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This paper offers a new vision of responsible service leadership for service organizations nested in economic, societal and environmental contexts across time to foster collective flourishing.

Following the call for novel perspectives that recognize service as a game among (vs between) people in service ecosystems, we build on service leadership theory to integrate insights from infinite (vs finite) games and biomimicry practices to propose a holistic model for responsible service leadership.

We extend the 3C (competence, character and care) model of service leadership (Shek et al., 2021) adding context and chronos as essential pillars of responsible service leadership in nested ecosystems. We offer new interpretations and applications of the 3Cs through the lens of context and chronos.

This paper furthers the emerging conversation about unique leadership approaches for service, linking existing service leadership theories with holistic views of service ecosystems and enabling a shift from decontextualized models of leadership to a more inclusive approach.

We propose that responsible service leadership can inspire new approaches to leadership development within organizations and in business education (e.g. competencies, settings) and a reconsideration of organizational structures (e.g. culture, selection and incentive design).

The proposed 5C model revisits foundational assumptions of responsibility in service leadership, integrating actors across and within service ecosystems, society at large and the environment in the present and future.

This paper offers a conceptual framework – the 5Cs model of responsible service leadership – aimed at reimagining service leadership.

This chapter demonstrates the strategic use of social media by firms interacting with stakeholders, not just customers. Corporations have seen how consumers can share their experiences with products and services. Smart companies find ways to leverage these conversations to engage current and potential customers and other stakeholders.

Our approach is theoretical, based on an examination of how the communication channel of social media can be used as a strategic tool throughout the organization, going well beyond marketing.

This chapter looks at four advantages of social media. Social media provides a mechanism for firms to not just find customers, but also allow customers to find firms. Social media can engage customers, industry influencers, and thought pundits in cost-effective ways that benefit both the firm and stakeholders. The largest contribution of social media is the information from big data on customer needs and wants as well as on the processes underlying consumer decisions. The greatest opportunity lies in how a firm converts data into actionable intelligence in real time.

We discuss how the words, social and media, need to be de-coupled: The concept of social can be practically applied very effectively to areas of strategy and internal learning, and product development. Taking advantage of the opportunities presented by social media requires a social enterprise to be effective.

This chapter presents an original theoretical framework of social media that can be used by a company to examine the social aspects and implications of every element in the value chain.

This chapter examines the integration of Industry 4.0 (IR 4.0) technologies and sustainability, proposing a people-centric strategy to align industrial progress with environmental responsibility. Through a synthesis of scholarly insights and practical applications, we delve into the potential of Industry 4.0 to drive economic, social, legal, and environmental sustainability while addressing key challenges and limitations. Acknowledging these obstacles associated with IR 4.0 implementation, including employment displacement and data security risks, we advocate for collaborative efforts among governments, businesses, and society, highlighting the imperative of optimizing the advantages conferred by IR 4.0 while mitigating its adverse effects on sustainability. It calls for strategic investments in education, innovation, and social protection mechanisms to navigate this transition while ensuring equitable and sustainable development for all stakeholders. As these technologies intertwine, it becomes increasingly crucial to establish regulatory frameworks that can endorse the “rule of law” in this ever-evolving legal landscape. Ensuring fairness and effectiveness in regulation is key to navigating this emergent reality. Through fostering synergistic partnerships, embracing sustainable methodologies, and ensuring that the promise of IR 4.0 is realized without sacrificing the fundamental principles of fairness, justice, and human rights, we can envision a future where innovation thrives in harmony with sustainability principles, paving the way for inclusive prosperity.

Diabetes is a chronic disease and the major types of diabetes are type 1 and type 2. On aging, people with diabetes tend to have long-term problems in hypertension, coronary artery disease, obesity, and nerves. Given the increasing number of complications in recent years, by 2040, 624 million people will have diabetes worldwide and l in 8 adults will have diabetes in the future. Machine learning (ML) is evolving rapidly, many aspects of medical learning use ML. In this study, tension-type headaches (TTH) were associated with diabetes using SPSS, Pearson correlation, and ANOVA tests. Data were collected from Delhi NCR Hospital. It contains 30 diabetic subjects. The purpose of this study was to correlate diabetes analysis from TTH and other diseases using the latest technologies to analyze the Internet of Things and Big Data and Stress Correlation (TTH) on human health. The authors used Pearson correlation to correlate study variables and see if there was any effect between them. There was an important relationship between the percent variable, the total number of individuals, the number of individuals, and the minimum variable. The age (field) of the number of individuals to one of the total number of individuals showed a strong correlation (1.000) with a significant value of p (1.000). Overall, cases of TTH increased with age in men and do not follow the pattern of change in diabetes with age, but in cases of TTH, patterns of headaches such as diabetes increase to age 60 and then tend to decrease.

The purpose of this paper is to investigate propagation characteristics of seismic waves at the welded interface of an elastic solid and unsaturated poro-thermoelastic solid.

A theoretical formulation of partially saturated poro-thermoelastic solid is used in this study established by Zhou et al. (2019). The incidence of two primary waves (P and SV) is taken. The incident wave from the elastic solid induces two reflected waves and five refracted waves. Due to viscous pore fluids, partially saturated poro-thermoelastic solid behave dissipative, whereas elastic solid behaves non-dissipative. As a result, both reflected and incident waves are homogeneous. However, all the refracted waves are inhomogeneous. A non-singular system of linear equations is formed by the coefficients of reflection and refraction for a specified incident wave. The energy shares of various reflected and refracted waves are determined by using these reflection and refraction factors. Finally, a sensitivity analysis is performed, and the effect of critical variables on energy partitioning at the interface is observed. The numerical example shows that throughout the process of reflection/refraction, the energy of incidence is conserved at all angles of incidences.

This study demonstrated two refracted (homogeneous) and five refracted (inhomogeneous) waves due to the incident wave from elastic solid. The reflection and refraction coefficients and partitioning of incident energy are acquired as a part of diverse physical parameters of the partially saturated poro-thermoelastic media. The interference energies between unlike pairs of refracted waves have been discovered due to the dissipative behavior of unsaturated poro-thermoelastic solid.

The sensitivity of different energy shares to various aspects of the considered model is graphically analyzed for a specific numerical model. The energy balance is maintained by combining interaction energy and bulk wave energy shares.

The tea plantation industry is characterized by the large-scale deployment of cheap women laborers and gender-blind practices that make the social positions of women workers vulnerable. This paper considers women temporary workers in tea gardens to study the exacerbated impact of Covid-19 on their lives. The impact of the pandemic on marginal tea garden women laborers has not received enough attention from researchers; hence, the authors have studied the gendered implications of the pandemic on Adivasi temporary women workers in tea gardens in India. “Adivasi” is an umbrella term to refer to all indigenous tribes in India.

The authors conducted a qualitative study with 26 in-depth interviews with women temporary workers who identify themselves as Adivasis. For the discussion, the authors have mainly borrowed from intersectionality and subalternity literature.

The analysis explored the intersectional experiences of the women temporary workers (1) as members of Tea Tribes who are compelled to continue working at tea gardens as wage laborers, (2) job insecurities at work due to their temporary worker status, (3) disadvantages faced by women workers for their gender identity and (4) the gendered impact of the pandemic on their lives.

This study has explored the gendered impact of the Covid-19 pandemic on the lives of temporary women workers who belong to ethnic minority groups in the global south. The exploitation of labor rights in the tea industry during the pandemic has not been discussed enough by researchers earlier.

Introduction: Historically, the corporate governance (CG) framework was designed primarily to safeguard the economic interests of shareholders, as a result of political and legal interventions, developing into an effective instrument for stakeholders and society in general.

Purpose: The core objectives of the study include: identifying journals/publications responsible for publishing CG studies in India, key CG issues covered by CG researchers, the amount of high-impact CG literature across different time periods, sectors/industries covered by CG researchers and different research instruments (quantitative or qualitative) used in CG studies in India.

Design/methodology: The chapter used a sample of 130 corporate governance studies that fulfil the selection criteria, drawn from the repository of over 100 reputed journals that are either recognised by the Australian Business Deans Council (ABDC) or indexed by SCOPUS. A systematic literature review has been carried out pertaining to CG issues in India, based on various statistical tools, data, industries, research outlets & citations, etc.

Findings: The results show an overwhelming number of studies have assessed the relationship between CG variables and firm performance, which could be measured through a variety of performance metrics such as ROA and ROI. Apart from empirical analysis, many conceptual studies use repetitive basic statistical tools like descriptive statistics or regression analysis. The chapter offers insights into current achievements and future development.

Originality/value: This bibliometric study is a useful guide for policymakers, corporate leaders, research organisations and management faculty to draw insights from work produced by eminent researchers in GC in India.

The banking sector is more revolutionized than ever, with advanced technologies driving a seismic change in the financial industry. This study aims to understand how digital technologies influence banking sector employees and their perception of working in an era of Banking 4.0.

This study incorporated qualitative analysis to gain different insights from diverse respondents from banking industries. A purposive sampling method was adopted, and semistructured interviews were conducted, taking a sample of 72 respondents. All the transcripts were then analyzed using NVivo.

The findings focus on challenges related to understanding technology phenomena, managing changes, infrastructure, skills, competitiveness and regulatory mechanisms. This is further followed by the favorable impact of Banking 4.0 on employees and future avenues, such as innovation in financial services, work productivity, career opportunities and change management, banking 4.0 and banking 5.0, and banking 4.0 management strategies identified as the significant findings.

This study provides guidelines for Banking 4.0 provision strategy and conceptual reference toward the development of Banking 4.0. It also supports the Enhanced Access and Service Excellence 4.0 program, driven by the Indian Bank’s Association, to focus more on digitization, automation and data analytics.

The novelty of this research provides a qualitative hierarchy of significant challenges, favorable impacts and future research avenues of Banking 4.0 in the Indian banking sector.

With the advent of Big Data, the ability to store and use the unprecedented amount of clinical information is now feasible via Electronic Health Records (EHRs). The massive collection of clinical data by health care systems and treatment canters can be productively used to perform predictive analytics on treatment plans to improve patient health outcomes. These massive data sets have stimulated opportunities to adapt computational algorithms to track and identify target areas for quality improvement in health care.

According to a report from Association of American Medical Colleges, there will be an alarming gap between demand and supply of health care work force in near future. The projections show that, by 2032 there is will be a shortfall of between 46,900 and 121,900 physicians in US (AAMC, 2019). Therefore, early prediction of health care risks is a demanding requirement to improve health care quality and reduce health care costs. Predictive analytics uses historical data and algorithms based on either statistics or machine learning to develop predictive models that capture important trends. These models have the ability to predict the likelihood of the future events. Predictive models developed using supervised machine learning approaches are commonly applied for various health care problems such as disease diagnosis, treatment selection, and treatment personalization.

This chapter provides an overview of various machine learning and statistical techniques for developing predictive models. Case examples from the extant literature are provided to illustrate the role of predictive modeling in health care research. Together with adaptation of these predictive modeling techniques with Big Data analytics underscores the need for standardization and transparency while recognizing the opportunities and challenges ahead.