Journal Articles
Permanent URI for this collectionhttp://10.0.100.92:4000/handle/123456789/21
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Item Knowledge Management(International Encyclopedia of Business Management, 2025) Lathabhavan, Remya; Padhy, Prabir C.; Panda, SmitaThe chapter on Knowledge Management (KM) comprehensively explores the multidimensional strategies, processes, and technologies organizations employ to systematically harness, organize, and leverage information. Beginning with a definition that encapsulates both explicit and implicit knowledge, the chapter delves into the historical evolution of KM, identifying key concepts such as the DIKW pyramid and intellectual capital. It thoroughly examines the components of KM, from knowledge creation and storage to retrieval, sharing, and transfer, emphasizing the role of technology in this dynamic process. The chapter sheds light on the challenges organizations faces in implementing KM, including cultural and technological barriers, while highlighting the benefits of improved decision-making, enhanced innovation, and increased organizational agility. Real-world case studies illustrate successful KM implementations and offer valuable insights from failures. The exploration extends to emerging technologies shaping the future of KM and anticipates evolving organizational cultures. The chapter provides a roadmap for navigating the intricate landscape of Knowledge Management. It underscores the pivotal role of KM in fostering a culture of continuous learning, innovation, and strategic advantage in today׳s information-driven era. As organizations strive to capitalize on their intellectual capital, this chapter serves as an indispensable guide to understanding, implementing, and maximizing the benefits of Knowledge Management. © 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.Item Does financial development matter for firm performance in Asia-Pacific markets? Evidence from large firm-level data(Eurasian Economic Review: A Journal in Applied Macroeconomics and Finance, 2026-01-20) Yadav, Inder Sekhar; Yadav, Akash SinghThis study investigates the impact of financial market development on the finan-cial performance of 18,751 non-financial listed and active firms across 12 Asian economies from 1996 to 2020. Financial development is measured using the IMF’s financial development index, while firm performance is assessed through return on investment, return on assets, and return on equity. The analysis incorporates macroeconomic and firm-level controls such as GDP per capita, employment, firm size, leverage, tangibility, current ratio, asset turnover, and sales growth in panel regression models. Results reveal a positive and significant effect of financial devel-opment on firm performance in countries like China, India, Indonesia, South Korea, Malaysia, Pakistan, and Thailand, but an insignificant effect in Israel, Singapore, Hong Kong, Japan, and the Philippines. The financial development negatively af-fects small firms’ performance relative to medium and large firms. No significant differences are observed between financially developing and developed economies in terms of the impact of financial development on firm performanceItem Making your CSR message effective: A language perspective on the impact of CSR on job seekers’ organizational attraction(Journal of Business Research, 2026) Choudhary, Suman; Mishra, Kirti; Budhwar, PawanEmployers are increasingly including information about their corporate social responsibility (CSR) effort (the CSR action) and its impact (results of the CSR action) in recruitment messages to attract job seekers. However, in contemporary times, when all employers are using these CSR claims to differentiate themselves in the labor market, evaluating the effectiveness of CSR messages in attracting job seekers becomes important. In this research, we suggest that to leverage the potential of CSR messages in attracting job seekers, it is pertinent to pay attention to the nuances of CSR language used in displaying CSR information. Drawing on the literature on language, communication, and organizational attraction, we claim that using personal compared to impersonal language in communicating CSR effort increases organizational attractiveness. Further, we also claim that the language of CSR effort (personal vs. impersonal) interacts with CSR impact’s goal framing (positive vs. negative) to influence job seekers’ attraction towards the organization, such that it is highest in the personal + negative condition. Using a pilot study, one preliminary study, two experimental studies, and a follow-up study, we also demonstrate that job seekers’ anticipated warmth and meaningfulness mediate the above interaction effect. This research provides important theoretical and practical insights for attracting high-quality applicants by effectively communicating CSR initiatives.Item Time-varying causality and correlations between spot and futures prices of natural gas, crude oil, heating oil, and gasoline(Resources Policy, 2024-06) Mensi, Walid; Brahim, Mariem; Hammoudeh, Shawkat; Tiwari, Aviral Kumar; Kang, Sang HoonThis paper examines the time-varying Granger causality between spot and futures prices of petroleum (oil, gasoline, and heating oil) and natural gas markets. The methodology involves computing time-point grey correlations, performing time-varying causality tests, and estimating dynamic equicorrelations between pairs of these markets. The estimated results show that the futures and spot prices of those petroleum and natural gas are highly correlated. The relationship dynamics of two variables in the pairs intensified during extreme economic and political events as well as during COVID-19 spread and the Russia-Ukraine conflict. Among all the energy commodities, heating oil and crude oil (natural gas) present the highest (lowest) integrated grey correlations. In addition, the time-varying Granger causality test results show evidence evolving bidirectional information spillovers between the futures and spot prices of natural gas and gasoline. Moreover, the dynamic equicorrelation estimates show an evolving relationship between the futures and spot prices and provide support for the findings of the causality tests.Item LGBTQ inclusion in the workplace: examining the roles of climate, leadership, and psychological empowerment to determine satisfaction(Social Responsibility Journal, 2024-11-21) Lathabhavan, Remya; Mishra, NidhiPurpose Organizations are moving beyond the gender binary in the workplace and are implementing diversity management practices, making Lesbian, Gay, Bisexual, Transgender and Queer (LGBTQ) inclusion increasingly important as they continue to remain a disadvantaged group. This paper aims to look into the factors that affect job and life satisfaction among LGBTQ employees in India. Design/methodology/approach Data were collected from 348 LGBTQ employees and analysed using structural equation modelling. Findings The results showed that psychological safety has a positive impact on psychological empowerment, job satisfaction and life satisfaction. Additionally, inclusive climate and inclusive leadership were found to have a significant moderating effect on the relationships. The study also revealed that psychological empowerment plays a mediating role between psychological safety and life satisfaction. Originality/value The study stands pioneers among the works that discuss workplace inclusion among LGBTQ employees in Indian context since LGBTQ acceptance in normal social system is still in nascent stage in Indian scenario. The findings can be used to improve LGBTQ inclusion and promote social development and well-being in organizations and society, as the inputs from the study can be taken up for inclusive leadership development and wellbeing of the employees.Item Online grocery services evolution and trends: a bibliometric approach(International Journal of Retail & Distribution Management, 2024-12-11) Khalek, Sk Abu; Samanta, Tamal; Behera, Chandan KumarPurpose Online grocery service (OGS) has significantly grown in recent years, particularly during the Covid-19 pandemic. This surge has attracted significant scholarly attention and resulted in many scientific articles in the last five years. Adopting a bibliometric review approach, this study attempts to comprehensively and systematically analyse the academic literature on OGS. Design/methodology/approach A Scopus search using pertinent keywords followed by PRISMA screening identified 384 relevant articles. Articles were analysed using VOSviewer and Biblioshiny, which employed citation and thematic analysis. Findings The study identifies the significantly impactful authors, articles, and journals. While the analysis reveals the evolution across four time-frames, it also highlights the clusters representing the literature strands. Six major themes are identified in the literature, and potential future research enquiries are suggested. Originality/value As the first study to include over 350 articles, it comprehensively represents the current state of the OGS literature, utilising performance and thematic analysis techniques. The article contributes significantly to the academic discourse surrounding OGS by synthesising and presenting diverse themes. Further, the future research questions provide a foundation for advancing the literature and guiding future scholarly work in the OGS domain.Item Public and scholarly interest in social robots: An investigation through Google Trends, bibliometric analysis, and systematic literature review(Technological Forecasting and Social Change, 2024-09) Mishra, Nidhi; Bharti, Teena; Tiwari, Aviral Kumar; Pfajfar, GregorThe COVID-19 pandemic has expedited the integration of social robots within the healthcare sector. This research employs a tripartite methodology, combining Google Trends analysis, bibliometric analysis, and a systematic literature review, to gauge both public and research interest in social robots within the healthcare domain. In the Google Trends analysis, search query data for “Social Robots” was retrieved, encompassing both “all categories” and the specific “health” category. Seasonal effects on relative search volumes (RSV) were assessed through the cosinor model. The analysis confirmed statistically significant seasonal patterns in RSV for “social robots” within the “health” category. Conversely, for the broader “all categories,” only the intercept showed significance, while sinw and cosw were deemed insignificant. For bibliometric analysis, the global literature on “robotics” and “healthcare” was examined in the SCOPUS database. From the extensive pool of publications, 144 relevant studies were identified out of 4037 publications. These studies were further analyzed using VOSviewer, providing insights into recent trends and hot topics concerning social robots in healthcare. The systematic literature review focused on 46 articles published from 2019 to the end of 2023. The findings revealed a lack of consensus on the drivers, barriers, and outcomes associated with social robot acceptance and human-robot interaction (HRI). The study systematically maps the existing research on these aspects, introducing a novel categorization and presenting the concept of a “robot user's ecosystem.” This concept emphasizes the imperative involvement of all stakeholders in the development and understanding of social robots. Ultimately, this methodological approach not only identifies nine research gaps in the current literature but also formulates numerous research questions to guide future researchers in this domain.Item Can generative AI motivate management students? The role of perceived value and information literacy(The International Journal of Management Education, 2024-11) Jose, Emily Maria K; Prasanna, Akshara; Kushwaha, Bijay Prasad; Madhumita DasGenerative Artificial Intelligence (GenAI) is a disruptive technology that has started to be used among students in management education. However, the question is whether the utilisation of GenAI in educational settings stimulates students to engage in learning activities and broaden their knowledge base. Hence, this study investigates student motivation and perception of using GenAI (ChatGPT) technology in management education. A random sampling technique was employed to survey 478 students from various educational institutions in the southern region of India. The outcomes revealed that GenAI presents both prospects and hurdles in the domain of management education. The disruptive nature of this technology brings forth numerous opportunities for acquiring knowledge and augmenting one's cognitive capacity. Nonetheless, a cautious and accountable approach is imperative for the successful integration of GenAI into the of management education. Consequently, this study provides pragmatic implications for students, educators, and educational institutions. The effectiveness of GenAI in practical settings can be heightened by arranging interactive training sessions led by AI experts, devising easily accessible online educational modules, embedding AI proficiencies into educators through collaborative endeavors or specialized training programs, and establishing systematic assessment protocols to ensure continual improvement.Item Decoding mood of the Twitterverse on ESG investing: opinion mining and key themes using machine learning(Management Research Review, 2024-07) Jaiswal, Rachana; Gupta, Shashank; Tiwari, Aviral KumarPurpose Grounded in the stakeholder theory and signaling theory, this study aims to broaden the research agenda on environmental, social and governance (ESG) investing by uncovering public sentiments and key themes using Twitter data spanning from 2009 to 2022. Design/methodology/approach Using various machine learning models for text tonality analysis and topic modeling, this research scrutinizes 1,842,985 Twitter texts to extract prevalent ESG investing trends and gauge their sentiment. Findings Gibbs Sampling Dirichlet Multinomial Mixture emerges as the optimal topic modeling method, unveiling significant topics such as “Physical risk of climate change,” “Employee Health, Safety and well-being” and “Water management and Scarcity.” RoBERTa, an attention-based model, outperforms other machine learning models in sentiment analysis, revealing a predominantly positive shift in public sentiment toward ESG investing over the past five years. Research limitations/implications This study establishes a framework for sentiment analysis and topic modeling on alternative data, offering a foundation for future research. Prospective studies can enhance insights by incorporating data from additional social media platforms like LinkedIn and Facebook. Practical implications Leveraging unstructured data on ESG from platforms like Twitter provides a novel avenue to capture company-related information, supplementing traditional self-reported sustainability disclosures. This approach opens new possibilities for understanding a company’s ESG standing. Social implications By shedding light on public perceptions of ESG investing, this research uncovers influential factors that often elude traditional corporate reporting. The findings empower both investors and the general public, aiding managers in refining ESG and management strategies. Originality/value This study marks a groundbreaking contribution to scholarly exploration, to the best of the authors’ knowledge, by being the first to analyze unstructured Twitter data in the context of ESG investing, offering unique insights and advancing the understanding of this emerging field.Item Do domestic and overseas technological innovations equally matter in renewable energy development? Empirical evidence from emerging economies(Economic Change and Restructuring, 2024-10-24) Yadav, Aneet; Mahalik, Mantu Kumar; Patel, Gupteswar; Pal, ShreyaSince fossil fuel consumption drives environmental degradation, existing studies highlight the role of clean energy development in climate change mitigation. The emerging economies can mitigate climate change and also achieve sustainable development if they focus on innovation-led renewable energy development. In this context, this study is motivated to analyze the impact of domestic and overseas innovations (i.e., resident and non-resident) on renewable energy generation for 16 emerging market economies by employing balanced panel data from 1996 to 2019. This study incorporates financial development, regulatory quality, and real economic growth as crucial control variables in renewable energy generation function. The long-run results of the PMG-ARDL estimation technique show the positive (negative) impact of domestic (overseas) innovation on renewable energy generation, while regulatory quality, financial development, and economic growth promote it. Our results are robust across FGLS, Driscoll–Kraay standard error, and dynamic GMM estimation techniques. The policy implications are also discussed.