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A crucial contemporary policy question for financial service organizations of being resilient across the globe calls for rethinking and renovating by adopting and adapting to the technologies of artificial intelligence (AI). The purpose of this study is to propose a policy framework for adoption of AI in the finance sector by exploring the driving factors through systems approach.

Based on literature review and discussions with experts from both industry and academia, nine enablers were shortlisted, which were used in the questionnaire survey to determine ranks of enablers. Further, the study developed the interpretive structural model (ISM) with the help of experts.

The ISM digraph developed with the help of the experts, resulted in the enablers like anticipated profitability, contactless solutions, credit risk management and software vendor support as dependent factors and stood at the top of the ISM. On the other hand, factors like availability of the data, technical infrastructure and funds are the most driving factors, which lie on the bottom of the ISM.

The study provides implications and policy recommendations for the practicing managers and government agencies approaching the digital transformation towards the adoption of AI in the finance ecosystem.

The paper uses the systems approach for the development of the ISM of the enabling factors for the adoption of AI technology. On the basis of the results, the study proposes a policy framework to accelerate the functioning of the finance ecosystem with AI technology.

Digital supply chains require nascent technologies like blockchain and Internet of Things (IoT). There is a need to develop a roadmap for the implementation of these technologies, as they require a huge amount of resources and infrastructure. The purpose of this paper is to analyze the challenges of implementing blockchain-IoT integrated architecture in the green supply chain and develop strategies for the same.

After a thorough literature survey of Scopus-indexed journals and books, 37 barriers were identified, which were then brought down to 15 barriers after confirming with industry and academic experts using the Delphi method. Using the total interpretive structural modeling (TISM) method and cross-impact matrix multiplication applied to classification (MICMAC) analysis, the barriers were modeled, and finally, strategies were formulated using a concept map to handle the barriers in the blockchain-IoT integrated architecture for a green supply chain.

This paper presents the research on barriers that can be considered for incorporating blockchain and IoT in the green supply chain. It was found from the TISM model that environmental concerns are Level-1 barriers and need to be addressed by developing appropriate technology and allocating funds for the same. An integrated ecosystem with blockchain and IoT is developed.

The focus of this study was on the challenges of blockchain and IoT; hence, it is required to extend the research and find challenges for different industries and also analyze the criteria using other multi-criteria decision-making (MCDM) methods. Further research is required for the integration of blockchain-IoT with supply chain functions.

The transformation of a traditional supply chain into a green supply chain is possible with the integration of technologies. This research work and the strategies developed are useful to managers and practitioners working on technology implementation. Planning resources and addressing key barriers is possible with the concept maps and architecture developed.

Green supply chain management (SCM) is gaining importance in industry as well as the academic sector due to government Policies and norms worldwide for reducing emissions and encouraging environment-friendly production systems. Incorporating blockchain and IoT in a green supply chain will further digitize and increase transparency in supply chains.

We have done a categorization of all barriers based on the expert survey by academicians and industry experts from industries in India. The concept map helps in identifying possible solutions for the challenges and initiatives to be taken for the smooth integration of technologies in the green supply chain.

The purpose of this paper is to examine the effects of financialisation on the changing structure of housing supply in Malaysia. The share of newly launched sub-MYR250,000 houses has been decreasing continuously in the past decade. This implies that housing developers are launching more expensive houses. The greater focus on higher cost housing could be attributed to inflation. But while input cost is rising, the housing sector has also become increasingly financialised. This claim can be supported by the rising share of mortgage and real estate loans in gross domestic product. Financialisation is a process in which the financial sector becomes more dominant relative to the real sector. The extent to which this process is responsible for the changing structure of housing supply in Malaysia is investigated.

A survey of the literature suggested that the decreasing the proportion of newly launched sub-MYR250,000 housing could be result of rising input cost, greater degree of financialisation and changing market concentration. Thus, long-run cointegrating equations were formulated and estimated. These equations linked housing share with financialisation, market structure and input cost. The quantitative and qualitative impact of financialisation on the structure of housing supply is of interest.

The analyses of secondary data suggested that financialisation and input cost did indeed contribute to the decrease in proportion of newly launched sub-MYR250,000 housing. However, the impact of market concentration on housing share was ambiguous. This conclusion survived several robustness checks.

The financialisation of the housing sector implies that developers are increasingly building for profits instead of accommodating the social objective of providing shelter. This result is unsettling because access to adequate housing is a human right. The transformation of housing from the concept of a shelter to a tradable, money-making asset could be a major contributor to the declining housing affordability in the country. Thus, efforts to improve affordability must take account of the effects of financialisation.

An empirical framework for assessing the changes in the structure of housing supply was developed. Existing studies tended to focus only on the volume of housing supply. It is a comprehensive study on changes in the structure of housing supply. Second, while existing studies on the financialisation of housing are mostly qualitative in methodology, this paper offers a quantitative assessment of the financialisation in the housing sector.

This study aims to examine the enablers of productivity of enterprise-level Agile development process using modified total interpretative structural modeling (TISM). The two main objectives of the current study are to determine the variables influencing enterprise-level agile development productivity and to develop modified TISM for the corresponding components.

To identify enablers of the productivity of enterprise-level agile software development process a literature review and opinions of domain experts were collected. A hierarchical relationship among variables that show direct and indirect influence is created using the modified TISM (M-TISM) technique with Cross Impact Matrix-Multiplication Applied to Classification analysis. This study examined and analyzed the relationships between the determinants within the enterprise using a M-TISM technique.

With the literature review, the study could identify ten enabling factors of the productivity of Agile development process at the enterprise level. Results depict that program increment (PI) planning and scalable backlog management, continuous integration and continuous delivery (CI/CD), agile release trains (ART), agile work culture, delivery excellence, lean and DevOps practices, value stream mapping (VMS), team skills and expertise, collaborative culture, agile coaching, customer engagement have an impact on the productivity of enterprise-level Agile development process. The results show that team collaboration, agile ways of working and customer engagement have a greater impact on productivity improvement for enterprise-level Agile development process.

The developed model is useful for organizations employing scaled Agile development processes in software development. This study provides a recommended listing of key enablers, that may enable productivity improvements in the Agile development process at the enterprise level. Strategists should focus on team collaboration and Agile project management. This study offers a modified TISM model to academicians to help them understand the effects of numerous variables on maintaining the productivity of an enterprise-level Agile. The identified characteristics and their hierarchical structure can help project managers during the execution of Agile projects at the enterprise level, more effectively, increasing their success and productivity.

The study addresses the gap in the literature by interpretative relationships between the identified enabling factors. The model validation is carried out by a panel of nine experts from several information technology organizations deploying Agile software development at the enterprise level. This unique method broadens the knowledge base in Agile software development at scale and provides project managers and practitioners with a practical foundation.

Since the introduction of the outstanding web AI chat system, ChatGPT, it has caused a significant impact in both academia and the business world. Many studies have started to explore its potential applications in various fields. However, there is a lack of research from the perspective of user experience. To fill this theoretical gap and provide a theoretical basis for the operation and design of related services, this study plans to develop a set of evaluation scales for AI chat system user experience and explore the relationship between various factors and user satisfaction.

This study obtained 41 evaluation indicators through literature review and user research. Subsequently, these indicators were used as questionnaire items, combined with satisfaction metrics. A total of 515 questionnaires were distributed, and factor analysis and linear regression were employed to determine the specific elements influencing user experience and the user satisfaction model.

This study found that the factors influencing user experience are usefulness, accuracy, logical inference, interactivity, growth, anthropomorphism, convenience, credibility, ease of use, creativity, and security. Among these factors, only accuracy, anthropomorphism, creativity, and security indirectly influence satisfaction through usefulness, while the rest of the factors have a direct positive impact on user satisfaction.

This study provides constructive suggestions for the design and operation of related services and serves as a reference for future theoretical research in this area.

With the increasing use of Internet and mobile handsets, mobile-based electronic payment apps (MEPAs) are becoming very popular around the world due to having various advantages. The intention of this exploratory study is to investigate the role/impact of selected factors in adopting three MEPAs of China: Wechat, Alipay and UnionPay based on the extended technology acceptance model (ETAM). After conducting a thorough and careful literature review, this study identified and divided seven such factors into three broad categories: (1) technological factors: perceived ease of use (PEU) and perceived usefulness (PU); (2) personal factors: perceived trust (PT), perceived privacy (PP) and perceived risk (PR); and (3) social factors: social influence (SI) and peer influence (PI) that were assumed to affect the intention to adopt MEPAs (IAMEPAs).

1,597 Chinese individuals were selected through purposive sampling technique who regularly used MEPAs at the time of collecting data. For collecting primary data from the selected respondents, a cross-sectional survey instrument was used. The study utilized IBM SPSS 25 for descriptive statistics and a second generation covariance-based structural equation modeling (CB-SEM) technique through AMOS 25 for testing the hypothesized relationships.

From statistical analysis, it was identified that five factors: PEU, PU, PT, SI and PI have significant positive impact on the dependent variable, IAMEPAs while PR and PP have insignificant influence on IAMEPAs.

This is one of the studies ever conducted to discover the factors that can have impact on the adoption of MEPAs using ETAM. It is strongly expected that this exploratory study can motivate the scholars to commence additional investigations regarding this increasingly popular financial technology (Fin-Tech). In addition, it can be anticipated that the MEPA service providers can widen their service effectiveness according to the users’ opinion reflected in this study. Furthermore, policymakers involved with Fin-Tech can also formulate and implement effective policies and guidelines based on the empirical outcomes.

This study aims to investigate the dynamic relationship between renewable and non-renewable energy sources on the economic growth of eight countries, the capital stock and labour force being used as control variables in each case. Questions that need to be asked include the following: Is there is an asymmetric and, hence, a non-linear relationship between variables? If yes, how does economic growth interact with both renewable and non-renewable energy consumption (EC)? How different are these relationships in the countries highly rated in the performance of renewable EC compared to those lowly rated?

This paper uses asymmetric quantile-based methods to extract possible asymmetric and, hence, non-linear relationships between the underlying variables.

A newly developed asymmetric panel quantile approach suggests that EC has a significant effect on economic growth in both directions of shocks as well as for the considered sample. The results further support the findings in recent literature on renewable energy deployment, given the importance of renewable EC for economic growth with the increased levels of renewable EC, although the initial investments may have a negative effect on economic growth for some countries.

This study contributes to the literature in twofold. Firstly, it aims to contribute to the ongoing debate in literature by incorporating both renewable and non-renewable energy resources in the production function with labour and capital to test their asymmetric impact on economic growth. Secondly, this paper uses asymmetric quantile-based methods to extract possible asymmetric and, hence, non-linear relationships between the underlying variables. Another point that should be emphasised in this study is the need for studies analysing economic growth and EC for a sample of G20 countries based on a comparative view for the renewable and non-renewable EC in literature.

This study aims to revisit the empirical debate about the asymmetric relationship between oil prices, energy consumption, CO₂ emissions and economic growth in a panel of 184 countries from 1981 to 2020.

A relatively new research method, the PVAR system GMM, is applied.

The outcome of the PVAR system GMM model at the group level in the study suggests that oil prices exert a positive but statistically insignificant effect on economic growth. Energy consumption is inversely related to economic growth but statistically significant, and the correlation between CO₂ emissions and economic growth is negative but statistically insignificant. The Granger causality test indicates that oil prices, CO₂ emissions, oil rents, energy consumption and savings jointly Granger-cause economic growth. A unidirectional causality runs from energy consumption, savings and economic growth to oil prices. At countries’ income grouping levels, oil prices, oil rent, CO₂ emissions, energy consumption and savings jointly Granger-cause economic growth for the high-income and upper-middle-income countries groups only, while those variables did not jointly Granger-cause economic growth for the low-income and lower-middle-income countries groups. The modulus emanating from the eigenvalue stability condition with the roots of the companion matrix indicates that the model is stable. The results support the asymmetric impacts of oil prices on economic growth and aid policy formulation, particularly the cross-country disparities regarding the nexus between oil prices and growth.

From a methodological perspective, to the best of the author’s knowledge, the study is the first attempt to use the PVAR system GMM and such a large sample group of 184 economies in the post-COVID-19 era to examine the impacts of oil prices on countries’ growth while controlling for other crucial variables, which is noteworthy. Two, using the World Bank categorisation of countries according to income groups, the study adds another layer of contribution to the literature by decomposing the 184 sample economies into four income groups: high-income, low-income, upper-middle-income and lower-middle-income groups to investigate the potential for asymmetric effects of oil prices on growth, the first of its kind in the post-COVID-19 period.

The liberalization initiative commenced in India from 1991 onwards, replacing the four-decade long import substitution policy. The primary objective was to enhance the role of foreign and private investment, in line with the newly embraced outward-oriented growth model. The government had undertaken several policy initiatives since then, especially to strengthen the manufacturing sector which plays an important role in the economic development of any country. The current study evaluates the effects of the liberalization policy in India on industrial outcomes. Recent studies have found that when firm heterogeneity is present in trade models, reforms will lead to a decrease in the number of firms and a rise in their average size (Melitz, 2003). A dataset of 24 manufacturing industries had been used in the current study. We test empirically whether liberalization had led to a rise in the average size of establishments as stated in the literature. We also attempt to analyze the magnitude of trade costs in terms of the impact of reforms on wages and prices. The empirical analysis based on the difference-in-difference (DID) estimation method shows that on average, trade reforms do not lead to an increase in the real wages and average size of establishments. In addition, prices appear to increase in the long run due to liberalization, with potential ramifications.

Decision-making, reinforced by artificial intelligence (AI), is predicted to become potent tool within the domain of supply chain management. Considering the importance of this subject, the purpose of this study is to explore the triggers and technological inhibitors affecting the adoption of AI. This study also aims to identify three-dimensional triggers, notably those linked to environmental, social, and governance (ESG), as well as technological inhibitors.

Drawing upon a six-step systematic review following the preferred reporting items for systematic reviews and meta analysis (PRISMA) guidelines, a broad range of journal publications was recognized, with a thematic analysis under the lens of the ESG framework, offering a unique perspective on factors triggering and inhibiting AI adoption in the supply chain.

In the environmental dimension, triggers include product waste reduction and greenhouse gas emissions reduction, highlighting the potential of AI in promoting sustainability and environmental responsibility. In the social dimension, triggers encompass product security and quality, as well as social well-being, indicating how AI can contribute to ensuring safe and high-quality products and enhancing societal welfare. In the governance dimension, triggers involve agile and lean practices, cost reduction, sustainable supplier selection, circular economy initiatives, supply chain risk management, knowledge sharing and the synergy between supply and demand. The inhibitors in the technological category present challenges, encompassing the lack of regulations and rules, data security and privacy concerns, responsible and ethical AI considerations, performance and ethical assessment difficulties, poor data quality, group bias and the need to achieve synergy between AI and human decision-makers.

Despite the use of PRISMA guidelines to ensure a comprehensive search and screening process, it is possible that some relevant studies in other databases and industry reports may have been missed. In light of this, the selected studies may not have fully captured the diversity of triggers and technological inhibitors. The extraction of themes from the selected papers is subjective in nature and relies on the interpretation of researchers, which may introduce bias.

The research contributes to the field by conducting a comprehensive analysis of the diverse factors that trigger or inhibit AI adoption, providing valuable insights into their impact. By incorporating the ESG protocol, the study offers a holistic evaluation of the dimensions associated with AI adoption in the supply chain, presenting valuable implications for both industry professionals and researchers. The originality lies in its in-depth examination of the multifaceted aspects of AI adoption, making it a valuable resource for advancing knowledge in this area.