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This paper aims to identify the key factors to design efficient, healthy and potentially economical neighbourhood places in the surroundings of smart cities to reduce the urban polarization for the sustainable urban development.

A two-stage methodology is followed. First, the key factors for neighbourhood are identified from literature studies. The selected factors are validated by sample t-tests. Second, the total interpretive structural modeling is used to interpret the complexity of relationships among various factors. Further, cross-impact matrix multiplication is applied for classification analysis to find the most driving factors for neighbourhood design.

The contribution of this research is to show hierarchical relationships among the various factors to design the neighbourhood places as smart from the perspectives of city planners and decision makers.

The applicability of the research findings is limited to developing countries mainly where population is large and most of cities have high pressure on its infrastructure to fulfil the citizens’ demands.

This paper will aid policymakers, city planners and government officials to design a sustainable smart city model in which smart neighbourhood would also be the potential solution to decrease pressure on a city’s critical infrastructure especially in developing countries.

A smart city could be considered as the centre point of smart initiatives to develop a place smart, and it should continue beyond the city boundaries to enhance the facilities, services, resources utilization and working environment in neighbourhood places also.

The study explores the various literature on neighbourhood planning and then link with smart city development as current need of urban development scenario. The authors propose a hierarchical relation framework to develop the neighbourhood places as smart places to meet the future demand of urbanization in developing countries like India.

The purpose of this paper is to identify and build a hierarchy of the factors influencing competitiveness of electronics manufacturing industry (EMI) at the industry level and apply the interpretive structural modeling, fuzzy Matriced’ Impacts Croisés Multiplication Appliquée á UN Classement (i.e. the cross-impact matrix multiplication applied to classification; MICMAC) and analytic hierarchy process (AHP) approaches. These factors have been explained with respect to managerial and government policymakers’ standpoint in Indian context.

This study presents a hierarchy and weight-based model that demonstrates mutual relationships among the significant factors of competitiveness of the Indian EMI.

This study covers a wide variety of factors that form the bedrock of the competitiveness of the EMI. Interpretive structural modeling and fuzzy MICMAC are used to cluster the influential factors of competitiveness considering the driving and dependence power. AHP is used to rank the factors on the basis of weights. Results show that the “government role” and “foreign exchange market” have a significantly high driving power. On the other hand, the “capital resource availability” and “productivity measures” come at the top of the interpretive structural modeling hierarchy, implying high dependence power.

The study has strong practical implications for both the manufacturers and the policymakers. The manufacturers need to focus on the factors of competitiveness to improve performance, and at the same time, the government should come forward to build a suitable environment for business in light of the huge demand and frame suitable policies.

The lackluster performance of the industry is because of the existing electronics policies and environmental conditions. The proposed interpretive structural modeling and fuzzy MICMAC and AHP frameworks suggest a better understanding of the key factors and their mutual relationship to analyze competitiveness of the electronics manufacturing industry in view of the Indian Government’s “Make in India” initiatives.

This paper contributes to the industry level competitiveness and dynamics of multi-factors approach and utilize the ISM–fuzzy MICMAC and AHP management decision tool in the identification and ranking of factors that influence the competitiveness of the EMI in the country.

The main aim of the existing study is to assess work-home interaction which is a challenge to human resource management (HRM).

The existing research is primary in nature. The sample size of the study is 285. The responses obtained from structured questionnaire. The qualitative data collected from secondary sources. For quantitative analysis various statistical tools have been applied, namely, Cronbach Alpha test, Correlation analysis and Regression analysis. The variables understudy was work environment, training opportunities, career opportunities, pay structure and technology factors.

As the recent pandemic continues to spread, all institutions should essentially remain alert and adaptable to new circumstances. HRM was essential throughout the pandemic, but now more than ever HR professionals must go above and beyond to meet the demands of organizations. The purpose of this study was to illuminate some of the most significant HR concerns that have surfaced in the aftermath of recent pandemics.

Employees' perspectives on work and life have shifted as a result of the added stress brought on by pandemic. So, businesses should provide crucial care to their workers' spirits and also HR professionals should pay close attention to their concerns and address the same thoughtfully.

The research adds to the current literature by illuminating the additional HR difficulties that have emerged in the wake of the global epidemic. More research may reveal additional significant HR difficulties, but these are a good place to start.