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Built asset management processes require a long transition period to collect, edit and update asset conditions information from existing data sets. This paper aims to explore and explain whether and how digital technologies, including asset information model (AIM), Internet of Things (IoT) and blockchain, can enhance asset conditions assessment and lead to better asset management.

Mixed methods are applied to achieve the research objective with a focus in universities. The questionnaire aims to test whether the integration of AIM, IoT and blockchain can enhance asset condition assessment (ACA). Descriptive statistical analysis was applied to the quantitative data. The mean, median, mode, standard deviation, variance, skewness and range of the data group were calculated. Semi-structured interviews were designed to answer how the integration of AIM, IoT and blockchain can enhance the ACA. Quantitative data was analysed to define and explain the essential factors for each sub-hypothesis. Meanwhile, to strengthen the evaluation of the research hypothesis, the researcher also obtained secondary data from the literature review.

The research shows that the integration of AIM, IoT and blockchain strongly influences asset conditions assessment. The integration of AIM, IoT and blockchain can improve the asset monitoring and diagnostics through its life cycle and in different aspects, including financial, physical, functional and sustainability. Moreover, the integration of AIM, IoT and blockchain can enhance cross-functional collaboration to avoid misunderstandings, various barriers and enhance trust, communication and collaboration between the team members. Finally, costs and risk could be reduced, and performance could be increased during the ACA.

The contribution of this study indicated that the integration of AIM, IoT and blockchain application in asset assessment could increase the efficiency, accuracy, stability and flexibility of asset assessment to ensure the reliability of assets and lead to a high-efficiency working environment. More importantly, a key performance indicator for ACA based on the asset information, technology and people experience could be developed gradually.

This study can break the gap between transdisciplinary knowledge to improve the integration of people, technology (AIM, IoT and blockchain) and process value-based ACA in built asset management within universities.

As the global financial ecosystem grapples with the complexities of modernization, blockchain technology emerges as a pivotal catalyst, offering the banking, financial services, and insurance (BFSI) industry unprecedented opportunities for secured digital transformation and enhanced customer trust. To gain a comprehensive understanding of blockchain technology adoption, this study aims to identify the factors and establish the contextual interrelationships among them.

The authors have identified the factors affecting blockchain technology adoption in BFSI industry through extensive literature review and experts’ interviews. After identification of factors, contextual relationship has been established based on experts’ opinion and total interpretive structural modeling (TISM) approach. Furthermore, factors are categorized into autonomous, dependent, linkage and driving variables using cross-impact matrix multiplication applied to classification analysis.

The TISM-based structural model is divided into eight different hierarchal levels in which Government support is placed on the lower most layer (level 8) which indicates that this is the most crucial factor in blockchain adoption. Further social influence and security are placed on seventh and sixth level in the hierarchy.

The results of this study will help the policymakers to direct the resources from the most crucial factor to other factors in the hierarchy as per their relevance. In essence, this study serves as a guiding compass, steering the course of blockchain technology adoption in the BFSI sector toward a more secure and digitally transformed future.

In the current landscape, blockchain technology remains in its nascent stage, leaving ample room for exploration and innovation. This study stands as the pioneering effort to comprehensively identify and establish the contextual relationships among the adoption factors of blockchain technology within BFSI industry. Through rigorous TISM analysis, this paper enriches the existing body of knowledge on blockchain technology adoption.

This study aims to explore how multinational lead buyers can play an active role in ensuring worker voices in garment supplier factories where workers have limited space to raise their voices, and how buyers’ involvement increases the possibilities of worker voices mitigating barriers to social dialogues and enhancing mutual interests of buyers and workers in garment factories.

Using a qualitative research approach and multiple embedded case study method, this study considered buyer−supplier dyads as the unit of analysis, i.e. two multinational lead buyers and their four corresponding suppliers in the garment industry of Bangladesh. Focus group discussion and key informant in-depth interviews were techniques applied to collect factory-level data, and within and cross-case analysis techniques were applied to develop an overall understanding.

The results of this study reveal that the opportunities for workers to voice their concerns through social dialogue in garment supplier factories are limited due to various obstacles. Similarly, the role of multinational lead buyers in addressing these issues is found to be less than ideal. This study also shows that buyers can take short-term and long-term initiatives to ensure social dialogues. Moreover, this study presents how social dialogues can meet the expectations of multinational buyers and their garment suppliers.

While this study focuses exclusively on the garment industry, similar scenarios also exist across a multitude of other industries. Thus, future research could extend this study’s scope to various sectors, providing a more comprehensive understanding of the general state of worker voices in Bangladesh. This study stands to make significant contributions to literature in the fields of global value chains, human relations and international business. It will pose critical perspectives on how upstream value chain suppliers can fortify worker rights through social dialogue, and elucidate the means and motives for lead buyers to play a more active role in this endeavour.

This study is distinct in its approach, integrating buyer−supplier roles to pave the way for enhanced worker voice opportunities through social dialogue in garment supplier factories.

The purpose of this study is to identify and assess the role of predictors to women’s economic empowerment (WEE). Moreover, the mediating role of digital banking usage (DBU) between financial literacy (FL) and WEE is empirically tested. The study also examines the moderation effect of educational level (EL) and employment sector (ES) on WEE.

Using a mixed-method approach, a comprehensive questionnaire was used to collect data of 482 women working in the formal ESs of Delhi-NCR. Partial least square structural equation modeling using SmartPLS-4 was used to test the explanatory and predictive power of the proposed model. This was followed by semi-structured interviews to collect qualitative data from 14 respondents.

The results present the following important findings: first, DBU, FL, women’s agency (WA) and workplace human resource policies (HR) significantly impact WEE, whereas government support (GS) and FL significantly impact DBU; second, DBU significantly mediates the relationship between FL and WEE; and third, ES significantly moderates the relationship between DBU and WEE.

This research also shares significant findings for practitioners and organizations by holistically identifying factors affecting WEE. These findings apply to both the human resource department of the employment sectors and the management of the banking sector.

The present study adds value to the scarce literature on the impact of DBU on WEE and highlights the mediating role of DBU along with the moderation effect of EL and ES. The study model incorporates novel constructs that impact WEE and offers new insights to various stakeholders in enhancing WEE. In addition, qualitative method was used to complement the quantitative findings.

Investors aim for returns when investing in stocks, making return volatility a crucial concern. This study compares symmetric and asymmetric GARCH models to forecast volatility in emerging nations like the G4 countries. Accurate volatility forecasting is vital for investors to make well-informed investment decisions, forming the core purpose of this study.

From January 1993 to May 2021, the study spans four periods, focusing on the global economic crisis of 2008, the Russian crisis of 2015 and the COVID-19 pandemic. Standard generalized autoregressive conditional heteroscedasticity (GARCH), exponential GARCH (E-GARCH) and Glosten-Jagannathan-Runkle GARCH models were employed to analyse the data. Robustness was assessed using the Akaike information criterion, Schwarz information criterion and maximum log-likelihood criteria.

The study's findings show that the E-GARCH model is the best model for forecasting volatility in emerging nations. This is because the E-GARCH model is able to capture the asymmetric effects of positive and negative shocks on volatility.

This unique study compares symmetric and asymmetric GARCH models for forecasting volatility in emerging nations, a novel approach not explored in prior research. The insights gained can aid investors in constructing more effective risk-adjusted international portfolios, offering a better understanding of stock market volatility to inform strategic investment decisions.

Given the growing emphasis among scholars, practitioners and policymakers on financial sustainability, this study aims to explore the applicability of machine learning techniques in predicting the financial performance of microfinance institutions (MFIs).

This study gathered 9,059 firm-year observations spanning from 2003 to 2018 from the World Bank's Mix Market database. To predict the financial performance of MFIs, the authors applied a range of machine learning regression approaches to both training and testing data sets. These included linear regression, partial least squares, linear regression with stepwise selection, elastic net, random forest, quantile random forest, Bayesian ridge regression, K-Nearest Neighbors and support vector regression. All models were implemented using Python.

The findings revealed the random forest model as the most suitable choice, outperforming the other models considered. The effectiveness of the random forest model varied depending on specific scenarios, particularly the balance between training and testing data set proportions. More importantly, the results identified operational self-sufficiency as the most critical factor influencing the financial performance of MFIs.

This study leveraged machine learning on a well-defined data set to identify the factors predicting the financial performance of MFIs. These insights offer valuable guidance for MFIs aiming to predict their long-term financial sustainability. Investors and donors can also use these findings to make informed decisions when selecting their potential recipients. Furthermore, practitioners and policymakers can use these findings to identify potential financial performance vulnerabilities.

This study stands out by using a global data set to investigate the best model for predicting the financial performance of MFIs, a relatively scarce subject in the existing microfinance literature. Moreover, it uses advanced machine learning techniques to gain a deeper understanding of the factors affecting the financial performance of MFIs.

The study aims to investigate the influence of corporate governance practices on enhancing firm value in manufacturing industries in Bangladesh.

The study sample consists of 131 companies from 10 manufacturing industries listed in Dhaka stock exchange (DSE). Using the multiple regression method, the study analyzed 1,193 firm-year observations from 2012 to 2021.

The outcome reveals that managerial ownership, foreign ownership, ownership concentration, board size, board independence, board diligence and auditor quality have a significant positive influence on firm value. In contrast, audit committee size has no significant influence on firm value.

The practical implications of the current study demonstrated that good corporate governance creates value and must be invigorated for the interest of all stakeholders. Policymakers should formulate specific guidelines regarding firms' ownership structure and audit quality issues.

Digitalization is one of the major factors that fosters economic growth across the world. However, the level of digitalization varies significantly between developed and developing countries, with the latter often lagging behind. To bridge this gap, it is crucial to pinpoint the drivers of digitalization, specifically from the macroeconomic and country-level governance dimensions. Therefore, this study aims to investigate the determinants of digitalization, particularly for countries in Asia and the Pacific region.

Our study utilizes unbalanced panel data from 46 Asian and Pacific countries for the period of 2001–2021. Initially, we analyzed the data using conventional econometric methods, such as pooled ordinary least squares (POLS), random-effects model (REM) and fixed-effects model (FEM). Moreover, we employed endogeneity-corrected techniques and alternative proxies to enhance the robustness and reliability of our findings.

Our findings reveal that economic development progress, government expenditure relative to country size and political stability are key drivers of digitalization. In contrast, corruption at the country level emerges as a significant impediment. Notably, our results remain robust to endogeneity-corrected techniques and alternative proxies of digitalization. Overall, these insights can inform policymakers, helping them to understand the macroeconomic and governance factors shaping digitalization and guide their decision-making toward effective policy interventions.

This study’s empirical findings add significant value to the existing literature by quantifying the impact of macroeconomic and governance factors on digitalization in selected countries. This offers valuable insights for policymakers, particularly in nations with lower levels of digitalization.

Entrepreneurship represents a complex decision-making process capable of influencing the conditions of a socio-economic system. For this reason, stimulating entrepreneurship is a topic that has always fascinated scholars and attracted the attention of public policy makers. This study, from the perspective of the theory of planned behaviour (TPB), aims to contribute to the analysis of entrepreneurial intention (EI) in university students. Factors such as entrepreneurship education (EE), mediation of personal attitude (PA), perceived behavioural control (PBC), EI, regulatory support (RS) and opportunity recognition (OR) for university students.

Research data was collected using a questionnaire, and a cross-sectional sample was selected from senior business and engineering students who are most likely to participate in entrepreneurial activities. The survey was conducted in the Delhi NCR region. 240 students were interviewed. Partial least square structural equation modelling using SmartPLS-4 was used to test the explanatory and predictive power of the proposed model.

The results of this study offer interesting contributions to the academic debate. First, EE has a significant impact on PA, PBC and entrepreneurial intentionality. Second, PBC, recognition of opportunities and EI have a significant impact on entrepreneurial education. Finally, PA and PBC significantly mediate the “entrepreneurial education – entrepreneurial intention” relationship.

Interesting elements of originality are offered by this study. First, entrepreneurship is studied as a decision-making process influenced by intentions and not behaviours. Second, the authors limited the efforts to unraveling the effect of the five variables on the formation of EI. Finally, the large size of the sample allows the authors to obtain significant results, directing future studies to other territorial contexts. Additionally, incorporating some control variables, such as gender and family background, would explore the relationship between the model variables more meaningfully.

Online shopping around the world is growing exponentially, especially during the COVID-19 pandemic. This study aims to examine how an online customer's purchasing experience influences his/her buying intention and willingness to believe in fraud news, as well as the ripple impact of satisfaction and trust, with gender as a moderator in an emerging economy during COVID-19.

Based on the underpinning of the stimulus-organism-behavior-consequence (SOBC) theory, the research model was developed, and collected data from 259 respondents using convenience samples technique. Next, the data were analyzed using partial least squares-based structural equation modeling (PLS-SEM), SPSS (Statistical Package for the Social Sciences) and Hayes Process Macro.

The study results confirmed that the online shopping experience (OSE) has positive impact on customers' satisfaction (CS), purchase intention (PI) and customer trust (CT); CS has positive effects on trust toward online shopping and their future product PI; future product PI significantly affects customers' propensity to believe and act on fraud news (PBAFN). The finding also states that gender moderates the relationships of CS to PI, OSE to PI and PI to PBAFN, but doesn't moderate the CT to PI relationship.

The study findings will assist policymakers and online vendors to win customers' hearts and minds' through confirming satisfaction, trust and a negative attitude toward fake news, which will lead to customer loyalty and the sustainable development of the industry. Finally, the limitations and future research directions are discussed.