Aug 2026· European Conference on Knowledge Management· Vol 27, pp. 954-962· 0 citations· 23 references
TL;DR
It is indicated that effectively implementing AI in management requires parallel development of employee competencies, building trust in technology and implementing ethical and supervisory standards, in which technology plays a supporting role rather than replacing the human factor.
Abstract
The study aimed to examine acceptance of, and trust in, artificial intelligence in organisational management. The study was based on a quantitative approach and conducted using a diagnostic survey. The CAWI method was used to conduct the study. The questionnaire was first verified by experts in artificial intelligence and business analysis before being made available to respondents. It included questions about respondents' experience with AI, their level of competence, the perceived benefits and threats of AI, their trust in AI-assisted decisions and their assessment of this technology's impact on organisational functioning. Trust in AI is conditional, acceptance of its use hinges on human control, transparent systems, and the ability to audit decisions. While AI is seen as conducive to the development of new management practices and innovation, its direct impact on operational efficiency remains unclear. Respondents emphasise the irreplaceable role of humans in areas requiring emotional intelligence, ethics, motivation and creativity. The conclusions indicate that effectively implementing AI in management requires parallel development of employee competencies, building trust in technology and implementing ethical and supervisory standards. These findings align with the socio-technical systems approach and the human–AI cooperation model, in which technology plays a supporting role rather than replacing the human factor.
Assessing the effects of technology reliability (RL), credibility (CR) and technical competence (TEC) on HR professionals’ trust and, subsequently, their intent to deploy AI tools reveals that technology RL, CR and TEC each enhance trust in AI.
R. Arora, Neha Kumari Siradhana· South Asian Journal of Human...· 0 citations
This study aims to examine the impact of artificial intelligence (AI)-driven workflows on efficiency and collaboration, shaping employees’ attitudes and intentions. In addition, it theoretically contributes by linking AI adoption to different levels of collaboration, showing how trust and risk influence engagement.
This study conducted a survey with remote work employees in Indian information technology (IT) firms and received 386 respondents. The study further extended the unified theory of acceptance and use of technology (UTAUT2) model, and for a comprehensive analysis, partial least squares structural equation modeling using SmartPLS4 was used.
The study findings underline the significant impact of AI adoption on employees’ attitudes and intentions. Results also demonstrate how trust and risk perceptions determine the depth of collaboration in remote AI-enabled work environments. It further provides insights into how traditional job practices adapt to an AI-integrated work environment.
Finally, this study contributes to understanding organizational adaptation in an AI-enabled environment and gives practical and managerial insights for organizational leaders, practitioners and policymakers while ensuring a trust- and ethics-focused AI system in remote work. The findings contribute to collaboration theory by empirically showing how trust enables, and risk constrains, effective collaborative engagement in remote work.
The rapid use of AI in remote work scenarios in Indian IT firms influences collaboration and work efficiency. However, this scenario is hindered by certain challenges related to stakeholder and employee trust and ethical concerns. This study provides a novel integration of UTAUT2 with collaboration frameworks, emphasizing the theoretical link between AI adoption, trust, risk and collaboration levels.
Suman Kumar, M. Moslehpour, A. Walawalkar et al.· Journal of Information, Comm...· 0 citations
Results have demonstrated that good practice in teaching and learning of AI requires responsible governance, and staff engagement, ethical practice, and access to continual AI-related information are needed to build confidence and attain institutional good practice.
Lal Mani Pokhrel· Academia Research Journal· 0 citations
The entry of Artificial Intelligence (AI) in an organization decision-making has attained international popularity but the strategic use of Artificial Intelligence in educational leadership is less researched. Although literature emphasizes the ability of AI to automate administrative tasks, there is little empirical evidence of its impact on strategic judgments involving educational leadership and decisions associated with high stakes and associated risks, especially in developing countries. The current study fills this knowledge blank by analyzing the impact of AI in decision-making processes in higher education leadership. Under a mixed-methods design, the data was drawn using a validated survey instrument and quantitative data collected on 214 senior leaders of various universities. The relationship between adoption of AI, quality of decision and speed of decision were measured using Structural Equation Modeling (SEM). Complementary qualitative information was received with the use of 20 semi-structured interviews with institutional leaders as well as using thematic analysis with the help of NVivo software to allow identifying the subtleties of the context. The results indicate that AI enjoys a positive relationship with the quality and the efficiency of strategic decisions, upon the digital literacy of leaders and the institutional preparedness. Nonetheless, the ethical concerns, the question of reliability of the data and the risks of overestimating AI became the important moderating factors. The research is helpful to the disciplines of socio-technical decision-making and rational decision-making as well since it is shown that AI continuously becomes a strategic resource but not an operational one. In practice, the study provides practical suggestions on leadership training, policymaking and ethical implementation of AI in educational establishments.
Anwar Saeed, A. Rizvi· Aposta: Revista de Ciencias...· 0 citations
The role of AI is changing and reshaping auditing processes. However, the adoption of AI in financial auditing remains limited due to auditors' lack of expertise, resistance to change, and unclear regulatory laws. The present study uses Innovation Resistance Theory (IRT) as a base to identify several functional (active) or psychological (passive) barriers on resistance to adopting AI in financial auditing. Open-ended survey questions were used to gather the responses from 41 auditors. The analysis identified five barriers: tradition, image, value, risk, and usage barriers associated with AI adoption. The study identified several concerns, including poor data quality, AI-biased outputs, regulatory and compliance issues, lack of qualitative and subjective judgment skills, lack of knowledge and technical expertise, high cost, and many more. The study extends the IRT framework and evaluates the relevance of both psychological and functional barriers when adopting AI. The study examines the five barriers' impact on users and the significance of each on their resistance to AI adoption.
S. Almasabi, Nidhi Singh, Danish Mehraj et al.· Journal of Global Informatio...· 0 citations
Computer science includes artificial intelligence (AI) as a field. It entails creating computer programs to carry out operations that would typically require intelligence from humans. This study explores the role of Artificial Intelligence (AI) in marketing and consumer behavior, investigating its benefits, challenges, impact on consumer trust, and ethical considerations. this study is in the Kurdistan Region-Iraq. The survey was in three cities (Erbil, Sulaymaniyah and Halabja). The participants we dealt with were over 18 years old, approximately 145 respondents were recruited who were recruited through an adaptive questionnaire. and the level of knowledge of the participants includes individuals with expertise in electronics, IT, computers, and technology updates in business. the research design included quantitative data collection techniques. The results showed that using AI in marketing has a number of advantages, including improved efficiency in processing and interpreting consumer data and more accuracy in forecasting customer behavior. But it also presents serious obstacles, such as worries about data privacy, losing one's work, and being overly dependent on technology. Furthermore, concerns of accountability, justice, and openness were noted as ethical implications related to the application of AI in marketing.