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The use of technology in 4th industrial revolution is at its peak. Industries are trying to reduce the consumption of resources by effectively utilizing information and technology to connect different functioning agents of the manufacturing industry. Without digitization “Industry 4.0” will be a virtual reality. The present survey-based study explores the factual status of digital manufacturing in the Northern India.

After an extensive literature review, a questionnaire was designed to gather different viewpoints of Indian industrial practitioners. The first half contains questions related to north Indian demographic factors which may affect digitalization of India. The latter half includes the queries concerned with various operational factors (or drivers) driving the digital revolution without ignoring Indian constraints.

The focus of this survey was to understand the current level of digital revolution under the ongoing push by the Indian government focused upon digital movement. The analysis included non-parametric testing of the various demographic and functional factors impacting the digital echoes, specifically in Northern India. Findings such as technological upgradations were independent of type of industry, the turnover or the location. About 10 key operational factors were thoughtfully grouped into three major categories—internal Research and Development (R&D), the capability of the supply chain and the capacity to adapt to the market. These factors were then examined to understand how they contribute to digital manufacturing, utilizing an appropriate ordinal logistic regression. The resulting predictive analysis provides seldom-seen insights and valuable suggestions for the most effective deployment of digitalization in Indian industries.

The country-specific Industry 4.0 literature is quite limited. The survey mainly focuses on the National Capital Region. The number of demographic and functional factors can further be incorporated. Moreover, an addition of factors related to ecology, environment and society can make the study more insightful.

The present work provides valuable insights about the current status of digitization and expects to facilitate public or private policymakers to implement digital technologies in India with less efforts and the least resistance. It empowers India towards Industry 4.0 based tools and techniques and creates new socio-economic dimensions for the sustainable development.

The quantitative nature of the study and its statistical predictions (data-based) are novel. The clubbing of similar success factors to avoid inter-collinearity and complexity is seldom seen. The predictive analytics provided in this study is quite elusive as it provides directions with logic. It will help the Indian Government and industrial strategists to plan and perform their interventions accordingly.

The annual holiday party is a long-standing tradition in many organizations, yet academic research has largely left the holiday party unexamined. The present study sheds light on this significant social event by exploring what factors help differentiate successful events from less successful ones.

First, the authors developed a taxonomy of characteristics of good holiday parties using a critical incident technique in which stories of holiday party experiences were analyzed following a mixed-method approach. Second, the authors quantitatively examined the relationships between these characteristics and three outcomes, including perceived organizational support, positive interpersonal interactions, and experienced fun.

The critical incident analysis revealed 11 key characteristics that distinguish good from bad holiday parties. Primary findings from the quantitative study are that games and activities, music, good food, and notable positive leader behavior are key characteristics of more successful events.

As the data were obtained using a traditional survey methodology, further research would be valuable that adopts an experience sampling methodology to capture employee experiences, perceptions, and feelings about holiday parties in real-time before, during, and after an event has occurred.

From an event planning standpoint, this research provides a framework for designing holiday parties and provides evidence as to which features matter most. From a strategic leadership perspective, this research signals that different features of holiday parties can influence different outcomes.

Beyond merely identifying important characteristics, this research provides a framework for further research on holiday parties and identifies theories that can be used in future research to explore the mechanisms that influence how and under what conditions holiday parties impact employees’ experiences at work.

This paper aims to explore how an academic researcher and a practitioner experience scepticism for their qualitative research.

The study applies Olt and Teman's new conceptual phenomenological polyethnography (2019) methodology, a hybrid of phenomenology and duoethnography.

For the researcher-participants, the essence of living with scepticism means feeling a sense of injustice; struggling with the desire for simplicity and quantification; being in a circle of uneasiness; having a survival mechanism; and embracing healthy scepticism. They experience the essence differently and similarly in varied cultural contexts. Through duoethnographic conversations, they acknowledge that while there can be scepticism of their work, it is important to remain sceptical, persistent and curious by challenging traditional concepts. Theoretical and practical advances in artificial intelligence (AI) continue to highlight the need for clarifying qualitative researcher roles in academia and practice.

This paper contributes to the debate of qualitative versus quantitative research. Its originality is in exploring scepticism as lived experience, from an academic and practitioner perspective and applying a phenomenological polyethnography approach that blends two different traditional research paradigms.

This paper aims to examine the mismatch between multidimensional deprivation and monetary poverty in identifying the poor in Egypt and investigates their determinants empirically.

The paper uses the Alkire-Foster multidimensional poverty measurement method using data from Egypt’s 2017/2018 Household Income, Expenditure and Consumption Survey (HIECS 2017/2018). Using a logistic regression model, the paper assesses the empirical relationship between multidimensional and monetary poverty and their determinants at the aggregate level and by dimension.

The paper demonstrates a significant mismatch between multidimensional and monetary poverty measures, underscoring their complementary nature. Statistics indicate that both measures overlap in classifying 35.81% of Egyptians, whereas monetary poverty ignores 63.12% of multidimensionally poor in at least one dimension. Regression estimates show a significant moderate negative association between expenditure per capita and multidimensional poverty and its dimensions. Moreover, they show that household head’s gender, age, education attainment, marital status, job proficiency, household size and location affect poverty mismatch and match in Egypt.

This paper offers Egyptian policymakers the multidimensional poverty index that enables more efficient designing and targeting of poverty alleviation programs and assessing current poverty alleviation programs to modify them if needed.

To the best of the authors’ knowledge, this study is the first to examine the mismatch between both poverty measures in Egypt, using the recent full data set of HIECS 2017/2018. This paper confirms that depending only on monetary measures can send inaccurate insights for crafting effective social policies. Also, it offers policymakers a comprehensive insight into the country’s poverty landscape, which enable more efficient design, targeting of poverty alleviation programs and monitoring their effectiveness.

This study aims to evaluate the dynamics between healthcare resource capacity expansion and disease spread. Further, the study estimates the resources required to respond to a pandemic appropriately.

This study adopts a system dynamics simulation and scenario analysis to experiment with the modification of the susceptible exposed infected and recovered (SEIR) model. The experiments evaluate diagnostic capacity expansion to identify suitable expansion plans and timelines. Afterwards, two popularly used forecasting tools, artificial neural network (ANN) and auto-regressive integrated moving average (ARIMA), are used to estimate the requirement of beds for a period when infection data became available.

The results from the study reflect that aggressive testing with isolation and integration of quarantine can be effective strategies to prevent disease outbreaks. The findings demonstrate that decision-makers must rapidly expand the diagnostic capacity during the first two weeks of the outbreak to support aggressive testing and isolation. Further, results confirm a healthcare resource deficit of at least two months for Delhi in the absence of these strategies. Also, the study findings highlight the importance of capacity expansion timelines by simulating a range of contact rates and disease infectivity in the early phase of the outbreak when various parameters are unknown. Further, it has been reflected that forecasting tools can effectively estimate healthcare resource requirements when pandemic data is available.

The models developed in the present study can be utilised by policymakers to suitably design the response plan. The decisions regarding how much diagnostics capacity is needed and when to expand capacity to minimise infection spread have been demonstrated for Delhi city. Also, the study proposed a decision support system (DSS) to assist the decision-maker in short- and long-term planning during the disease outbreak.

The study estimated the resources required for adopting an aggressive testing strategy. Several experiments were performed to successfully validate the robustness of the simulation model. The modification of SEIR model with diagnostic capacity increment, quarantine and testing block has been attempted to provide a distinct perspective on the testing strategy. The prevention of outbreaks has been addressed systematically.

The study aims to examine the impact of social assistance schemes introduced by the Indian government on poverty alleviation in urban slums.

To accomplish the study's objectives, primary data were collected from 585 beneficiaries of government schemes operating in India's northwestern state using a multistage sampling technique (Punjab). The exploratory factor analysis (EFA) technique reduced the total dataset to its minimum factors. Then, using second-order confirmatory factor analysis, the data's validity and reliability were determined. The data were analyzed using statistical techniques such as one-way ANOVA, t-test and structural equation modeling (SEM).

The study's findings indicate that social assistance provided by the Government of India has a direct and substantial influence on poverty alleviation in urban slums. The study demonstrates how access to basic credit financial services through social assistance programmes has improved the lives of urban poor households living in slums and assisted them in escaping poverty.

The investigation was undertaken among a few limitations. First, the in-depth investigation of the study is restricted to only the northwestern state of India solely because of limited resources and time availability. Second, the study focuses primarily on the perspectives of beneficiaries of the social assistance schemes in India. Still, it might be expanded in the future to include additional stakeholders such as bank executives, business colleagues and municipal town panchayats.

Due to policymakers' increased emphasis on poor households living in urban slums, this topic is critical for studying many issues.

The research explores gaps in social welfare schemes to direct policymakers and government authorities to take appropriate steps to aid the urban poor people in sliding out of poverty.

By examining the influence of the Indian government's social welfare schemes on poverty reduction in slums, this study contributes to the literature on public assistance schemes and poverty alleviation. This article can assist policymakers in developing nations in increasing financial capability among disadvantaged urban families on a national and international level.