درخت حوزه‌های تخصصی

This study examines the use of AI tools within work environments, particularly Generative AI (Gen-AI). Its objective is to comprehend the factors affecting employees' adoption and utilization of such tools. The research applies the Technology, Organization, and Environment (TOE) framework to pinpoint potential factors and formulate hypotheses regarding their influence on employees' Gen-AI usage frequency. A quantitative research approach was conducted among a sample of 316 American employees. Results suggest that employees' perceived Gen-AI intelligence and warmth positively impact their usage through the mediation of performance expectancy. Effort expectancy only mediates the relationship between perceived Gen-AI intelligence and Gen-AI employee usage. Findings also show that the perceived severity of Gen-AI has a negative influence on employees’ usage and that an organization's absorptive capacity of Gen-AI does not influence employees’ usage. Critical drivers for Gen-AI utilization encompass technological proficiency, peer influence, and regulatory backing. These outcomes underscore the significance of nurturing a corporate culture that encourages innovation and adherence to regulations to successfully integrate Gen-AI in workplaces.

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.

This paper investigates the success of the EHR system and its impacts on the knowledge-sharing process by healthcare professionals in a public medical complex in Saudi Arabia. It aims to reexamine and evaluate the usefulness of the updated DeLone & McLean IS success model considering IS in the healthcare context. The study is based on a quantitative methodology conducted at a public medical complex in Saudi Arabia. The data analysis was performed by combining descriptive and exploratory analysis. SPSS was used to test the constructs' validity and scale reliability. Additionally, using AMOS to test the model fit and examine the direct and indirect relationships among dependent and independent variables, structural equation modeling was performed. The results support the role of EHR use in enhancing knowledge-sharing practices within the medical care complex. The findings show that EHR users appreciate knowledge transfer and collaboration between medical staff. The findings suggest that the EHR’s characteristics of information quality, system quality, and service quality promote medical care knowledge-sharing through system satisfaction and use. This study helps medical staff and health decision-makers to understand the EHR benefits and the medical digital innovations that may contribute to improving the work conditions at care organizations. It shows that healthcare organizations can identify various benefits from the use of the EHR**, ** especially in terms of knowledge management and sharing to propose better medical services. The paper contributes to the existing empirical literature by demonstrating the confirmation of the D&M Model as a relevant instrument for IS success within the healthcare sector.

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.