Journal Articles

Permanent URI for this collectionhttp://10.0.100.92:4000/handle/123456789/21

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A comparative study of DEA-based ranking methods applied in multi-sport events
(Managing Sport and Leisure, 2026-01-19) Shriya, Shimona; Ranjan, Prabhat
urpose: To compare DEA-based ranking methods and develop a fair approach to evaluate states’ efficiency and rankings at the National Games, India's national-level multi-sport event, adjusted for population and wealth, while excluding less relevant macroeconomic factors. Design/methodology/approach: Using data from the 2023 Games (37th edition), we compare three widely applied Data Envelopment Analysis (DEA) models: variable returns to scale (VRS), zero-sum gains (ZSG), and cross-efficiency. Results are validated using data from the 35th edition of the National Games to ensure robustness. Findings: The cross-efficiency method best produces complete rankings without ties. The VRS method identifies peer groups for the seventeen inefficient states out of 29 participating teams. Originality: This study contributes by providing a comparative evaluation comparing DEA models and identifying the most suitable method to assess states’ efficiency in promoting athletes at multi-sport events. It also highlights each method’s particularities, suitability, and characteristics. Research Contribution: By combining technical rigor with practical relevance, the paper addresses a gap in the literature by comparatively evaluating VRS, ZSG, and cross-efficiency models, underscoring their methodological distinctiveness and applicability. Practical implications: It provides actionable insights for state sports bodies, organizers, and policymakers to optimize resources, support talent development, and strengthen future National Games performance.
A comparative study on the moderating impact of renewable energy and innovation on environmental quality
(Natural Resources Forum, 2025-05) Pal, Shreya;; Villanthenkodath, Muhammed Ashiq; Ansari, Mohd Arshad
This study explores the complex interactions between renewable energy production, innovation, economic growth, institutional quality, economic globalization, and CO2 emissions in OECD countries and emerging economies from 1996 to 2021. Results from Driscoll–Kraay standard error and feasible generalized least square reveal distinct trends: renewable energy production leads to increased CO2 emissions in emerging economies but significantly reduces emissions in OECD countries. Besides, residential and non‐residential innovation, along with total innovation, show similar effects. Notably, technology‐moderated renewable energy production effectively lowers CO2 emissions in both country groups. Similarly, economic growth enhances environmental quality in both sets of countries. However, institutional quality needs improvement in emerging economies, while current levels suffice in OECD nations to maintain environmental quality. Moreover, the study emphasizes the importance of considering globalization's impact on CO2 emissions, advocating for international agreements to leverage globalization for environmental benefits. Overall, these findings provide valuable insights for shaping renewable energy policies, fostering innovation, promoting economic growth, enhancing institutional quality, and harnessing globalization efforts to reduce CO2 emissions and enhance environmental quality
A Consumer Typology Based on Celebrity Endorsement Factors
(FIIB Business Review, 2022-08-26) Arora, N; Prashar, S; Vijay, T.S; Parsad, Chandan
Celebrity endorsement is one of the most prominent advertising and promotional strategies used by organizations to seek consumer attention. It has been examined extensively by academicians in the last few decades. Still, existing studies have not focused much on understanding the typology of shoppers related to specific celebrity-related factors. This study endeavours to segment the consumer market and develop corresponding profiles based on shoppers’ importance to various celebrity factors. Survey-based research was undertaken using the mall-intercept technique, and data were collected from shoppers in India. Cluster and correspondence analysis techniques were used on the responses received from 232 survey participants. The results suggested the presence of three distinct clusters, namely ‘Indifferent-to-celebrity Buyer’, ‘Fascinated Buyer’ and ‘Star Power Buyer’. The findings suggest the clusters—fascinated buyers and star power buyers—are influenced by different components related to celebrity endorsement. The present study contributes to the marketing and celebrity endorsement literature by offering an insight into the need of identifying the differences among the consumers with respect to their propensity of acceptance of a celebrity endorser. These results would help managers in mapping them to target consumer segments with an appropriate celebrity effectively.
A cross-quantile correlation and causality-in-quantile analysis on the relationship between green investments and energy commodities during the COVID-19 pandemic period
(Studies in Economics and Finance, 2024) Sharma, Aarzoo; Tiwari, Aviral Kumar; Abakah, Emmanuel Joel Aikins; Owusu, Freeman Brobbey
Purpose This paper aims to examine the cross-quantile correlation and causality-in-quantiles between green investments and energy commodities during the outbreak of COVID-19. To be specific, the authors aim to address the following questions: Is there any distributional predictability among green bonds and energy commodities during COVID-19? Is there exist any directional predictability between green investments and energy commodities during the global pandemic? Can green bonds hedge the risk of energy commodities during a period of the financial crisis. Design/methodology/approach The authors use the nonparametric causality in quantile and cross-quantilogram (CQ) correlation approaches as the estimation techniques to investigate the distributional and directional predictability between green investments and energy commodities respectively using daily spot prices from January 1, 2020, to March 26, 2021. The study uses daily closing price indices S&P Green Bond Index as a representative of the green bond market. In the case of energy commodities, the authors use S&P GSCI Natural Gas Spot, S&P GSCI Biofuel Spot, S&P GSCI Unleaded Gasoline Spot, S&P GSCI Gas Oil Spot, S&P GSCI Brent Crude Spot, S&P GSCI WTI, OPEC Oil Basket Price, Crude Oil Oman, Crude Oil Dubai Cash, S&P GSCI Heating Oil Spot, S&P Global Clean Energy, US Gulf Coast Kerosene and Los Angeles Low Sulfur CARB Diesel Spot. Findings From the CQ correlation results, there exists an overall negative directional predictability between green bonds and natural gas. The authors find that the directional predictability between green bonds and S&P GSCI Biofuel Spot, S&P GSCI Gas Oil Spot, S&P GSCI Brent Crude Spot, S&P GSCI WTI Spot, OPEC Oil Basket Spot, Crude Oil Oman Spot, Crude Oil Dubai Cash Spot, S&P GSCI Heating Oil Spot, US Gulf Coast Kerosene-Type Jet Fuel Spot Price and Los Angeles Low Sulfur CARB Diesel Spot Price is negative during normal market conditions and positive during extreme market conditions. Results from the non-parametric causality in the quantile approach show strong evidence of asymmetry in causality across quantiles and strong variations across markets. Practical implications The quantile time-varying dependence and predictability results documented in this paper can help market participants with different investment targets and horizons adopt better hedging strategies and portfolio diversification to aid optimal policy measures during volatile market conditions. Social implications The outcome of this study will promote awareness regarding the environment and also increase investor’s participation in the green bond market. Further, it allows corporate institutions to fulfill their social commitment through the issuance of green bonds. Originality/value This paper differs from these previous studies in several aspects. First, the authors have included a wide range of energy commodities, comprising three green bond indices and 14 energy commodity indices. Second, the authors have explored the dependency between the two markets, particularly during COVID-19 pandemic. Third, the authors have applied CQ and causality-in-quantile methods on the given data set. Since the market of green and sustainable finance is growing drastically and the world is transmitting toward environment-friendly practices, it is essential and vital to understand the impact of green bonds on other financial markets. In this regard, the study contributes to the literature by documenting an in-depth connectedness between green bonds and crude oil, natural gas, petrol, kerosene, diesel, crude, heating oil, biofuels and other energy commodities.
Adaptive selling, anxiety and emotional exhaustion among salespeople
(Journal of Marketing Theory and Practice, 2025) Sharma, Aditi; Chawla, Vaibhav; Guda, Sridhar; Rangarajan, Deva; Swain, Anjan Kumar
The need to constantly adapt to the changes in the business environment and selling situations places enormous demands on salespeople. Applying the Job Demands-Resources theory, this study examines the negative impact of adaptive selling on salespeople. Using survey data from 384 sales professionals in different industries in an emerging market, we find that adaptive selling directly increases cognitive anxiety among salespeople. Furthermore, our research suggests that cognitive anxiety mediates the relationship between adaptive selling and emotional exhaustion. However, our study did not find support for the direct impact of adaptive selling on emotional exhaustion, suggesting that in some instances, the need to practice adaptive selling by itself might motivate salespeople to get more involved. Our study also demonstrates that salesperson perseverance reduces the negative impact of adaptive selling on cognitive anxiety, suggesting a moderating effect. The study concludes by providing theoretical and practical implications.
Adaptive selling, anxiety and emotional exhaustion among salespeople
(Journal of Marketing Theory & Practice, 2025) Sharma, Aditi; Chawla, Vaibhav; Guda, Sridhar; Rangarajan, Deva; Swain, Anjan Kumar
The need to constantly adapt to the changes in the business environment and selling situations places enormous demands on salespeople. Applying the Job Demands-Resources theory, this study examines the negative impact of adaptive selling on salespeople. Using survey data from 384 sales professionals in different industries in an emerging market, we find that adaptive selling directly increases cognitive anxiety among salespeople. Furthermore, our research suggests that cognitive anxiety mediates the relationship between adaptive selling and emotional exhaustion. However, our study did not find support for the direct impact of adaptive selling on emotional exhaustion, suggesting that in some instances, the need to practice adaptive selling by itself might motivate salespeople to get more involved. Our study also demonstrates that salesperson perseverance reduces the negative impact of adaptive selling on cognitive anxiety, suggesting a moderating effect. The study concludes by providing theoretical and practical implications
An asymmetric analysis of overall globalization on financial inclusion
(Journal of Financial Economic Policy, 2025-02-21) Villanthenkodath, Muhammed Ashiq; Pal, Shreya
Purpose Financial inclusion is acknowledged as a critical facilitator of the United Nations Sustainable Development Goals agenda for 2030. Therefore, this study aims to examine the asymmetric role of overall globalization on financial inclusion by controlling economic growth, urbanization and population for the selected South Asian countries. Design/methodology/approach Applying the nonlinear autoregressive distributed lag approach to cointegration explores the impact of overall globalization on financial inclusion in the presence of additional variables like economic growth, urbanization and population in the designed financial inclusion function. Findings The estimated econometric outcomes show that increasing overall globalization fosters financial inclusion while decreasing overall globalization reduces financial inclusion. Furthermore, a positive (negative) change in economic growth leads to an increase (decrease) in financial inclusion while varying short-run findings. Moreover, both positive and negative changes increase financial inclusion in the long run in connection with urbanization. Although the short-run results are not significant, the study finds that an increase (decrease) in population leads to a decrease (increase) in financial inclusion. Finally, to support the promotion of financial inclusivity throughout South Asia, several policies pertaining to financial inclusion are suggested. Originality/value To the best of the authors’ knowledge, this is the first study to examine the asymmetries related to overall globalization on financial inclusion by controlling economic growth, urbanization and population.
An Evaluation Framework for Machine Learning and Data Science (ML/DS) Based Financial Strategies: A Case Study Driven Decision Model.
(IEEE Transactions on Engineering Management, Engineering Management, 2025) Saadatmand, M.; Daim, T. Mena,; C. Yalcin, H.; Bolatan, G.; Chatterjee, M
Big data and computational technologies are increasingly important worldwide in asset and investment management. Many investment management firms are adopting these data science (DS) methods and technologies to improve performance across all investment processes. A good question is whether we can make better decisions in developing quantitative strategies. Therefore, the main objective of this research was to develop a multicriteria assessment framework and scoring decision support system to evaluate quantitative investment strategies that apply machine learning (ML) and DS techniques in their research and development. Subject matter experts will assess all framework perspectives from a systematic literature review to approve their reliability. The perspectives consist of economic and financial foundations , data perspective , features perspective , modeling perspective , and performance perspective . The research methodology applied was the hierarchical decision model (HDM) to provide a 360° view of the quantitative investment strategy and improve and generalize the concept to other asset classes and regions. This study accomplished a rigorous integration of an extensive literature review connecting DS, ML, and investment decision-making in developing quantitative investment strategies. As a result, the major contribution of this study is the comprehensive examination, which included identifying and quantifying perspectives and criteria. The results, while limited indicated significant gaps in strategies examined and therefore generated critical knowledge to improve ML/DS-driven investment strategies, which are valuable for financial companies and policymakers.
An Evidence-Based CoCoSo Framework with Double Hierarchy Linguistic Data for Viable Selection of Hydrogen Storage Methods.
(Computer Modeling in Engineering & Sciences, 2024) Krishankumar, Raghunathan;; Sundararajan, Dhruva;; Ravichandran, K. S.
Hydrogen is the new age alternative energy source to combat energy demand and climate change. Storage of hydrogen is vital for a nation's growth. Works of literature provide different methods for storing the produced hydrogen, and the rational selection of a viable method is crucial for promoting sustainability and green practices. Typically, hydrogen storage is associated with diverse sustainable and circular economy (SCE) criteria. As a result, the authors consider the situation a multi-criteria decision-making (MCDM) problem. Studies infer that previous models for hydrogen storage method (HSM) selection (i) do not consider preferences in the natural language form; (ii) weights of experts are not methodically determined; (iii) hesitation of experts during criteria weight assessment is not effectively explored; and (iv) three-stage solution of a suitable selection of HSM is unexplored. Driven by these gaps, in this paper, authors put forward a new integrated framework, which considers double hierarchy linguistic information for rating, criteria importance through inter-criteria correlation (CRITIC) for expert weight calculation, evidence-based Bayesian method for criteria weight estimation, and combined compromise solution (CoCoSo) for ranking HSMs. The applicability of the developed framework is testified by using a case example of HSM selection in India. Sensitivity and comparative analysis reveal the merits and limitations of the developed framework
Analysis of critical success factors of Quality 4.0 implementation in manufacturing SMEs using best–worst method
(The TQM Journal, 2024-12-19) Vinodh,Sekar; Wankhede, Vishal Ashok; Muruganantham, Ganesan
Purpose To attain a competitive edge, it is essential to realize the significant critical success factors (CSFs) that contribute to the adoption of Quality 4.0 (Q4.0) in manufacturing organizations. Therefore, the study aimed to analyze CSFs for Q4.0 implementation in manufacturing small and medium-sized enterprises (SMEs) using multi-criteria decision-making (MCDM) tool. Design/methodology/approach The present study begins with a systematic literature review of past studies about Q4.0 implementation in manufacturing, followed by the identification of CSFs. Further, a case study was conducted wherein 42 CSFs identified were grouped into five dimensions. Best–worst method is a MCDM tool applied as a solution methodology for the analysis of CSFs based on expert opinion and priority order of CSFs attained. Findings The priority order of CSFs is obtained. Based on the findings, significant CSFs are “Data prediction and Analytics,” “Organizational culture towards Quality 4.0” and “Machine to Machine communication.” Practical implications The shifting market dynamics incorporate Q4.0 inclusion for realizing zero defects and high traceability in automotive SMEs. The present study offers implications for industry managers and practitioners by delivering insights on how Q4.0 could be serving automotive systems and CSFs that industry authorities need to pay attention to effectively adopt Q4.0 in the current quality systems. The study will facilitate industry practitioners to meticulously examine CSFs for Q4.0 toward the improvement of SME performance. Originality/value The identification of CSFs for Q4.0 adoption in manufacturing SMEs, along with the prioritization of CFSs using the MCDM tool, is the original contribution by the authors.
Analyzing the static and dynamic dependence among green investments, carbon markets, financial markets and commodity markets
(International Journal of Managerial Finance, 2025-01-17) Abakah, Emmanuel Joel Aikins; Tiwari, Aviral Kumar; Oliyide, Johnson Ayobami; Appiah, Kingsley Opoku
Purpose This paper investigates the static and dynamic directional return spillovers and dependence among green investments, carbon markets, financial markets and commodity markets from January 2013 to September 2020. Design/methodology/approach This study employed both the quantile vector autoregression (QVAR) and time-varying parameter VAR (TVP-VAR) technique to examine the magnitude of static and dynamic directional spillovers and dependence of markets. Findings Results show that the magnitude of connectedness is extremely higher at quantile levels (q = 0.05 and q = 0.95) compared to those in the mean of the conditional distribution. This connotes that connectedness between green bonds and other assets increases with shock size for both negative and positive shocks. This further indicates that return shocks spread at a higher magnitude during extreme market conditions relative to normal periods. Additional analyses show the behavior of return transmission between green bond and other assets is asymmetric. Practical implications The findings of this study offer significant implications for portfolio investors, policymakers, regulatory authorities and investment community in terms of carefully assessing the unique characteristics offered by each markets in terms of return spillovers and dependence and diversifying the portfolios. Originality/value The study, first, uses a relatively new statistical technique, the QVAR advanced by Ando et al. (2018), to capture upper and lower tails’ quantile price connectedness and directional spillover. Therefore, the results possess adequate power against departure from mean-based conditional connectedness. Second, using a portfolio of green investments, carbon markets, financial markets and commodity markets, the uniqueness of this study lies in the examination of the static and dynamic dependence of the markets examined.
Analyzing time-varying tail dependence between leveraged loan and debt markets in the U.S. economy
(International Review of Finance, 2024-06) Tiwari, Aviral Kumar; Trabelsi, Nader; Abakah, Emmanuel Joel Aikins; Lee, Chi-Chuan
This study analyzes the time-varying dependence between U.S. leveraged loan and debt markets within a highly linked financial system using a quantile-based time-varying connectedness framework to determine the hedging benefits of leveraged loans for financial investors at various quantiles. Based on daily closing price data from November 28, 2008 to October 3, 2023, the evidence demonstrates considerable (moderate) spillovers across the leveraged loan and debt markets for severe (normal) occurrences, with additional results indicating symmetric interaction. In terms of risk spillover, we also affirm the dominance of short-term fixed-income instruments over leveraged loans and long-term bonds. These findings indicate that no hedging or diversification occurred among the investigated markets.
Antecedents and job outcomes from a self-efficacy perspective while working from home among professionals during the COVID-19 pandemic
(International Journal of Manpower, 2024) Lathabhavan, Remya; Griffiths, Mark D.
Purpose Working from home (WFH) was one of the major changes that occurred in many organizations during the COVID-19 pandemic. This also led to online training being conducted during this WFH period. The present study investigated the role of technology, manager support and peer support on self-efficacy and job outcomes (i.e. training transfer, work engagement and job satisfaction) of employees while WFH. Design/methodology/approach The study framework incorporated Bandura's self-efficacy theory. Data were collected from 852 employees in India, and structural equation modeling was used to analyze the data. Findings The study found positive relationships between ease of technology use, manager support and peer support on self-efficacy and a negative relationship between self-efficacy and technostress. The study also found significant positive relationships between self-efficacy and training transfer, work engagement and job satisfaction. Moreover, the study also identified the moderating effects of WFH and technical issues in the relationships of self-efficacy with training transfer, work engagement and job satisfaction. Originality/value The study is novel in that it extended self-efficacy theory regarding the WFH context with influencers such as technology, managers and peers as organizational factors. It also demonstrated the effectiveness of remote working and online training considering the potential antecedents while WFH. Moreover, the study highlighted the simultaneous role of technology and people (managers and peers) in enhancing job outcomes by increasing self-efficacy among employees.
Are emerging technologies unlocking the potential of sustainable practices in the context of a net-zero economy?
(Environmental Science and Pollution Research, 2023-03-18) Agrawal, Rohit; Priyadarshinee, Pragati; Kumar,Anil
Increasing globalization and climate change have significantly affected business activities. Government and other stakeholders are creating pressure to have a sustainable business model for efficient resource utilization and minimizing negative environmental impact. Many organizations have started focusing on sustainable and cleaner production through the adoption of net-zero economy (NZE) practices. Certain technological advancements are required to put these concepts into practice. Firms have begun to adopt digital technologies (such as big data analytics, artificial intelligence, and internet of things), and have been widely used in practice to achieve NZE. Is digitalization unlocking the potential of sustainable practices in the context of a net-zero economy? This question is still unanswered; therefore, this study aims to identify and analyze the drivers of digitalization that ensure sustainable practices to achieve net-zero economy. Through an extensive literature review and experts’ opinions, a list of drivers was identified. An empirical investigation was conducted to validate the identified drivers and further understand the influencing relationship among the drivers, Pythagorean fuzzy decision-making trial and evaluation laboratory (PF-DEMATEL) was employed. The findings of the study show that “high degree of automation,” “enhancing the flexibility in the manufacturing process,” and “real-time sensing capability” are the main influencer drivers among all cause group forces. The present study can be a source for industrial practitioners and academia that can provide significant guidance on how the adoption of digitalization can unlock the potential to achieve CE, which can lead us toward net-zero.
Are metaverse applications in quality 4.0 enablers of manufacturing resiliency? An exploratory review under disruption impressions and future research
(The TQM Journal, 2024-06-25) Jaouhari, Asmae El; Arif, Jabir; Samadhiya, Ashutosh; Kumar, Anil; Jain, Vranda; Agrawal, Rohit
Purpose The purpose of this paper is to investigate, from a thorough review of the literature, the role of metaverse-based quality 4.0 (MV-based Q4.0) in achieving manufacturing resilience (MFGRES). Based on a categorization of MV-based Q4.0 enabler technologies and MFGRES antecedents, the paper provides a conceptual framework depicting the relationship between both areas while exploring existing knowledge in current literature. Design/methodology/approach The paper is structured as a comprehensive systematic literature review (SLR) at the intersection of MV-based Q4.0 and MFGRES fields. From the Scopus database up to 2023, a final sample of 182 papers is selected based on the inclusion/exclusion criteria that shape the knowledge base of the research. Findings In light of the classification of reviewed papers, the findings show that artificial intelligence is especially well-suited to enhancing MFGRES. Transparency and flexibility are the resilience enablers that gain most from the implementation of MV-based Q4.0. Through analysis and synthesis of the literature, the study reveals the lack of an integrated approach combining both MV-based Q4.0 and MFGRES. This is particularly clear during disruptions. Practical implications This study has a significant impact on managers and businesses. It also advances knowledge of the importance of MV-based Q4.0 in achieving MFGRES and gaining its full rewards. Originality/value This paper makes significant recommendations for academics, particularly those who are interested in the metaverse concept within MFGRES. The study also helps managers by illuminating a key area to concentrate on for the improvement of MFGRES within their organizations. In light of this, future research directions are suggested.
Are we making progress in developing knowledge management strategies that support organizational performance?
(Kybernetes, 2024-12-09) Kaushal, Sanjay; Nyoni, Austin Milward; Sharma. Aarti
Purpose The purpose of the present study is to establish the emerging trend of studies on knowledge management (KM) strategy from 2007 to 2021 and identify the most studied constructs, methodologies used and gaps, thereby suggesting future directions. Design/methodology/approach Guided by items on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, the study analyzed 46 articles published within the 15 years under review. Findings An upward trend in KM strategy studies published from 2007 to 2021 emerged, indicating researchers' growing interest in the topic. Further, the studies reaffirmed the essence of having a KM strategy alongside other functional strategies for an organization's outstanding performance. Key KM strategy antecedents were identified: resource availability, communication, business environment, stakeholder participation, organizational culture and incentives. The need to align the KM strategy and other functional strategies with the overall business strategy was also established as critical. Finally, gaps in study methodologies and extant literature were identified, leading to suggestions for future directions. Originality/value The study provides valuable insights regarding the emerging trend of studies on KM strategy over the 15 years, identification of methodologies used in the studies and the most studied constructs. To this effect, the study's uniqueness lies in the identified gaps and recommendations made for future research directions as it strives to bridge the identified gaps.
Artificial intelligence an enabler for sustainable engineering decision-making in uncertain environment: a review and future propositions
(Journal of Global Operations and Strategic Sourcing, 2024) Wankhede, Vishal Ashok; Agrawal, Rohit; Kumar, Anil; Luthra, Sunil; Pamucar, Dragan; Stević, Željko
Purpose Sustainable development goals (SDGs) are gaining significant importance in the current environment. Many businesses are keen to adopt SDGs to get a competitive edge. There are certain challenges in realigning the present working scenario for sustainable development, which is a primary concern for society. Various firms are adopting sustainable engineering (SE) practices to tackle such issues. Artificial intelligence (AI) is an emerging technology that can help the ineffective adoption of sustainable practices in an uncertain environment. In this regard, there is a need to review the current research practices in the field of SE in AI. The purpose of the present study is to comprehensive review the research trend in the field of SE in AI. Design/methodology/approach This work presents a review of AI applications in SE for decision-making in an uncertain environment. SCOPUS database was considered for shortlisting the articles. Specific keywords on AI, SE and decision-making were given, and a total of 127 articles were shortlisted after implying inclusion and exclusion criteria. Findings Bibliometric study and network analyses were performed to analyse the current research trends and to see the research collaboration between researchers and countries. Emerging research themes were identified by using structural topic modelling (STM) and were discussed further. Research limitations/implications Research propositions corresponding to each research theme were presented for future research directions. Finally, the implications of the study were discussed. Originality/value This work presents a systematic review of articles in the field of AI applications in SE with the help of bibliometric study, network analyses and STM.
Artificial Intelligence-Based Cybersecurity for the Metaverse: Research Challenges and Opportunities
(2025-04) Awadallah, Abeer; Eledlebi, Khouloud; Zemerly, Mohamed Jamal; Puthal, Deepak; Damiani, Ernesto; Taha, Kamal
The metaverse, known as the next-generation 3D Internet, represents virtual environments that mirror the physical world. It is supported by innovative technologies such as digital twins and extended reality (XR), which elevate user experiences across various fields. However, the metaverse also introduces significant cybersecurity and privacy challenges that remain underexplored. Due to its complex multi-tech infrastructure, the metaverse requires sophisticated, automated, and intelligent cybersecurity measures to mitigate emerging threats effectively. Therefore, this paper is the first to explore Artificial Intelligence (AI)-driven cybersecurity techniques for the metaverse, examining academic and industrial perspectives. First, we provide an overview of the metaverse, presenting a detailed system model, diverse use cases, and insights into its current industrial status. We then present attack models and cybersecurity threats derived from the unique characteristics and technologies of the metaverse. Next, we review AI-driven cybersecurity solutions based on three critical aspects: User authentication, intrusion detection systems (IDS), and the security of digital assets, specifically for Blockchain and Non-fungible Tokens (NFTs). Finally, we highlight challenges and suggest future research opportunities to enhance metaverse security, privacy, and digital asset transactions
Assessing the impact of renewable energy and non-renewable energy use on carbon emissions: evidence from select developing and developed countries
(Environment, Development and Sustainability, 2025-02) Rai, Pratibha; Gupta, Priya; Saini, Neha; Tiwari, Aviral Kumar
Renewable Energy (RE) is essential for balancing economic and environmental conditions to attain Sustainable Development Goals (SDGs). This paper investigates the relationship between carbon emissions (CO2) and RE use, considering Non-renewable Energy (NRE) and macroeconomic variables such as Foreign Direct Investment, Gross Domestic Product, and Trade in eight major polluting nations from 1990 to 2019, constrained by data idiosyncratic features. The Error Correction Model using Autoregressive Distributed Lag methodology reveals that RE effectively lowers carbon emissions on average, but high economic growth and NRE use significantly contribute to environmental degradation. Additionally, while a reduction in CO2 emissions with RE use is evident through panel data analysis using the random-effect model. However, country-wise and panel data analyses do not substantiate the Environmental Kuznets Curve (EKC) hypothesis. The study confirms a long-run cointegrated relationship among the variables. It highlights the necessity for tailored energy innovations, as the weak validation of the overemphasized EKC hypothesis indicates that a generic solution is only sometimes applicable for mitigating emissions that facilitate the achievement of SDGs. This inquiry contributes to the extant literature by providing a nuanced understanding of the associations amongst macroeconomic variables, renewable and non-renewable energy consumption, and carbon emissions and offers critical insights for policy formulation. The requirement of indispensable energy innovations to achieve SDGs is emphasized. It is necessary to decrease the share of NRE use in total energy consumption and to increase the percentage share of RE use.
Assessing the relative impact of inland and marine fish production on fishing load capacity factor: Insights for sustainable fisheries management
(Marine Pollution Bulletin, 2025-06) Villanthenkodath, Muhammed Ashiq; Pal, Shreya
Promoting environmental sustainability, including the preservation of marine ecosystems, is a shared responsibility that requires the active engagement of diverse stakeholders. Thus, this study aims to find out how fish production in inland and marine areas affects the fishing load capacity factor in India from 1990 to 2022, considering economic growth and renewable energy consumption as additional covariates. For this purpose, the study employs both the Dynamic Auto-Regressive Distributed Lag (DYNARDL) simulations and the Auto-Regressive Distributed Lag (ARDL) bound testing models. The findings reveal a valid long-run relationship for the fishing load capacity factor function. Further, the findings show that inland fish production improves the fishing load capacity factor in the long run, though this effect is statistically insignificant in the short run. In contrast, marine fish production degrades the fishing load capacity in both the short and long run. Similarly, economic growth has significantly reduced the fishing load capacity factor in both time frames. Conversely, renewable energy consumption also shows a long-run negative impact on the fishing load capacity factor, although the short-run effect is insignificant. Therefore, the research findings suggest that the Indian government should prioritize implementing green economic policies for the fisheries sector, including environmental financing and promoting sustainable fishery products not limited to inland fish production but for marine fish production, to improve the fishing load capacity factor.