outcomes during a crisis such as a pandemic.

The integration of Lean Six Sigma (LSS) and Industry 4.0 (I4.0) is in the nascent stage and promises to achieve new optimums in operational excellence. This study aims to empirically examine the enablers, barriers, benefits and application of I4.0 technologies in LSS and I4.0 integration.

A pilot survey was chosen as an appropriate methodology, as LSS and I4.0 integration is still budding. The survey targeted senior quality management professionals, quality managers, team leaders, LSS Black Belts and operations managers to collect the relevant research data. The questionnaire was sent to 200 respondents and received 53 valid responses.

This study reveals that “top management support” is an essential enabler for LSS and I4.0 integration. The most significant barrier was “poor understanding of data analysis” and “lack of top management support”. The findings further illustrated that LSS and I4.0 integration resulted in greater efficiency, lower operational costs, improved productivity, improved customer satisfaction and improved quality. Regarding I4.0 technology integration at different phases of LSS, the authors noticed that big data analytics and artificial intelligence (AI) are the most prominent technologies used in all phases of LSS implementation.

One of the limitations of this study is the sample size. LSS and I4.0 are emerging concepts; hence, obtaining a larger sample size is difficult. In addition, the study used non-parametric tests to analyse the data. Therefore, future studies should be conducted with large sample sizes across different continents and countries to understand differences in the key findings.

The outcomes of this study can be useful for organisational managers to understand the enablers and barriers before integrating LSS and I4.0 for adoption in their organisations. Secondly, it helps to convince top management and human resource personnel by providing a list of benefits of LSS and I4.0 integration. Finally, it can help decision-makers understand which I4.0 technologies can be used in different stages of LSS methodology.

LSS and I4.0 integration was studied at a conceptual level. This is the first empirical study targeted toward understanding the LSS and I4.0 integration. In addition, this study investigates the application of widely used I4.0 technologies in different phases of LSS.

Surface acoustic wave (SAW) sensors have attracted great attention worldwide for a variety of applications in measuring physical, chemical and biological parameters. However, stability has been one of the key issues which have limited their effective commercial applications. To fully understand this challenge of operation stability, this paper aims to systematically review mechanisms, stability issues and future challenges of SAW sensors for various applications.

This review paper starts with different types of SAWs, advantages and disadvantages of different types of SAW sensors and then the stability issues of SAW sensors. Subsequently, recent efforts made by researchers for improving working stability of SAW sensors are reviewed. Finally, it discusses the existing challenges and future prospects of SAW sensors in the rapidly growing Internet of Things-enabled application market.

A large number of scientific articles related to SAW technologies were found, and a number of opportunities for future researchers were identified. Over the past 20 years, SAW-related research has gained a growing interest of researchers. SAW sensors have attracted more and more researchers worldwide over the years, but the research topics of SAW sensor stability only own an extremely poor percentage in the total researc topics of SAWs or SAW sensors.

Although SAW sensors have been attracting researchers worldwide for decades, researchers mainly focused on the new materials and design strategies for SAW sensors to achieve good sensitivity and selectivity, and little work can be found on the stability issues of SAW sensors, which are so important for SAW sensor industries and one of the key factors to be mature products. Therefore, this paper systematically reviewed the SAW sensors from their fundamental mechanisms to stability issues and indicated their future challenges for various applications.

Asynchronous Video Interviews (AVIs) incorporating Artificial Intelligence (AI)-assisted assessment has become popular as a pre-employment screening method. The extent to which applicants engage in deceptive impression management (IM) behaviors during these interviews remains uncertain. Furthermore, the accuracy of human detection in identifying such deceptive IM behaviors is limited. This study seeks to explore differences in deceptive IM behaviors by applicants across video interview modes (AVIs vs Synchronous Video Interviews (SVIs)) and the use of AI-assisted assessment (AI vs non-AI). The study also investigates if video interview modes affect human interviewers' ability to detect deceptive IM behaviors.

The authors conducted a field study with four conditions based on two critical factors: the synchrony of video interviews (AVI vs SVI) and the presence of AI-assisted assessment (AI vs Non-AI): Non-AI-assisted AVIs, AI-assisted AVIs, Non-AI-assisted SVIs and AI-assisted SVIs. The study involved 144 pairs of interviewees and interviewers/assessors. To assess applicants' deceptive IM behaviors, the authors employed a combination of interviewee self-reports and interviewer perceptions.

The results indicate that AVIs elicited fewer instances of deceptive IM behaviors across all dimensions when compared to SVIs. Furthermore, using AI-assisted assessment in both video interview modes resulted in less extensive image creation than non-AI settings. However, the study revealed that human interviewers had difficulties detecting deceptive IM behaviors regardless of the mode used, except for extensive faking in AVIs.

The study is the first to address the call for research on the impact of video interview modes and AI on interviewee faking and interviewer accuracy. This research enhances the authors’ understanding of the practical implications associated with the use of different video interview modes and AI algorithms in the pre-employment screening process. The study contributes to the existing literature by refining the theoretical model of faking likelihood in employment interviews according to media richness theory and the model of volitional rating behavior based on expectancy theory in the context of AVIs and AI-assisted assessment.

The purpose of this study was to investigate the state of appraisal with regard to compliance with archives and records management legislations (ARML), the archives and records management policy (ARMP), reappraisal of records, capacity building, archives building and electronic records management (ERM) and to make a recommendation based on the research findings.

This study was based on the qualitative research technique. The research approach was a multiple case study comparing the previously advantaged and disadvantaged universities in South Africa.

The study found that ARML, ARMP, reappraisal of records and capacity building are essential for the appraisal of university records. The lack of appropriate appraisal theory/strategy led to a loss of institutional memory.

This research was limited to the University of the Witwatersrand (WITS) and the University of Venda (UNIVEN). These two institutions are representatives of the state of archiving in South Africa.

This research will serve a benchmark for other South African universities, intending to implement systematic disposal of records in compliance with legislations and policies.

Failure by universities to appraise records will lead to the loss of institutional memory. This implies that history of institution will be lost if necessary measures are not taken.

There is very little, if any, research on the appraisal of South African universities’ records. The outcome of this research will benefit universities that are seeking to develop and implement appraisal strategies.