Search results

Economies and societies are not digitally isolated. Digital technologies are widely recognised as key drivers of information dissemination, knowledge sharing, income and employment. Digital technologies also influence the interlinkages of digitalisation, gender and labour market outcomes. Digital technologies impact every sphere of day-to-day life. It impacts ways of communication, trade and business; influences networking abilities; shapes societal norms, attitudes and behaviours. It is hence argued that digital technologies may have crucial implications for women's participation in the workforce.

Gender equality and increasing women's workforce participation is an important goal under the United Nations Sustainable Development Goals (SDGs). Research indicates that women are mainly involved in agricultural work, blue-collar formal work, while collar formal work, and entrepreneurship. Digital technologies significantly impact the ways of working in all these sectors. Consequently, it is argued that digital technologies influence women's participation across all such types of work.

This chapter aims at unravelling the linkages between digital technologies and women's workforce participation. To this end, the influences of digital technologies on women's participation in agricultural work, blue-collar formal work, white-collar formal work and entrepreneurship are discussed. The implications and impacts of the use of broadband, internet and mobile technologies are also discussed. This chapter also includes important theories of women's workforce participation and discusses them in light of digitalisation.

The present study aimed to explore the consequences of perceived authenticity in artificial intelligence (AI) assistants and develop a serial-mediation architecture specifying causation of loyalty in human–AI relationships. It intended to assess the predictive power of the developed model based on a training-holdout sample procedure. It further attempted to map and examine the predictors of loyalty, strengthening such relationship.

Partial least squares structural equation modeling (PLS-SEM) based on bootstrapping technique was employed to examine the higher-order effects pertaining to human–AI relational intricacies. The sample size of the study comprised of 412 AI assistant users belonging to millennial generation. PLS-Predict algorithm was used to assess the predictive power of the model, while importance-performance analysis was executed to assess the effectiveness of the predictor variables on a two-dimensional map.

A positive relationship was found between “Perceived Authenticity” and “Loyalty,” which was serially mediated by “Perceived-Quality” and “Animacy” in human–AI relational context. The construct “Loyalty” remained a significant predictor of “Emotional-Attachment” and “Word-of-Mouth.” The model possessed high predictive power. Mapping analysis delivered contradictory result, indicating “authenticity” as the most significant predictor of “loyalty,” but the least effective on performance dimension.

The findings of the study may assist marketers to understand the relevance of AI authenticity and examine the critical behavioral consequences underlying customer retention and extension strategies.

The study is pioneer to introduce a hybrid AI authenticity model and establish its predictive power in explaining the transactional and communal view of human reciprocation in human–AI relationship. It exclusively provided relative assessment of the predictors of loyalty on a two-dimensional map.

The study aims to fulfil two objectives, namely, to explore what dimensions comes into play in the current consumer EV purchase behaviour based on relevant literature identified in the past and to find out whether there are other reasons that are peculiar to the Indian automobile market which influenced consumers to adopt this eco-friendly technology.

Mixed method approach for collecting the primary data was used. The study was conducted in five cities across India, through a questionnaire administered to 266 existing EV owners using judgemental sampling, that included dimensions as to what made them purchase EVs (any variant whether hybrid or battery) and encouraged their green behaviour. Moreover, the survey also took care of gathering other motivational constructs that may have played a significant role in the Indian EV market. To take care of this, use of single open-ended question was done. Statistical techniques like exploratory factor analysis (EFA) and Partial Least Square-Structural Equation Modelling (PLS-SEM) version 4.0 were performed to gain meaningful insights from this investigation.

Based on our analysis, EV purchase behaviour in the Indian market could be explained by six configurational paths identified in conceptual framework of this study. The data analysed revealed long-term operational benefits or relative advantages, trialability or prior driving experience and positive attitude towards EVs playing an important role towards EV purchase decision followed by environmental concern, social norms and personal values of Indian consumers. Besides, several other factors were identified in open comments like favourable government policies, brand consciousness, compatibility and functional benefits in terms of comfortable and noiseless driving which tend to drive the decision of Indian consumers towards buying EVs. The identification of these motivations can help policymakers in targeting the customer market accordingly.

Although the study is restricted by its scope of sampling criteria, yet is distinct in its methodological approach and thereby adds value to existing literature on marketing and diffusion of EVs. The study explores the untapped gap in the current literature by helping identify reasons on the basis of which consumers made their choices of different EVs. Instead of analysing the potential antecedents of EV uptake, the study brings to light the actual reasons of EV purchase behaviour in the context of an emerging economy like India. That is to say, the horizon of EV motivations spans across technological, individual and regulatory attributes in the Indian automobile market.

This study is relevant and fulfils the underlying gap in the existing literature related to actual motivations that consumers look for before buying a high involvement product such as EVs. The results can be beneficial for marketers to design effective marketing strategies from the perspective of consumers. Promotional strategies addressing ease of using EVs and how battery technology is better than before needs to be clearly communicated by the marketers. Consumer education is necessary, in this regard, to make people aware of the rising air pollution levels. They have to be made understood the real advantages of owning EVs and how these non-conventional vehicles are far better when compared to traditional cars. In a nutshell, the study underscored potential determinants or reasons that made Indian consumers to go for EVs, based on statistically significant evidences which can be crucial for stakeholders operating in the Indian EV industry.

The study has important implications for members of the community. By sacrificing their egoistic tendencies or individual preferences for energy intensive products such as ICE based vehicles, people can imbibe altruistic orientation and can contribute to the collective benefit of all by shifting towards energy rich vehicles powered by electricity. This is necessary to achieve the broader goals of sustainable development and to curtail the rising climate change phenomenon.

The study applied both quantitative (deductive) and qualitative (inductive) approach to study into the reasons for consumers’ purchase motivations towards electric cars. Taking a distinct methodological approach, the study enhances the knowledge on EVs by asking consumers to comment in their own words as to why they purchased EVs. This was necessary so as to better explore the needs of the Indian automobile market and which factors govern the purchase of EVs in domestic market. Past literature majorly focused on purchase intention towards EVs but this study in unexampled and analysed the purchase behaviour towards EVs. It should be noted that our investigation reveals the complexity of EV purchase reasons than those identified in past literature and it adds to the current pool of literature on EVs by explaining factors with unrivalled illustrative power.

Electric cars have very little market share in developing countries despite their environmental benefits. Thus, governments have started promoting electric cars by providing financial incentives to consumers. The current article aims to examine the direct and indirect effects of government financial incentives on consumer electric car adoption in India.

The study followed a quantitative research method that employed a self-administered survey questionnaire. Structural Equation Modelling and Multi-Group Analysis were followed for data analysis.

The study revealed that financial incentives have an indirect effect on electric car adoption intention rather than a direct effect. In addition, financial incentives were found to have a direct effect on attitude and Perceived Behavioural Control (PBC). Attitude and PBC positively influenced consumer adoption intention.

The insights and implications from the present study would help policymakers and marketers to formulate better incentive policies and market strategies to increase consumer acceptance of electric cars in developing countries.

The study contributes to the literature by analysing the underlying mechanism that links government financial incentives to electric car adoption intention. This study also explored the direct effect of financial incentives on attitude and PBC, which are less investigated in electric vehicle literature. In addition, the present article also assessed the moderating role of age in electric car adoption, which has mixed evidence in the literature, and such studies are scarce in the Indian context.

COVID-19 placed millions of employees under work-from-home/telework. Employers intend extending telework for the long-term, anticipating business benefits. But the benefits are impacted by employees' well-being/ill-being, which is affected by the satisfaction of psychological needs. In turn, need satisfaction is influenced by employees' personal/job attributes. As work-from-home's blended environment disrupts routines, the satisfaction of the psychological need for structure or routines was examined in this study, along with the effect of personal/job attributes.

Cross-section primary data were collected from 500 teleworking information technology employees from India and analysed using partial least squares structural equation modelling. Vigour and exhaustion represented well-being and ill-being. Telework self-efficacy, standardised job, technology assistance and supervisor social support were the determinants or personal/job attributes. Need for structure satisfaction was the mediator.

Telework self-efficacy, technology assistance and supervisor social support were positively associated with structure satisfaction. In turn, structure satisfaction was related positively with vigour and negatively with exhaustion, and thus mediated between personal/job attributes and vigour/exhaustion. Standardised job did not affect vigour, exhaustion or structure satisfaction.

Need for structure is mostly studied as a trait, with implications of greater/lesser preference for structure examined. However, this work acknowledges structure as a basic ubiquitous need. Everyone needs some structure. Hence, need for structure is researched from the novel perspective of its satisfaction. This paper also uniquely combines job demands–resources model which identifies personal/job attributes, with concepts of epistemic which posit the need for structure.

The United Nations (UN) adopted Sustainable Development Goals (SDGs); agenda 2030 focuses on Climate Action (goal 13), targeting climate adaptability, as well as resilience, awareness and improving policy mechanisms on climate change. In order to enhance climate adaptability, climate-smart agricultural practices (CSAP) is a necessary step. CSAP is a sustainable agriculture approach with a strong focus on climate dimensions. The three pillars of climate-smart agriculture (CSA) are ‘Adaptation’: adapting to climate change; ‘Resilience’: building resilience against it and ‘Remove’: reducing carbon emissions. The new world economy uses Industry 4.0 technologies for sustainable advancement, including blockchain technology, big data analytics, artificial intelligence (AI), augmented and virtual reality, industrial Internet of Things (IoT) and services. Hence, technology plays a significant role in climate sustainable agriculture practices. This chapter shall consider three technologies consisting of IoT, AI and blockchain technology which contribute to CSAP in pre-harvesting (monitoring climate as well as fertility status, soil testing, etc.), harvesting (tilling, fertilisation, seed operations, etc.) and post-harvesting (predicting weather factors, seed varieties, etc.) periods of agriculture. All these three technologies work like the human nervous system; IoT helps in converting various information regarding demography, climate change, local agricultural needs, etc. into world data; AI works like a brain in combination with IoT, helps predict the use of climate-smart technology and blockchain, the memory part of the nervous system which deals with supply-side and ensures traceability as well as transparency for consumers as well as farmers. Hence, this chapter shall contribute to the importance of these three technologies in adopting CSAP in three stages of agriculture.

This study aims to identify demographic characteristics, personal attributes and attitudes and social support factors that adversely or favourably affect the likelihood of career persistence amongst women workers of the Indian IT sector.

The research, grounded in the social cognitive career theory, analyses primary data collected from 850 women working in IT via a survey. Based on an original definition of career persistence, the sample was segregated into 427 persistent and 423 non-persistent women. Logistic regression was performed to test for the effect of various determinants on the likelihood of women being career persistent versus non-persistent.

Being married, having children, as well as high levels of belief in gender disadvantage and work–family conflict lowered the likelihood of career persistence amongst women. While being a manager, possessing high career identity, high occupational culture fit, positive psychological capital and family support boost the likelihood.

The study examines women's actual continuance in an IT career vis-à-vis exit from the workforce/IT field, rather than women's stated intent to persist/quit as previously investigated. It uses logistic regression to identify both hurdles and aids on the path of women's career persistence. The findings can help recognize women more likely to struggle, thus be a first step in targeted organizational interventions to plug a leaky talent pipeline.

Despite efforts to reduce environmental pollution and wasteful fossil fuel use, electric vehicles (EVs) are still rare on the road. Why is it so challenging to get widespread EV adoption? One significant factor on which it heavily depends is one's awareness and understanding of EVs. However, due to an absolute lack of knowledge on the part of the populace, this factor becomes a huge impediment to the uptake of EVs. A systematic review of the electronic database Scopus for the years 2003–2022 was carried out on ‘EV awareness and adoption of EV’ while considering the ‘Preferred Reporting Items for Systematic Reviews and Meta-analysis’ (PRISMA) standards. A three-step identification process resulted in the ultimate detection of 41 papers, which were then thoroughly examined. A conceptual framework that encompasses the three key awareness aspects that influence EV adoption is developed. To encourage greater uniformity among EV researchers, this study's conclusions serve as a foundation for operationalising upcoming research efforts within a predetermined framework. The authors must therefore be optimistic that lingering technological, legislative, cultural, behavioural and business-model barriers may be overcome over time through widespread dissemination of knowledge and awareness related to EVs, making it possible for everyone to switch to greener, more economical and more efficient transportation solutions.

Despite India achieving many milestones under MGNREGA, the external and internal shocks result in below potential outcomes in employment demand and generation. This study examines how these shocks matter and how the migration-prone regions perform.

This study, employing district-level data from 2018 to 2021, investigates how climate change and COVID-19 have affected the employment demand and supply. We applied RE-GLS and IV-2SLS regressions to examine the effects of shocks on employment demand and generation, respectively. The difference in difference panel model is employed to test the spatial effects of the pandemic. Further, we used RE-GLS and extended regression model to examine how external shocks interacting with migration affect unemployment rates.

It was found that the pandemic increased employment demand and generation. This reflects the adverse effects of the pandemic and the swift action by the government. However, the responses were possibly different during climate shocks. The wage differential increased employment demand. However, demand decreased due to poor support from the support staff. The employment generation was higher in migration-prone districts, indicating that seasonal migration, being a lean-season phenomenon, continues to occur despite employment generation.

This study contributes to the literature in several ways. It captures spatial variations while examining the impact of climate change and COVID-19. It investigates the performance of MGNREGA in migration-prone areas. In effect, the findings provide policymakers with greater insight into the issues.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2024-0132.