Future of Work and Digital Management Journal

This study aimed to investigate the relationship between managerial flexibility and operational efficiency, with role consistency examined as a potential mediating variable. A descriptive correlational design was employed, and data were collected from 419 participants working in medium and large-scale organizations across Pakistan. The sample size was determined using the Morgan and Krejcie table, and participants were selected through stratified random sampling. Standardized instruments were used to measure managerial flexibility (Combe & Greenley, 2004), role consistency (adapted from Rizzo et al., 1970), and operational efficiency (Ward et al., 1998). Data analysis included Pearson correlation to examine bivariate relationships using SPSS-27 and Structural Equation Modeling (SEM) in AMOS-21 to test the hypothesized mediation model and evaluate model fit indices. Pearson correlation results showed that managerial flexibility was significantly correlated with operational efficiency (r = .54, p = .001) and role consistency (r = .47, p = .002), while role consistency was also positively correlated with operational efficiency (r = .49, p = .003). SEM results indicated excellent model fit (χ² = 164.87, df = 84, χ²/df = 1.96, CFI = 0.96, RMSEA = 0.046). The direct effect of managerial flexibility on operational efficiency was significant (B = 0.44, β = .46, p = .001), as were the paths from managerial flexibility to role consistency (B = 0.41, β = .47, p = .001) and role consistency to operational efficiency (B = 0.36, β = .42, p = .002). The indirect effect of managerial flexibility on operational efficiency through role consistency was also significant (B = 0.15, β = .20, p = .003), confirming partial mediation. The results underscore the importance of embedding role consistency within flexible managerial environments to optimize operational efficiency, highlighting a balanced strategic approach for enhancing organizational performance.

This study aimed to investigate the relationship between perceived AI fairness and organizational commitment, with psychological safety examined as a potential mediating variable. A descriptive correlational design was employed using a sample of 443 employees from public and private sector organizations in Indonesia. The sample size was determined using the Morgan and Krejcie table for large populations. Standardized instruments were used to measure organizational commitment (Organizational Commitment Questionnaire by Mowday et al., 1979), psychological safety (Psychological Safety Scale by Edmondson, 1999), and perceived AI fairness (Perceived Fairness in Algorithmic Decision-Making Scale by Lee, 2018). Data were analyzed using SPSS-27 for descriptive and Pearson correlation statistics, and AMOS-21 for structural equation modeling (SEM). Model fit was assessed using established indices including χ²/df, CFI, TLI, GFI, AGFI, and RMSEA. Perceived AI fairness was significantly and positively correlated with organizational commitment (r = .39, p < .001) and psychological safety (r = .51, p < .001). Psychological safety was also positively correlated with organizational commitment (r = .47, p < .001). SEM results confirmed good model fit (χ²/df = 2.47, CFI = 0.96, RMSEA = 0.057) and demonstrated that perceived AI fairness significantly predicted psychological safety (β = 0.51, p < .001), which in turn predicted organizational commitment (β = 0.39, p < .001). The indirect effect of AI fairness on commitment through psychological safety was also significant (β = 0.20, p < .001), indicating partial mediation. The findings suggest that perceived fairness in AI systems contributes to higher organizational commitment, both directly and through enhanced psychological safety. Organizations should consider both technological transparency and supportive interpersonal environments to foster employee trust and engagement in AI-integrated workplaces.

This study aimed to investigate the mediating role of work meaningfulness in the relationship between job autonomy and organizational commitment among Chinese employees. A descriptive correlational design was employed, and data were collected from 490 full-time employees in China using stratified random sampling. Standardized instruments were used to measure job autonomy, work meaningfulness, and organizational commitment. Descriptive statistics and Pearson correlation coefficients were computed using SPSS-27, while Structural Equation Modeling (SEM) was conducted using AMOS-21 to test the hypothesized mediation model and assess the overall fit of the structural model. The results of Pearson correlations indicated significant positive relationships among all study variables: job autonomy was significantly correlated with work meaningfulness (r = .49, p < .001) and organizational commitment (r = .38, p < .001), while work meaningfulness was strongly correlated with organizational commitment (r = .56, p < .001). SEM analysis demonstrated good model fit (χ² = 148.72, df = 84, χ²/df = 1.77, CFI = .96, TLI = .95, RMSEA = .039). Path analysis revealed that job autonomy significantly predicted work meaningfulness (β = .49, p < .001) and organizational commitment directly (β = .26, p < .001). Work meaningfulness significantly predicted organizational commitment (β = .52, p < .001), and the indirect effect of job autonomy on commitment through meaningfulness was also significant (β = .26, p < .001), indicating partial mediation. The findings confirm that job autonomy enhances organizational commitment both directly and indirectly through the experience of work meaningfulness. This highlights the importance of designing autonomous and meaningful work environments to foster long-term employee commitment, particularly in collectivist cultural contexts such as China.

This study aimed to investigate the relationship between remote work autonomy and employee productivity, and to examine whether intrinsic motivation mediates this relationship among remote workers. A descriptive correlational research design was employed with a sample of 394 remote employees from various sectors in Bulgaria, selected based on Krejcie and Morgan’s sample size table. Standardized measurement tools were used to assess remote work autonomy (Work Design Questionnaire – autonomy subscale), intrinsic motivation (Work Extrinsic and Intrinsic Motivation Scale – WEIMS), and productivity (Individual Work Performance Questionnaire – IWPQ). Data were analyzed using SPSS-27 for Pearson correlation and AMOS-21 for Structural Equation Modeling (SEM) to test direct and indirect relationships between variables and assess model fit. Pearson correlation analysis revealed significant positive associations among all study variables: remote work autonomy and intrinsic motivation (r = .41, p = .001), remote work autonomy and productivity (r = .38, p = .002), and intrinsic motivation and productivity (r = .46, p < .001). SEM results indicated that remote work autonomy had both a direct effect on productivity (β = 0.38, p = .002) and an indirect effect through intrinsic motivation (β = 0.19, p = .003), with a total effect of β = 0.57 (p < .001). The model showed a good fit to the data (χ²/df = 2.08, CFI = .96, RMSEA = .053, TLI = .95). The findings demonstrate that intrinsic motivation partially mediates the relationship between remote work autonomy and productivity, highlighting the critical role of psychological motivation in optimizing performance in autonomous work environments. Organizations should focus on fostering both structural autonomy and motivational support to enhance remote employee productivity.

This study aimed to identify and conceptualize key organizational metrics that define digital transformation readiness across infrastructural, cultural, and strategic domains. This qualitative research employed a constructivist approach using semi-structured interviews with 26 participants from various organizations based in Tehran, including IT professionals, innovation managers, and strategic decision-makers. Participants were selected via purposive sampling, and data collection continued until theoretical saturation was reached. Interviews were transcribed verbatim and analyzed using thematic analysis, facilitated by NVivo software. The analysis process included open, axial, and selective coding to extract and refine categories and subcategories related to digital transformation readiness. Thematic analysis revealed three overarching categories of digital transformation readiness: organizational capability and infrastructure, human capital and culture, and strategic alignment and governance. Within these categories, key subthemes included IT infrastructure preparedness, cybersecurity infrastructure, data governance, employee digital competency, leadership commitment, learning culture, strategy integration, KPI systems, and policy adaptability. Participants highlighted the interplay between technical systems and human readiness factors, underscoring the importance of alignment between strategic intent and operational capacity. The findings support the notion that readiness is a dynamic, multi-layered construct involving both internal and external organizational dimensions. Comparisons with existing literature further validated these metrics as practical indicators of readiness across sectors. This study provides a comprehensive, empirically grounded framework for assessing digital transformation readiness by integrating infrastructural, human, and strategic components. The findings offer actionable insights for organizations seeking to benchmark their preparedness and design interventions that foster successful digital transitions. Readiness should be treated as an evolving organizational capability that requires continuous evaluation and adaptation in response to technological and environmental change.

This study aimed to explore the organizational, sociotechnical, and cultural barriers to implementing transparent AI governance within human-centered work environments. A qualitative research design was employed, utilizing semi-structured interviews with 27 participants from diverse public and private sector organizations based in Tehran. Participants were selected using purposive sampling to capture a range of roles including management, IT, policy, and human resources. Interviews were conducted until theoretical saturation was achieved. Each interview was transcribed and analyzed using inductive qualitative content analysis. NVivo software facilitated systematic coding, enabling the emergence of main themes and subthemes from the data. The analysis yielded three overarching themes: structural and organizational constraints, sociotechnical misalignments, and cultural and cognitive barriers. Participants identified key challenges such as the absence of AI policy frameworks, siloed decision-making, and lack of ethical oversight mechanisms. Sociotechnical barriers included exclusion of stakeholders in AI development, technological opacity, and poor integration with human workflows. Cultural issues like distrust in AI systems, low AI literacy, and fear of job displacement further hindered transparent governance. These findings align with prior studies emphasizing the regulatory, ethical, and human-centered dimensions of AI transparency. The results demonstrate that transparency is not solely a technical feature but a multidimensional construct requiring organizational capacity, ethical culture, and inclusive governance processes. Transparent AI governance in human-centered work design is undermined by intersecting structural, technical, and cultural barriers. Overcoming these challenges requires a shift from compliance-based models to participatory and ethics-driven approaches that prioritize stakeholder engagement, organizational learning, and adaptive oversight. Addressing these barriers is essential for building trust, accountability, and fairness in the workplace as AI technologies become increasingly embedded in decision-making systems.

This study aimed to explore the key mechanisms and behaviors that enable trust-building among users of anonymous digital labor platforms. A qualitative research design was employed, relying on semi-structured interviews with 30 participants—17 freelancers and 13 clients—active on anonymous digital labor platforms in Germany. Participants were selected using purposive sampling to ensure relevance, and interviews continued until theoretical saturation was achieved. Data collection focused on user experiences and perceptions related to trust in environments lacking identity transparency. All interviews were transcribed verbatim and analyzed using thematic analysis with the aid of NVivo software. An inductive coding approach was applied to identify recurring patterns, behaviors, and technological features that contribute to trust formation within anonymous digital transactions. Thematic analysis revealed four overarching themes that underpin trust-building in anonymous digital labor platforms: (1) platform-based trust mechanisms, such as reputation systems, algorithmic accountability, and identity verification; (2) communication and interaction quality, including clarity, responsiveness, and tone; (3) individual professional behaviors, such as reliability, proactive engagement, and quality of deliverables; and (4) socio-psychological factors, including perceived integrity, empathy, and comfort with anonymity. Participants emphasized the importance of a combination of technological infrastructure and consistent professional behavior in sustaining trust despite the absence of personal identification. Quotations from participants illustrated the nuanced ways in which trust is negotiated through systemic cues and interpersonal practices. Trust in anonymous digital labor platforms is a dynamic, multi-dimensional process shaped by platform design, user behavior, and social interpretation. Technological tools such as escrow systems and algorithmic enforcement must be complemented by clear communication and ethical engagement to foster sustainable trust in anonymous environments.

This study aimed to explore how organizational ambidexterity is enacted within digitally mediated teams, with a focus on the mechanisms through which team members balance exploration and exploitation in virtual work environments. This qualitative study employed a thematic analysis approach using semi-structured interviews with 24 professionals from various industries in Bulgaria, all of whom had experience working in digitally mediated teams. Participants were selected through purposive sampling, and data collection continued until theoretical saturation was achieved. The interviews were conducted via video conferencing platforms, transcribed verbatim, and analyzed using NVivo software. The coding process followed an inductive strategy to identify emergent themes related to role management, communication practices, learning behaviors, and adaptive strategies in virtual settings. Three main categories emerged from the data: Balancing Exploration and Exploitation, Digital Communication and Team Dynamics, and Adaptive Learning and Innovation. Participants emphasized the importance of role flexibility, strategic time management, trust-building mechanisms, continuous skill development, and psychological safety in navigating dual demands. They reported challenges such as digital fatigue, asynchronous misalignment, and unclear performance metrics. However, teams that fostered reflexivity, encouraged safe failure, and integrated digital tools thoughtfully were better equipped to sustain both innovative and operational goals. Quotations from participants illustrated how ambidexterity was embedded in team routines and decision-making processes. The study reveals that organizational ambidexterity in digitally mediated teams is not solely a technological or structural outcome but a dynamic, human-centered process shaped by leadership behavior, communication practices, and team reflexivity. The findings contribute practical insights for organizations seeking to enhance team adaptability, innovation, and execution in digital work environments.