Future of Work and Digital Management Journal

In the era of digital transformation, public sector organizations face significant challenges in updating the capabilities of their human capital. Digital competencies have emerged as one of the most critical components for the successful implementation of innovative policies and the effective utilization of emerging technologies. Purposeful development of employees’ digital competencies not only enhances productivity but also plays a pivotal role in organizational flexibility and sustainability. This study aims to design a conceptual model for the development of digital competencies in governmental organizations in the city of Kermanshah and seeks to identify and explain the dimensions, requirements, and barriers to this development. Employing a qualitative approach and thematic analysis method, data were collected through semi-structured interviews with 12 academic and executive experts in the fields of human resource management and information technology. For data analysis, the four-stage thematic analysis process based on the Clarke and Braun model was used, including initial coding, theme searching, reviewing, naming, and presenting the final model. The findings of the study led to the identification of four main themes: employee digital competencies, organizational enabling factors, barriers to competency development, and the role of external stakeholders. Furthermore, 13 sub-themes and more than 20 initial codes were identified to support the development of the conceptual model. According to the results, developing digital competencies in public sector organizations requires a systemic, interdisciplinary perspective grounded in continuous learning, leadership support, and inter-organizational collaboration. The final proposed model can serve as a practical guide for policymakers and human resource managers in designing and implementing digital transformation programs.

The aim of this study is to investigate the factors influencing branding on Instagram digital media among students at the University of Baghdad, Iraq. Utilizing a qualitative research method and data analysis through interviews with 25 experts in the field of branding and digital media, this research identifies the key factors contributing to digital branding on Instagram. The quantitative section of the study focused on the student population of the University of Baghdad, Iraq, comprising 8,000 individuals. Based on Morgan’s table, the sample size was determined to be 366. The validity and reliability of the study were assessed using expert evaluations and Cronbach’s alpha coefficient. The interview results were analyzed and categorized through primary and secondary coding, which ultimately led to the identification of factors in five main categories: causal factors, including audience needs and consumption patterns, advertising opportunities on Instagram, and effective content production; intervening factors, such as the importance of content creation and audience engagement, political and socio-cultural factors, and the visibility of produced content; contextual factors, including competitiveness in branding and content production, brand positioning, and economic and social influences on customer purchasing; outcomes, including building trust and audience loyalty, increased sales and revenue, and meeting customer expectations while maintaining interaction and trust; and strategies, which encompass trust-building and engagement with the audience, aligning with audience culture and values, and leveraging technology. These factors can be considered significant in the process of digital branding on Instagram among students at the University of Baghdad, Iraq.

This study aims to evaluate the structural and operational impacts of linguistic and visual artificial intelligence (AI) on media systems, proposing a three-tier governance model to enhance transparency, editorial integrity, and ethical accountability in AI-integrated content ecosystems. The research employs a mixed-methods design combining comparative conceptual analysis, scenario-based sensitivity modeling, and two case studies conducted in Iranian private media organizations. Data were collected over six months through qualitative thematic coding, algorithmic configuration experiments, and expert panel reviews. The study assessed three key dimensions of AI-media interaction: content production, content distribution, and policy-level governance. Quantitative metrics such as semantic error rates, discursive diversity indices, and user complaint frequencies were triangulated with editorial satisfaction surveys and internal policy evaluations to validate the proposed framework. Results indicate that AI-generated content, if unreviewed, leads to a high incidence of factual and semantic inaccuracies (up to 34%), while multi-stage human oversight reduces errors to 3% and improves editorial satisfaction. Engagement-only recommender systems significantly decreased discursive diversity (index dropped to 0.38) and increased cognitive polarization (62%), while hybrid algorithms improved diversity (0.63) and reduced polarization (29%). Media organizations implementing a formal AI ethics charter reported a 64% reduction in content-related complaints and a 27% increase in public trust. The study confirms that internal governance frameworks and ethical transparency significantly enhance audience perception, editorial control, and institutional resilience. The integration of AI into media demands proactive, data-driven, and ethics-oriented governance. The proposed three-tier model offers a scalable framework for managing AI’s risks while fostering editorial responsibility and content authenticity. Institutions that embed human oversight and algorithmic transparency are better positioned to preserve public trust and adapt to the evolving information landscape.

This study was conducted with the aim of designing an effective decision-making model for the branches of the Social Security Organization in Kerman Province, intending to provide a localized framework for improving decision-making processes, enhancing organizational accountability, and increasing stakeholder satisfaction under the region’s complex conditions. A sequential mixed-methods approach (qualitative and quantitative) was employed. In the qualitative phase, thematic analysis of semi-structured interviews with 12 experts (branch and headquarters managers and specialists with a minimum of 10 years of experience) was carried out to identify the dimensions and components of effective decision-making. In the quantitative phase, exploratory factor analysis (EFA) was applied to a sample of 384 branch employees selected through multistage cluster sampling, using a questionnaire based on a five-point Likert scale. Thematic analysis of the interviews revealed that decision-making in the Kerman branches is often unsystematic, based on individual experience, and confronted with challenges such as lack of accurate data, insufficient transparency, and weak documentation practices. Key themes included the need for precise problem definition, strengthening of informational infrastructure, enhancement of employee participation, and continuous monitoring of decision outcomes. The exploratory factor analysis extracted eight key dimensions: strategic and policy-making, operational and executive, information and technology, data-driven analysis, organizational participation, organizational culture, evaluation and learning, and adaptability and innovation. These dimensions were validated using indicators such as financial sustainability rate with a factor loading of 0.82, clarity of SMART decision-making goals with a factor loading of 0.80, data accuracy rate with a factor loading of 0.80, and employee participation percentage with a factor loading of 0.79, altogether explaining 89.79% of the total variance.

The aim of this study is to develop a model of organizational culture that facilitates the implementation of the general policies issued for Iran’s administrative system. This research employed a qualitative method based on grounded theory. A semi-structured interview tool and purposive sampling method were used for data collection. The validity of the data was evaluated through formal validation techniques. Interviews were conducted with cultural managers who possess extensive organizational management experience, continuing until theoretical saturation was achieved. Ultimately, 12 individuals participated in the study. To analyze the collected data, the researchers applied open coding, axial coding, and selective coding techniques. Based on the research findings and data analysis, 64 concepts, 14 subcategories, and 4 main themes were identified as the components constituting the organizational culture model for implementing the general administrative system policies. These components are analyzed within five domains: causal conditions, contextual conditions, intervening conditions, strategies, and consequences. According to the findings, the proposed model suitable for implementing the general policies of the administrative system must be grounded in four core elements: excellence orientation, rule of law orientation, transformational orientation, and idealism orientation. This model should provide an appropriate foundation for the development of an excellence-oriented organizational culture, and more specifically, it should be shaped based on a rule-of-law organizational culture. Moreover, the model should establish a suitable environment for transformational practices in the structure and function of organizational culture and foster the emergence of an ideal-oriented organizational culture.

This study aims to investigate the moderating effect of artificial intelligence use on the relationship between environmental attitude and green marketing. This research adopts a quantitative correlational approach, employing structural equation modeling for analysis. A cross-sectional survey design was utilized to collect primary data from participants. Initially, 450 paper questionnaires were distributed, resulting in 400 completed responses, which reflects a participation rate of 89%. After discarding sixteen incomplete questionnaires, the final valid sample consisted of 384 responses. Data collection was conducted using standardized questionnaires, and the analysis involved Pearson correlation tests alongside structural equation modeling. The findings reveal that environmental attitudes significantly influence green marketing, evidenced by a T-value of 4.527. Furthermore, artificial intelligence use plays a crucial moderating role in the connection between environmental attitudes and green marketing, with a T-value of 6.957. The model fit analysis indicates that the research framework demonstrates a strong fit. The findings highlight the significant impact of artificial intelligence on business operations, ultimately offering actionable insights for implementing AI-enhanced green marketing strategies.

The aim of the present study is to design a marketing model for nature tourism under sanction conditions. To evaluate the validity of the nature tourism marketing model under sanctions, a quantitative research method was employed for data analysis, and the Smart PLS software was used to implement structural equation modeling (SEM). The statistical population consisted of employees of the Iranian Ministry of Cultural Heritage and Tourism, which spans 31 provinces and includes over three thousand permanent and contractual staff members. However, the sample was selected using convenience sampling. Cochran's formula was used to determine the sample size, which was calculated to be 366 individuals. After extracting the questions, the questionnaire was distributed among the selected individuals and subsequently collected. Investigation of the relationships between variables: After evaluating the goodness-of-fit for the measurement models, the structural model, and the overall model—according to the data analysis algorithm in the PLS method—to confirm or reject the hypotheses, the t-value must be greater than 1.96 or less than -1.96. Values falling between these two thresholds indicate the absence of a statistically significant difference between the calculated regression weights and zero at the 95% confidence level. One of the essential and pivotal topics in tourism planning is determining the position of infrastructure and the current status of this sustainable industry in a given region. Achieving economic dynamism and prosperity in the country requires optimal utilization of infrastructure and identification of inequalities, making the regional stratification of nature tourism areas a necessity. By identifying and ranking infrastructure at the regional level, more effective management of tourists can be achieved.