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The current era of the fourth industrial revolution has attracted significant research on the use of digital technologies in improving construction project delivery. However, less emphasis has been placed on how these digital tools will influence the management of the construction workforce. To this end, using a review of existing works, this chapter explores the fourth industrial revolution and its associated technologies that can positively impact the management of the construction workforce when implemented. Also, the possible challenges that might truncate the successful deployment of digital technologies for effective workforce management were explored. The chapter submitted that implementing workforce management-specific digital platforms and other digital technologies designed for project delivery can aid effective workforce management within construction organisations. Technologies such as cloud computing, the Internet of Things, big data analytics, robotics and automation, and artificial intelligence, among others, offer significant benefits to the effective workforce management of construction organisations. However, several challenges, such as resistance to change due to fear of job loss, cost of investment in digital tools, organisational structure and culture, must be carefully considered as they might affect the successful use of digital tools and by extension, impact the success of workforce management in the organisations.

Lean Six Sigma (LSS) implementation follows a structured approach called define-measure-analyze-improve-control (DMAIC). Earlier research about its application in emergency healthcare services shows that it requires organizational transformation, which many healthcare setups find difficult. The Kotter change management model facilitates organizational transformation but has not been attempted in LSS settings till now. This study aims to integrate the LSS framework with the Kotter change management model to come up with an integrated framework that will facilitate LSS deployment in emergency health services.

Two-stage Delphi method was conducted by using a literature review. First, the success factors and barriers of LSS are investigated, especially from an emergency healthcare point of view. The features and benefits of Kotter's change management models are then reviewed. Subsequently, they are integrated to form a framework specific to LSS deployment in an emergency healthcare set-up. The elements of this framework are analyzed using expert opinion ratings. A new framework for LSS deployment in emergency healthcare has been developed, which can prevent failures due to challenges faced by organizations in overcoming resistance to changes.

The eight steps of the Kotter model such as establishing a sense of urgency, forming a powerful guiding coalition, creating a vision, communicating the vision, empowering others to act on the vision, planning for and creating short-term wins, consolidating improvements and producing still more change, institutionalizing new approaches are derived from the eight common errors that managers make while implementing change in the institution. The study integrated LSS principles and Kotter’s change management model to apply in emergency care units in order to reduce waste and raise the level of service quality provided by healthcare companies.

The present study could contribute knowledge to the literature by providing a framework to integrate lean management and Kotter's change management model for the emergency care unit of the healthcare organization. This framework guides decision-makers and organizations as proper strategies are required for applying lean management practices in any system.

The proposed framework is unique and no other study has prescribed any integrated framework for LSS implementation in emergency healthcare that overcomes resistance to change.

The purpose of this paper is to develop optimal estimation procedures for the stress-strength reliability (SSR) parameter R = P(X > Y) of an inverse Pareto distribution (IPD).

We estimate the SSR parameter R = P(X > Y) of the IPD under the minimum risk and bounded risk point estimation problems, where X and Y are strength and stress variables, respectively. The total loss function considered is a combination of estimation error (squared error) and cost, utilizing which we minimize the associated risk in order to estimate the reliability parameter. As no fixed-sample technique can be used to solve the proposed point estimation problems, we propose some “cost and time efficient” adaptive sampling techniques (two-stage and purely sequential sampling methods) to tackle them.

We state important results based on the proposed sampling methodologies. These include estimations of the expected sample size, standard deviation (SD) and mean square error (MSE) of the terminal estimator of reliability parameters. The theoretical values of reliability parameters and the associated sample size and risk functions are well supported by exhaustive simulation analyses. The applicability of our suggested methodology is further corroborated by a real dataset based on insurance claims.

This study will be useful for scenarios where various logistical concerns are involved in the reliability analysis. The methodologies proposed in this study can reduce the number of sampling operations substantially and save time and cost to a great extent.

Wearable medical devices (WMDs) are improving people’s health and well-being in a noble way, as these aid in effective personal health monitoring, remote surveillance and overall illness management. Despite its wider applicability and usage, it is prevalent that users discontinue its usage, which presents an obstacle in the proliferation of such vital innovations among the masses. Therefore, relying on the expectation-confirmation model (ECM), this study aims to delve deeper to explain the factors that motivate users to continually use WMDs by incorporating novel variables, namely, health belief, health information accuracy and privacy protection.

The study proposes and tests an extended ECM perspective to predict the continuance intention (CI) of WMDs among users. By using structural equation modelling using SmartPLS, the authors tested the model on Indian people (n = 451) who had an erstwhile experience of using WMDs.

The study results show that confirmation of users’ expectations positively impacts their usefulness and satisfaction towards WMDs. Moreover, satisfaction towards WMDs is the strongest predictor of users’ CI, followed by perceived usefulness. Interestingly, personal factor such as health beliefs reveals a greater influence on perceived usefulness than technological factors like health information accuracy and privacy protection.

The study findings demonstrate the significance of using the expectation-confirmation perspective in technology-based studies in general and WMDs, in particular. This study aids by offering an integrated model of WMD’s continued usage intention for the users, in addition to practical implications for marketers and policymakers.

A paucity of research exists when understanding the predictors of CI for WMDs. This study fills this gap and adds to behavioural literature by offering a noble viewpoint involving an extended ECM perspective.

Due to herbs and plants’ therapeutic properties and simplicity of availability in nature, humans have used them to treat a variety of maladies and diseases since ancient times. Later, as technology advanced, these plants and herbs gained significant relevance in some industries due to their suitable chemical composition, abundant availability and ease of access. Aegle marmelos is a species of plant that may be found in nature. Yet, little or very little literature was located on the coloration behavior of this plant’s leaves. This study aims to focus on the effect of different parameters on the extraction of colorant from Aegle marmelos leaves.

Some factors that affected on the extraction processes were examined and found to have significant impacts on the textile dyeing such as the initial dye concentration, extracted temperature, extracted bath pH and extracted time were all changed to see how they affected color extraction. The authors report a direct comparison between three heating methods, namely, microwave irradiation (MWI), ultrasonic waves (USW) and conventional heating (CH). The two kinetic models have been designed (pseudo-first and pseudo-second orders) in the context of these experiments to investigate the mechanism of the dyeing processes for fabrics under study. Also, the experimental data were analyzed according to the Langmuir and Freundlich isotherms.

From the result, it was discovered these characteristics were found to have a substantial effect on extraction efficiency. Temperature 90°C and 80°C when using CH and USW, respectively, while at 90% watt when using MWI, period 120 min when using CH as well as USW waves, while 40 min when using MWI, and pH 4, 5 and 10 for polyamide, wool and cotton, respectively, were the optimal extraction conditions. Also, the authors can say that wool gives a higher absorption than the other fabric. Additionally, MWI provided the best color strength (K/S) value, and homogeneity, at low temperatures reducing the energy and time consumed. The coloring follows the order: MWI > USW > CH. The adsorption isotherm of wool could be well fitted by Freundlich isotherm when applying CH and USW as a heating source, while it is well fitted by the Langmuir equation in the case of MWI. In the study, it was observed that the pseudo-first-order kinetic model fits better the experimental results of CH with a constant rate K₁ = −0.000171417 mg/g.min, while the pseudo-second-order kinetic model fits better the experimental results of absorption of both MWI (K2 = 38.14022572 mg/g.min) and USW (K₂ = 12.45343554 mg/g.min).

There is no research limitation for this work. Dye was extracted from Aegle marmelos leaves by applying three different heating sources (MWI, ultrasonic waves [USWW] and CH).

This work has practical applications for the textile industry. It is concluded that using Aegle marmelose leaves can be a possible alternative to extract dye from natural resource by applying new technology to save energy and time and can make the process greener.

Socially, it has a good impact on the ecosystem and global community because the extracted dye does not contain any carcinogenic materials.

The work is original and contains value-added products for the textile industry and other confederate fields.

Interrelationships among some common factors of human resource (HR) management and quality management are still unexplored. Changes in work patterns due to the Covid-19 pandemic have aroused interest in some of these factors, such as working-hours, work pressure, work–life balance practices, job satisfaction. The purpose of this study is to explore the interrelationships among such factors. Specifically, the influence of work hours, work pressure, job rotation and work–life balance on job satisfaction is evaluated both directly and under the mediating influence of working conditions.

A questionnaire-based survey was conducted in Indonesia among diversified organisations. A total of 432 responses were gathered, and they were examined using hypothesis testing and partial least square based structural equation modelling.

The study confirms the statistically proven impact of work pressure, job rotations and work–life-balance practices on working conditions. Job rotations, work–life balance practices and working conditions directly influenced job satisfaction. Work pressure did not influence job satisfaction directly, but it significantly influenced working conditions, which eventually affected job satisfaction. Working hours neither affected working conditions nor job satisfaction in a significant manner.

Covid-19 necessitated working from home, which is a peculiar work–life balance situation. The findings are helpful for organisations in planning strategies related to work–life-balance, working hours, multi-skilling, working conditions and other quality of work life factors in both regular working conditions and under Covid-19 conditions.

The proven influence of work pressure and work–life-balance practices may result in the formation of informal organisations, social groups and increased social networking. As working hours are not diagnosed as an influencing factor for job satisfaction, organisations may think about increasing them, affecting the social fabric of the working community.

Previously unexplored interrelationships among various quality of work life factors are established. Under Covid-19 circumstances, factors such as working hours, work–life-balance and work pressure are investigated in a novel manner. The factors and their interrelationships are important to both quality management professionals and HR professionals.

The purpose of this paper is to investigate students' adoption intention (ADI) and actual usage (ATU) of artificial intelligence (AI)-based teacher bots (T-bots) for learning using technology adoption model (TAM) and context-specific variables.

A mixed-method design is used wherein the quantitative and qualitative approaches were used to explore the adoption of T-bots for learning. Overall, 45 principals/directors/deans/professors were interviewed and NVivo 8.0 was used for interview data analysis. Overall, 1,380 students of higher education institutes were surveyed, and the collected data was analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique.

The T-bot's ADI’s antecedents found were perceived ease of use, perceived usefulness, personalization, interactivity, perceived trust, anthropomorphism and perceived intelligence. The ADI influences the ATU of T-bots, and its relationship is negatively moderated by stickiness to learn from human teachers in the classroom. It comprehends the insights of senior authorities of the higher education institutions in India toward the adoption of T-bots.

The research provides distinctive insights for principals, directors and professors in higher education institutes to understand the factors affecting the students' behavioral intention and use of T-bots. The developers and designers of T-bots need to ensure that T-bots are more interactive, provide personalized information to students and ensure the anthropomorphic characteristics of T-bots. The education policymakers can also comprehend the factors of T-bot adoption for developing the policies related to T-bots and their implications in education.

T-bot is a new disruptive technology in the education sector, and this is the first step in exploring the adoption factors. The TAM model is extended with context-specific factors related to T-bot technology to offer a comprehensive explanatory power to the proposed model. The research outcome provides the unique antecedents of the adoption of T-bots.