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This study aims to delve into the interplay between didactic audiovisual translation (DAT) and computer-assisted language learning (CALL), exploring their combined impact on the development of intercultural competence (IC) among learners of English as a foreign language (EFL).

Using a quasi-experimental approach with a quantitative research design, the study analyses the outcomes of a questionnaire answered by 147 students across 15 language centres in Spanish Universities. These participants actively engaged in completing the lesson plans of the Traducción audiovisual como recurso didáctico en el aprendizaje de lenguas extranjeras project, a Spanish-Government funded research initiative aimed at assessing the effects of DAT on language learning.

The current study confirms the reliability of the instrument developed to measure students’ perceived improvement. Beyond validating the research tool, the findings of the current study confirm the significant improvement in intercultural learning achieved through DAT, effectively enhancing students’ motivation to engage in language learning.

The current research solely examines students enrolled in higher education language centres. This paper closes with a CALL for additional research, including participants from other educational stages, such as primary or secondary education. In the broader context of CALL research, this study serves as a valuable contribution by exploring the potential of DAT in fostering IC in EFL settings.

This research confirms the potential of DAT and CALL to promote students’ learning process, as the combination of these approaches not only yields linguistic benefits but also intercultural learning.

This paper aims to identify the relevant contributing constructs of readiness for the implementation of intelligent contracts (iContracts) in the construction industry. This study investigates the relationship between the personality dimensions of technology readiness index (TRI) and the system specific factors of technology acceptance model (TAM) within the context of iContracts.

Drawing insights from the extant literature and the author's previous qualitative investigations into iContract readiness constructs, a quantitative approach is used to operationalise the constructs by offering relevant statements to be measured and validated through a multiple-item scale against the users intent to accept the future iContract technology.

This study confirms and validates the relationship of the proposed iContract readiness index (iCRI) statements against the established TAM factors by offering 18 new constructs influencing technology readiness of the iContract technology. This study proves 9 of the 12 hypotheses highlighting key factors to be addressed for the successful development of the iContract technology.

This paper contributes to the body of knowledge by proposing a novel iCRI that informs an iContract technology readiness acceptance model (iCTRAM) for a trending technology. The iCTRAM can guide developers in producing an appropriate iContract solution and assess the readiness of users and organisations for the successful adoption of the iContract concept.

This study offers a unique theoretical framework, in an embryonic field, for predicting the success of iContract implementation within construction organisations. This study combines the established studies of TRI and TAM in producing a predictive iContract readiness assessment tool.

This study aims to identify an automated machine learning algorithm with high accuracy that sport practitioners can use to identify the specific factors for predicting Major League Baseball (MLB) attendance. Furthermore, by predicting spectators for each league (American League and National League) and division in MLB, the authors will identify the specific factors that increase accuracy, discuss them and provide implications for marketing strategies for academics and practitioners in sport.

This study used six years of daily MLB game data (2014–2019). All data were collected as predictors, such as game performance, weather and unemployment rate. Also, the attendance rate was obtained as an observation variable. The Random Forest, Lasso regression models and XGBoost were used to build the prediction model, and the analysis was conducted using Python 3.7.

The RMSE value was 0.14, and the R2 was 0.62 as a consequence of fine-tuning the tuning parameters of the XGBoost model, which had the best performance in forecasting the attendance rate. The most influential variables in the model are “Rank” of 0.247 and “Day of the week”, “Home team” and “Day/Night game” were shown as influential variables in order. The result was shown that the “Unemployment rate”, as a macroeconomic factor, has a value of 0.06 and weather factors were a total value of 0.147.

This research highlights unemployment rate as a determinant affecting MLB game attendance rates. Beyond contextual elements such as climate, the findings of this study underscore the significance of economic factors, particularly unemployment rates, necessitating further investigation into these factors to gain a more comprehensive understanding of game attendance.

This study aims to investigate the factors associated with the intention to use information technology in audit (ITIA) in Iraq.

The study uses a quantitative approach based on a questionnaire survey of 186 respondents. The study population includes respondents who are board members, senior executives, internal auditors and information technology (IT) assistants in various Iraqi organizations from different sectors. Structural equation modeling has been used to estimate the results.

The findings exhibit that most auditors in Iraq use basic IT software. However, among several specialized and advanced IT audit software packages, only generalized audit software is used by about 20%. The results also indicate that social factors significantly and positively impact auditors’ and practitioners’ perceptions of ITIA use. Moreover, the results reveal that companies and auditors who use or audit complex accounting systems perceive higher benefits and intent to adopt ITIA. However, the results report that organizational support, professional support, competency and IT education have an insignificant effect on ITIA adoption.

The originality of the present research lies in several aspects. First, the research study focuses specifically on Iraq, which is an emerging and less developed country influenced by social and economic. This research context provides a unique perspective and contributes to the understanding of ITIA adoption in less developed countries. The study investigates how external factors, including social and external pressure and the support of government professional bodies, affect the adoption of ITIA. Further, it assesses the influence of firms’ specific factors such as management support, level of competency and complexity of accounting information systems. Second, the study uses a quantitative approach with a questionnaire survey from various Iraqi organizations and sectors. The specific sample composition adds originality by capturing insights from different levels of organizational hierarchy and diverse professional backgrounds. Third, the findings shed light on the current IT usage in auditing practices in Iraq, highlighting that most auditors use basic IT software and the limited adoption of specialized IT audit software packages. Finally, the study’s originality is also reflected in its contribution to expanding knowledge on the perceived benefits and challenges associated with ITIA adoption in less developed countries. By emphasizing the need for broader awareness of emerging technology-enabled auditing software and considering the unique characteristics of less developed countries, the research provides valuable insights and implications for practitioners, policymakers and researchers.

The purpose of this study is to investigate the factors affecting behaviour intention (BI) to use and actual usages of investment-related FinTech services among the zoomers (Gen Z) and millennials (Gen M) retail investors of India.

The study explores the predictive relevance of actual adoption behaviour among the two different age categories of Indian retail investors. It uses the Unified Theory of Acceptance and Use of Technology-2 and the prospect theory framework as guiding frameworks. Data has been collected from 294 retail investors, actively engaged in the investment-related FinTech services. The multi-group analysis using variance-based partial least square structured equation modelling has been used to compare the two groups. The invariance between the two groups was achieved through measurement invariance assessment.

The study reveals distinct factors significantly affecting BI to use investment-related FinTech services among Gen Z and Gen M retail investors are performance expectancy (PE) to BI, perceived risk (PR) to BI, price value (PV) to BI and PR to service trust (ST).

This study provides insights for financial providers and policymakers, emphasizing different factors influencing BI to use investment-related FinTech services in both age groups. Notably, habit emerges as a common factor influencing the actual usage of investment-related FinTech services across Gen M and Gen Z retail investors in India.

This study explores the heterogeneous behaviour of the heterogenous population in the domain of technological adoption of investment-related FinTech services in India.

This study is the first to investigate the causal relationship between Bitcoin and equity price returns by sectors. Previous studies have focused on aggregated indices such as S&P500, Nasdaq and Dow Jones, but this study uses mixed frequency and disaggregated data at the sectoral level. This allows the authors to examine the nature, direction and strength of causality between Bitcoin and equity prices in different sectors in more detail.

This paper utilizes an Unrestricted Asymmetric Mixed Data Sampling (U-AMIDAS) model to investigate the effect of high-frequency Bitcoin returns on a low-frequency series equity returns. This study also examines causality running from equity to Bitcoin returns by sector. The sample period covers United States (US) data from 3 Jan 2011 to 14 April 2023 across nine sectors: materials, energy, financial, industrial, technology, consumer staples, utilities, health and consumer discretionary.

The study found that there is no causality running from Bitcoin to equity returns in any sector except for the technology sector. In the tech sector, lagged Bitcoin returns Granger cause changes in future equity prices asymmetrically. This means that falling Bitcoin prices significantly influence the tech sector during market pullbacks, but the opposite cannot be said during market rallies. The findings are consistent with those of other studies that have established that during market pullbacks, individual asset prices have a tendency to decline together, whereas during market rallies, they have a tendency to rise independently. In contrast, this study finds evidence of causality running from all sectors of the equity market to Bitcoin.

The findings have significant implications for investors and fund managers, emphasizing the need to consider the asymmetric causality between Bitcoin and the tech sector. Investors should avoid excessive exposure to both Bitcoin and tech stocks in their portfolio, as this may lead to significant drawdowns during market corrections. Diversification across different asset classes and sectors may be a more prudent strategy to mitigate such risks.

The study's findings underscore the need for investors to pay close attention to the frequency and disaggregation of data by sector in order to fully understand the true extent of the relationship between Bitcoin and the equity market.

Might the impact of the financial stress on exchange markets be asymmetric and exposed to regime changes? Departing from the existing literature, highlighting that the domestic and foreign financial stress in terms of money market have substantial effects on exchange market, this paper aims to investigate the impacts of the bond yield spreads of three emerging countries (Mexico, Russia, and South Korea) on their exchange market pressure indices using monthly observations for the period 2010:01–2019:12. Additionally, the paper analyses the impact of bond yield spread of the US on the exchange market pressure indices of the three mentioned emerging countries. The authors hypothesized whether the negative and positive changes in the bond yield spreads have varying effects on exchange market pressure indices.

To address the research question, we measure the bond yield spread of the selected countries by using the interest rate spread between 10-year and 3-month treasury bills. At the same time, the exchange market pressure index is proxied by the index introduced by Desai et al. (2017). We base the empirical analysis on nonlinear vector autoregression (VAR) models and an asymmetric quantile-based approach.

The results of the impulse response functions indicate that increases/decreases in the bond yield spreads of Mexico, Russia and South Korea raise/lower their exchange market pressure, and the effects of shocks in the bond yield spreads of the US also lead to depreciation/appreciation pressures in the local currencies of the emerging countries. The quantile connectedness analysis, which allows for the role of regimes, reveals that the weights of the domestic and foreign bond yield spread in explaining variations of exchange market pressure indices are higher when exchange market pressure indices are not in a normal regime, indicating the role of extreme development conditions in the exchange market. The quantile regression model underlines that an increase in the domestic bond yield spread leads to a rise in its exchange market pressure index during all exchange market pressure periods in Mexico, and the relevant effects are valid during periods of high exchange market pressure in Russia. Our results also show that Russia differs from Mexico and South Korea in terms of the factors influencing the demand for domestic currency, and we have demonstrated the role of domestic macroeconomic and financial conditions in surpassing the effects of US financial stress. More specifically, the impacts of the domestic and foreign financial stress vary across regimes and are asymmetric.

This study enriches the literature on factors affecting the exchange market pressure of emerging countries. The results have significant economic implications for policymakers, indicating that the exchange market pressure index may trigger a financial crisis and economic recession.

This study aims to synthesize and conceptualize the highly fragmented yet important literature on racial discrimination in entrepreneurship.

A bibliometric analysis and literature review were performed that involved 523 articles containing 26,926 references.

The bibliometric analysis identified three dominant research themes that comprehensively illustrate the state of research in this domain: strategic, sociocultural and individual-level perspectives. The synthesis of extant literature helped in formulating a holistic conceptual model that portrays the genuineness of racial discrimination in entrepreneurship. The sources, factors and impact of racial discrimination faced by entrepreneurs were identified. Based on the review and analysis of keywords, certain fruitful future research directions were formulated that will take the field forward.

This work is the first attempt to review the literature that narrows down the focus to racial discrimination in entrepreneurship (from other discriminations such as gender, cultural and religious discrimination) as one form of discrimination due to its unique origins and consequences.

Developing a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.

Due to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021–2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).

This research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes ($100,000-$300,000, $300,000-$500,000, $500,000-$1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.

The current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.

This research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.

This study aims to explore the relationship between risk governance characteristics (chief risk officer [CRO], chief financial officer [CFO] and senior directors [SENIOR]) and regulatory adjustments (RAs) in Organization for Economic Cooperation and Development public commercial banks.

Using principal component analysis (PCA) and regression models, the research analyzes a representative data set of these banks.

A significant negative correlation between risk governance characteristics and RAs is found. Sensitivity analysis on the regulatory Tier 1 capital ratio and the total capital ratio indicates mixed outcomes, suggesting a complex relationship that warrants further exploration.

The study’s limited sample size calls for further research to confirm findings and explore risk governance’s impact on banks’ capital structures.

Enhanced risk governance could reduce RAs, influencing banking policy.

The study advocates for improved banking regulatory practices, potentially increasing sector stability and public trust.

This study contributes to understanding risk governance’s role in regulatory compliance, offering insights for policymaking in banking.