Journal of Resource Management and Decision Engineering

Technological transformations resulting from the Fourth Industrial Revolution (Industry 4.0) have led to a paradigm shift in quality and safety management practices within manufacturing industries. In this regard, the present study aims to design an integrated model for quality and safety management under the Industry 4.0 framework. This research is exploratory in nature and adopts a qualitative methodology based on grounded theory using the Strauss and Corbin (1998) approach. Data were collected through semi-structured interviews with 15 experts from industry, academia, and technology organizations. The data were analyzed using open, axial, and selective coding methods. As a result, 22 conceptual categories were extracted and organized into six core dimensions: causal conditions, contextual conditions, intervening factors, core phenomenon, strategies, and outcomes. The central category was identified as “Designing an Integrated Model of Quality and Safety Management in the Context of Industry 4.0.” According to the findings, factors such as weak technological infrastructure, cultural resistance, insufficient professional training, and lack of supportive policies are among the key barriers to the implementation of this model. Conversely, strategies such as process digitalization, formulation of modern standards, and the development of employees’ digital competencies play a crucial role in the realization of the model. The outcomes of implementing this model include enhanced productivity, improved product quality, and increased safety within industrial environments. By presenting a localized and data-driven model, this study offers a strategic framework that can guide industrial managers, policymakers, and educational institutions on the path toward digital transformation and the simultaneous advancement of organizational quality and safety.

This study aimed to design and validate a human resource allocation model based on a soft skills approach in knowledge-based companies operating in Iraq. The research employed an applied, quantitative, and descriptive-correlational design. The statistical population consisted of employees working in knowledge-based companies across Iraq. Given the infinite size of the population, the sample size was determined to be 384 using Cochran’s formula. Data were collected through a researcher-made questionnaire designed to capture various dimensions and indicators related to soft skills in human resource management. The constructs included identifying required skills, assessing current employee skills, identifying job requirements, appropriate selection and training, and monitoring and evaluating soft skills. Data analysis was conducted using structural equation modeling (SEM) with SmartPLS software, and model fit was evaluated using the GOF index. The results indicated that all hypothesized relationships within the model were statistically significant (t > 1.96). Specifically, human resource allocation based on soft skills showed strong and significant relationships with: evaluation of current employee skills (t = 175.84; factor loading = 0.93), appropriate selection and training of employees (t = 196.219; factor loading = 0.93), identification of required skills (t = 49.372; factor loading = 0.82), identification of job requirements (t = 197.961; factor loading = 0.93), and monitoring and evaluating soft skills (t = 222.504; factor loading = 0.94). The overall model demonstrated a strong fit with a GOF value of 0.67. The findings highlight the strategic role of soft skills in effective human resource allocation. Organizations can enhance employee performance and organizational alignment by investing in identifying, developing, and assessing soft skills. Training programs, continuous feedback, and cross-functional project participation are recommended to improve soft skill capacities. This model provides a robust framework for integrating soft skills into HR strategies in knowledge-based environments.

The aim of the present study is to design and evaluate an organizational green chain model based on intellectual capital in the textile industry of Iraq. This research is exploratory-applied in terms of its objective and descriptive-survey in terms of methodology. The research design follows a mixed-methods approach, combining both qualitative and quantitative methods. The statistical population in the qualitative section includes experts and managers in Iraq's textile industry as well as university professors. A total of 10 individuals were selected for interviews using a combination of snowball and random sampling methods. In the quantitative section, the statistical population includes employees in Iraq’s textile industry. The population is considered unlimited; based on Cochran’s formula, a sample size of 384 participants was determined. The data analysis tool in the qualitative section is thematic analysis, while in the quantitative section it is structural equation modeling using PLS software. The qualitative findings reveal that five main dimensions, along with their components, were extracted from expert interviews. These dimensions include green raw materials, green production processes, sustainable transportation, collaboration with green suppliers, and transparency in sustainable communication. In the quantitative section, the overall model fit, based on the GOF formula, was calculated at 67%, indicating a strong model fit. Additionally, the factor loadings in both first- and second-order constructs were greater than 0.40, confirming the validity of the confirmatory factor analysis.

The objective of the present study is to design a strategic human resource planning model based on the development of artificial intelligence (Case Study: Advertising Companies in Karbala). From a purpose perspective, this study is applied, and in terms of method, it is descriptive-survey. The research follows a mixed-methods approach combining qualitative and quantitative methods. The qualitative population includes advertising companies in Karbala, while the qualitative sample comprises academic experts in the fields of human resources, computer science and artificial intelligence, information systems, and managers of advertising firms in Iraq, selected for qualitative analysis and interviews. The participants in this phase were chosen purposively based on the principle of theoretical saturation. The sampling method employed was snowball sampling. In the quantitative phase, the statistical population consists of employees of advertising companies in Iraq who were asked to complete a questionnaire developed based on factors derived from the qualitative analysis. The quantitative sample size was determined using Cochran’s formula. Given the unlimited population size, the sample size was calculated as 384 respondents, selected randomly. In the qualitative part, data analysis was conducted using thematic analysis, while in the quantitative part, structural equation modeling was used via SmartPLS software. The qualitative results revealed five dimensions for the model: development of organizational culture, recruitment and retention of artificial intelligence-related talents, design of AI systems to enhance HR processes, data analysis and forecasting, and training and development. The model's validity was examined using structural equations. Based on the calculations, the overall model fit index (GOF) was found to be 0.66, indicating a strong model fit. A significant relationship value of 223.512 was obtained between strategic human resource planning and training and development. The second-order factor loading was calculated as 0.949. For the relationship between strategic human resource planning and data analysis and forecasting, the significance value was 169.426, confirming a meaningful relationship. The second-order factor loading was 0.940. For the relationship between strategic human resource planning and organizational culture development, a significance value of 210.672 was obtained, confirming the relationship. The second-order factor loading was 0.948. Regarding the relationship between strategic human resource planning and the recruitment and retention of AI-related talents, a significance value of 167.958 was observed, indicating a significant relationship. The second-order factor loading was calculated as 0.938.

The objective of this study is to design and validate a human resource performance management model with a strategic value development approach to enhance organizational effectiveness and sustainability. This study employed an exploratory-applied research design using a mixed-methods approach. In the qualitative phase, thematic analysis was applied to semi-structured interviews with 10 purposively selected experts from knowledge-based companies in Iraq, based on the principle of theoretical saturation. In the quantitative phase, a cross-sectional survey was conducted with 384 randomly selected employees from the same population, using a researcher-developed questionnaire derived from the qualitative findings. Data were analyzed using structural equation modeling (SEM) via SmartPLS software. Model fit was assessed through GOF (Goodness of Fit) indices, and relationships between latent variables were evaluated using t-values and path coefficients. The findings revealed five key dimensions of the proposed model: goal and standard setting, work performance development, regular feedback and evaluation, reward and incentive system development, and skill and knowledge development. A total of 142 initial codes were extracted in the qualitative phase, which were synthesized into 24 secondary codes. The quantitative analysis confirmed the significance of all hypothesized relationships, with t-values exceeding the 1.96 threshold and second-order factor loadings ranging from 0.921 to 0.942. The GOF value of 0.64 indicated a strong overall model fit, confirming the robustness of the proposed structure. The results demonstrate that strategic HR performance management significantly contributes to setting clear goals, developing competencies, providing effective feedback, and aligning reward systems with organizational strategy. The integration of these components enables organizations to enhance employee motivation, foster continuous improvement, and make data-driven HR decisions. This model provides a comprehensive framework for aligning HRM practices with strategic value creation and offers practical insights for managers seeking to optimize human capital in dynamic environments.

The objective of this study is to examine and evaluate an innovative human resources model tailored to the context of higher education institutions in Iraq, with the aim of enhancing organizational innovation, adaptability, and performance through strategic HR practices. This research adopts a mixed-method exploratory-applied design, combining qualitative and quantitative approaches. The qualitative phase involved thematic analysis of semi-structured interviews with 10 academic experts and senior managers in Iraq’s higher education sector, selected through purposive sampling until theoretical saturation was achieved. The quantitative phase used a researcher-developed questionnaire distributed to 137 participants, including managers, deputies, and HR specialists from higher education institutions, identified using Cochran’s formula. Structural equation modeling (SEM) was applied using Smart PLS to assess the relationships between the components and dimensions of the proposed model. Data were collected through both library and field methods. The results of the qualitative phase led to the identification of five primary dimensions: innovation strategy formulation, development of an innovation-based organizational structure, promotion of an innovative organizational culture, development of innovative systems and processes, and enhancement of human capabilities based on innovative behavior. Quantitative analysis confirmed the statistical significance of all relationships, with all t-values exceeding 1.96 and factor loadings above 0.94, indicating strong model validity. The overall model fit index (GoF) was calculated at 0.69, suggesting a robust model capable of explaining the interactions among variables influencing innovative HR practices in higher education. The study concludes that implementing an innovative human resources model significantly contributes to fostering creativity, collaboration, and strategic adaptability within higher education institutions. The integration of innovation-focused HR practices—such as training, empowerment, participatory decision-making, and performance incentives—creates a conducive environment for sustainable institutional development. These findings offer actionable insights for academic administrators aiming to align HR strategies with organizational innovation goals.

This study aimed to design and evaluate a model for employee acceptance of information technology (IT) with a specific focus on the role of transformational leadership within public sector organizations. The research employed a mixed-method design combining qualitative and quantitative approaches. In the qualitative phase, thematic analysis was conducted based on semi-structured interviews with 10 experts and managers from public organizations in Karbala, selected through purposive and snowball sampling. In the quantitative phase, a survey was administered to 384 randomly selected employees from the same organizational setting using a researcher-developed questionnaire. Data were analyzed using SPSS for descriptive statistics and SmartPLS for structural equation modeling to test the model’s dimensions and paths. The model's goodness-of-fit was evaluated using the GOF index, confirming a strong overall fit (GOF = 0.67). The results revealed four main components in the IT acceptance model: leadership characteristics (e.g., motivating employees, fostering innovation), leadership impact (e.g., promoting knowledge sharing, enhancing security), organizational trust in technology (e.g., managing change, encouraging creativity), and value recognition and rewards (e.g., professional growth, acknowledgment of achievements). All hypothesized relationships were statistically significant with high t-values (e.g., t = 222.35 for the relationship between transformational leadership and trust in technology) and strong second-order factor loadings (ranging from 0.93 to 0.95), confirming the model’s validity.  Transformational leadership significantly influences the acceptance of IT among employees by fostering motivation, building trust in technology, encouraging innovation, and recognizing contributions. These leadership behaviors not only reduce resistance to change but also facilitate effective implementation of digital tools. The study recommends that public organizations invest in transformational leadership development to enhance IT-driven performance and innovation outcomes.

This study aims to design an interpretive structural model (ISM) of human resource productivity with a specific focus on occupational health and safety management systems in Iraq’s construction industry. This research employs a mixed-methods design with both qualitative and quantitative components. Qualitatively, thematic analysis was applied to data gathered through expert interviews using a snowball sampling approach involving 10 university professors and industry managers. In the quantitative phase, the ISM technique was used to examine the structural relationships among dimensions and to stratify them hierarchically. The structural equation modeling (SEM) approach was applied using SmartPLS software with a sample of 384 civil engineers selected based on Cochran’s formula. The reliability and validity of the measurement model were assessed through composite reliability, AVE, and confirmatory factor analysis, while model fit was evaluated using the Goodness of Fit (GoF) index. The qualitative analysis identified five key dimensions of human resource productivity: creating suitable employment, scientific training and development, effective communication, risk management, and rewards and incentives. ISM analysis stratified these dimensions into three levels, with "creating suitable employment" being the most dependent, and "scientific training and development" the most influential. SEM results confirmed all hypothesized relationships among constructs, with scientific training significantly impacting risk management, communication, and incentive systems. The model demonstrated strong convergent validity (AVE > 0.50) and composite reliability (> 0.70), and the overall model fit (GoF = 0.56) was deemed robust. The study confirms that investments in scientific training and development play a central role in enhancing human resource productivity through effective risk management, communication, and reward mechanisms in construction settings. Furthermore, implementing structured risk management systems and integrating immersive safety training technologies can lead to safer, more innovative, and productive work environments in Iraq’s construction industry.