Automated chatbots powered by artificial intelligence (AI) can act as a ubiquitous point of contact, improving access to healthcare and empowering users to make effective decisions. However, despite the potential benefits, emerging literature suggests that apprehensions linked to the distinctive features of AI technology and the specific context of use (healthcare) could undermine consumer trust and hinder widespread adoption. Although the role of trust is considered pivotal to the acceptance of healthcare technologies, a dearth of research exists that focuses on the contextual factors that drive trust in such AI-based Chatbots for SelfDiagnosis (AICSD). Accordingly, a contextual model based on the trust-in-technology framework was developed to understand the determinants of consumers' trust in AICSD and its behavioral consequences. It was validated using a free simulation experiment study in India (N = 202). Perceived anthropomorphism, perceived information quality, perceived explainability, disposition to trust technology, and perceived service quality influence consumers' trust in AICSD. In turn, trust, privacy risk, health risk, and gender determine the intention to use. The research contributes by developing and validating a context-specific model for explaining trust in AICSD that could aid developers and marketers in enhancing consumers' trust in and adoption of AICSD.

Automated chatbots powered by artificial intelligence (AI) can act as a ubiquitous point of contact, improving access to healthcare and empowering users to make effective decisions. However, despite the potential benefits, emerging literature suggests that apprehensions linked to the distinctive features of AI technology and the specific context of use (healthcare) could undermine consumer trust and hinder widespread adoption. Although the role of trust is considered pivotal to the acceptance of healthcare technologies, a dearth of research exists that focuses on the contextual factors that drive trust in such AI-based Chatbots for SelfDiagnosis (AICSD). Accordingly, a contextual model based on the trust-in-technology framework was developed to understand the determinants of consumers' trust in AICSD and its behavioral consequences. It was validated using a free simulation experiment study in India (N = 202). Perceived anthropomorphism, perceived information quality, perceived explainability, disposition to trust technology, and perceived service quality influence consumers' trust in AICSD. In turn, trust, privacy risk, health risk, and gender determine the intention to use. The research contributes by developing and validating a context-specific model for explaining trust in AICSD that could aid developers and marketers in enhancing consumers' trust in and adoption of AICSD.

The role of information systems (IS) were widely discoursed during the spread of the COVID-19 outbreak. We have focused on developing a decision support systems (DSS) based on a combined prediction model, that can essentially be used at the start of any pandemic. Convalescent plasma therapy is generally applied during the spread of a pandemic as a therapy method that transfuses blood plasma from the people, who have recovered from an illness to treat critical cases. We observe, analyse, and predict the risks associated with the treatment effects of convalescent plasma therapy on COVID-19 patients. Based on the secondary data, we build a prediction model to evaluate and predict the trends in the clinical characteristics and laboratory findings for critically ill patients infected with COVID-19 and treated with convalescent plasma. Here, we use a combined prediction model utilizing three models; the grey prediction model (GM (1, 1)), the residual prediction model (residual GM (1, 1)), and a back propagation artificial neural network (BP-ANN) based residual sign prediction model. Also, a validation of the results of the study has been presented at two levels. On analysis of the results from the prediction model, it is observed that the convalescent plasma therapy can show progressive signs on COVID-19 infected patients. Health practitioners can understand, analyze, and predict the potential risks of convalescent plasma therapy based on the proposed model.

Purpose-Research on human resource management (HRM) and technology has gained momentum recently. This review aims to create a bibliographic profile of the field of HRM and technology using bibliometric techniques, complemented by qualitative analysis, examining 239 articles published in the four key human resource (HR) journals. Design/methodology/approach-First, using VOSviewer software, we analysed the research productivity by identifying authors, journals and influential articles, followed by insights on research themes and their evolution. Next, integrating bibliometric and qualitative approaches, we conducted a hybrid inquiry of the field to analyse current theories, methods and variables. Findings-The bibliometric analysis highlighted the intellectual structure, key themes and distinctive developments categorised under four temporal phases that have shaped research in this field. In addition, qualitative analysis presents significant theoretical perspectives, the methods employed and the nomological framework of variables. Originality/value-Our study advances the extant literature on HRM and technology by quantifying the leading bibliometric performance indicators complemented by qualitative evaluation of the field, which entails exploring the possible research strands and related trends that have emerged in the past two decades.

Purpose - Research on human resource management (HRM) and technology has gained momentum recently. This review aims to create a bibliographic profile of the field of HRM and technology using bibliometric techniques, complemented by qualitative analysis, examining 239 articles published in the four key human resource (HR) journals. Design/methodology/approach - First, using VOSviewer software, we analysed the research productivity by identifying authors, journals and influential articles, followed by insights on research themes and their evolution. Next, integrating bibliometric and qualitative approaches, we conducted a hybrid inquiry of the field to analyse current theories, methods and variables. Findings - The bibliometric analysis highlighted the intellectual structure, key themes and distinctive developments categorised under four temporal phases that have shaped research in this field. In addition, qualitative analysis presents significant theoretical perspectives, the methods employed and the nomological framework of variables. Originality/value - Our study advances the extant literature on HRM and technology by quantifying the leading bibliometric performance indicators complemented by qualitative evaluation of the field, which entails exploring the possible research strands and related trends that have emerged in the past two decades.

This study deals with airlines' handling of customer complaints pre- and post-Covid 19. Authors extracted airlines' responses regarding redress, timeliness, apology, credibility, facilitation, and attentiveness and analyzed them pre- and post-pandemic. The results indicate the differential impacts of organizational responses on recovery satisfaction pre- and post-pandemic. Attentiveness (timeliness) had a significant role only pre- (post-) pandemic. Redress, credibility and facilitation remained crucial in both scenarios. Further, pre-pandemic results showed that the impact of organizational responses (credibility, timeliness, and facilitation) on recovery satisfaction was greater for failures associated with core rather than augmented elements of airline service. In the post-pandemic scenario, the impact of organizational responses on consumers' recovery satisfaction was the same for both categories of service failures. Thus, post-pandemic, airlines may emphasize redress, credibility, timeliness, and facilitation responses in their social media posts for both service failure types.

We attempt to study the psycho -social conditions of people during the COVID-19 and observe the conditions of distress, which are closely associated to each other. Using incidence analysis and grey theory, we examined uncertainty with various psycho -social conditions, indicating stress, anxiety, depression, sleep impairment, stigma issues, other psychiatric issues, and social needs reported across the 14 districts of Kerala, as a case. On critical analysis of the results, we have observed that different psycho -social conditions of distress are mostly closely associated. Anxiety appears as the psycho -social condition, which is observed to having the highest degree of incidences to other conditions. And, stigma issues show second highest degree of incidences, as observed from the study. Observing from the results, anxiety, at a higher level can be a reason for various other psycho -social conditions and is needed to have urgent attention, if not can lead to several conditions of phobia, panic disorder, social anxiety disorder, and obsessive -compulsive disorder during the time of the COVID-19 pandemic. Based on the proposed six incidence sequences, we measure the geometric pattern of their developmental tendencies and propose suggestions for doctors and health practitioners.

Research Question/IssueThis paper primarily explores the relationship between busy boards and firm performance. Additionally, we have performed a quasi-natural experiment to evaluate the impact of SEBI regulatory restrictions on multiple directorships on the firm performance.Research Findings/InsightsThe study sample includes all firms listed on the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE). Our primary findings indicate a nonlinear relationship between busy boards and firm performance. These results are more pronounced for the non-business group firms and firms with high promoter ownership. Our quasi-natural experiment results indicates that the treatment firms had a significant improvement in firm performance. The results of event study analysis and difference-in-difference analysis are robust for both short- and long-term measures of firm performance. Furthermore, we observe an increase in the board meeting attendance of independent directors in the post-mandate period.Theoretical/Academic ImplicationsTwo competing views prevalent in busy board literature are reputational effect hypothesis and distraction effect hypothesis. Our findings support the limit on number of outside directorships; if this number exceeds a certain threshold, the directors become less effective monitors and exacerbate the firm performance.Practitioner/Policy ImplicationsThe results are in favor of restriction on multiple directorship positions of independent directors by the regulators in emerging markets. Further, the study demonstrates that firms that adhere to good governance practices protect the investors' interests and improve the firm's performance.

Extant research emphasizes the need for research artifacts to span the academic boundaries, to result in societal benefits. Digital boundary objects (DBOs), with their inherent ability of boundary spanning, are the key to translate research artifacts for non-academic audiences like practitioners. We explore the creation and dissemination of DBOs as a mechanism to bridge the epistemic divide between academics and practitioners. We employ the Gioia methodology and conduct in-depth interviews, netnography and archival data analysis. This method is apt, given our exploratory research objective of understanding the creation of DBOs through the lens of Social Construction of Technology (SCOT) theory. Our findings reveal a three-phase cyclical process model, termed epistemic framing for DBO creation. We identify DBOs as key in bridging the epistemic divide and the novelty of our research lies in identifying the iterative epistemic framing process model that helps in knowledge translation for societal advancement.

We connect business group research with literature on competitive repertoire-a firm's portfolio of actions that characterize its strategy-to examine the influence of business group affiliation on firm strategy. We theorize that access to a larger stock and a broad range of resources, both within the firm and through the business group network, enables affiliate firms to execute a higher number (competitive repertoire intensity) and a wider variety (competitive repertoire complexity) of competitive actions. Our analysis of a sample of Indian firms during 2009-2017 supports our hypotheses. In supplementary analyses, we find that business group characteristics-size and affiliate intra-group position-positively influence competitive repertoire. We also find that competitive repertoire intensity mediates affiliation-firm performance relationship. Our findings enhance our understanding of business groups by establishing the link between affiliation-firm strategy-performance. Our work also extends research on network-based and ownership-based antecedents of competitive repertoire. We discuss these implications in detail.

To combat emissions from transportation sector, India is transitioning to a zero or low carbon emission transportation paradigm by encouraging the use of hydrogen fuel cars and electric vehicles (EVs). The study aims to investigate the challenges impeding the adoption of electric vehicles and to understand the causal relations among them. The novel Grey Influence Analysis (GINA) methodology has been utilised to study the causal relations among the challenges and managers and practitioners are benefitted as they are able to identify the most important challenges in the adoption of EVs in India and act accordingly. The findings of a survey-based study show that lack of charging infrastructure comes in second place and the reliability and lifespan of EV components come in first place, when evaluating the overall or total influence scores. The third place among the most important drivers is occupied by lack of servicing/workshops, low awareness of battery characteristics, high upfront cost, and fear of battery explosions. On detailed analysis of the driving forces for adoption, it is apparent that the reliability and lifespan of EV components is the most important driving factor for adoption; hence, policy makers and practitioners can focus on this for augmenting the adoption rates.

We use the daily data from 1 October 2010 to 1 March 2021 on the stock prices of several global, regional, and country-specific ESG indices of the Dow Jones Sustainability Index family (DJSI) and MSCI family to analyse the relative performance of sustainable indices (ESG) and corresponding conventional benchmarks. In terms of classic risk and return characteristics and modern portfolio metrics, we report mixed evidence with some sustainability indices marginally outperforming the traditional benchmarks and others staying at parity. Regionally, the evidence is heterogeneous; the Pan Arab, the Middle East (including Israel), the United States, Emerging Markets, and Europe indices reported a slightly superior performance and those belonging to Asia Pacific, Emerging Africa, and Latin America marginally underperformed compared to their conventional counterparts. The findings of this study imply that sustainable investments appear to be an encouraging investment option, although their progress has not been substantial. For an appreciable outperformance of sustainable investments, a more conducive regulatory framework should be established, including robust incentivizing policies concerning tax rebates or low capital costs.

PurposeThis study aims to investigate the co-volatility patterns between cryptocurrencies and conventional asset classes across global markets, encompassing 26 global indices ranging from equities, commodities, real estate, currencies and bonds.Design/methodology/approachIt used a multivariate factor stochastic volatility model to capture the dynamic changes in covariance and volatility correlation, thus offering empirical insights into the co-volatility dynamics. Unlike conventional research on price or return transmission, this study directly models the time-varying covariance and volatility correlation.FindingsThe study uncovers pronounced co-volatility movements between cryptocurrencies and specific indices such as GSCI Energy, GSCI Commodity, Dow Jones 1 month forward and U.S. 10-year TIPS. Notably, these movements surpass those observed with precious metals, industrial metals and global equity indices across various regions. Interestingly, except for Japan, equity indices in the USA, Canada, Australia, France, Germany, India and China exhibit a co-volatility movement. These findings challenge the existing literature on cryptocurrencies and provide intriguing evidence regarding their co-volatility dynamics.OriginalityThis study significantly contributes to applying asset pricing models in cryptocurrency markets by explicitly addressing price and volatility dynamics aspects. Using the stochastic volatility model, the research adding methodological contribution effectively captures cryptocurrency volatility's inherent fluctuations and time-varying nature. While previous literature has primarily focused on bitcoin and a few other cryptocurrencies, this study examines the stochastic volatility properties of a wide range of cryptocurrency indices. Furthermore, the study expands its scope by examining global asset markets, allowing for a comprehensive analysis considering the broader context in which cryptocurrencies operate. It bridges the gap between traditional asset pricing models and the unique characteristics of cryptocurrencies.

In recent years, the rapid proliferation of economic activities in India is accompanied by a surge in the demand for electric vehicles (EVs) and charging infrastructure development. This study analyses the performance of the EV adoption across the Indian states using a modified context dependent data envelopment analysis. We identify key macroeconomic factors that influence EV and CI expansion and provide a framework for informing targeted policy interventions for states to optimize their EV ecosystem development. Our analysis reveals that states with higher socioeconomic indicators and comprehensive EV policies exhibit superior performance in EV adoption. Additionally, we propose a benchmarking strategy for lower performing states, guiding them towards a pathway for efficiency improvement through targeted infrastructure investments. This data-driven approach can inform policymakers in creating effective strategies to accelerate the development of a robust EV ecosystem.

Prior research has primarily concentrated on non-digital and process-oriented methods for achieving carbon neutrality (CN) in the context of mitigating climate change (CC), while the potential of digital technology (DT) has hardly been investigated. This study addresses this gap by answering four research questions: How are firms utilizing DT to achieve CN? What drives the adoption of DT for achieving CN? What are the barriers that prevent the adoption of DT for achieving CN? What risk mitigation strategies can be adopted by firms to overcome these barriers? An inductive method using open-ended essays has been adopted to gather data from firms that have already implemented DT for CN. The findings revealed four distinct dimensions. Utilization of DT for CN includes enhancing business value, managing one's carbon footprint, enabling smart solutions, and enhancing efficiency. The drivers for adopting DT for CN included driving business growth, external pressures, competitive advantage, and environmental consciousness. Key barriers included financial barriers, technological barriers, human resource barriers, and external barriers. Risk mitigation strategies included pre-implementation strategies, detailed planning and evaluation, ensuring employees' buy-in and readiness, and external stakeholder engagement. This study offers a broad-based foundation for designing DT strategies for achieving CN.

PurposeThis study investigates the impact of pre-IPO earnings management on investor demand in the Indian IPO market. It also examines whether earnings management by issuer firms affects IPO valuation, a topic that is underexplored in accounting research.Design/methodology/approachThe study uses the data of 310 IPOs from India during the period 2000-2021. The association between pre-IPO earnings management with investor demand and valuation is tested using cross-sectional ordinary least squares regression models with heteroscedasticity-robust standard errors.FindingsThe study finds that the degree of pre-IPO earnings management impacts retail investor demand, measured as their over-subscription multiple. Pre-IPO earnings management is unrelated to institutional investor bidding. Further, this paper suggests no relation between pre-IPO earnings management and IPO valuation.Research limitations/implicationsFuture studies could explore various other forms of earnings management and their impact on investor demand and valuation.Practical implicationsThe findings of this study will help the investors and regulators to understand the practice of earnings management among IPO firms and how it is related to IPO demand and valuation.Originality/valueThis study contributes to the existing literature on IPO-earnings management and investor demand by documenting that issuer firms engage in earnings management to influence investor demand, particularly retail investor demand. Analysis of IPO valuation reveals that earnings management is mostly unrelated to IPO valuation, contrary to the general perception in the literature.

PurposeThis paper aims to examine the relationship between earnings quality and corporate cash holdings in an emerging economy. Existing literature posits that earnings quality is a result of information asymmetry and firms with lower earnings quality increases cash holdings, to shield the firm from future uncertainties. In this paper, the authors propose a 'private benefits hypothesis', which suggests that lower earnings quality is an indicator of opportunism and expropriation of resources in the firm, through tunneling or excessive executive compensations. As a result, firms with lower earnings quality increase cash holdings in their control, to increase their private benefits and to avoid the scrutiny of the external stakeholders. The authors further examine the monitoring role played by institutional investors on cash holdings, with varying degrees of earnings quality.Design/methodology/approachThis study uses an unbalanced panel data sourced from Prowessdx, from 2000 to 2019. The analysis employs 20,231 firm-year observations from 2,421 firms. Earnings quality is calculated following Dechow and Dichev (2002).FindingsEmpirical analysis confirms that the firms with higher earnings quality reduce cash. Further, institutional investors reduce the cash holdings in firms with higher earnings quality. Institutional investors effectively reduce the cash only in firms with at least 10% of equity shareholding. The results are robust to alternative measures of earnings quality and endogeneity concerns.Originality/valueThis study diverges from the information asymmetry hypothesis in the existing literature on earnings quality and cash holdings and highlights the underlying private benefits hypothesis, that will impact cash holdings. Next, the 10% institutional shareholding is important in the Indian context as it represents the minimum threshold at which block holders can request extraordinary general meetings (Section 100 of the Companies Act 2013) or the involvement of the National Company Law Tribunal (NCLT) (Section 213 of the Companies Act 2013). This study highlights that unlike in Anglo-Saxon economies, institutional investors or other minority shareholders are empowered by the Companies Act 2013 to play a vital role in corporate governance with a mere 10% equity.

The micro-entrepreneurship sector, as the second-largest employment generator in rural areas, plays a crucial role in alleviating poverty. This study explores how social enterprises can assist rural micro-entrepreneurs in mobilizing and leveraging indigenous knowledge to align production with demand. We propose technoficing as a strategic approach to rural microentrepreneurship. We conducted a case study of CommunityLink, a social enterprise implementing ICT-enabled practices to facilitate the growth of rural micro-entrepreneurship in India. Our findings reveal knowledge disconnections impeding the development of rural microentrepreneurship. Importantly, we propose a four-phase approach of technoficing - basic, internal, external, and offline-online - that can effectively mobilize and leverage indigenous knowledge for rural micro-entrepreneurs with minimal resource investment. We synthesize our findings into a comprehensive process model. Our research contributes to the discourse on ICT for development and the emerging academic dialogue on decoloniality by highlighting the significance of empowering local institutions and practices through the strategic use of off-the-shelf technologies. Practitioners and policymakers can leverage our findings to propel indigenous efforts toward rural development and improve rural livelihoods.

PurposeThis research paper aims to provide a comprehensive analysis of the ergonomic risks faced by employees in the IT industry who work from home (WFH). With the increasing prevalence of remote work, understanding and addressing ergonomic challenges are crucial to ensure the well-being, productivity and long-term health of IT professionals.Design/methodology/approachThe data are gathered through a survey questionnaire which includes demographic factors, job-related factors, quick video display terminal (VDT) workspace evaluation factors, quick chair design usability evaluation factors and the Cornell Musculoskeletal Discomfort Questionnaire (CMDQ) in order to measure the ergonomic risks among them. The risk factors associated with each body part are determined using ordinal logistic regression.FindingsHighest occurrence of musculoskeletal disorders was observed in the neck, hip and lower back among WFH employees. By identifying and prioritizing these risks, organizations and individuals can implement effective strategies to promote a healthy and ergonomic work environment for remote IT employees.Originality/valueThis research contributes new insights that will be helpful for researchers and practitioners working in the areas of ergonomic risks, improving performance of work from home employees and provides suggestions for future research to explore and enrich the existing knowledge base.

PurposeThe main purpose of this paper is to determine the bias-corrected efficiencies and rankings of the selected hotels and restaurants (H&Rs) in India.Design/methodology/approachThe data for the Indian H&R sector are collected from the Prowess database. The bootstrap data envelopment analysis (DEA) based on a constant return to scale (CRS), variable return to scale-input oriented (VRS-IP) and variable return to scale-output oriented (VRS-OP) are applied on H&Rs to obtain the bias-corrected efficiencies.FindingsIt is found that relative efficiencies using basic DEA methods of all the 45 H&Rs of India are overestimated. These efficiencies are corrected using bias correction through bootstrap DEA methods. The bounds for the efficiencies of each H&R are computed using all the adopted methods. All H&Rs are ranked using bias-corrected efficiencies, and the linear trend between ranks suggests that the H&Rs are ranked almost similarly by all the adopted methods.Practical implicationsTo improve efficiency, Indian H&R companies must rethink their personnel needs by enhancing their workforce management capabilities. The government needs to extend more support to this sector by introducing a liberal legislation framework and supporting infrastructure policies.Originality/valueThere is a paucity of studies on H&Rs in India. The current study focused on measuring bias-corrected efficiencies of the selected H&Rs of India. This study is one of the few initiatives to explore bias-corrected efficiencies extensively using the bootstrap DEA method.