Gupta, Sanjay

Gupta S., Arora A., Singh S., Jain J.

Purpose In the present era, artificial intelligence (AI) is transforming and redefining the lifestyles of society through its applications, such as chatbots. Chatbot has shown tremendous growth and has been used in almost every field. The purpose of this study is to identify and prioritize the factors that influence millennial’s technology acceptance of chatbots. Design/methodology/approach For the present research, data were collected from 432 respondents (millennials) from Punjab. A fuzzy analytical hierarchy process was used to prioritize the factors influencing millennials’ technology acceptance of chatbots. The key factors considered for the study were information, entertainment, media appeal, social presence and perceived privacy risk Findings The findings of the study revealed media appeal as the top-ranked prioritized factor influencing millennial technology acceptance of chatbots. In contrast, perceived privacy risk appeared as the least important factor. Ranking of the global weights reveals that I3 and I2 are the two most important sub-criteria. Research limitations/implications Data were gathered from the millennial population of Punjab, and only a few factors that influence the technology acceptance of chatbots were considered for analysis which has been considered as a limitation of this study. Practical implications The findings of this study will provide valuable insights about consumer behaviour to the business firm, and it will help them to make competitive strategies accordingly. Originality/value Existing literature has investigated the factors influencing millennials’ technology acceptance of chatbots. At the same time, this study has used the multi-criteria decision-making technique to deliver valuable insights for marketers, practitioners and academicians about the drivers of millennials’ technology acceptance regarding chatbots which will add value to the prevailing knowledge base.

Gupta S., Bhandari R.S., Aggarwal A., Gupta S.

The purpose of this research is to concretise reactions of Indian millennials towards online advertisements and to prioritise these reactions. A questionnaire was circulated among 450 college students (online), from which 378 responses were used out of 415 responses received. Confirmatory Factor Analysis followed by the Fuzzy multi-criteria assessment method (F-AHP) was applied, initially to confirm the factors in the context of Indian millennials and then they were prioritized. The results show that among the 8 factors youth like the amusing factor very much with 21% while the authoritative nature of advertisements are not much appreciated by them and ranked lowest among all. Future research can be conducted using different methodologies, such as interviews, focus groups discussion, and expert talks to study the relationship between these factors and their impact on the youth, and various other dimensions can also be involved. The uniqueness of the study lies in the fact that the researchers have identified factors that affecting reactions of Millennials towards online advertisements. Further, these factors have been prioritized using Fuzzy AHP technique.

Gupta S., Kaur S., Gupta M., Singh T.

Purpose The rapid expansion of artificial intelligence (AI) is progressively reshaping the dynamics of human interaction, communication, lifestyle, education and professional endeavors. The purpose of the study is to comprehend and address the barriers which are impeding the implementation of Generative AI Technologies, such as ChatGPT in the educational landscape. Design/methodology/approach The study used the Fuzzy analytic hierarchy process (AHP) model to analyze the responses gathered from 149 academicians belonging to the northern states of India. Findings The study established that the three most important criteria that influence the adoption of generative AI in the education sector are Risk of Academic Integrity, Risk of biased outcomes and Erosion of Critical Thinking. Research limitations/implications The present study was confined to Fuzzy AHP to extract the critical criteria influencing the decision-making. Various other techniques such as PF-Delphi and PF-CoCoSo can be used further. The results provide significant inputs for future research to understand the effect of adoption of Generative AI in different contexts including both opportunities and the challenges faced by them. Practical implications The study will be beneficial to various stakeholders including students, educators, society and policymakers as the study will highlight the importance of AI tools, introduce the various challenges associated with and explain the use of these tools as productivity-enhancing tools. Originality/value To the best of the author’s knowledge, the present study is a novice as the use of AI in Academia is unexplored and the major criteria influencing the choices have yet been undiscovered.

Gill A.S., Narwana K., Gupta S.

Gupta S., Raj S., Garg A., Gupta S.

PurposeThe primary purpose of this study is to examine the factors leading to shopping cart abandonment and construct a model depicting interrelationship among them using interpretive structural modeling (ISM) and Matriced Impact Croises Multiplication Appliquee an un Classement (MICMAC).Design/methodology/approachInitially, 20 factors leading to shopping cart abandonment were extracted through a systematic literature review and expert opinions. Fifteen factors were finalized using the importance index and CIMTC method, for which consistency has been checked in SPSS software through a statistical reliability test. Finally, ISM and MICMAC approach is used to develop a model depicting the contextual relationship among finalized factors of shopping cart abandonment.FindingsThe ISM model depicts a technical glitch (SC8), cash on delivery not available (SC4), bad checkout interface (SC9), just browsing (SC11), and lack of physical examination (SC12) are drivers or independent factors. Additionally, four quadrants have been formulated in MICMAC analysis based on their dependency and driving power. This facilitates technical managers of e-commerce companies to focus more on factors leading to shopping cart abandonment according to their dependency and driving power.Research limitations/implicationsTaking an expert’s opinion as a base may affect the results of the study due to biases based on subjectivity.Practical implicationsThis study’s outcomes would accommodate practitioners, researchers, and multinational or national companies to indulge in e-commerce to anticipate factors restricting the general public from online shopping.Originality/valueFor the successful running of an e-commerce business and to retain the confidence of e-shoppers, every e-commerce company must make a strategy for controlling factors leading to shopping cart abandonment at the initial stage. So, this paper attempts to highlight the main factors leading to shopping cart abandonment and interrelate them using ISM and MICMAC approaches. It provides a clear path to technical heads, researchers, and consultants for handling these shopping cart abandonment factors.

Sood K., Pathak P., Gupta S.

PurposeInvestment decisions hold immense significance for investors and eventually affect their portfolio performance. Investors are advised to weigh the costs and benefits associated with every decision in order to make rational investment decisions. However, behavioral finance research reveals that investors' choices often stem from a blend of economic, psychological and sociological factors, leading to irrationality. Moreover, environmental, social and corporate governance (ESG) factors, aligned with behavioral finance hypotheses, also sway opinions and stock prices. Hence, this study aims to identify how individual equity investors prioritize key determinants of investment decisions in the Indian stock market.Design/methodology/approachThe current research gathered data from 391 individual equity investors through a structured questionnaire. Thereafter, a fuzzy analytic hierarchy process (F-AHP) was used to meet the purpose of the research.FindingsInformation availability, representative heuristics belonging to psychological factors and macroeconomic indicators falling under economic factors were discovered to be the three most prioritized criteria, whereas environmental issues within the realm of ESG factors, recommendations of brokers or investment consultants of sociological factors, and social issues belonging to ESG factors were found to be the least prioritized criteria, respectively.Research limitations/implicationsOnly active and experienced individual equity investors were surveyed in this study. Furthermore, with a sample size of 391 participants, the study was confined to individual equity investors in one nation, India.Practical implicationsThis research has implications for individual investors, institutional investors, market regulators, corporations, financial advisors, portfolio managers, policymakers and society as a whole.Originality/valueTo the best of the authors' knowledge, no real attempt has been made to comprehend how active and experienced individual investors prioritize critical determinants of investment decisions by taking economic, psychological, sociological and ESG factors collectively under consideration.

Kajla T., Sood K., Gupta S., Raj S., Singh H.

PurposeThe objective of this research is to identify and prioritize the critical factors that influence the adoption of blockchain technology within the banking sector.Design/methodology/approachA well-known theoretical framework, the “Technology Organization Environment (TOE),” was chosen to analyze what criteria and sub-criteria affect blockchain adoption in the banking sector after a thorough assessment of the prior literature. Following that, 3 evaluation criteria and 14 sub-criteria were selected and verified using expert opinion. A survey design was created, and data for the study has been collected from various information technology (IT) managers/officers in the banking sector. A fuzzy analytic hierarchy process (Fuzzy-AHP) was then used to meet the purpose of the research.FindingsThe study identified that the organizational dimension is the most significant criteria for blockchain adoption in the banking sector, followed by the environmental dimension. In contrast, the technological dimension is the least influential criterion. Clientele pressure, IT resources, financial resources, pressure from competitors and relative advantage are the most influential sub-criteria for blockchain adoption.Research limitations/implicationsThis study provides valuable insights to bank managers, blockchain and IT developers, third-party service providers and policymakers. For instance, adopting the same blockchain platform is easier for both large and small banks for banking operations by using third-party service provider. At the same time, banks should have the banks' own core team to implement the blockchain-based systems or to have control over the third-party service providers during the adoption stage.Originality/valueTo the best of the authors' knowledge, no empirical studies have used a holistic organizational context to understand the factors influencing the adoption of blockchain technology from traditional to blockchain-based banking systems.

Gupta S., Gupta S.

The objective of the study was to investigate different factors that influence the intention to purchase green vehicles and prioritize these factors to identify the most critical factor. The use of green vehicles, such as electric or hybrid vehicles, can significantly reduce greenhouse gas emissions, air pollution, and dependence on fossil fuels. Therefore, purchasing green vehicles can contribute to sustainable development by reducing the negative impact of transportation on the environment. Fuzzy AHP is an extension of the traditional AHP method that incorporates fuzzy logic. AHP breaks down complex problems into hierarchies of criteria and alternatives, enabling evaluation and prioritization. The study used fuzzy AHP to rank factors influencing individuals' intention to purchase green vehicles, based on 302 responses. Fuzzy AHP allows for flexible assessment considering uncertainties in decision-making. Harman's single-factor test was conducted to verify common method bias. The results revealed six factors that affect intentions to purchase green vehicles, with environmental concern (EC) being the most crucial and mass media influence (MMI) the least significant factor. However, as the study was conducted in India only, the generalizability of the findings to other countries may be limited. Additionally, the study is cross-sectional, and several factors that could have a significant impact on customers' purchase intentions are not included in the analysis. The study has implications for managers, practitioners, and government agencies. The use of fuzzy AHP in the proposed conceptual framework adds to the literature by providing a better empirical understanding of the primary determinants of customers' purchasing behaviour concerning green vehicles in India. The research also introduces fuzzy AHP as one of the multi-criteria decision-making (MCDM) techniques in the field of green vehicle purchasing, offering a comprehensive view of the significant motivations behind people's favourable attitude towards green vehicles from a theoretical perspective.

Sood K., Pathak P., Jain J., Gupta S.

PurposeResearch in the domain of behavioral finance has proven that investors demonstrate irrational behavior while making investment decisions. In a similar domain, the primary objective of this research is to prioritize the behavioral biases that influence cryptocurrency investors' investment decisions in the Indian context.Design/methodology/approachA fuzzy analytic hierarchy process (F-AHP) was used to prioritize the behavioral factors impacting cryptocurrency investors' investment decisions. Overconfidence and optimism, anchoring, representativeness, information availability, herding, regret aversion, and loss aversion are among the primary biases evaluated in the present study.FindingsThe findings suggested that the two most important influential criteria were herding and regret aversion, with loss aversion and information availability being the least influential criteria. Opinions of family, friends, and colleagues about investment in cryptocurrency, the sale of cryptocurrencies that have increased in value, the avoidance of selling currencies that have decreased in value, the agony of holding losing cryptocurrencies for too long rather than selling winning cryptocurrencies too soon, and the purchase of cryptocurrencies that have fallen significantly from their all-time high are the most important sub-criteria.Research limitations/implicationsThis survey only covered active cryptocurrency participants. Additionally, the study was limited to individual crypto investors in one country, India, with a sample size of 467 participants. Although the sample size is appropriate, a larger sample size might reflect the more realistic scenario of the Indian crypto market.Practical implicationsThe study is relevant to individual and institutional cryptocurrency investors, crypto portfolio managers, policymakers, researchers, market regulators, and society at large.Originality/valueTo the best of the authors' knowledge, no prior research has attempted to explain how the overall importance of various criteria and sub-criteria related to behavioral factors that influence the decision-making process of crypto retail investors can be assessed and how the priority of focus can be established, particularly in the Indian context.

Gupta S., Singh S., Garg A., Goel P.

Payments bank are a new addition to the family of digital payment services and are largely unexplored by the consumers. Hence there is a need to comprehend the underlying critical success factors that play a crucial role in the mass adoption of Payments bank. Further, to make Payments bank a standard digital payment option, converting imprecise information acquired from the respondents into key business insights is required. Hence, the present study aims to explore and prioritize the key factors responsible for the adoption of Payments bank. The study used the Fuzzy Analytic Hierarchy Process on the sample obtained from the northern region of India to identify the structure of the pertinent concerns and determine the criteria and sub-criteria of Payments bank services. Results of the study postulated that ease of use and facilitating conditions are the most prioritized factors. At the same time, personalization is ranked as the least preferred factor among all the factors. The present study sample is limited to a few parts of India, so generalizations of results should be made with caution. However, the current study strengthens the literature on Payments bank as it is one of the early studies in this area. Further, the insights generated by the study will help marketers and policymakers to pay broader attention to key parameters responsible for the success of Payments bank.

Wats S., Singh S., Gupta S.

Kaur S., Singh S., Gupta S., Wats S.

Decentralized finance is disrupting the financial ecosystem through innovative, transparent, and interoperable financial solutions. Based on distributed ledger technology, decentralized finance is a nascent and rapidly evolving area. Decentralized finance protocols are witnessing a perfect storm (in terms of growth). However, this emerging area needs sober consideration as these financial technologies possess unique risks for users, makers, regulators, and other stakeholders. The current research aims to identify and prioritize risks in decentralized finance. The present study conducted an extensive survey of the literature to identify various risks involved in decentralized finance. For empirical analysis, the study collected data from 90 experts. A fuzzy analytical hierarchical process (F-AHP) was applied to prioritize various risks in decentralized finance. Pairwise comparison and weights of all the criteria and sub-criteria revealed that technical risks are the most significant ones, followed by legal, regulatory, and financial risks. Among the sub-risks, financial risks are at the highest level, followed by smart contract risks and transaction risks. The outcomes of this research have several implications for regulators, policymakers, entrepreneurs, technologists, and practitioners. These stakeholders can focus on these vulnerabilities and offer more sustained solutions in the future.

Arora A., Gupta S., Devi C., Walia N.

PurposeThe financial technology (FinTech) era has brought a revolutionary change in the financial sector’s customer experiences at the national and global levels. The importance of artificial intelligence (AI) in the context of FinTech services for enriching customer experiences has become a new norm in this modern era of technological advancement. So, it becomes crucial to understand the customer’s perspective. The current research ranks the factors and sub-factors influencing customers’ perceptions of AI-based FinTech services.Design/methodology/approachThe sample size for this study was decided to be 970 respondents from four Indian cities: Mumbai, Delhi, Kolkata and Chennai. The Fuzzy-AHP technique was used to identify the primary factors and sub-factors influencing customers’ experiences with AI-enabled finance services. The factors considered in the study were service quality, trust commitment, personalization, perceived convenience, relationship commitment, perceived sacrifice, subjective norms, perceived usefulness, attitude and vulnerability. The current research is both empirical and descriptive.FindingsThe study’s three top factors are service quality, perceived usefulness and perceived convenience, all of which have a significant impact on customers’ experience with AI-enabled FinTech services discussing sub-criteria three primary criteria for customers’ experience for FinTech services include: “Using FinTech would increase my effectiveness in managing a portfolio (A2)”, “My peer groups and friends have an impact on using FinTech services (SN3)” and “Using FinTech would increase my efficacy in administering portfolio (PU2)”.Research limitations/implicationsThe current study is limited to four Indian cities, with 10 factors to understand customers’ preferences in FinTech. Further research can focus on other dimensions like perceived ease of use, familiarity, etc. Future studies can have a broader view of different geographical locations and consider new tech to understand customer perceptions better.Practical implicationsThe study’s findings will significantly assist businesses in determining the primary aspects influencing customers’ experiences with AI-enabled financial services. As a result, they will develop strategies and policies to entice clients to use AI-powered FinTech services.Originality/valueExisting AI research investigated several vital topics in the context of FinTech services. On the other hand, the current study ranked the criteria in understanding customer experiences. The research will substantially assist marketers, business houses, academicians and practitioners in understanding essential facets influencing customer experience and contribute significantly to the literature.

GUPTA S., Gupta S., Narang L.

Yadav S., Gill A.S., Narwana K., Gupta S.

This exploratory article, on the basis of a comprehensive field survey, identifies and empirically examines the various key quantitative and qualitative determinants of female child labour in Haryana (India). These factors have been examined on three broader parameters, viz. economic, sociocultural and institutional by way of using a multiple-criteria decision-making (MCDM) technique, called the analytic hierarchy process model. The analysis clearly establishes that while the economic dynamics are certainly the elementary drivers of the supply of female child labour, this phenomenon is also attributed, and that too up to a significant extent, to several area-specific sociocultural factors, which are many times ignored. The study finds that three sub-criteria, viz. inadequate annual adult earnings, patriarchy and alcoholism have emerged as major sub-factors in this context. The outcomes of this study have several connotations, both for mitigation of the problem of female child labour as well as further research in this area. The government may emphasise more appropriate strategies like the improvements in labour market outcomes for socio-economically underprivileged sections, restraining alcoholism as well as creating awareness among the masses for bringing changes in the state’s orthodox cultural norms with regard to the girl children and child labour.

Luo J., Zhang S., Zhang C.

Al-Okaily A.

Purpose This study aims to empirically examine and analyze the technological factors that influence the adoption of blockchain technology, particularly within the financial industry. It also predicts how adopting blockchain technology may affect financial firms’ performance from a multiple-measure perspective. Design/methodology/approach The research model was validated using quantitative data collected from 144 decision-makers working in Jordanian financial firms. The data was obtained through a cross-sectional survey approach and analyzed using partial least squares structural equation modeling. Findings The research results demonstrated that technological factors such as data security, technology compatibility, technology readiness and relative advantage significantly influenced blockchain technology adoption. The findings also provide evidence that blockchain technology adoption positively impacts innovation, operational, market and financial performance. Research limitations/implications This research was focused solely on technological factors that affected blockchain technology adoption in the financial industry in the Jordanian context. Further research can investigate organizational, environmental or economic factors that affect blockchain adoption to better generalize the findings. Practical implications The study offers valuable insights into the determinants and benefits of blockchain technology adoption in the financial industry. The results of this research can be used to guide practitioners and policymakers toward promoting and facilitating the use of blockchain technology in financial firms. Originality/value The study fills a significant gap in the academic literature since only a few studies have endeavored to ascertain the determinants of blockchain technology adoption and its impact on the performance of financial firms in the context of a developing country like Jordan.

Esmaeili R., Yazdi M., Rismanchian M., Shakerian M.

BackgroundEffective emergency response in various industries depends on the synergy between team coordination and cognitive abilities. Industries should prioritize investing in the development of team cognition to improve readiness and ensure swift, effective responses to emergencies and crises. This study aimed to identify and model factors influencing team cognition within Emergency Response Teams (ERTs).MethodsThis cross-sectional study undertook two principal phases: qualitative research using meta-synthesis and quantitative research using Best Worst Method (BWM), Interpretive Structural Modeling (ISM), and Fuzzy Cognitive Mapping (FCM). These methods were employed to assign weights to factors, establish their hierarchy, and determine cause-and-effect relationships among team cognition shaping factors (TCSFs).ResultsThrough a comprehensive evaluation of the articles, 13 dimensions were identified as the primary TCSFs influencing team cognition. The reliability of the extracted factors was validated using the Kappa indicator, with a value of 0.63 signifying an acceptable level of agreement. Using BWM analysis, “Team maturity (The team members’ harmonization)” and “Inefficient 4Cs (communication, coordination, cooperation, and collaboration)” were identified as the most influential factors shaping team cognition, with weights of 0.132 and 0.112, respectively. ISM analysis revealed “Improper team training programs” as a critical independent factor influencing other dimensions. FCM modeling further emphasized the significance of “Failure in decision-making” and “Leadership behavior and performance” as pivotal contributors to team cognition, with “Team maturity” and “Inefficient 4Cs” achieving the highest centrality scores of 13.44 and 13.28, respectively.ConclusionStakeholders can enhance team performance and effectiveness in emergency situations by understanding the relative importance of various factors, their hierarchical relationships, and the causal links between them. This allows for informed decision-making and targeted interventions, such as training programs to improve team maturity and team communication.

Linh H., Salloum C., Dana L., Voak A.

Baptista G., Pereira A.

The travel and transportation sectors continuously fight to stay up to date with new advancements in technology. Disruptive technologies, such as Artificial Intelligence (AI), are being used to develop businesses, enhance economic growth, revolutionize existing industries, create new opportunities, and increase productivity and efficiency. Notwithstanding the several advantages that this technology may bring, there is still little research on AI use in the travel and transportation sectors. This research contributes to this still understudied field to fill a gap in the literature by putting out a novel, thorough, and as far as we know not yet tested until now theoretical model, designed with the combination of the outcome of a literature meta-analysis study with Travel Experience and the Intention to Recommend technology constructs. A quantitative investigation using an online questionnaire was administered through social media and reached a total of 100 European participants. Structural equation modelling (SEM) was employed to test the suggested model empirically. The findings highlight that the user’s attitude towards AI is strongly influenced by Performance Expectancy and that the Intention to Use this technology is significantly influenced by Initial Trust and Attitude. Theoretical and practical contributions, limitations, and future areas of research are discussed.

Nguyen T.H., Vu T.Q.

Artificial intelligence (AI) has evolved for several decades. Much research has focused on how financial institutions should design, build, and operate AI-assisted financial products and services and how customers perceive and adopt individual AI assistants. Nonetheless, separating the perceptions of the AI assistants and the intentions toward their respective offerings is inadequate. This study implemented a case study with Vietnamese university students (n = 458) to examine a theoretical model involving four AI-assistant attributes (anthropomorphism, security, performance, and effort), trust, and six intentions. It found that perceived anthropomorphism, performance (usefulness), and effort (ease of use) could significantly affect intentions to various degrees; the tendency to trust significantly mediated these associations. Perceived security did not show any significant influence. Implications for improving students' financial knowledge and habits and scheduling financial products and services were discussed based on these observations.

Kumar Chaudhary M., Adhikari M., Mani Ghimire D., Raj Bhattarai D.

This study examines the connections between heuristic prejudices, risk perceptions, and investment decisions among stock market investors in Nepal. The study explores how prejudices such as overconfidence, representativeness, availability, and anchoring and adjustment shape investment choices, with a specific emphasis on the mediating influence of risk perception. Through a quantitative approach, data were collected from 404 respondents via a self-administered survey, and Structural Equation Modeling (SEM) was used for analysis. The findings reveal that risk perception significantly mediates the effect of these biases on investment decisions, highlighting the complex interplay between behavioral factors and investor behavior. By highlighting the necessity of taking risk perceptions into consideration when addressing behavioral biases in investment strategies, these results have practical consequences for investors, financial consultants, and legislators. This research pays attention to the understanding of behavioral finance, particularly within the context of Nepal’s capital market, and lays the groundwork for further studies on factors affecting investment decisions in real-world settings.

Maiti M., Kayal P., Vujko A.

Abstract Widespread adaptation and implementation of artificial intelligence (AI) across the businesses make ethical implications increasingly important. This study explores the ethical challenges and best practices surrounding the adoption of AI in various business contexts. The study finds that following ethical concerns are the hinderance in the adaptation of AI in business (Privacy and data protection, bias and fairness, transparency and explainability, job displacement and workforce changes, algorithmic influence, and manipulation, accountability, and liability, and ethical decision making). It also shows that these challenges vary across gender, age group, country, profession area, and age of the organizations. Lastly, the study provides insights on how businesses can navigate these challenges while upholding ethical standards. The study finding is highly useful for the business leaders, policymakers, and researchers in ensuring responsible and ethical AI deployment in the business ecosystem.

Gopal S., V. S., N. E.

With the increasing global emphasis on responsible investing, this study explores the tradeoff between ESG and traditional financial metrics in shaping the investment decisions of retail investors in India. A within-subject experimental design was employed at Christ University, India, involving an initial sample of 75 participants, with 55 completing all three experiment rounds. The sample respondents evaluated masked stock profiles across three rounds, where updated financial and ESG information on masked stock was provided at each round. The results indicate that though ESG metrics are getting attention among retail investors, financial metrics are still the main determining factor for investment. It was found that ROE (52 responses), 3-year CAGR Net Profit (36 responses), and P/E ratios (48 responses) are the most influencing factors to make investment decisions. Similarly, ESG factors (Governance, Environmental, and Sustainability scores) are also frequently mentioned, with 74 citations. Retail investors mainly consider profitability and view ESG as risk-mitigating or neutralizing factors. While evaluating the ESG factors, retailers mainly look at the firm’s environmental concerns, followed by governance and social factors. This result contrasts with the previous studies in this domain, where the literature emphasized governance factors more than environmental factors. These results highlight the integration of ESG elements, as retail investors remain with favorable returns and sacrifice sustainability. Further, this study spots the need for better and quantifiable ESG performance reports to consider alternative data comparable to financial data for better investment decisions.

Djabang P., Shubita M., Konstantopoulou A.

Purpose The paper aims to examine pecking order theory (POT) impact on the financing of small- and medium-sized enterprises (SMEs) in the UK. Design/methodology/approach The paper adopts a qualitative method based on semi-structured interviews of 45 owners/managers to examine the POT impact on SMEs’ financing in the UK. Findings A total of 11% of owners rely on debt/equity primarily as their source of finance before internally generated funds. This suggests that the alternative POT approach is adopted in the financing of business activities. This manifests in three ways: first because there is a tax shield on the interest paid on debt; second, because the internal source of finance is not enough to cater for capital investment; and third, because an expert may be required in the businesses, in terms of venture capitalists, to help support growth and expansion. The study assesses that 89% of owners/managers will use POT in their financing judgements. Research limitations/implications Owing to the number of SMEs that are concentrated within the UK, the study adopted a non-probability sampling technique to collect data from owners/managers of SMEs. Practical implications The study extends the POT in assessing the combination of capital strands in SME financing. Further, it informs SME owners/managers of the alternative forms of financing available for their business activities. Originality/value The last study conducted on POT within the context of the UK was in 2011 by Zata-Poutziouris. This study is timely in adding to the study of SME financing using the POT approach within the context of the UK and modestly contributes to the knowledge based on SME financing.

Hidegföldi M., Csizmazia G.L., Karpavičė J.

Bin-Nashwan S.A., Mohamed I., Muneeza A., Sadallah M., Ya’u A., Ma’aji M.M.

Purpose This study aims to investigate the intentions of Muslim cryptocurrency (CC) holders to fulfil their zakat obligations on digital assets, exploring the unique motivations and barriers within this emerging financial landscape. Design/methodology/approach The research uses a quantitative approach and a cross-sectional research design through online surveys, using purposive sampling to gather data from Muslim CC holders. The integrated model, known as the theory of planned behaviour and social cognitive theory (TPB-SCT) model, is used to comprehensively analyse the key factors influencing intentions to pay zakat on cryptocurrencies (CCs). Findings The study reveals that attitude towards zakat on CCs and perceived behavioural control regarding zakat on CCs have a significant and positive effect on the intention to pay. In contrast, subjective norms show no significant influence. CCs-related financial risk exerts a negative impact on intention. Moreover, CCs-related zakat knowledge and adherence to Shariah compliance are strongly associated with intention. These findings provide insights into the intricate dynamics of religious compliance within the evolving realm of digital assets. Practical implications Outcomes offer profound indications to stakeholders, including financial institutions, zakat agencies, policymakers and the community, on how to integrate zakat into this new and rapidly evolving financial paradigm like CC. Originality/value A pioneering effort was made in this study by exploring the intentions of Muslim CC holders to fulfil zakat obligations, bridging a significant gap in the existing literature. Developing and validating an integrated model of TPB-SCT in the realm of zakat on CC enriches the literature with a novel theoretical framework.

Olutimehin A.T.

Gupta S., Jaiswal R.

Purpose This study explores the factors influencing artificial intelligence (AI)-driven decision-making proficiency (AIDP) among management students, focusing on foundational AI knowledge, data literacy, problem-solving, ethical considerations and collaboration skills. The research examines how these competencies enhance self-efficacy and engagement, with curriculum design, industry exposure and faculty support as moderating factors. This study aims to provide actionable insights for educational strategies that prepare students for AI-driven business environments. Design/methodology/approach The research adopts a hybrid methodology, integrating partial least squares structural equation modeling (PLS-SEM) with artificial neural networks (ANNs), using quantitative data collected from 526 management students across five Indian universities. The PLS-SEM model validates linear relationships, while ANN captures nonlinear complexities, complemented by sensitivity analyses for deeper insights. Findings The results highlight the pivotal roles of foundational AI knowledge, data literacy and problem-solving in fostering self-efficacy. Behavioral, cognitive, emotional and social engagement significantly influence AIDP. Moderation analysis underscores the importance of curriculum design and faculty support in enhancing the efficacy of these constructs. ANN sensitivity analysis identifies problem-solving and social engagement as the most critical predictors of self-efficacy and AIDP, respectively. Research limitations/implications The study is limited to Indian central universities and may require contextual adaptation for global applications. Future research could explore longitudinal impacts of AIDP development in diverse educational and cultural settings. Practical implications The findings provide actionable insights for curriculum designers, policymakers and educators to integrate AI competencies into management education. Emphasis on experiential learning, ethical frameworks and interdisciplinary collaboration is critical for preparing students for AI-centric business landscapes. Social implications By equipping future leaders with AI proficiency, this study contributes to societal readiness for technological disruptions, promoting sustainable and ethical decision-making in diverse business contexts. Originality/value To the author’s best knowledge, this study uniquely integrates PLS-SEM and ANN to analyze the interplay of competencies and engagement in shaping AIDP. It advances theoretical models by linking foundational learning theories with practical AI education strategies, offering a comprehensive framework for developing AI competencies in management students.

Carbó-Valverde S., Cuadros-Solas P.J., Rodríguez-Fernández F.

Ding Y., Najaf M.

This study aims to investigate the impact of interactivity and perceived humanness on trust toward AI chatbots in the e-commerce setting. Moreover, this study also aims to examine the mediation effect of trust toward AI chatbots in the relationship between interactivity and intention to adopt AI chatbots for e-commerce as well as in the relationship between perceived humanness and intention to adopt chatbots for e-commerce. This study used a time lag approach to collect the data from 343 customers from the southern region of China. The data were collected online through a questionnaire designed in Chinese language using a survey firm. The findings of this study indicated that there is a significant impact of interactivity and humanness on the trust toward chatbots. Moreover, the findings of this study indicated that there is a significant mediating effect of trust toward chatbots in the relationships of interactivity and perceived humanness to adopt chatbots for e-commerce. In addition, this study found a significant moderating influence on the perceived enjoyment of using chatbots in e-commerce settings. This study provides a unique perspective of expectation-confirmation theory for adopting emerging technologies for online shopping and also provides insights for designers and business firms to develop businesses to facilitate the AI chatbot feature for e-commerce.

Chen T., Gasco-Hernandez M.

Getawa Ayalew G., Admasu Alemneh L., Melkamu Ayalew G.

BAİDOO-ANU D., OWUSU ANSAH L.

Since its maiden release into the public domain on November 30, 2022, ChatGPT garnered more than one million subscribers within a week. The generative AI tool ⎼ChatGPT took the world by surprise with it sophisticated capacity to carry out remarkably complex tasks. The extraordinary abilities of ChatGPT to perform complex tasks within the field of education has caused mixed feelings among educators, as this advancement in AI seems to revolutionize existing educational praxis. This is an exploratory study that synthesizes recent extant literature to offer some potential benefits and drawbacks of ChatGPT in promoting teaching and learning. Benefits of ChatGPT include but are not limited to promotion of personalized and interactive learning, generating prompts for formative assessment activities that provide ongoing feedback to inform teaching and learning etc. The paper also highlights some inherent limitations in the ChatGPT such as generating wrong information, biases in data training, which may augment existing biases, privacy issues etc. The study offers recommendations on how ChatGPT could be leveraged to maximize teaching and learning. Policy makers, researchers, educators and technology experts could work together and start conversations on how these evolving generative AI tools could be used safely and constructively to improve education and support students’ learning.

Gill A.S., Narwana K., Choudhury P.K.

Based on a recent primary survey, this study aims to examine the magnitude, patterns and determinants of household expenditure on compulsory schooling in Punjab. Our findings suggest that the increasing presence of private schools in the education marketplace, particularly in the contemporary era of neoliberalism, has commodified the education in the state, and these education providers offer compulsory schooling in diverse forms, with varying costs depending on the demand of their student clientele. Parents who enroll their children in elite private schools pay approximately seven times more than those choosing low-fee private schools. Private schools exert undue financial burden on their parent clientele (particularly those from low- and middle-income groups). This situation highlights the concept of ‘compulsion to pay’, especially in the absence of an acceptable quality of service provided by state-owned schools. Our regression analysis results on determinants of household expenditure reveal that households from socially and economically marginalised sections (for example, scheduled castes and the poor) spend significantly less than their more affluent counterparts. We recommend that the state take a more stringent approach to regulating these non-state education providers, particularly in terms of exorbitant fees, which would prevent further widening of the existing socio-economic inequality in the state.

Anlesinya A., Dadzie S.A.

Van de Wetering R., Doe J., van den Heuvel R., Al Halbusi H.

Li L., Ma Z., Fan L., Lee S., Yu H., Hemphill L.

The rapid advancements in generative AI models present new opportunities in the education sector. However, it is imperative to acknowledge and address the potential risks and concerns that may arise with their use. We analyzed Twitter data to identify critical concerns related to the use of ChatGPT in education. We employed BERT-based topic modeling to conduct a discourse analysis and social network analysis to identify influential users in the conversation. While Twitter users generally expressed a positive attitude toward using ChatGPT, their concerns converged into five categories: academic integrity, impact on learning outcomes and skill development, limitation of capabilities, policy and social concerns, and workforce challenges. We also found that users from the tech, education, and media fields were often implicated in the conversation, while education and tech individual users led the discussion of concerns. Based on these findings, the study provides several implications for policymakers, tech companies and individuals, educators, and media agencies. In summary, our study underscores the importance of responsible and ethical use of AI in education and highlights the need for collaboration among stakeholders to regulate AI policy.

Boxleitner A.

Kaur M., Jain J., Sood K.

The study's purpose is to examine the effect of herding, loss aversion, overconfidence, and fear of missing out (FOMO) biases on crypto investors’ investment decisions. The study also looks at how FOMO plays a mediating role between herding, loss aversion, overconfidence, and crypto investment decisions. To acquire data from crypto retail investors, the study used a questionnaire survey. A total of 473 responses were gathered and analyzed with SmartPLS. To achieve the study's aims, factor analysis and partial least square structural equation modelling were used. The study's findings found that FOMO, herding, loss aversion, and overconfidence biases have a substantial effect on the investment decisions of crypto investors, in respective order. In addition, FOMO bias establishes a complementary partial mediation on the relationship between herding, loss aversion, and crypto investors’ decision-making behavior. Ergo, the present study assisted individual and institutional cryptocurrency investors, crypto portfolio managers, policymakers, researchers, and market regulators in broadening their knowledge base about cryptocurrency and forecasting investors' behavior. Hence, this study contributes to the field of behavioral finance.

Panigrahi R.R., Shrivastava A.K., Qureshi K.M., Mewada B.G., Alghamdi S.Y., Almakayeel N., Almuflih A.S., Qureshi M.R.

AI chatbots (AICs) have the potential to increase the sustainability of a manufacturing supply chain (SC) through sales engagement and customer engagement to accomplish various activities related to logistics and SC in real time. Industry 4.0 (I4.0) has opened up several opportunities with internet-based technologies, along with challenges for small and medium enterprises (SMEs). SMEs are beginning to adopt such technologies for their competitive advantages and the required sustainability in the manufacturing supply chain. AICs may help in accomplishing supply chain visibility (SCV) to enhance sustainable supply chain performance (SSCP). Innovation capability (IC) is also due to disruptive technologies being adopted by SMEs. The present research investigates the role of AICs in SCV and IC, which lead to SSCP, by employing structural equation modeling (SEM). An empirical study based on dynamic capability (DC) theory was carried out using 246 responses, and later Smart PLS-4.0 was used for SEM. The analysis revealed that AICs positively influence SCV and IC to support SSCP. SCV and IC also partially mediate the relationship between the adoption of AICs and SSCP.

Adeshola I., Adepoju A.P.

Kajla T., Sood K., Gupta S., Raj S., Singh H.

PurposeThe objective of this research is to identify and prioritize the critical factors that influence the adoption of blockchain technology within the banking sector.Design/methodology/approachA well-known theoretical framework, the “Technology Organization Environment (TOE),” was chosen to analyze what criteria and sub-criteria affect blockchain adoption in the banking sector after a thorough assessment of the prior literature. Following that, 3 evaluation criteria and 14 sub-criteria were selected and verified using expert opinion. A survey design was created, and data for the study has been collected from various information technology (IT) managers/officers in the banking sector. A fuzzy analytic hierarchy process (Fuzzy-AHP) was then used to meet the purpose of the research.FindingsThe study identified that the organizational dimension is the most significant criteria for blockchain adoption in the banking sector, followed by the environmental dimension. In contrast, the technological dimension is the least influential criterion. Clientele pressure, IT resources, financial resources, pressure from competitors and relative advantage are the most influential sub-criteria for blockchain adoption.Research limitations/implicationsThis study provides valuable insights to bank managers, blockchain and IT developers, third-party service providers and policymakers. For instance, adopting the same blockchain platform is easier for both large and small banks for banking operations by using third-party service provider. At the same time, banks should have the banks' own core team to implement the blockchain-based systems or to have control over the third-party service providers during the adoption stage.Originality/valueTo the best of the authors' knowledge, no empirical studies have used a holistic organizational context to understand the factors influencing the adoption of blockchain technology from traditional to blockchain-based banking systems.

Dwivedi Y.K., Kshetri N., Hughes L., Slade E.L., Jeyaraj A., Kar A.K., Baabdullah A.M., Koohang A., Raghavan V., Ahuja M., Albanna H., Albashrawi M.A., Al-Busaidi A.S., Balakrishnan J., Barlette Y., et. al.

Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT’s capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT’s use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts.

Chan C.K., Hu W.

AbstractThis study explores university students’ perceptions of generative AI (GenAI) technologies, such as ChatGPT, in higher education, focusing on familiarity, their willingness to engage, potential benefits and challenges, and effective integration. A survey of 399 undergraduate and postgraduate students from various disciplines in Hong Kong revealed a generally positive attitude towards GenAI in teaching and learning. Students recognized the potential for personalized learning support, writing and brainstorming assistance, and research and analysis capabilities. However, concerns about accuracy, privacy, ethical issues, and the impact on personal development, career prospects, and societal values were also expressed. According to John Biggs’ 3P model, student perceptions significantly influence learning approaches and outcomes. By understanding students’ perceptions, educators and policymakers can tailor GenAI technologies to address needs and concerns while promoting effective learning outcomes. Insights from this study can inform policy development around the integration of GenAI technologies into higher education. By understanding students’ perceptions and addressing their concerns, policymakers can create well-informed guidelines and strategies for the responsible and effective implementation of GenAI tools, ultimately enhancing teaching and learning experiences in higher education.