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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.

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.

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.

This paper aims to investigate the effects of build parameters and strain rate on the mechanical properties of three-dimensional (3D) printed polylactic acid (PLA) by integrating digital image correlation and desirability function analysis. The build parameters included in this paper are the infill density, build orientation and layer height. These findings provide a framework for systematic mechanical characterization of 3D-printed PLA and potential ways of choosing process parameters to maximize performance for a given design.

The Taguchi method was used to shortlist a set of 18 different combinations of build parameters and testing conditions. Accordingly, 18 specimens were 3D printed using those combinations and put through a series of uniaxial tensions tests with digital image correlation. The mechanical properties deduced for all 18 tests were then used in a desirability function analysis where the mechanical properties were optimized to determine the ideal combination of build parameters and strain rate loading conditions.

By comparing the tensile mechanical experimental properties results between Taguchi's recommended parameters and the optimal parameter found from the response table of means, the composite desirability had increased by 2.08%. The tensile mechanical properties of the PLA specimens gradually decrease with an increase in the layer height, while they increase with increasing the infill densities. On the other hand, the mechanical properties have been affected by the build orientation and the strain rate in similar increasing/decreasing trends. Additionally, the obtained optimized results suggest that changing the infill density has a notable impact on the overall result, with a contribution of 48.61%. DIC patterns on the upright samples revealed bimodal strain patterns rendering them more susceptible to failures because of printing imperfections.

These findings provide a framework for systematic mechanical characterization of 3D-printed PLA and potential ways of choosing process parameters to maximize performance for a given design.

This study aims to evaluate the efficiency of using some polymers at different concentrations in the consolidation of vegetable-tanned leather artifacts.

New vegetable-tanned leather samples were prepared. The consolidants used were polyacrylamide (PAM) and polymethyl methacrylate/hydroxyethyl methacrylate (MMA-HEMA). Accelerated heat aging was applied to the untreated and treated samples. Analytical techniques used were Fourier transform infrared spectroscopy (FTIR), digital microscope, scanning electron microscope (SEM), change of color and mechanical properties.

The characteristic FTIR bands showed the effect of accelerated heat aging on the molecular structure of the studied samples, but treated and aged treated samples used were better than aged untreated samples. Microscopic investigations (digital and SEM), and mechanical properties proved that 2% was the best concentration for polymers used. The change in the total color difference of the treated and aged treated samples was limited.

This study presents the important results obtained from PAM and poly(MMA-HEMA) used for the consolidation of vegetable-tanned leather artifacts. The best results of the studied polymers can be applied directly to protect historical vegetable-tanned leathers.

In this study, the authors investigate the effect of big data analytics capability (BDAC) on supply chain performance (SCP) to assess the mediating effect of supply chain innovation (SCI) and the moderating effect of a data-driven culture (DDC).

The authors collected the primary data through an online questionnaire survey from the manufacturing sector operating in Jordan. The authors used 420 samples for the final data analysis, which the authors performed via partial least squares structural equation modelling using SmartPLS 3.3.9 software.

The results indicate that BDAC has a strong relationship with SCI and SCP. SCI shows a positive relationship with SCP as well as a mediating effect on SCI. The authors confirmed that DDC moderated the relationship between SCI and SCP.

The authors developed a conceptual and empirical model to investigate the relationship between BDAC, SCI, DDC and SCP. The authors contributed new theoretical and managerial insights that add value to the supply chain management literature through testing the moderated-mediated model of these constructs in Jordan’s manufacturing sector.