مطالب

This research aims to examine the impact of Big Data Quality (BDQ) components, including completeness, accuracy, format, and currency, on Big Data-driven Human Resources (BDHRP) management practices and Decision-Making Quality (DMQ) from the viewpoint of HR managers. It also seeks to identify the most impactful components among completeness, accuracy, format, and currency in the context of BDHRP and DMQ. A survey of HR professionals in 108 French organizations deploying Big Data Analytics systems revealed positive relationships between BDQ, BDHRP, and DMQ. Statistical analyses conducted with the Partial Least Squares Structural Equation Modeling (PLSEM) method showed a positive relationship between BDQ components and BDHRP, with currency and accuracy emerging as the most influential factors. Additionally, a strong positive relationship was found between BDQ components and DMQ, with currency and accuracy leading the way. The research also found a significant connection between BDHRP and DMQ, further underscoring the importance of effective HRM practices in enhancing decision-making quality. These findings contribute significantly to understanding the crucial role played by big data quality in BDHRP and decision-making, highlighting the potential for organizations to improve outcomes by focusing on currency and accuracy-related concerns. In practical terms, this research offers insights that can guide data quality practices, resource allocation, and strategic decision-making within organizations.

Purpose: The purpose of this research is to develop a model of export market orientation based on competitive intelligence in the era of the digital economy. Method: This study employed a qualitative approach, utilizing grounded theory as the research method. Sampling was conducted purposefully and continued until theoretical saturation was achieved, involving a total of 15 managers and experts from selected exporting companies. The systematic research process comprised open coding, axial coding, and selective coding, facilitated by strategies such as analysis, questioning, comparison, reminders, schemas, and paradigms. During the coding process, revisions were made, and classifications were validated through feedback from the participants. Findings: The findings revealed the paradigmatic pattern of export market orientation based on competitive intelligence in the era of digital economy including six main categories of causal conditions, context, phenomenon or central category, intervening conditions, strategies and the consequences. Causal conditions have three sub-categories of learning abilities promotion of customer-oriented culture, knowledge of export technology and R&D processes and identification of export market needs; background conditions include three sub-categories of competitive thinking, competitive export processes (technical evaluation) and competitive governance; intervening conditions  include three sub-categories of market technical structure (new technologies), environmental uncertainty and global markets (export risks); the central phenomenon has three sub-categories of value chain (reward strategy), human resource empowerment and employee training (export intelligence); The strategies include three sub-categories of competitive value creation, development of export competitiveness and strategic evaluation of resources and competitive agility and strategic entrepreneurship; The results include three sub-categories of developing entrepreneurial activities, competitive advantage and promoting innovation and dynamic export capabilities. Conclusion: Based on the results, successful implementation of the export market orientation model, grounded in competitive intelligence, requires managers of exporting companies to develop a deeper understanding of the export market. This can be achieved by allocating greater effort to market-oriented activities. Furthermore, by emphasizing competitive intelligence, these companies can improve the perception of their services and create a positive association with their brand in the minds of international customers.

Accurate stock market forecasting is a challenging and complex problem for the market analysts and decision makers. During the past decade’s accuracy of different methods are examined yet there is no consensus on optimum forecasting method. In this regard, the main objective of present study is to investigate eligibility of nonlinear time series, such as exponential smoothing and regime-switching models beside Box-Jenkins scheme in forecasting of stock return time series. Data set consist of daily observations of Apple and Microsoft corporations as of 2024 to 2025. The Terasvirta-Lin-Granger procedure chaotic behavior of data generating process of the selected samples being examined. The Self-Exciting Threshold Autoregressive procedure combined with GARCH component (SETARMA-GARCH) and ARMA model combined with EGARCH component (ARMA-EGARCH) in order to capture the heterogeneous variance of financial time series, which yield dynamic hybrid models. Moreover, due to the overwhelming application of Artificial Intelligence methods in computation, besides the Exponential Smoothing (ES) approach as a non-parametric method, a recently developed Multilayer Perceptron Network (MLP) based on Feed-Forward-Back Propagation (FF-BP) algorithm being developed either. Both of the in-sample and out-sample forecasting are carried out and performance of models is evaluated using standard error criteria. Finally, the Diebold-Mariano test is employed in order to determine the significance of forecasting differences among the models. Findings indicated that the behavior of the return series for the both of the corporations are chaotic and nonlinear methods are appropriate in modeling. The exponential smoothing method outperformed the developed SETARMA-GARCH and ARMA-EGARCH procedures in terms of the majority of error criteria in the both of in-sample and out-sample forecasting. However, the MLP has outweighed the ES model based on every calculated error criteria. The estimated S-statistic of Diebold-Mariano test confirmed results of the forecasting in favor of the MLP method. This finding suggests application of the dynamic nonparametric methods in modeling and forecasting of the selected time series. Implication of such finding recommends use of dynamic nonlinear and nonparametric methods in financial series prediction.

Every ethical system possesses at least six core elements: a theory of moral value, fundamental moral principles, rules of inference, incentives for adhering to ethical principles and rules, enforcement mechanisms for those principles and rules, and a justification for the necessity of being moral and for the validity of the said ethical system. This system is constructed and refined based on sources of moral knowledge and ethical foundations and is presented to its target society. Within this system, the theory of moral value is recognized as the principal element, upon which all other elements rely in some manner. Within Jewish ethical systems, two distinct theories are observed concerning the theory of moral value: "A deontological perspective and a teleological perspective. Some scholars currently argue that Jewish ethics is deontological and present reasoning for this position." This paper, with reference to the Tanakh (the Old Testament) and the foundations of ethics in Judaism, emphasizes the teleological viewpoint and critiques the arguments put forth by deontologists. The difference between these two perspectives has been notably evident in the conduct of the Zionist regime and its supporters over the past two years. From a deontological viewpoint, it is sufficient for a religious scholar to justify, based on their own interpretation, the massacre of the people of Gaza, Lebanon, Yemen, and Iran by citing a divine command; in this scenario, no rational, empirical or even emotional argument can prevent such crimes.