FUTURE OF WORK AND DIGITAL MANAGEMENT JOURNAL
Year:2024 | Volume:2 | Issue:2|Papers Count:7

The purpose of this study is to examine the effect of artificial intelligence (AI) on supply chain efficiency, strategic decision-making, customer satisfaction, and organizational productivity in business management. This research employed a quantitative methodology using a standardized questionnaire with a five-point Likert scale. Data were collected from companies and organizations active in the field of business management. For data analysis, SPSS and SmartPLS software were utilized, and the reliability and validity of the data collection instrument were confirmed by Cronbach’s alpha coefficient (≥0. 7) and expert review. The findings revealed that AI has a positive and significant impact on all four examined dimensions. Specifically, the application of AI improves supply chain efficiency, enhances the quality of strategic decision-making, increases customer satisfaction through intelligent customer relationship management (CRM) and personalized experiences, and boosts organizational productivity. These results align with previous studies and confirm the importance of AI adoption to strengthen competitiveness and performance in business organizations. Ultimately, the study suggests that business managers should leverage AI for process automation, improved decision-making, and enhanced customer experience. Future research is recommended to explore the role of AIin sustainable supply chains, product innovation, and other industries.

This study aimed to identify and prioritize the key strategies that enablestartups to effectively implement process automation using artificial intelligence (AI), addressing both organizational and technological dimensions. A qualitative research design was employed, using purposive sampling to select 21 participants, includingfounders, senior managers, and technical experts from technology-driven startups in Tehran, Iran. Data were collected through in-depth semi-structured interviews designed to explore participants’ experiences with AI-driven automation adoption. Interviews continued until theoretical saturation was reached. All interviews were transcribed verbatim and analyzed using NVivo 14 through open, axial, and selective coding to generate themes. After the qualitative phase, the identified strategies were subjected to quantitative prioritization,participants rated the relative importance of each strategy, and descriptive statistical analysis was performed using SPSS to establish ranking and mean scores. Six major strategic factors emerged: Strategic Alignment & Vision, Data Governance & Quality, Resource & Infrastructure Readiness, Technology Selection & Integration, Change Management & Culture, and Performance Measurement & Continuous Improvement. Ranking results indicated that Strategic Alignment & Vision was perceived as the most critical (M = 4. 72,22. 5%), followed closely by Data Governance & Quality (M = 4. 56,21. 7%) and Resource & Infrastructure Readiness (M = 4. 31,20. 5%). Technology Selection & Integration (M = 4. 18,19. 9%) and Change Management & Culture (M = 3. 97,19. 0%) followed, while Performance Measurement & Continuous Improvement (M = 3. 85,18. 6%) was ranked lowest but still recognized as essential for long-term success. The study provides a practical, evidence-based roadmap for startups seeking AI-driven process automation. Aligning automation with strategic vision, ensuring robust data governance, and preparing technical and financial infrastructure are foundational. Equally, careful technology selection and fostering cultural adaptability support effective and sustainable automation

The present study aims to examine managerial factors influencing the tendency of Bandar Abbas citizens to use Instagram. The research method employed in this study is cross-sectional in terms of temporal dimension,operationally, it is an applied investigation, as the researcher seeks to establish a logical relationship between scholarly work and the target population. Given the nature of the topic, the data collection procedure, and the characteristics of the statistical population, a survey method was utilized. The statistical population of this study consists of the citizens of Bandar Abbas. The sample size includes 384 citizens of Bandar Abbas. To collect data, the study used the Virtual Network Usage Motivation Questionnaire (Gülnar et al., 2010). The data obtained from the questionnaires were analyzed statistically using SPSS software. The findings revealed a significant relationship between the use of the Instagram social network and immersion in virtual space (r = 0. 001), between the use of the Instagram social network and the motivation for interaction and communication in virtual space (r = 0. 001), between the use of the Instagram social network and self-expression in virtual space (r = 0. 001), and between the use of the Instagram social network and entertainment motivation in virtual space (r = 0. 001). Based on the results, it can be concluded that individuals’ motivation to join Instagram is not unidimensional,rather, it is multidimensional and multifaceted, varying from one person to another and from one culture to another. The results of this study may offer valuable theoretical and practical implications for managers and policymakers in this domain

This study aimed to examine the predictive roles of manager support and digital feedback quality on employee engagement in the Malaysian workforce. A correlational descriptive design was used to investigate the relationships between the variables. The study involved 379 full-time employees from various sectors in Malaysia, selected using stratified random sampling based on the Krejcie and Morgan sample size table. Data were collected via an online self-report survey comprising three standardized instruments: the Utrecht Work Engagement Scale (UWES-17), the Perceived Supervisor Support Scale (PSS), and the Feedback Environment Scale (FES). Statistical analyses were performed using SPSS-27, including descriptive statistics, Pearson correlation, and multiple linear regression to determine the strength and significance of relationships among variables. Inferential analysis revealed significant positive correlations between employee engagement and both manager support (r =. 61, p <. 001) and digital feedback quality (r =. 54, p <. 001). Multiple regression analysis showed that manager support and digital feedback quality jointly predicted employee engagement, F(2, 376) = 159. 84, p <. 001, with an R² of. 44. In the regression model, manager support (β =. 48, p <. 001) emerged as a stronger predictor than digital feedback quality (β =. 33, p <. 001), indicating that both interpersonal and digital communication factors significantly influence employee engagement. The findings underscore the critical importance of both managerial support and digital feedback quality in promoting employee engagement within digitally evolving organizations. Managerial behaviors that convey support and well-designed digital feedback systems together form a robust foundation for sustaining employee motivation and involvement. These insights are particularly relevant for organizations operating in hybrid or technology-mediated environments and offer strategic directions for leadership development and digital communication practices

This study aimed to investigate the predictive roles of remote work experience and perceived career development on organizational loyalty among employees in Greece. The study adopted a correlational descriptive design involving 360 full-time employees from public and private organizations across Greece, selected using Krejcie and Morgan’s sample size determination table. Standardized tools were used to measure the dependent variable (Organizational Loyalty) and the independent variables (Remote Work Experience and Perceived Career Development). Data were analyzed using SPSS-27, including Pearson correlation to assess the relationships between variables and multiple linear regression to evaluate the predictive power of the independent variables on organizational loyalty. Assumptions of normality, linearity, multicollinearity, and homoscedasticity were tested and confirmed before regression analysis. Pearson correlation analysis showed that organizational loyalty was significantly correlated with both remote work experience (r =. 42, p <. 001) and perceived career development (r =. 53, p <. 001). Multiple linear regression analysis revealed that the model was statistically significant, F(2, 357) = 90. 36, p <. 001, with an R² of. 34, indicating that 34% of the variance in organizational loyalty was explained by the two predictors. Both remote work experience (β =. 27, t = 5. 71, p <. 001) and perceived career development (β =. 42, t = 7. 32, p <. 001) significantly contributed to the prediction of organizational loyalty. The results underscore the critical importance of fostering high-quality remote work experiences and providing visible career development opportunities to enhance organizational loyalty. Organizations aiming to retain talent in hybrid or remote environments should prioritize both flexible work structures and long-term growth pathways for employees.

This study aimed to investigate the relationship between digital monitoring and employee burnout, examining the mediating role of perceived control in this association. A descriptive-correlational design was employed with a sample of 285 full-time employees from various sectors. Participants completed three standardized instruments: the Electronic Monitoring and Surveillance Scale (EMSS) for digital monitoring, the Pearlin Mastery Scale (PMS) for perceived control, and the Maslach Burnout Inventory (MBI) for burnout. Data were analyzed using Pearson correlation, structural equation modeling (SEM), and mediation analysis in SPSS-27 and AMOS-24. The goodness-of-fit indices were used to evaluate the model’s fit, including χ²/df, GFI, AGFI, CFI, TLI, and RMSEA. Inferential analysis revealed that digital monitoring was positively associated with burnout (β = 0. 34, p <. 001) and negatively associated with perceived control (β = −0. 31, p <. 001). Perceived control was significantly negatively associated with burnout(β = −0. 43, p <. 001). Mediation analysis indicated a significant indirect effect of digital monitoring on burnout through perceived control (β = 0. 13, p =. 001), and the total effect of digital monitoring on burnout was significant (β = 0. 47, p <. 001). Model fit indices confirmed good model fit (χ²/df = 2. 05, GFI = 0. 94, AGFI = 0. 91, CFI = 0. 96, TLI = 0. 95, RMSEA = 0. 061). The findings suggest that digital monitoring contributes to higher levels of employee burnout both directly and indirectly by reducing perceived control. Perceived control plays a protective, mediating role and should be considered a crucial psychological factor when designing and implementing monitoring technologies in the workplace.

The objective of this study is to investigate how modern financial technologies influence social trust among financial managers through mechanisms of transparency, information asymmetry reduction, and agency cost reduction. This applied research employed a mixed-methods design combining quantitative and qualitative approaches. The quantitative phase utilized a descriptive-survey method, collecting data from 300 financial managers, senior accountants, and board chairpersons of companies listed on the Tehran Stock Exchange through a researcher-developed questionnaire assessing financial technology adoption, social trust, information asymmetry, and agency costs. The qualitative phase involved semi-structured interviews with 20 senior financial managers selected purposively to capture experiential insights into the effects of financial technologies on trust and organizational dynamics. Quantitative data were analyzed using SPSS and AMOS through descriptive statistics, correlation analysis, and structural equation modeling (SEM), while qualitative data were examined via thematic analysis to extract contextual patterns. The inferential results indicated that modern financial technologies significantly increased social trust among financial managers (β = 0. 71, p < 0. 001). The technologies also had strong positive effects on reducing information asymmetry (β = 0. 66, p < 0. 001) and lowering agency costs (β = 0. 58, p < 0. 001), both of which further contributed to higher social trust (β = 0. 32 and β = 0. 28, respectively, p < 0. 001 for both). Financial technology usage showed strongcorrelations with social trust (r = 0. 69), information asymmetry reduction (r = 0. 63), and agency cost reduction (r = 0. 61). The structural model demonstrated excellent fit (χ²/df = 2. 14, CFI = 0. 95, RMSEA = 0. 056), explaining 68% of the variance in social trust. The study concludes that modern financial technologies significantly enhance social trust among financial managers by creating transparency, reducing information imbalances, and minimizing agency-related inefficiencies, thereby shifting trust frominterpersonal dependence to system-based reliability