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A comprehensive literature review was conducted to identify the lean principles and the challenges of building refurbishment. To have an in-depth investigation of the application of lean principles to address the challenges of refurbishment projects, ten expert interviews following a qualitative research approach were utilised in this research. Data were analysed using manual content analysis to derive the framework.

The refurbishment of buildings has attracted the attention of the present construction industry. However, uncertain project characteristics, information deficiency, limited space for construction activities and less stakeholder involvement make it complex. Since the lean concept effectively deals with complex and uncertain projects, this study focusses to investigate the application of lean principles to overcome the challenges of refurbishment projects in Sri Lanka by developing a framework.

It was found that the five main lean principles of customer value, value stream, value flow, pull and perfection are appropriate for building refurbishment projects in Sri Lanka. Precise identification of clients and end-users, value adding and non-value adding activities, interruptions and stakeholder communication chains, setting scope, examining the possible technologies and taking measures to deliver the exact product to ensure the successful application of lean principles for refurbishment projects. Further, 27 benefits of five lean principles were identified which can be used to address the 13 identified challenges of building refurbishment of projects. Finally, a framework has developed portraying the application of lean principles in building refurbishment.

The framework developed is beneficial for the building refurbishment project team to address the barriers of refurbishment projects by applying lean principles.

This framework can be used as a guideline for the implementation of building refurbishment projects by addressing their challenges with lean principles.

Millennials are a vital generational cohort of the Indian population, and understanding their motivation to participate in the stock market is crucial. This study aims to understand the investment decision-making behavior among millennials in the Indian Stock Market.

Using a cross-sectional research design that entails in-depth personal interviews, this study aims to understand the equity investment behavior of millennials. Verbatim texts from interview transcripts were used to analyze the content and arrive at themes.

The study investigated the motivation to enter the stock market and gained insights into how individuals make equity investment decisions considering economic and behavioral dimensions. The basis for stock selection was predominantly on the self-analysis of investors. Multiple stock selection priorities are also discussed. In addition, informants ensured asset diversification and exercised various strategies to overcome emotions. Furthermore, they suffered from various behavioral biases.

Individual investors are the least informed and most impacted stakeholders in the stock markets; therefore, this study contributes fresh insights to enhance their financial security. The paper also examines some noticeable behavioral tendencies retail investors exhibit and gathers helpful strategies for mitigating behavioral biases.

The uniqueness of the research lies in its adoption of a qualitative methodology that uses the investment experience of millennial investors to reveal the components of decision-making behavior and investor psychology. The findings are thereby unique and have significant managerial implications.

Variations due to errors and mistakes have caused cost overruns in building projects. Therefore, it is undeniable that the gravity of such variations will be a critical factor in deciding the success of any building project. In addition, the design stage of a building project is considered the most suitable stage to identify and mitigate the causes of potential variations. However, there are no proper mechanisms to minimise the frequency or gravity of variations. Many researchers experienced the promising essence of Poka-Yoke, a mistake-proofing method aimed at increasing efficiency by early detection and eradication of the causes of potential errors. However, less attention has been paid so far to implement Poka-Yoke principles to minimise variations in the building project. Therefore, this study aims to develop a framework to minimise variations in building projects through the integration of the Poka-Yoke principles.

An interpretivism stance is adopted, and a qualitative research approach is used. The data collection technique adopted is semi-structured interviews with ten experts, and the data is analysed using code-based content analysis through NVivo12.

Research findings revealed 23 causes of variations, categorised under client-originated, consultant-originated, contractor-originated and other variations. The identified causes were then mapped with the Poka-Yoke principles to develop the framework. The research findings could prove useful to researchers, academics, government agencies and construction professionals in developing nations that have demographic/cultural and socioeconomic characteristics such as Sri Lanka.

The findings benefitted the Sri Lankan construction sector by minimising the causes of variations. To the best of the authors’ knowledge, this study will be the first of its kind in the Sri Lankan construction industry, leading to a better understanding of the “Poka-Yoke” principle within the building construction context.

Previous research provides adequate evidence on performance management (PM) for hospitals and healthcare providers; however, less is known about their individual and PM appraisal process. Additionally, there is limited research exploring PM in the Middle Eastern context. This study investigates PM practices in the Middle Eastern healthcare industry.

This study adopts the qualitative research methodology through semi-structured interviews of healthcare professionals in Kingdom of Saudi Arabia and the United Arab Emirates. Thematic analysis was adopted for analyzing this qualitative data.

The main findings have uncovered different facets of appraisal challenges for both the appraiser (i.e. manager) and the appraisee (i.e. employee). These challenges include communication deficits, lack of goal setting standards and regular meeting updates in order to ensure employee satisfaction and motivation in the workplace.

This study has significant implications for policymakers in Middle Eastern hospitals in terms of implementing PM for their staff. Moreover, future studies can conduct in-depth analysis and provide comparison between public and private sectors in the Gulf countries.

This study is one of the first to portray challenges involved in conducting PM in the Middle East healthcare sector specifically in the UAE and Kingdom of Saudi Arabia (KSA), both from the perspectives of the appraiser and appraisee.

The study directs attention to the psychological conditions experienced and knowledge management practices leveraged by faculty in higher education institutes (HEIs) to cope with the shift to emergency remote teaching caused by the COVID-19 pandemic. By focusing attention on faculty experiences during this transition, this study aims to examine an under-investigated effect of the pandemic in the Indian context.

Interpretative phenomenological analysis is used to analyze the data gathered in two waves through 40 in-depth interviews with 20 faculty members based in India over a year. The data were analyzed deductively using Kahn’s framework of engagement and robust coding protocols.

Eight subthemes across three psychological conditions (meaningfulness, availability and safety) were developed to discourse faculty experiences and challenges with emergency remote teaching related to their learning, identity, leveraged resources and support received from their employing educational institutes. The findings also present the coping strategies and knowledge management-related practices that the faculty used to adjust to each discussed challenge.

The study uses a longitudinal design and phenomenology as the analytical method, which offers a significant methodological contribution to the extant literature. Further, the study’s use of Kahn’s model to examine the faculty members’ transitions to emergency remote teaching in India offers novel insights into the COVID-19 pandemic’s effect on educational institutes in an under-investigated context.

This paper explores the current state of Robo-advisory services in India. This paper further highlights the problems experienced by the service providers in disseminating the innovative business model among the Indians.

The study adopts a qualitative approach to investigate the industry experts by conducting semi-structured interviews. The data collected were transcripted and further analyzed using the content analysis technique. Finally, the authors utilized categorization and coding techniques to frame broad study themes.

The study findings reveal that the three pillars of Robo-advisory are ease and convenience, the time factor and transparency in operations. Robo-advisory services are still at a nascent stage in India. Furthermore, keeping the sentiments of Indians in mind, FinTech companies could combine automated Robo-advisory with a human touch of a wealth manager for optimal advisory services.

Since the present study is qualitative, the authors cannot generalize the study results. Future research can focus on empirically proving the constructs of the study using quantitative methods.

Robo-advisors have a well-established market in developed nations but are still nascent in developing countries like India. The current focus of service providers and regulatory authorities must be to increase awareness among investors by educating the investors and building trust.

The present study is the first to qualitatively synthesize the challenges faced by the FinTech service providers in the Indian market.

Human skin temperature data can provide reference advice for diagnosing many diseases, but current wearable skin temperature monitoring systems have few points and may miss temperature information in critical areas. This paper aims to develop a wearable system that can be used for local temperature monitoring and restore better the actual state of local human temperature by exploring ways to extend more temperature measurement points. This will provide better assistance in developing medical targeting and intelligent clothing.

The temperature measurement system contains modules for temperature monitoring, digital-to-analog conversion and regulated power supply, enabling fast reading of 12 channels of monitoring data. The microprocessor unit is the STM32F407. The instructions and control modes are written in C. The waist area was chosen as the monitoring area because it is susceptible to temperature, and many diseases are associated with skin temperature. Twelve points, including temperature-related acupuncture points and heat-sensitive points, were selected for testing and the data results agreed well with the infrared imaging results.

The waist is selected as the monitoring object, and an easy-to-wear waist temperature monitoring belt is designed to verify the application value of the system. The development of the system provides reference suggestions for the exploration of multi-point temperature monitoring systems and the integration capabilities of temperature measurement modules in wearable multifunctional systems.

In addition to waist temperature monitoring, the wearable temperature measurement system developed in this study can also be applied to other body parts. In addition, the system can be efficiently and effectively combined with various garments, making it a useful tool for researching human skin temperature.

The wearable temperature monitoring system designed in this paper extends the number of temperature test points to 12. The number of test points covers as many localized body areas as possible to indeed reproduce the temperature distribution of the human body. In addition, the selection of test points combines medically relevant body points with physiologically relevant heat-sensitive areas, which makes the temperature measurement data more valuable.

Human–robot collaboration (HRC) is an emerging research field for the construction industry along with construction robot adoption, but its implementation remains limited in construction sites. This paper aims to identify critical risk factors and their interactions of HRC implementation during engineering project construction.

Literature research, expert interviews, a questionnaire survey and a social network analysis (SNA) method were used. First, literature research and expert interviews were employed to identify risk factors of HRC implementation and preliminarily understand factor interactions. Second, a questionnaire survey was conducted to determine the degree of interactions between risk factors. Third, based on the data collected from the questionnaire survey, SNA metrics were used to find critical risk factors and critical interactions.

The critical risk factors consist of robot technology reliability, robot-perceived level, conflict between designers and users of construction robots, organisational culture, organisational strength, project cost requirements, changeability of project construction, project quality requirements and project safety requirements. The interactions between risk factors are strong and complex. Robot technology risk factors were relatively fundamental risk factors, and project risk factors had a direct influence on the risk of HRC implementation. The implementation cost of HRC was not identified as a critical risk factor. Individual risk factors could be mitigated by improving technical and organisational factors.

This paper contributes to the body of knowledge in the field of both HRC behaviours and its risk management in construction project management. Identifying the critical risk factors and their interactions of HRC implementation in the construction industry and introducing social network theory to the research on critical risk factors are the innovations of this paper. The findings and proposed suggestions could help construction professionals to better understand the HRC risk factors and to manage the risk of HRC implementation more effectively.

In the digital transformation era, the construction industry is not immune to unintended consequences and disruptions of distributed ledger technologies like blockchain. At the micro-level, construction organizations need an in-depth understanding of blockchain risks to take proactive strategies for being on the safe side. This study seeks to answer “What are the risks associated with blockchain technology from the firm-level perspective? And how can this disruptive technology overshadow the business objectives and impact organizational criteria?”

The current research proposes a novel model for risk assessment based on the trapezoidal fuzzy ordinal priority approach (OPA-F) in the multi-criteria decision-making (MCDM) context. The proposed model handles uncertainties of experts' judgment around three primary parameters: the importance of organizational criteria, the impact of blockchain risks on criteria and the probability of risk occurrence.

The case study shows that organizational “communication and information” is exposed to the most blockchain risk. On the contrary, blockchain has less to do with an organization's “corporate social responsibility.” Furthermore, effective blockchain risk management can bring about cost efficiency, quality and improved customer experience for this case study. In the end, the authors develop a conceptual blockchain risk management framework based on findings.

This study will broaden researchers' horizons regarding “blockchain in construction context” and “blockchain risk management.”

Furthermore, executives looking for blockchain-based solutions can benefit from research findings and lessons learned from this case study before decision-making. Lastly, the risk assessment model based on trapezoidal OPA-F can be used both for research purposes and industrial decision problems.

To the best of the authors’ knowledge, it is for the first time that the OPA-F is employed in a risk assessment model. Also, the original OPA-F is extended to trapezoidal OPA-F using trapezoidal fuzzy numbers, and it is the first attempt to evaluate blockchain risks facing construction organizations and develop a blockchain risk management framework accordingly.