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This paper aims to empirically investigate the dynamic interlinkages among globalization, digitalization and economic development in the top 75 most globalized countries from 2000 to 2019. The selection of the 75 most globalized developing countries is based on the overall scores of the KOF Globalization Index (2021).

The research design is based on secondary data collected from the World Bank (2021), the International Telecommunication Union (2021) and the KOF Globalization Index (2021). The study uses panel unit root tests followed by the panel cointegration techniques. Further, the estimation uses panel fully modified ordinary least squares and panel dynamic ordinary least squares methods.

The empirical results reveal that the effect of globalization on economic development is sensitive to different estimation procedures; in some cases, but not in every case, the effect is positive and significant. However, the positive and significant effect of digitalization on economic development is robust across all estimated models. Long-run equilibrium relationships and bidirectional causalities strongly affirm the nexus among globalization, digitalization and economic development, substantiating the interconnectedness among 75 developing economies.

The study reinstates that the forces of globalization and digitalization will be instrumental in shaping the selected most globalized economies in the long run. Adopting various econometric methodologies takes care of the time-specific and cross-sectional dynamics, as evident in the panel framework considered in this study. The empirical findings truly ascertain the theoretical synergy among the forces of globalization leading to more digitalization and economic development. This makes the empirical interplay highly conducive to framing long-term policies to expand the information communication network in terms of its access and reach.

This study aims to comprehensively inspect the impact of blockchain technology in the context of globalization, focusing on the most influential research works, scientific production by countries, commonly used keywords, trending topics and the conceptual and intellectual structure of this field.

A bibliometric analysis was conducted in R Studio, involving the review and scrutiny of 194 research papers indexed in the Scopus database. This analytical approach allowed for a systematic examination of the literature and the identification of key patterns and trends in blockchain and globalization research.

The analysis revealed the most influential research contributions, highlighted countries with significant scientific production, identified frequently used keywords and unveiled emerging trends and areas of focus in the field of blockchain and digitization. Additionally, the study explores prospective research directions.

This research provides insights into the current state of blockchain and globalization research, offering valuable information for scholars, policymakers and industry professionals. The recognized themes provide a systematic structure for upcoming inquiries, directing studies toward vital domains like supply chain management and sustainable development. Moreover, the analysis emphasizes the significance of interdisciplinary collaboration and provides practical insights, enabling businesses and policymakers to enhance transparency, counteract fraud and adjust to the digital era for societal and economic advancement.

The study contributes to the prevailing literature by steering a comprehensive analysis of the interplay between blockchain technology and globalization, shedding light on influential works and emerging trends. It adds value by providing a structured overview of the field's intellectual landscape.

There is an increasing shift toward cashless societies worldwide, with electronic payment networks at the forefront of facilitating this transition. The purpose of this research is to explore the critical role of domestic payment networks and to propose recommendations for the effective implementation of such networks.

This research paper uses a multiple-case study design, informed by global best practices in domestic payment systems. Using thematic and content analysis methodologies, this research rigorously analyzes secondary data sources to investigate the strategic importance of domestic payment networks to national economies and the motivations driving their developments.

This paper illuminates the role of domestic payment networks in advancing cost-effective transactions, enhancing financial inclusion and safeguarding national sovereignty. It highlights the growing trend among nations to prioritize the development of their own payment systems. The research further explores the strategic initiatives undertaken by governments to prefer domestic over multinational networks, thereby maintaining control over their financial systems and safeguarding economic interests. Additionally, the study addresses the challenges these networks face, providing a thorough analysis that serves as insight for policymakers and financial institutions aiming to develop and improve their domestic payment infrastructures amidst current and future challenges.

This study contributes to the existing literature on domestic payment networks by studying their significance within the global financial ecosystem, particularly highlighting their role in advancing financial inclusion and ensuring national financial sovereignty. This research paper uses competition state theory as a foundation for its arguments and provides policy and practical recommendations for policymakers and financial institutions. Through this synthesis, the research aims to facilitate the enhancement and strategic development of domestic payment infrastructures globally.

The purpose of this study is to comprehensively examine the ethical implications surrounding generative artificial intelligence (AI).

Leveraging a novel methodological approach, the study curates a corpus of 364 documents from Scopus spanning 2022 to 2024. Using the term frequency-inverse document frequency (TF-IDF) and structural topic modeling (STM), it quantitatively dissects the thematic essence of the ethical discourse in generative AI across diverse domains, including education, healthcare, businesses and scientific research.

The results reveal a diverse range of ethical concerns across various sectors impacted by generative AI. In academia, the primary focus is on issues of authenticity and intellectual property, highlighting the challenges of AI-generated content in maintaining academic integrity. In the healthcare sector, the emphasis shifts to the ethical implications of AI in medical decision-making and patient privacy, reflecting concerns about the reliability and security of AI-generated medical advice. The study also uncovers significant ethical discussions in educational and financial settings, demonstrating the broad impact of generative AI on societal and professional practices.

This study provides a foundation for crafting targeted ethical guidelines and regulations for generative AI, informed by a systematic analysis using STM. It highlights the need for dynamic governance and continual monitoring of AI’s evolving ethical landscape, offering a model for future research and policymaking in diverse fields.

The study introduces a unique methodological combination of TF-IDF and STM to analyze a large academic corpus, offering new insights into the ethical implications of generative AI across multiple domains.

The impact of digitalisation on smaller firms remains sparsely studied across emerging economies. The paper aims to examine the relationship between digital adoption and multiple performance parameters of micro, small and medium enterprises (MSME) in a prominent emerging economy.

The study employs data from the World Bank Enterprise Survey (WBES) 2022, capturing 9,024 Indian MSME firms spread across the country. Performance indicators are derived from growth in sales, employment and labour productivity (LPROD). Multiple regression estimates are derived that also correct for sample selection bias using Heckman’s two-step process.

Digital proliferation is found to increase as firms mature up in terms of age, size and constitution. A significant difference could also be observed in business performance across digital and non-digital businesses, with sales growth (SG) and productivity higher for digital firms. Digital financial variables are found to have a significant impact on SG but not as much in the case of employment growth and LPROD. The results are robust to correction for sample selection bias in digital adoption using inverse mills ratio (IMR).

The study highlights digital adoption gaps across various strata of MSMEs, highlighting lower adoption when firms are younger, smaller and lacking formal constitutional setup. Digital variables indicating positive association with SG highlight the need for concerted efforts at the public policy level for building appropriate skills and infrastructure for micro and small enterprises to boost their digital adoption to promote growth.

There is a lack of micro-level empirical evidence measuring the impact of advanced digital technology usage on multiple aspects of enterprise performance amongst micro and small firms. The study deploys unique digital variables including TReDS and use of online credit applications to assess the impact on business performance. The findings provide insights for practice and public policy, besides making the case for a higher focus on launching digital initiatives for smaller enterprises.

One of the achievements of the fourth industrial revolution is smart manufacturing, a manufacturing system based on Industry 4.0 technologies that will increase systems' reliability, efficiency and productivity. Despite the many benefits, some barriers obstruct the implementation of this manufacturing system. This study aims to analyze these barriers.

One of the measures that must be taken is to identify and try to remove these barriers, which involves identifying the stakeholders and components of technology associated with each barrier. As such, the primary purpose of this paper is to present a systematic literature review in the field of smart manufacturing with a focus on barriers to implementation related to the stakeholders and components of technology.

This research conducted a systematic literature review in Scopus and Web of Science databases and considered the studies published until 2021 were examined. The central question of this paper is answered based on this literature review, in which 133 related studies and 15 barriers were identified.

The significant gap observed in the literature review is that no research has been conducted to determine the stakeholders and components of technology related to the barriers, making it a potentially worthwhile subject for future research. In addition, the results of this study may help managers to implement smart manufacturing.

This study provides two main originalities. The former is helpful information for managers to make effective decisions when they face smart manufacturing barriers. The latter is related to identifying critical research gaps through systematic literature review.

This study aims to explore the factors affecting investment behaviour in cryptocurrencies among Jordanian investors. Specifically, it aims to assess how various motivational and behavioural drivers impact the intention to use cryptocurrencies, grounded in the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. The choice of Jordan as the research context is particularly relevant due to the lack of adequate regulations on cryptocurrency investment.

This study uses a quantitative research approach, using an online survey as the primary method for data collection. The final data set consists of 285 responses collected through a self-administered questionnaire to cryptocurrency users in Jordan. Next, structural equation modelling (SEM) was used to test the developed theoretical framework based on the UTAUT2 model.

The findings reveal that performance expectancy, trust, hedonic motivation and price value significantly enhance the intention to invest in cryptocurrencies, with performance expectancy acting as a mediator. Effort expectancy is not directly related to behavioural intention; however, it positively impacts performance expectancy, validating the mediation hypothesis. Trust affects both the intention to use and the performance expectancy, reinforcing its role as a mediator in cryptocurrency adoption. Hedonic motivation and price value also positively affect the intention to use cryptocurrency. In contrast, social influence and facilitating conditions do not significantly impact behavioural intention, suggesting that cryptocurrency adoption decisions are less influenced by external opinions or the availability of necessary conditions. The findings also show that the demographic profiles of the cryptocurrency users were young, educated males, which suggests a demographic skew in cryptocurrency usage in Jordan.

This study innovatively adapts the UTAUT2 model, focusing on the mediating role of performance expectancy between effort expectancy, trust, and behavioural intention. This study pioneers by examining the mediation effect of performance expectancy, showing how users' ease in using cryptocurrencies positively affects their belief in positive outcomes, subsequently influencing their behavioural intention to use cryptocurrencies. Moreover, this study sheds light on the factors driving cryptocurrency adoption in developing countries like Jordan. It also underscores the demographic trends in cryptocurrency use and proposes targeted recommendations for policymakers and cryptocurrency platforms to foster more inclusive and informed investment environments.

Bank–fintech associations are significant globally, establishing purposeful eco-systems towards extending and complementing capabilities, reach and customer experiences. This paper aims to explore 39 leading fintechs in India catering across payments, lending, wealth management, regulation, neo-banks and other banking functions. Alongside fintechs, the research studies 19 leading banks (public and private) to understand the nature of bank–fintech associations in the Indian context.

The research focuses on narratives from leading banks and top fintechs in India, captured from public disclosures and leadership interviews. The study leverages qualitative research techniques, including grounded theory approaches of inductive analysis, to codify interview and narrative observations to discover relevant objectives, scenarios, challenges and outcomes in India-centric bank–fintech associations.

Bank–fintech associations in India are increasingly focusing on financial services portfolio diversification and improvement in customer experience. Simultaneously, both banks and fintechs, differentiate with innovations and extend offerings to target underserved customer segments. The associations are beneficial for both banks and fintechs in transforming offerings and improving efficiency, scale across channels. Through codification of observations, review of existing literature and evaluation of best practices, alongside subject matter expertise, the study evolves a generalized “Association Model”. The model can steer meaningful bank–fintech associations in India and globally. The association model relates to observables like objectives, enablers of bank–fintech associations, challenges and association-driven value outcomes. Built from study of practices, the proposed model is relevant for strategic orientation in bank–fintech associations.

The findings reveal practices in bank–fintech associations in India with significant learning opportunity for organizational leaders globally. Understanding the nature of association is relevant for strategic interventions, particularly in scenarios of inter-organization collaborations. Central banks, policymakers, governments, investors, banks and fintechs can use the derived association model to establish, govern and steer purposeful value-driven associations.

The study aims to investigate the impact of industrial robot application on corporate labor cost stickiness and labor investment efficiency in China.

Using the textual analysis to construct firm-level industrial robot application indicators in China, we implement the methodology in Anderson et al. (2003) and Banker and Byzalov (2014) to estimate cost stickiness.

We argue that the industrial robot uses in China would increase firms’ labor adjustment costs by increasing the employment scale and upgrading the employment structure (i.e. by employing more high-skilled and high-educated labor). Consistent with our expectation through the channel of labor adjustment costs, the use of robotics increases firms’ labor cost stickiness. We further find that the positive impact is more significant among labor-intensive industries, and among state-owned enterprises with lower labor adjustment flexibility. We also find that industrial robot uses do not decrease the labor cost stickiness even when robots are more likely to substitute labor. Finally, we find that industrial robot uses significantly facilitate more efficient hiring practices by mitigating overinvestment in labor (i.e. over-hiring).

Against the backdrop of intelligent manufacturing worldwide, our study sheds new insight into the effects of new technologies on corporate labor cost behavior in developing countries. We contribute to scant studies examining how robotics, AI adoption or other automation technologies (e.g. specialized machinery, software, etc.) affect corporate cost behavior.

Data analytics (DA) is an emerging topic in management science at large organizations; however, accountants in small and medium-sized enterprises (SMEs) are believed to be lagging far behind in the usage of DA. This study aims to provide a deep understanding of the actual DA activities undertaken by management accountants (MA) and to draw on SMEs’ characteristics to take advantage of DA applications.

This study used a qualitative approach by conducting in-depth interviews with 31 accounting and finance practitioners at senior levels of SMEs, following the use of the MAXQDA 2022 application for data analysis.

Findings in this study suggest variance and trend analysis as the two most popular tasks of DA, and advanced tasks such as contingency analysis, financial modeling, sentiment analysis and regression analysis are unfully performed in SMEs. The outcomes revealed that DA is not anticipated to affect the responsibilities but to expand the role and scope of management accounting.

This study simultaneously builds a theoretical framework about the antecedents, in terms of the external and internal drives and the characteristics of SMEs’ owner-managers, that are most common in all types of SMEs that encourage MA to use a specific technology, data analysis in a more advanced way instead of searching for determinants that affect the adoption of technology in general, as previous studies have conducted.

Although there are studies on DA usage, little has approached the mutual interconnections between how DA is applied by MA in SMEs and what changes in management accounting responsibilities by DA affect. Therefore, the point of this study was to look into how SMEs use DA and what activities MA actually do with DA to discover what traits SMEs need to use DA applications effectively as DA applications become more advanced.