Future of Work and Digital Management Journal

This study aimed to identify and explain the challenges associated with employee recruitment in food-industry enterprises located in Afghanistan’s industrial parks. The study adopted a qualitative and inductive approach grounded in the interpretivist paradigm, which emphasizes subjectivity, social interaction, and the interpretation of meaning. A phenomenographic research strategy was selected to examine the lived experiences of experts and human resource managers. Data were collected through semi-structured interviews and analyzed using thematic analysis with the assistance of MAXQDA software. The study population consisted of experts and senior human resource managers with at least 10 years of professional experience. Purposive and snowball sampling continued until theoretical saturation was achieved. To establish the trustworthiness of the findings, several techniques were employed, including member checking, constant data comparison, detailed documentation of the research stages, and the provision of a rich description of the research context. Accordingly, the study provided a foundation for an in-depth examination of the challenges associated with inappropriate employee recruitment. The findings indicated that a total of 15 components and 78 categories were identified. The components included institutional, governance, and company macro-environment challenges; structural and systemic challenges in the recruitment process; managerial and leadership challenges in the recruitment process; workforce skill-related challenges; economic and financial challenges affecting the recruitment process; infrastructural and technological challenges in the recruitment process; cultural, social, and ethical challenges associated with recruitment and hiring; challenges related to the localization of the recruitment process; political challenges and human resource risks in recruitment and promotion; challenges in the employee attraction and selection process before hiring; challenges related to standards, regulations, and compliance during recruitment; communication and employer-branding challenges; legitimacy and trust challenges during recruitment; and challenges associated with recruiting applicants with low levels of literacy. The results demonstrated that identifying these challenges, which essentially involves recognizing the existing problems, can contribute to improving the development of employee attraction and recruitment processes.

The present study aimed to identify the dimensions and components of smart manufacturing systems in the automotive industry with an emphasis on the application of Artificial Intelligence (AI) and the Internet of Things (IoT), and to develop a comprehensive implementation model based on the perspectives of industry and academic experts. This study was conducted using a qualitative exploratory approach. Participants consisted of 14 experts from the automotive industry and academia who were selected through purposive sampling based on their expertise in smart manufacturing, Industry 4.0 technologies, artificial intelligence, industrial automation, and digital transformation. Data were collected through semi-structured, in-depth interviews and analyzed using thematic analysis with the support of MAXQDA 2020 software. The coding process was performed in two iterative stages, including initial and secondary coding. Through continuous comparison, refinement, and integration of codes, 84 unique open codes were extracted and subsequently organized into axial categories and higher-order themes. To ensure rigor and trustworthiness, member checking, peer review, and audit trail procedures were employed throughout the analytical process. The findings revealed a comprehensive paradigm model for the implementation of smart manufacturing systems in automotive parts manufacturing. The model identified two major causal conditions, including domestic and international competitive pressure and infrastructural, functional, and technological challenges. Contextual conditions comprised human resources, skills and organizational culture, as well as economic and implementation considerations. Intervening conditions included cybersecurity and risk management, and system quality and reliability. Four major strategic dimensions were identified, namely advanced automation and robotics, artificial intelligence-driven data analytics, Industrial Internet of Things (IIoT) and connectivity development, and smart supply chain and logistics management. The implementation of these strategies was found to result in structural and functional improvements, economic efficiencies, enhanced customer orientation, production flexibility and personalization, and the development of management and technological knowledge. Collectively, the findings demonstrated that successful smart manufacturing implementation requires the integrated alignment of technological, organizational, strategic, and human-resource factors. The study provides a holistic framework for understanding and implementing smart manufacturing systems within the automotive industry. The proposed model demonstrates that artificial intelligence and Internet of Things technologies function as central enablers of intelligent production environments, but their effectiveness depends on organizational readiness, technological infrastructure, cybersecurity capabilities, and strategic management support.

Educational planning based on the agricultural value chain, as a comprehensive and systematic approach, can make a substantial contribution to improving quality and productivity in the agricultural sector. Therefore, the aim of the present study was to design an educational planning model based on the agricultural value chain in citrus orchards in northern Iran. This study is classified as qualitative grounded-theory research. The statistical population consisted of 20 experts and faculty members of the Agricultural Research, Education and Extension Organization. Interviews were used for data collection, and MAXQDA 2020 software was employed for data analysis. The findings identified the research components, including causal conditions consisting of core content, educational activities, and supplementary programs; the central phenomenon, namely educational programs based on the agricultural value chain; contextual factors including macro-level policies and culture; intervening factors including evaluation, core policies, competition, and educational approaches; strategies including organizational strategies and environmental strategies; and outcomes including optimal value chain management, quality orientation, and educational enhancement.

The objective of this study was to systematically identify, synthesize, classify, and prioritize the barriers to open innovation adoption by developing an integrated multidimensional framework that explains the interrelated organizational, behavioral, structural, capability-based, inter-organizational, and institutional obstacles that hinder effective implementation of open innovation across diverse organizational contexts. This study employed a qualitative meta-synthesis design following the seven-step procedure proposed by Sandelowski and Barroso. A systematic search was conducted across Web of Science, Scopus, ScienceDirect, Google Scholar, and specialized innovation management journals. The search covered studies published between 2003 and 2025 and used combinations of keywords related to open innovation barriers, challenges, and resistance. An initial pool of 312 studies was identified and subjected to relevance screening, conceptual boundary assessment, and quality appraisal. Ultimately, 24 peer-reviewed empirical studies were retained for analysis. Data extraction and synthesis were performed through open coding, axial coding, and selective coding procedures. A qualitative prioritization matrix based on frequency of occurrence and depth of emphasis across studies was subsequently applied to determine the relative significance of identified barriers. The meta-synthesis revealed a comprehensive taxonomy consisting of six major dimensions and 26 subcategories of barriers to open innovation adoption. The six dimensions included cultural-behavioral, organizational-managerial, knowledge and capability, structural-process, inter-organizational and network, and institutional-environmental barriers. Qualitative prioritization demonstrated that the most critical barriers were resistance to external ideas (Not-Invented-Here syndrome), organizational risk aversion, structural rigidity, misaligned incentive systems, and weak absorptive capacity. The findings further indicated that barriers are highly interdependent and operate as a systemic configuration rather than as isolated constraints. Contextual analysis showed substantial variation across organizational settings: bureaucratic constraints dominated public organizations, resource limitations and skill deficits were particularly salient in SMEs, structural inertia and cultural resistance characterized large firms, and ecosystem weakness and inter-organizational mistrust were especially prominent in emerging economies. Overall, cultural and structural barriers emerged as the most influential constraints on successful open innovation adoption, exceeding the relative importance of technological factors. The study concludes that the successful adoption of open innovation depends primarily on overcoming deeply embedded cultural, organizational, and governance-related barriers rather than merely addressing technological challenges. The proposed multidimensional framework advances understanding of how barriers interact across organizational levels and provides a practical basis for prioritizing interventions.

In today's fast-paced and highly competitive environment, small and medium-sized enterprises (SMEs) play a crucial role in the economic development of countries. However, one of the major challenges facing these organizations is employee retention and the prevention of workforce turnover. Employee turnover is a complex and multifaceted phenomenon that can lead to reduced productivity, increased recruitment and training costs, and the erosion of an organization's competitive advantage. In this context, quality management, as one of the fundamental components of organizational performance improvement, can play a significant role in enhancing job satisfaction, increasing organizational commitment, and ultimately reducing turnover rates. The primary objective of this study is to evaluate the role of quality management in human resource retention, with a particular focus on the factors influencing employees' turnover intentions in small and medium-sized enterprises. In other words, this study seeks to answer the key question of whether the implementation of quality management principles and practices can influence employees' decisions to remain with the organization. The research employed a descriptive-survey design. Data were collected using two standardized questionnaires: a Quality Management Questionnaire and a Turnover Intention Questionnaire, which measures employees' propensity to leave the organization. The study population for the turnover intention questionnaire consisted of employees working in active SMEs in Iran, while the study population for the quality management questionnaire consisted of managers of these organizations. To analyze the data, appropriate statistical models, including multiple regression analysis using the Enter method, were applied to examine the relationships among variables accurately. The statistical analyses indicate that all dimensions of the quality management system, including organizational structure, leadership, planning, support, operational activities, employee identification and training, empowerment, performance evaluation, and continuous improvement, are significantly associated with reduced employee turnover intention. These findings demonstrate that the implementation of comprehensive quality management processes not only enhances organizational performance but also serves as an effective strategy for controlling and reducing employee turnover rates. Overall, the results of this study emphasize the importance of quality management and demonstrate that improving quality across different organizational levels not only leads to enhanced operational performance but also serves as a critical factor in preserving organizational human capital and preventing employee turnover.