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This paper presents the features and relevance of open distance learning in the context of the socio-economic conditions of people living in the Union Territory of Jammu and Kashmir (J&K) in India. Although the primary purpose of the present study was to ascertain the effectiveness of the learner support services (LSSs) and different facilities offered by the open distance learning (ODL) institutions in that specific region. On the basis of these findings, few suggestions are made for the improvement in the support services, in order to make the ODL system more responsive to the area-specific needs of the learners, especially those residing in inaccessible areas such as J&K.

A survey-based descriptive methodology was applied to conduct this study. The data extracted from primary and secondary sources were analysed to reach certain conclusions. In total, 1,200 distance learners constituted the sample of the research. A semi-structured questionnaire was designed and distributed amongst learners and academics in the region to obtain their feedback and opinion on different aspects of support services and facilities offered to ODL learners.

Results of the study revealed that overall, the LSSs were satisfactory on different parameters, although they slightly varied from institution to institution. However, there was still scope for improvement in certain areas, where institutions further need to make serious efforts to address the identified gaps for improving the LSS and make teaching-learning more learner-centric in view of their specific requirements and local circumstances of J&K.

The present study focusses on presenting an overview of the only major components of LSS offered by ODL institutions and covers six distance education institutions (DEIs) located in different geographical locations within the limited jurisdiction of J&K. There is still scope to make focused (institution-wise) studies in a more specific situation and context to ascertain the problems in a more realistic way.

Although the study has been conducted in a specific territory of J&K, the findings reported in the paper may be equally useful for the other ODL institutions with similar characteristics or located in similar geographical locations, as support services are essential common components of institutions offering distance education programmes or courses.

The study is original research work conducted in the specific context of J&K, and its findings are based on the primary and secondary data collected from learners and other stakeholders of the selected ODL institutions. This work may be significant for the DEIs of J&K to get familiar with the essential components of LSS and develop a mechanism to further improve the academic and administrative support services for distance learners.

Corporate social responsibility (CSR) is viewed as a differentiating strategy that wins over stakeholders’ confidence. Due to the potential strategic and positive effects on businesses, the study of CSR and its relationship to competitiveness has gained relevance. While studies have examined the impact of CSR activities on firm competitiveness, the findings so far remain contradictory. Further research on the underlying processes/mechanisms that explain how CSR contributes to competitiveness remains scarce. Accordingly, this study aims to look into the link between CSR and competitiveness with a focus on Asian business and management studies.

By using a bibliometric approach, this paper aims to provide a review of the state-of-the-art research on the linkage between CSR and competitiveness in Asian context. The sample for this research included all 538 studies from the period of 2001–2023 in the Scopus database. A bibliometric study included both co-occurrence and co-citation analysis.

The study’s findings made significant contributions by identifying seven distinct clusters of co-occurrences. Using co-citation, three journals-based co-citation clusters and another three authors-based co-citation clusters are identified. The findings show how processes/mechanisms such as – accountability, multi-stakeholder dialogue/engagement, resource generation, emphasizing sustainable development goals and emerging markets, redefining strategy, cultivating value/vision and CSR leadership – are increasing in importance.

Overall, the authors argue that CSR-led competitiveness is indeed one of the key drivers for improved sustainability performance of a firm.

Based on findings, a conceptual framework has been proposed highlighting different processes and mechanisms that influence the CSR-led competitiveness – outcomes relationship.

The primary objective of this study was to understand the impact of Corporate Social Responsibility on Employee Green Behavior, examining the mediating role played by Green Human Resource Management Practices and the moderating influence of Employee Green Culture.

To accomplish this, a careful research approach was taken, using a thoughtfully designed random sampling method to encompass 300 banking employees, ensuring a robust representation of the diverse workforce in the banking sector.

The empirical findings identified green human resource management practices as a pivotal mediator and employee green culture as a significant moderator. It elucidated how the strategic implementation of green human resource management practices can act as an amplifier, strengthening the positive effects of corporate social responsibility on employee green behavior. This insight underscores the strategic importance of aligning human resource practices with sustainability goals to further enhance the environmental consciousness of employees. It was revealed that the presence of a nurturing organizational culture, one that encourages and supports environmentally responsible behaviors can significantly bolster the association between corporate social responsibility and green behavior among employees.

These findings underscore the essential role of organizational culture as a catalyst for the successful implementation of corporate social responsibility initiatives and the cultivation of a sustainable corporate ethos. This comprehensive research underscores the profound significance of corporate social responsibility, green human resource management practices and employee green culture in fostering and promoting environmentally responsible behaviors within the banking industry. These findings hold substantial implications not only for businesses but also for policymakers.

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Gray Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Gray Wolf Optimization, inspired by the social hierarchy and hunting behavior of gray wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

The tourism has become more responsible in terms of tourists choosing environmentally friendly alternatives and resorting to green options in food, travel, and purchase. In view of this, “ecotourism” appears as a sustainable means of promoting tourism in the less explored areas which are endowed with esthetic beauty of nature. Ecotourism is based on the pillars of biodiversity, wildlife, natural wealth, rivers, and authentic cuisine. The proposed chapter is an attempt to explore the ecotourism potential of the Southern Rajasthan in India which has a rich heritage of natural wealth and cultural diversity.

A prime objective of the Swachh Bharat Abhiyan (SBA; Clean India Mission) is to motivate people to segregate their household waste. The purpose of this study is to assess the ground reality of waste management behaviour of Delhi residents with the help of a modified Value–Belief–Norm (VBN) model. Past researches point the need to include cost as a variable in the VBN model. This study fulfils this need and tests cost as one of the variables on the gathered data.

The research data were gathered by interacting with the people and the civic staff in the jurisdiction of the three Delhi municipalities through a stratified sampling technique (N = 250). The structural equation modelling was used to analyse the collected data.

The modified VBN model explains the waste management behaviour, but the variables do not follow the exact causal chain. Values, awareness of consequences, ascription of responsibility and personal norms all explain the resident's waste management behaviour. However, cost limits the resident's waste management behaviour.

The study could only achieve a moderate model fit; its sample size was small; and data were collected through self-reported questionnaire.

Three main practical implications of the study are: (1) While designing waste management solutions, due importance must be given to the cost to be borne by people for adopting these solutions. (2) Design such interventions that target residents' values to convince them to make the desired behavioural change. (3) People need be educated about the ways to sort waste and made aware of the importance of waste segregation in eradicating the urban waste mess.

The paper is an original contribution to testing a modified VBN model in predicting waste management behaviour. The modified model includes cost as a variable missing in the previous research. This research is useful in the backdrop of the SBA and provides suggestions for policymakers and pro-environment researchers.

With the change in climate and increased pollution, there has been a need to reduce environmental carbon emissions. This research aims to develop a framework for reducing environmental carbon footprints to improve business performance.

This study uses Scientific Procedures and Rationales for the Systematic Literature Reviews (SPAR-4-SLR) approach. Articles are searched in the Scopus database using various keywords and their combinations. It resulted in 651 articles initially. After applying different screening criteria, 61 articles were considered for the final study.

This study provided four themes and sub-themes within each category. This research also used theories, methodologies and context (TMC) framework to provide future research questions. This study used the antecedents, decisions and outcomes (ADO) framework for synthesising the findings. The ADO framework will help to achieve carbon neutrality and improve firms' supply chain (SC) performance.

This study provides theoretical implications by highlighting the various theories that can be used in future research. This study also states the practical implications for the achievement of carbon neutrality by the firms.

This study contributes to the literature linking carbon neutrality with business performance.

This paper aims to establish and empirically investigate a research model examining the effect of four dimensions of the technology readiness index – optimism, innovativeness, discomfort and insecurity – on customer engagement that further influences purchase intention in the context of online shopping through artificial intelligence voice assistants (AI VAs).

Data were collected in India from 429 customers in a self-administered online survey. Data analysis uses the structural equation modelling technique.

Technology readiness dimensions, e.g. optimism, innovativeness, discomfort and insecurity, are critical factors driving customer engagement. Customer engagement further results in purchase intention in online shopping through AI VAs.

This study adds to the literature by understanding how customers’ technology readiness levels drive engagement and purchase intention. However, this study includes customer engagement as a unidimensional construct. Further research can consist of customer engagement as a multidimensional construct.

The findings offer guidelines for e-retailers to enhance customer engagement that matches their personality traits, thereby strengthening their purchase intention through AI VAs.

The research contributes to the literature by empirically investigating a research model, revealing optimism, innovativeness, discomfort and insecurity as crucial parameters for customer engagement and purchase intention.

Sharing and obtaining information over social media has enabled people to express their opinions regarding any event. Since the tweets regarding the Russia-Ukraine war were extensively publicized on social media, this study aims to analyse the temporal sentiments people express through tweets related to the war.

Relevant hashtag related to the Russia-Ukraine war was identified, and tweets were downloaded using Twitter API, which were later migrated to Orange Data mining software. Pre-processing techniques like transformation, tokenization, and filtering were applied to the extracted tweets. VADER (Valence Aware Dictionary for Sentiment Reasoning) sentiment analysis module of Orange software was used to categorize tweets into positive, negative and neutral ones based on the tweet polarity. For ascertaining the key and co-occurring terms and phrases in tweets and also to visualize the keyword clusters, VOSviewer, a data visualization software, was made use of.

An increase in the number of tweets is witnessed in the initial days, while a decline is observed over time. Most tweets are negative in nature, followed by positive and neutral ones. It is also ascertained that tweets from verified accounts are more impactful than unverified ones. russiaukrainewar, ukraine, russia, false, war, nato, zelensky and stoprussia are the dominant co-occurring keywords. Ukraine, Russia and Putin are the top hashtags for sentiment representation. India, the USA and the UK contribute the highest tweets.

The study tries to explore the public sentiments expressed over Twitter related to Russia-Ukraine war.

The study aims to access, monitor and visualize the scientific progress of Twitter-based research through a bibliometric analysis of scientific publications.

The data was retrieved from 2006 to February 23, 2022 using the Web of Science, a leading indexing and abstracting database. In response to the authors’ query, 6,193 items with 101,037 citations, an average citation of 16.31 and an h index of 126 were received. The “Biblioshiny” extension of the “Bibliometrics” package (www.bibliometrix.org) of R software was used to evaluate and visualize the data.

The present study highlighted the scientific progress of the field evolved over a period of time. The obtained results uncovered the publication trends, productive countries and their collaboration pattern, active authors who nurture the field by making their contribution, prolific source titles adopted by authors to publish the literature on the topic, most productive language in which literature was written, productive institutions, funding agencies that sponsor the research, influential articles, prominent keywords used in publications were also identified which will aid scientists in identifying research gaps in a particular area.

This study comprehensively illustrates the research status of Twitter-related research by conducting a bibliometric analysis. The study’s findings can assist relevant researchers in understanding the research trend, seeking scientific collaborators and funding for their research. Further, the study will act as a ready reference tool for the scientific community to identify research gaps, select research topics and appropriate platforms for submitting their scholarly endeavors.