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This study aims to investigate the organizational and individual factors that influence the adoption of artificial intelligence (AI) in Kuwait's public accounting sector.

The methodology of this study is a cross-sectional survey of 393 experienced accounting professionals, using partial least square structural equation modeling to analyze the data.

The findings show that organizational culture, regulatory support, perceived usefulness and ease of use have a direct positive effect on AI adoption, while perceived usefulness and ease of use also have an indirect positive effect through accounting profit and behavioral intention. However, the availability of resources, effective communication channels and competition pressure have an insignificant impact on AI adoption.

This study pioneers a structural framework to elucidate the perceived enhancement of accounting quality through AI system integration. Further, this research adds to the literature on AI adoption in accounting. This study also offers empirical evidence regarding how organizations in Kuwait's public accounting sector view AI systems in accounting.

The paper uses firm level data for the top listed firms in New York exchange stock over the period 2000–2017. The analysis is mainly based on 237 firms that already experienced losses at the end of the fiscal year. The study aims to use the properties of the dynamic panel data, specifically the methodology proposed by Arenllo and Bond (1991), to fulfill the objectives of the paper.

This paper focuses on the dividend policy management of the firms when they experience a loss at the end of the fiscal year. The objective is to examine how such a policy management affects the sustainability of the firm (measured by the future sales and total factor productivity[TFP]) and the wealth of its shareholders (measured by the Stock Returns).

The results show that the distressed firms that distribute dividends at the end of the loss period are able to maintain sustainability and to reach more favorable wealth situation of their shareholders relative to the firms who abstain to pay; the dividend policy during periods of loss is still able to send positive signals about the firm in the market; and the dividend policy can be considered as a predictive indicator for a sustainable firm whose shareholders can also predict their capital gains.

Agreed upon the literature that the firms during the period of crisis are likely to change their dividend policy, this study offers robust evidence that the dividend policy of distressed firms affects their sustainability (measured by sales and TFP) and the wealth status of their shareholders (measured by the Stock Returns).

This study aims to dissect firm knowledge into two main types: internal firm knowledge (knowledge workers) and external firm knowledge (relational knowledge with other firms). This study aims to investigate how each type affects the productivity of the firms. This study also examines how this effect differs among Egyptian firms in the agriculture, manufacturing and service sectors.

The authors use firm-level data in Egypt on the sectoral level. The properties of instrumental variables regression using two-stage least-squares estimation are adopted to overcome endogeneity and omitted variable bias in the empirical estimations.

The study’s findings reveal that the effects of internal and external knowledge on the firm productivity are sector-specific; knowledge-workers and relational knowledge are two times more effective for agriculture than manufacturing and service firms; external knowledge plays a vital role in increasing productivity relative to internal knowledge for the manufacturing sector; finally, internal and external knowledge has the same effect on the service firms.

This research adds to the body knowledge-based theory of the firm by examining the effects of internal and external knowledge on the firms’ productivity. In particular, the paper differentiates this effect across three sectors: agriculture, manufacturing and services. This paper also suggests a novel empirical methodology to address endogeneity and omitted variable bias in this literature of firm knowledge and productivity.

This paper examines the impact of cryptocurrency market on the stock market performance in Middle East and North Africa (MENA) region. A comparative analysis is extended to distinguish this impact between Gulf countries and other economies in the region.

The analysis uses the information of cryptocurrencies and the stock market indices of the Gulf countries for the period 2014–2018 on a daily basis. Two strategies have been implemented to fulfill the goal of the study: first, the tests strategy, which is applied using the cointegration analysis and panel-specific forms of Granger causality; second, the regression strategy, which is applied mainly using the instrument variable with generalized method of moments (IV-GMM) method.

The results show that there is a significant relationship between the cryptocurrency market and the stock market performance in the MENA region. On the one hand, for the Gulf countries that claim full obedience to the Islamic Sharia rules, each 1% increase in the cryptocurrency returns reduces the stock market performance by 0.15%. On the other hand, for the non-Gulf (other MENA) countries that have flexibility in applying the Islamic Sharia rules or do not follow it, the stock market performance increases by 0.13%, for each 1% increase in the cryptocurrency returns.

The paper proposes two main contributions: First, the paper introduces the cryptocurrency returns as one of the determinants of the stock market performance in the MENA region. This impact is distinguished based on the degree of applying the Islamic Sharia rules and the vision of the government to the stock market. Second, the paper provides an empirical guideline for governments in the MENA region for efficient measures in their stock market, given the important expansion of the cryptocurrency market and the government type.

This study aims to examine the nexus between digital financial literacy and customers’ perceived financial behavior within the Kuwaiti context. Moreover, it will further explore how digital financial literacy relates to financial behavior dimensions.

Data collection was facilitated by creating a questionnaire derived from multiple literature sources. This study used a cross-sectional, time-based dimension. Data was analyzed using the partial least square (PLS) structural equation modeling approach, using the Smart-PLS 4 software for computation.

Findings demonstrated a significant relationship between digital financial literacy and financial behavior, with a path coefficient of 0.542, a p-value of 0.000 and an R² value of 0.581. The explorative model revealed substantial relationships between many dimensions of digital financial literacy and various dimensions of financial behavior. More precisely, financial knowledge, awareness and decision-making were the factors that had the most significant impact on financial behavior.

Kuwaiti policymakers should consider including digital financial literacy programs in comprehensive financial education programs to improve public understanding of digital financial instruments and their consequences.

As the authors know, this is the initial endeavor to evaluate the relationship between digital financial literacy, financial behavior and their respective dimensions.

The purpose of this study is to investigate the relationship between the level of sustainability reporting [environmental, social and governance (ESG)] and sectorial energy performance across both developed and emerging economies.

Using data culled from 3,311 observations from 50 different countries over a ten-year period (2008–2017), an ESG-score-derived independent variable is regressed against dependent performance indicator variables (operation ratio, return on equity and Tobin’s Q). Two types of control variables complete the regression analysis in this study: firm-specific and macroeconomic.

The findings of this study elicited from the empirical results demonstrate that there is a significant relationship between ESG and operational performance (operation ratio). However, there is no significant relationship between ESG and financial performance (return on equity) and market performance (Tobin’s Q). However, the relationship between ESG and operation ratio is stronger in emerging than in developed economies.

The model in this study presents a valuable analytical framework for exploring sustainability reporting as a driver of performance across energy sectors in both developed and emerging economies. In addition, this study highlights energy-sectorial managerial implications contrasting developed, as juxtaposed with, emerging economies.

The purpose of this paper is to explore the relationship between organizational learning and patient safety culture in hospital pharmacy settings as determined by the learning organization survey short-form (LOS-27) and pharmacy survey on patient safety culture instruments, and to further explore how dimensions of organizational learning relate to dimensions of pharmacy patient safety culture.

This study is a cross-sectional study. Data were obtained from three public hospital pharmacies and three private hospital pharmacies in Kuwait. Partial least square structural equation modeling was used to analyze the data.

A total of 272 surveys (59.1 percent response rate) were completed and returned. The results indicated a significant positive relationship between organizational learning and patient safety culture in hospital pharmacy settings (path coefficient of 0.826, p-value <0.05 and R² of 0.683). Several dimensions of the organizational learning showed significant links to the various dimensions of the pharmacy patient safety culture. Specifically, training (TRN), management that reinforces learning (MRL) and supportive learning environment (SLE) had the strongest effects on the pharmacy patient safety culture dimensions. Moreover, these effects indicated that MRL, SLE and TRN were associated with improvements in most dimensions of pharmacy patient safety culture.

To the best of the authors’ knowledge, this is the first attempt to assess the relationship between organizational learning, patient safety culture and their dimensions in hospital pharmacy settings.

This study investigates the impact of big data analytics capabilities on export performance. Moreover, it assesses the mediating effect of the supply chain innovation and moderating effect of supply chain agility.

This study is based on primary data that were collected from the manufacturing sector operating in Jordan. A total of 327 responses were used for the final data analysis. Data analysis was performed via a partial least square structural equation modeling (PLS-SEM) approach.

The results of the data analysis supported a positive relationship between big data analytics capabilities and the export performance as well as a mediating effect of supply chain innovation. It was confirmed that supply chain agility moderated the relationship of supply chain innovation and export performance.

This study developed a theoretical and empirical model to investigate the relationship between big data analytics capabilities, export performance, supply chain innovation and supply chain agility. This study offers new theoretical and managerial contributions that add value to the supply chain management literature by testing the moderated-mediated model of these constructs in the manufacturing sector in Jordan.

The purpose of this paper is to formulate a specialized artificial neural network algorithm utilizing radial basis function (RBF) for modeling of time to failure of aircraft engine turbines.

The model uses training failure data collected from operators of turboprop aircraft working in harsh desert conditions where sand erosion is a detrimental factor in reducing turbine life. Accordingly, the model is more suited to accurate prediction of life of critical components of such engines. The used RBF employs a closest neighbor type of classifier and the hidden unit’s activation is based on the displacement between the early prototype and the input vector.

The results of the algorithm are compared to earlier work utilizing Weibull regression modeling, as well as Feed Forward Back Propagation NN. The results show that the failure rates estimated by RBF more closely match actual failure data than the estimations by both other models. The trained model showed reasonable accuracy in predicting future failure events. Moreover, the technique is shown to have comparatively higher efficiency even with reduced number of neurons in each layer of ANN. This significantly decreases computation time with minimum effect on the accuracy of results.

Using RBF technique significantly decreases the computational time with minimum effect on the accuracy of results.

The current work sought to investigate the mediating effect of supply chain ambidexterity on the relationship between Industry 4.0 capabilities and operational performance of manufacturing firms in Jordan.

Data collection was carried out through a survey with 253 respondents from manufacturing firms in Jordan through the first quarter in 2023. The quantitative approach and structural equation modeling (SEM) were applied to analyze the collected data. Dynamic capabilities view (DCV) theory was the adopted theoretical lens for this work.

The results demonstrated that Industry 4.0 capabilities positively and significantly affect exploration, exploitation and operational performance. In addition, the results confirmed that exploration and exploitation positively and significantly affect operational performance. Further, it is also found that exploration and exploitation in the supply chain positively and significantly mediate the relationship between Industry 4.0 capabilities and operational performance.

This study focuses on this gap to deepen the understanding of operational performance in a recent manufacturing environment under various factors and perspectives (Industry 4.0 capabilities and supply chain ambidexterity).