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The COVID-19 pandemic and its aftermath sent the entire educational system across the globe topsy-turvy. Virtual classrooms, online lectures and online evaluations became the order of the day, replacing traditional face to face classroom interactions and examinations conducted physically. While it may be possible to reach out to a larger audience in remote places via online platforms, the new medium lacks personal touch of the past, and is known to cause physical and psychological problems for participants. This study collects primary data from a representative sample of students from emerging economies to study the factors that influence intention to pursue online education.

ANOVA, Kruskal–Wallis test, exploratory factor analysis (EFA) and multiple linear regression (MLR) have been used to test our hypothesis. We have also used text mining to corroborate statistical test results and ascertain the sentiment of students towards online learning.

This study not only confirms findings in extant literature that “benefits” is an important factor. It also identifies new factors such as “health”, “evaluation”, “class duration” and “student qualification”, that influence student intention to pursue online education. Sentiment analysis shows that students have positive sentiment coupled with trust towards online education. Text mining shows that “mode of class”, “time or duration of class” and “quality of learning” are important features that students consider.

This is one of the few studies to use quantitative plus text mining method of research to understand intention to pursue online education.

India has taken a successful step towards meeting Sustainable Development Goals (SDG) by providing access to basic amenities such as safe drinking water, waste management, drainage systems and bio-compost pits in households. The purpose of this study is to identify factors that significantly impact access to such basic amenities in villages in two states in the hilly regions of India.

Village-level secondary data collected from the Unnat Bharat Abhiyan (UBA) website has been analyzed using multiple linear regression and non-parametric statistical tests. Socio-economic and demographic variables are the independent factors in regression whereas availability of basic amenities is the dependent variable.

Findings reveal that in Himachal Pradesh, gender ratio, annual income per family, percentage of BPL households, percentage of pucca houses, and percentage of village population above graduation, significantly impact access to piped water in village households. Also, literacy rate and percentage of population with education above graduation significantly impact availability of compost pits. Further, in Uttarakhand, percentage of pucca houses influences access to waste collection system and availability of compost pits. Availability of drainage systems is influenced by literacy rate. A comparison between the two hilly states reveals that Himachal Pradesh is better off in terms of ease of access to drinking water whereas Uttarakhand is ahead in terms of other amenities.

To the best of the authors’ knowledge, no other studies have used socio-economic and demographic variables to study access to basic amenities in villages in hilly states in India.

The current study investigates the impact of the coronavirus disease 2019 (COVID-19) lockdown restrictions on air quality in an industrial town in Himachal Pradesh (HP) (India) and recommends policies and strategies for mitigating air pollution.

The air quality parameters under study are particulate matter₁₀ (PM₁₀), PM_2.5, SO₂ and NO₂. One-way ANOVA with post-hoc analysis and non-parametric Kruskal–Wallis test, and multiple linear regression analysis are used to validate the data analysis results.

The findings indicate that the lockdown and post-lockdown periods affected pollutant levels even after considering the meteorological conditions. Except for SO₂, all other air quality parameters dropped significantly throughout the lockdown period. Further, the industrial and transportation sectors are the primary sources of air pollution in Paonta Sahib.

Future research should include other industrial locations in the state to understand the relationship between regional air pollution levels and climate change. The findings of this study may add to the discussion on the role of adopting clean technologies and also provide directions for future research on improving air quality in the emerging industrial towns in India.

Very few studies have examined how the pandemic-induced lockdowns impacted air pollution levels in emerging industrial towns in India while also considering the confounding meteorological factors.

Social media sites are one of the vital technological developments of WEB 2.0. This study aims to emphasize on building an empirical model to investigate the impending determinants of users’ intention to use social media sites in higher education. Depending on the existing theories such as the social media acceptance model, e-learning acceptance model, unified theory of acceptance and use of technology and existing literature, determinants such as “performance,” “communication functionality” and “self” have been identified to test. Further, the mediation effect of “peer influence” on the relationship has also been tested.

A total of 310 students of different private and public Indian institutions have participated in an online survey. Exploratory factor analysis and multiple linear regression analysis are performed and results were analyzed.

The results of this study demonstrated substantial evidence of the impact of performance, communication functionality and self on the intention to use social media in higher education. The mediation of peer influence has also been seen between all the relations.

An empirical model of intention to use social media in higher education is built. The median of peer influence is tested.

Opportunistic and delayed maintenances are increasingly becoming important strategies for sustainable maintenance practices since they increase the lifetime of complex systems like aircrafts and heavy equipment. The objective of the current study is to quantify the optimal time window for adopting these strategies.

The current study considers the trade-offs between different costs involved in the opportunistic and delayed maintenances (of equipment) like the fixed cost of scheduled maintenances, the opportunistic rewards that may be earned and the cost of premature parts replacement. The probability of the opportunistic maintenance has been quantified under two different scenarios – Mission Reliability and Renewal Process. In the case of delayed maintenance, the cost of the delayed maintenance is also considered. The study uses optimization techniques to find the optimal maintenance time windows and also derive useful insights.

Apart from finding the optimal time window for the maintenance activities the study also shows that opportunistic maintenance is beneficial provided the opportunistic reward is significantly large; the cost of conducting scheduled maintenance in the pre-determined slot is significantly large. Similarly, the opportunistic maintenance may not be beneficial if the pre-mature equipment parts replacement cost is significantly high. The optimal opportunistic maintenance time is increasing function of Weibull failure rate parameter “beta” and decreasing function of Weibull failure rate parameter “theta.” In the case of optimal delayed maintenance time, these relationships reverse.

To the best of our knowledge, very few studies exist that have used mission reliability to study opportunistic maintenance or considered the different cost trade-offs comprehensively.

In current era, retaining the best-performing employees has become essential for businesses to compete in the dynamic technological landscape. Consequently, organizations must ensure that their star performers believe that company’s reward and recognition (R&R) system is fair and equal. This study aims to use an explainable machine learning (eXML) model to develop a prediction algorithm for employee satisfaction with the fairness of R&R systems.

The current study uses state-of-the-art machine learning models such as Naive Bayes, Decision Tree C5.0, Random Forest and support vector machine-RBF to predict employee satisfaction towards fairness in R&R. The primary data used in the study has been collected from the employees of a large public sector undertaking from an emerging economy. This study also proposes a novel improved Naïve Bayes (INB) algorithm, the efficiency of which is compared with the state-of-the-art algorithms.

It is seen that the proposed INB model outperforms the state-of-the-art algorithms in many scenarios. Further, the proposed model and feature interaction are explained using the explainable machine learning (XML) concept. In addition, this study incorporates text mining techniques to corroborate the results from XML and suggests that “Transparency”, “Recognition”, “Unbiasedness”, “Appreciation” and “Timeliness in reward” are the most important features that impact employee satisfaction.

To the best of the authors’ knowledge, this is one of the first studies to use INB algorithm and mixed method research (text mining along with machine learning algorithms) for the prediction of employee satisfaction with respect to the R&R system.

Sustainable human resource management (SuHRM), which aims to achieve positive environmental, social and economic outcomes at the same time, has gained prominence across industries. However, the challenges of implementing SuHRM across industries are largely under-studied. The purpose of this study is to identify the grey areas in the field of SuHRM by using an unsupervised learning algorithm on the abstracts of 607 papers published in prominent journals from 1995 to 2023. Most of the articles have been published post-2018.

The analysis of the data (abstracts of the selected articles) has been done using topic modelling via latent Dirichlet algorithm (LDA).

The output from topic modelling-LDA reveals nine primary focus areas of SuHRM research – the link between SuHRM and employee well-being; job satisfaction; challenges of implementing SuHRM; exploring new horizons in SuHRM; reaping the benefits of using SuHRM as a strategic tool; green HRM practices; link between SuHRM and organisational performance; link between corporate social responsible and HRM.

The insights gained from this study along with the discussions on each topic will be extremely beneficial for researchers, academicians, journal editors and practitioners to channelise their research focus. No other study has used a smart algorithm to identify the research clusters of SuHRM.

By utilizing topic modeling techniques, the study offers a novel approach to analyzing and understanding trends and patterns in HRM research related to sustainability. The significance of the paper would be in its potential to shed light on emerging areas of interest and provide valuable implications for future research and practice in Sustainable HRM.

The COVID-19 pandemic has impacted 222 countries across the globe, with millions of people losing their lives. The threat from the virus may be assessed from the fact that most countries across the world have been forced to order partial or complete shutdown of their economies for a period of time to contain the spread of the virus. The fallout of this action manifested in loss of livelihood, migration of the labor force and severe impact on mental health due to the long duration of confinement to homes or residences.

The current study identifies the focus areas of the research conducted on the COVID-19 pandemic. Abstracts of papers on the subject were collated from the SCOPUS database for the period December 2019 to June 2020. The collected sample data (after preprocessing) was analyzed using Topic Modeling with Latent Dirichlet Allocation.

Based on the research papers published within the mentioned timeframe, the study identifies the 10 most prominent topics that formed the area of interest for the COVID-19 pandemic research.

While similar studies exist, no other work has used topic modeling to comprehensively analyze the COVID-19 literature by considering diverse fields and domains.