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This chapter challenges the urban bias in studies on the middle class in India and underlines the need to focus on the significant role rural middle class plays in economic diversification. Given that more than 23% of the upper middle class are located in rural India, it is surprising to note that their contribution in supporting experience economy remains under-researched. Thus, this chapter fills a key gap in existing studies on rural tourism in India by underlining how rural middle-class has triggered a huge demand for travel within the country itself for rural cultural programmes and different schemes to promote rural heritage sights.

The purpose of the chapter is to integrate the understanding of diversity from different perspectives in Indian context and see how the holistic view emerges.

The methodology used is primarily the literature review of the concepts and their evolution in Indian context and the use of secondary sources to extract praxis information.

It emerged from the exploration on diversity practices at the societal as well as organizational level in India that the country demonstrates intent to mainstream the people from different wakes, but with the changing context the format of the practices has changed.

The basic premise of the chapter needs to be explored further through primary data from practice.

This chapter is novel in a way that it integrates the diversity scholarship of four different streams viz. caste, gender, disability, and generation. Most of the existing research focuses only on a thin slice/one key dimension of diversity.

An electrical power distribution network is expected to deliver uninterrupted power supply to the customers. The disruption in power supply occurs whenever there is a fault in the system. Therefore, fast fault detection and its precise location are necessary to restore the power supply. Several techniques are proposed in the past for fault location in distribution network but they have limitations as their fault location accuracy depends on system conditions. The purpose of this paper is to present a travelling wave-based fault location method, which is fast, accurate and independent of system conditions.

This paper proposes an effective method for fault detection, classification and location using wavelet analysis of travelling waves for a multilateral distribution network embedded with distributed generation (DG) and electric vehicle (EV) charging load. The wavelet energy entropy (WEE) is used for fault detection and classification purpose, and wavelet modulus maxima (WMM) of aerial mode component is used for faulted lateral identification and exact fault location.

The proposed method effectively detects and classifies the faults, and accurately determines the exact fault location in a multilateral distribution network. It is also found that the proposed method is robust and its accuracy is not affected by the presence of distributed generation and electric vehicle charging load in the system.

Travelling wave based method for fault location is implemented for a multilateral distribution network containing distributed generation and electric vehicle load. For the first time, a fault location method is tested in the presence of EV charging load in distribution network.

The aim of this chapter is to delineate a product framework concerning managed visitor attractions (MVA), which highlights the supply-chain aspects of destinations. It first touches on the rationales for developing such a framework and then constructs a framework composed of a set of product components deriving from the extant literature. Consequently, an version of a product component framework, fastening on an accumulated sample of attraction cases, is presented through three illustrative cases. In the conclusion section, this study elaborates on the study limitation while connoting how the resultant data could shed light on the role of the components of the MVA product in the creation of visitor experiences.

This case discusses the issue and challenges faced by MoveInSync during the COVID-19 pandemic lockdown and its Corporate Social Responsibility (CSR) initiative that contributed to the employees’ wellbeing. India was significantly hit by the pandemic and the impact has been severe. The corporate sector of India irrespective of the company size has worked for hand in hand with the government to contribute to handling the pandemic. One such company is MoveInSync which offers transport and security operations to enhance employee commute space. Despite the COVID-19 pandemic severely impacting their business operations, MoveInSync continued its CSR effort to help the needy and cared for its own employees.

One of the main advantages of the proposed model is that it is flexible to generate n-day pairings simultaneously. It means that, despite previous researches, one-day to n-day pairings can be generated in a single model. The flexibility in generating parings causes that the proposed model leads to better solutions compared to existing models. Another advantage of the model is minimizing the risk of COVID-19 by limitation of daily flights as well as elapsed time minimization. As airports are among high risk places in COVID-19 pandemic, minimization of infection risk is considered in this model for the first time. Genetic algorithm is used as the solution approach, and its efficiency is compared to GAMS in small and medium-size problems.

One of the most complex issues in airlines is crew scheduling problem which is divided into two subproblems: crew pairing problem (CPP) and crew rostering problem (CRP). Generating crew pairings is a tremendous and exhausting task as millions of pairings may be generated for an airline. Moreover, crew cost has the largest share in total cost of airlines after fuel cost. As a result, crew scheduling with the aim of cost minimization is one of the most important issues in airlines. In this paper, a new bi-objective mixed integer programming model is proposed to generate pairings in such a way that deadhead cost, crew cost and the risk of COVID-19 are minimized.

The proposed model is applied for domestic flights of Iran Air airline. The results of the study indicate that genetic algorithm solutions have only 0.414 and 0.380 gap on average to optimum values of the first and the second objective functions, respectively. Due to the flexibility of the proposed model, it improves solutions resulted from existing models with fixed-duty pairings. Crew cost is decreased by 12.82, 24.72, 4.05 and 14.86% compared to one-duty to four-duty models. In detail, crew salary is improved by 12.85, 24.64, 4.07 and 14.91% and deadhead cost is decreased by 11.87, 26.98, 3.27, and 13.35% compared to one-duty to four-duty models, respectively.

The authors confirm that it is an original paper, has not been published elsewhere and is not currently under consideration of any other journal.

Industry 4.0 defines the application of digital technologies on business infrastructure and processes. With the increasing need to take into account the social and environmental impact of technologies, the concept of Society 5.0 has been proposed to restore the centrality of humans in the proper utilization of technology for the exploitation of innovation opportunities. Despite the identification of humans, resilience and sustainability as the key dimensions of Society 5.0, the definition of the key factors that can enable Innovation in the light of 5.0 principles has not been yet assessed.

An SLR, followed by a content analysis of results and a clustering of the main topics, is performed to (1) identify the key domains and dimensions of the Industry 5.0 paradigm; (2) understand their impact on Innovation 5.0; (3) discuss and reflect on the resulting implications for research, managerial practices and the policy-making process.

The findings allow the elaboration of a multileveled framework to redefine Innovation through the 5.0 paradigm by advancing the need to integrate ICT and technology (Industry 5.0) with the human-centric, social and knowledge-based dimensions (Society 5.0).

The study detects guidelines for managers, entrepreneurs and policy-makers in the adoption of effective strategies to promote human resources and knowledge management for the attainment of multiple innovation outcomes (from technological to data-driven and societal innovation).

Contextual to the recognition of the complex interplay between health and behavioral aspects, integrated behavioral health (IBH) has emerged. Although this model is becoming popular in the Western world, its presence in the global context is not promising. This paper aims to explore the need for IBH in India and address its barriers to implementation and possible solutions.

We analyzed the case of IBH and its potential implications for India using the current evidence base, authors' reflections and experience of implementing similar programs.

This paper identifies contextual factors, including increased instances of non-communicable diseases and psychosocial and cultural determinants of health, that necessitate the implementation of IBH programs in India. The key features of different IBH models and their applicability are outlined. The current status of IBH and potential challenges in implementation in India in terms of human resources and other factors are delineated. We also discuss the potential models for implementing IBH in India.

Integrating behavioral health in primary care is considered an effective and sustainable model to promote health and well-being across various target populations. Towards this end, this paper is the first to discuss the contextual factors of IBH in India. It is a significant addition to the knowledge base on IBH and its possible implementation barriers and strategies in low- and middle-income countries.

Purpose – The aim of this study is to determine the impact of feeling bored on managers' decision-making in the digital age under conditions of increased uncertainty by examining the role of personality trait openness and empirically testing such relationships within the context of retail middle managers.

Design/methodology/approach – Feeling bored was defined within a broader Decision-Making Process Model, which included the personality trait openness. An empirical study with retail middle managers was conducted to examine the relationships between feeling bored and decision-making competence (DMC). Regression models were fit to test whether feeling bored affects DMC and whether the associations were moderated by personality trait openness.

Findings – In the relationship between feeling bored and DMC, the moderating role of the personality trait openness was established. Results showed that feeling bored has a significant negative association with middle managers' confidence levels and risk perceptions when making decisions. Results also provided evidence that the learning component of personality trait openness plays a moderating role in the relationship between feeling bored and DMC. Most notably, the learning component of personality trait openness neutralizes the negative effects of feeling bored on managers' ability to remain appropriately confident when making decisions. In addition, the learning and inquisitive components temper the positive association between mood excited and risk perceptions. Limitations to the study are outlined.

Practical implications – Since trait openness (specifically its learning component) benefits decision-making contexts, it makes trait openness a worthy criterion to include when screening aspirant retail middle managers. The benefits of trait openness (specifically its learning component) for middle managers and their teams (especially when they are feeling bored) are indicated, since learning neutralizes the negative effect feeling bored has on appropriate confidence levels in retail management decision-making contexts.