Industrial management
Bahareh Deljoo; Rohollah Ghasemi; Mohsen Moradi moghadam; Ali Mohaghar
Abstract
The food industry has been transformed by the Fourth Industrial Revolution, particularly through the application of the Internet of Things (IoT). This technology enhances efficiency by connecting various components of the factory—both wired and wirelessly—and paves the way for smart factories ...
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The food industry has been transformed by the Fourth Industrial Revolution, particularly through the application of the Internet of Things (IoT). This technology enhances efficiency by connecting various components of the factory—both wired and wirelessly—and paves the way for smart factories aligned with sustainability goals. The aim of this research is to analyze the capability–attractiveness of IoT applications in the food industry based on sustainability indicators and the readiness of selected companies in the food industry of Tehran province to implement these technologies. First, a systematic literature review was conducted to identify relevant IoT applications in the food industry, along with sustainability-based attractiveness indicators and capability criteria. The case study is selected companies in the food industry of Tehran province and their subsidiaries, which are currently deploying IoT technologies across various areas. Using the Best-Worst Method (BWM), the weights of the indicators were determined. Then, decision matrices were developed separately for evaluating the applications based on attractiveness (sustainability) and capability indicators, and each application was scored accordingly. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was then used to obtain final rankings. Based on the capability–attractiveness matrix, the most promising IoT applications identified for implementation in the company include “real-time data collection,” “inventory management and shelf replenishment,” “energy consumption management,” and “smart fire detection systems.” The findings offer valuable insights for identifying and adopting IoT applications in the food industry, considering the capacities and infrastructure of companies.
Introduction
The food industry, a fundamental sector for human needs, faces increasing demand, customer expectations, and intense competition. To ensure food safety and profitability, companies are adopting advanced technologies like the Internet of Things. IoT, through networks of sensors and smart devices, enables intelligent interaction between equipment, machinery, and information systems, enhancing efficiency, streamlining processes, and supporting sustainable development. The Iranian food industry faces challenges such as high food waste, weak supply chain traceability, inefficient resource management, and ongoing quality concerns. IoT can effectively address these issues, yet many companies have not fully adopted it. This research provides a systematic approach to examine and prioritize IoT applications based on sustainability attractiveness and the capability of active companies in Tehran province. The main research question is: What is the implementation priority of IoT applications in active companies in Tehran province based on the attractiveness of each application and the companies' capability to acquire this technology?
Research Background
The Iranian food industry confronts significant challenges that threaten its sustainability and competitiveness, including extensive food waste during production, storage, and distribution, weak traceability in the supply chain, inefficient management of critical resources such as water and energy, and persistent product quality and safety risks. Lack of effective infrastructure for tracking and verifying food authenticity reduces consumer trust and enables food fraud. In this context, IoT technology emerges as a modern and efficient solution. By employing smart sensors to monitor storage and transportation, tracking systems in the supply chain, real-time monitoring of raw materials and products, and wearable devices to enhance worker safety, IoT can increase productivity, improve food safety, reduce waste, and strengthen consumer confidence. Targeted IoT adoption can address structural problems and enhance Iran’s national and international food industry standing. Despite this potential, many companies have not yet fully embraced IoT. This research seeks to provide a systematic approach to examining and prioritizing IoT applications while considering their sustainability benefits and internal company capabilities.
Methodology
This applied research adopts a quantitative, descriptive-survey, and cross-sectional approach, utilizing both library and field methods for data collection. The initial phase involved a systematic literature review to identify IoT applications relevant to the food industry, along with sustainability-based attractiveness indicators and capability criteria. Through this process, twelve key IoT applications were identified, such as real-time data collection, smart fire detection systems, and energy management. Additionally, nine sustainability indicators were defined across economic dimensions—including operational cost savings—social dimensions, such as customer satisfaction, and environmental dimensions, like waste reduction. Furthermore, eight capability indicators were established, covering areas such as platform development, security, and regulatory compliance.
The study targeted experts from the food industry in Tehran province, all with a minimum of five years of experience in IoT-related projects. A judgmental sampling method was employed, and data were collected from seven selected experts. To determine the weights of the attractiveness and capability indicators, the Best-Worst Method (BWM) was applied. The experts completed BWM questionnaires, and the final group weights were derived by calculating the arithmetic mean of their responses. Consistency ratios were also computed to verify the reliability of the comparisons.
Following this, separate decision matrices were constructed to evaluate the twelve IoT applications based on the weighted attractiveness and capability indicators. Each application was scored by the experts using a 10-point Likert scale. The TOPSIS method was subsequently employed to process these matrices, yielding final scores and rankings for the applications according to each dimension.
Finally, a Capability-Attractiveness Matrix (ACM) was developed. The TOPSIS scores for attractiveness, represented on the vertical axis, and capability, on the horizontal axis, were plotted for each application. The mean scores of all applications served as cutoff points, dividing the matrix into four distinct quadrants and thereby enabling strategic prioritization of the IoT applications.
Findings
The BWM analysis revealed the relative importance of the indicators. For attractiveness, the economic dimension was the most critical (0.725), followed by the social (0.175) and environmental (0.100) dimensions. Among all sub-indicators, "operational cost savings" (EC1) had the highest final weight (0.494), underscoring its paramount importance. For capability, "IoT platform development" (CAP3) was the most significant indicator (0.305), followed by "application development" (CAP4) and "security capability" (CAP5). All consistency ratios were within acceptable limits, confirming the reliability of the expert judgments.
The TOPSIS analysis provided separate rankings based on attractiveness and capability. Based on attractiveness (sustainability benefits), the top applications were "real-time data collection" (A1), "inventory management" (A10), and "energy consumption management" (A11). Based on capability (ease of implementation), the top applications were "smart fire detection" (A2), "real-time data collection" (A1), and "energy consumption management" (A11).
The integration of the TOPSIS results into the ACM yielded the final strategic prioritization. The applications were categorized into four quadrants: Quadrant 1 (High Attractiveness, High Capability) contained the most promising applications for immediate implementation: A1 (Real-Time Data Collection), A10 (Inventory Management), A11 (Energy Management), and A2 (Smart Fire Detection). These represent the first priority. Quadrant 2 (High Attractiveness, Low Capability) included applications A5 (Operational Cost Control), A4 (Process Automation), and A8 (Remote Facility Control). They are desirable but require capability-building efforts, marking them as a second priority. Quadrant 3 (Low Attractiveness, High Capability) contained applications A6 (Quality Monitoring) and A7 (Worker Health Monitoring). While companies have the capability, the perceived sustainability benefits are lower. These could be developed after Quadrant 1 applications. Quadrant 4 (Low Attractiveness, Low Capability) included applications A12 (Supplier Tracking), A3 (Worker Tracking), and A9 (Environmental Monitoring), indicating the lowest priority for implementation.
Discussion and conclusion
This study identified and prioritized IoT applications for the food industry in Tehran province using a structured Capability-Attractiveness framework. The findings indicate that the primary focus for companies should be on applications in Quadrant 1, which offer high sustainability benefits and align with existing organizational capabilities. The prominence of real-time data collection, inventory management, and energy management aligns with global trends emphasizing operational efficiency and resource optimization.
The placement of environmental monitoring (A9) in the low-priority quadrant (4) contrasts with international research that emphasizes green technologies. This discrepancy may be attributed to weaker environmental regulations, lower technological infrastructure, or a primary focus on immediate economic gains within the Iranian context.
The prioritization based on the ACM provides a more comprehensive strategy than ranking by attractiveness or capability alone. It allows decision-makers to select applications that not only offer high value but also have a lower implementation risk, considering their specific resources and infrastructure.
In conclusion, this research enhances our understanding of IoT as an emerging and transformative technology in the food industry. It assesses various applications from economic, social, and environmental perspectives while evaluating implementation feasibility. The results can serve as a valuable guide for decision-makers and policymakers in the Iranian food industry, enabling a more strategic and effective adoption of IoT technologies. A limitation of this study is the lack of a detailed technical-economic feasibility analysis for each application. Future research should conduct in-depth studies on the selected applications to identify implementation challenges and provide practical solutions.
project management
Roya Soltani; Ali Nobakhti
Abstract
In this paper, a recommender system based on a multilayer feedforward artificial neural network (ANN) trained by the Levenberg–Marquardt backpropagation algorithm, optimized using a genetic algorithm (GA) to fine-tune both network structure and weights, is proposed to predict competency and recommend ...
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In this paper, a recommender system based on a multilayer feedforward artificial neural network (ANN) trained by the Levenberg–Marquardt backpropagation algorithm, optimized using a genetic algorithm (GA) to fine-tune both network structure and weights, is proposed to predict competency and recommend project managers in project-oriented organizations. The system considers both hard and soft skills, which are essential for sustainable development. The performance of the proposed system was evaluated by a case study within the Iranian construction industry, utilizing the experience of 80 senior managers and experts from the Ministry of Roads and Urban Development of Iran. The results demonstrate the high accuracy of the proposed system in identifying competent project managers. To validate the system, its performance was compared with existing methods in the literature, showing superior accuracy in terms of MSE and RMSE metrics.
Introduction
In today’s dynamic business environment, projects operate within a VUCA context—characterized by volatility, uncertainty, complexity, and ambiguity—that significantly influences managerial decision-making and project outcomes. Rapid technological advancements, economic fluctuations, the complex nature of stakeholder interactions, and resource constraints have made project management an increasingly challenging undertaking. Consequently, the competence of project managers to address a wide range of human resource, technical, and economic challenges, along with their ability to build effective communication and collaboration networks, is a crucial determinant of project success (Omoush, 2020). To ensure successful project delivery, project managers must demonstrate a sound understanding of environmental dynamics and make informed, adaptive decisions that integrate both hard and soft managerial skills—skills that are now more critical than ever for achieving sustainable development. Such abilities reflect the professional competence and strategic agility required for timely and effective decision-making (Karki & Hadikusumo, 2023).
Selecting competent project managers through a data-driven recommender system that matches the desired managerial skills can substantially enhance the effectiveness of project-based organizations. Such a system can transform the manager selection process from subjective judgments to evidence-based decision-making. This approach not only improves the precision of identifying qualified managers but also contributes to better human resource allocation, reduced managerial risk, and enhanced overall project performance. Ultimately, adopting a data-driven recommender framework enables project-based organizations to strengthen their managerial capabilities and achieve a sustainable competitive advantage.
Research Questions
How can a smart recommender system be designed—by integrating ANN and GA—to accurately identify competent project managers in project-based organizations?
To what extent can optimizing the parameters of an ANN using a GA enhance the accuracy of the recommender system?
Literature review
In the literature, various data-driven methods have been developed using machine learning approaches to enhance decision-making, resolve conflicts, and improve project performance, productivity, safety, and workflow in the field of project management. A comprehensive review of the literature reveals that existing predictive models in project management have predominantly focused on forecasting various project outcomes such as quality (Najafi Zangeneh et al., 2020; Fan, 2025), infrastructure costs (Soltanian et al., 2023; Dan, 2024; Chen, 2024; Effat, 2025; Al-Gahtani et al., 2025), dispute occurrences and litigation outcomes (Ayhan et al., 2021), delays (Awada et al., 2021), and construction crew productivity (Sadatnya et al., 2023) through the application of diverse classification algorithms (see Table 1). Despite these advances, the literature lacks studies that aim to develop a predictive model capable of accurately assessing project manager competency using a hybrid framework that combines ANN with metaheuristics. Employing such an approach could provide a robust mechanism for identifying competent project managers and, consequently, enhance the likelihood of successful project delivery in complex and dynamic construction environments.
However, in the literature, the combination of ANN with metaheuristics has been employed to improve prediction accuracy across various domains. These domains include stock market forecasting (Sharma et al., 2022), electricity consumption demand prediction (Azadeh et al., 2007), and patient mortality prediction (Dybowski et al., 1996) (see Table 2).
Methodology
The steps of the proposed recommender system for identifying competent project managers are as follows:
Data preparation: First, a database comprising data related to competency is established and quantified based on the experience of 80 senior managers and specialists. Subsequently, data cleaning is performed, and records with missing values, outliers, or inconsistencies are removed from the database. Finally, 70% of the data is randomly selected for training and 30% for testing.
Neural network architecture design: A feedforward multilayer ANN is designed based on the number of hidden layers (i.e., 1, 2, or 3) and the number of neurons per layer (i.e., 2, 4, or 8). The network is trained using the Levenberg–Marquardt algorithm. After training and testing, the optimal network structure is selected based on MSE and RMSE metrics.
Optimization of neural network weights using genetic algorithm: The weights of the designed ANN are optimized using a GA to improve the network’s predictive performance.
Training and testing the ANN-GA recommender system: The ANN optimized by the GA is first trained and then tested. The performance of the proposed ANN-GA recommender system in identifying competent project managers is evaluated based on the MSE and RMSE criteria.
Results
The ANN model with a three-hidden-layer architecture and 2 neurons per layer demonstrated the best performance in terms of MSE and RMSE, with values of 0.351 and 0.593, respectively. This indicates that the designed network effectively predicts project manager competency. To further enhance prediction accuracy, the network weights were optimized using a GA. The resulting ANN-GA recommender system achieved an MSE of 0.094 and an RMSE of 0.307, showing significantly higher accuracy in identifying competent project managers compared to the non-optimized network (MSE = 0.351, RMSE = 0.593). These findings highlight the effectiveness of combining ANN with GA for data-driven competency assessment.
To validate the proposed recommender system for identifying competent project managers, its prediction error was compared with the algorithms reported by Karki and Hadikusumo (2023). As shown in Table 12, the proposed system demonstrates superior accuracy, highlighting its effectiveness over existing methods.
Discussion
Effective project management in the construction industry, a complex and high-risk sector, requires managers capable of making informed decisions under VUCA conditions. Instead of subjective judgments, experiential biases, and unstructured evaluations, the proposed recommender system can help project-based companies use data-driven, intelligent tools to identify more competent managers, improving project productivity while significantly reducing costs associated with poor managerial decisions. Additionally, the proposed system can serve as a decision-support tool for hiring new project managers or promoting existing ones by analyzing past performance and predicting their potential success in future projects.
Conclusion
The proposed recommender system integrates an ANN with a GA to identify and select competent project managers with high accuracy. Leveraging historical data and uncovering hidden patterns, the optimized ANN accurately predicts managerial competencies based on defined criteria. Validation against existing approaches demonstrates that the GA significantly enhances predictive accuracy, highlighting the system’s potential to improve managerial selection and project outcomes in practice.
Industrial management
Maghsoud Amiri; HamidReza Talaie; Shahab Bayatzadeh
Abstract
In an era of intensifying global competition and unprecedented environmental, economic, and technological changes, organizations require a high level of readiness to adopt Industry 5.0-based technologies and business models. This study aims to investigate the impact of Industry 5.0 readiness on sustainable ...
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In an era of intensifying global competition and unprecedented environmental, economic, and technological changes, organizations require a high level of readiness to adopt Industry 5.0-based technologies and business models. This study aims to investigate the impact of Industry 5.0 readiness on sustainable business growth, considering the mediating roles of efficiency, responsiveness, and competitive advantage, with a case study of Mobarakeh Steel Company in Iran. Despite the growing body of research on Industry 5.0, a literature review indicates that the impact of Industry 5.0 readiness on efficiency, responsiveness, competitive advantage, and sustainable business growth has not yet been examined within an integrated framework. The research is applied in nature and follows a descriptive-correlational design. Data were collected using a standardized questionnaire from a purposive sample of 105 employees. The validity of the instrument, including content, convergent, and discriminant validity, as well as its reliability, was confirmed. The data were analyzed using structural equation modeling (SEM). The results revealed that Industry 5.0 readiness significantly enhances organizational efficiency and responsiveness. These two factors, in turn, strengthen competitive advantage and ultimately lead to sustainable business growth. Moreover, efficiency and responsiveness were found to mediate the relationship between Industry 5.0 readiness and competitive advantage. These findings offer practical guidance for industrial managers aiming to strategically transition toward Industry 5.0 and effectively leverage emerging technologies through a human-centric approach to achieve sustainable growth.
Introduction
The growing complexities of global supply chains, the imperative for sustainability, and the limitations of automation-focused paradigms have accelerated the shift toward Industry 5.0. Unlike Industry 4.0, which predominantly emphasizes automation and digitalization, Industry 5.0 integrates human-centricity, resilience, and sustainability as core values. Industry 5.0 readiness promotes a collaborative interface between humans and smart machines to enhance operational performance while considering social and environmental responsibilities. In this context, Industry 5.0 readiness emerges as a critical construct that reflects an organization’s capability to adopt, internalize, and benefit from emerging technologies such as artificial intelligence, collaborative robots, digital twins, blockchain, and big data analytics in a manner aligned with human and environmental values. While this concept has gained attention globally, empirical investigations into its impact on business outcomes remain limited. The steel industry, given its scale, energy intensity, and central role in economic development, represents a compelling sector for exploring Industry 5.0 transformation. Among leading firms in this domain, Mobarakeh Steel Company (MSC) in Iran has launched several digital transformation initiatives aligning itself with the broader agenda of Industry 5.0. This study examines the impact of Industry 5.0 readiness on sustainable business growth, considering the mediating roles of operational efficiency, organizational responsiveness, and competitive advantage. Despite the growing body of research on Industry 5.0, a literature review indicates that the impact of Industry 5.0 readiness on efficiency, responsiveness, competitive advantage, and sustainable business growth has not yet been examined within an integrated framework.
Methodology
This study employed a quantitative, applied, and correlational design to investigate the effect of Industry 5.0 readiness on sustainable business growth, mediated by efficiency, responsiveness, and competitive advantage. The target population comprised employees of Mobarakeh Steel Company, which has undertaken several initiatives aligned with Industry 5.0 principles. A structured questionnaire was designed to capture respondents' perceptions of their organization's readiness for Industry 5.0, its operational and strategic capabilities, and sustainable growth outcomes. The instrument included 17 items distributed across six constructs: Industry 5.0 readiness, efficiency, responsiveness, competitive advantage, and sustainable business growth. All items were measured on a five-point Likert scale, ranging from strongly disagree (1) to strongly agree (5). The sampling strategy was purposive, aimed at selecting employees involved in digital transformation initiatives or operational excellence programs. A total of 105 valid responses were collected. Despite the limitation of not including executive-level policymakers, the selected respondents possessed relevant knowledge of the technological and operational transformations within MSC. To ensure validity and reliability, several procedures were undertaken. Convergent and discriminant validity were assessed through Confirmatory Factor Analysis (CFA) using outer loadings, Average Variance Extracted (AVE), and the Fornell–Larcker criterion. The composite reliability (CR) and Cronbach’s alpha values for all constructs exceeded the accepted threshold of 0.7, indicating acceptable internal consistency. The hypothesized relationships were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 3.0.
Findings
The analysis of the structural model using PLS-SEM revealed several statistically significant relationships that validate the hypothesized impact pathways of Industry 5.0 readiness on sustainable business growth. The R² values for key endogenous variables, efficiency (0.57), responsiveness (0.54), competitive advantage (0.62), and sustainable business growth (0.66), indicate a good level of explanatory power for the model. The effect sizes (f²) were also moderate to strong, particularly for the paths from Industry 5.0 readiness to efficiency and responsiveness. The results confirmed that Industry 5.0 readiness has a significant and positive impact on both operational efficiency and organizational responsiveness. This finding aligns with Nazarian and Khan (2024), who demonstrated similar performance outcomes in European manufacturing contexts, and supports the idea that transitioning toward human-machine collaboration and real-time data systems yields operational improvements. In turn, efficiency and responsiveness were found to enhance competitive advantage, highlighting their mediating roles significantly. This corroborates Madhavan et al. (2024), who found that Industry 5.0-oriented capabilities in Thai marine SMEs improved competitive positioning through operational excellence. In the case of MSC, efficiency gains through AI-based predictive maintenance and responsiveness improvements via flexible scheduling systems contributed to a stronger market stance. Moreover, competitive advantage was shown to influence sustainable business growth significantly, suggesting that firms that achieve superior operational and strategic performance are more likely to maintain long-term viability and growth. This is consistent with studies by Alabi et al. (2025) and Bayatzadeh & Talaei (2024), who emphasized the link between technological transformation and long-term sustainability in industrial ecosystems. Importantly, the indirect effects of Industry 5.0 readiness on competitive advantage, through both efficiency and responsiveness, were also significant, confirming the partial mediating roles of these two capabilities. These results suggest that readiness for Industry 5.0 contributes not only to immediate performance benefits but also to longer-term strategic positioning, especially when internal capabilities are leveraged effectively.
Discussion and Conclusion
This study provides a nuanced exploration of how Industry 5.0 readiness contributes to sustainable business growth by enhancing efficiency, responsiveness, and competitive advantage, using the case of Mobarakeh Steel Company (MSC) in Iran. The study confirms the arguments of Nazarian and Khan (2024) that efficiency and responsiveness are critical conduits for the value generated by Industry 5.0 principles, such as smart automation and AI-human collaboration. It also aligns with the model proposed by Madhavan et al. (2024), showing that Industry 5.0 readiness can trigger significant organizational improvements when accompanied by complementary capabilities. Practically, the research illustrates how Industry 5.0 readiness serves as an enabler of sustainable growth, even in emerging economies where full deployment of Industry 5.0 technologies is not yet widespread. In the case of MSC, evidence from internal reports and strategic documents confirms the the Industry 5.0 readiness. Moreover, the results indicate that efficiency and responsiveness function as effective mediators, reinforcing the notion that performance benefits are not direct consequences of technology adoption, but instead are of the organization’s capacity to integrate and leverage such technologies. Looking ahead, these findings suggest that companies aiming to embark on their Industry 5.0 journey should focus not only on acquiring advanced technologies but also on developing internal capabilities, promoting a human-centric culture, and aligning operations with sustainability goals. Future research could explore cross-industry comparisons or longitudinal analyses to assess the evolution of Industry 5.0 readiness and its impact over time.
uncertainty
Faramarz Nouri; Alireza Fazlzadeh; Seyedsamad Hosseini; Samad Rahimiaghdam
Abstract
This research aims to design a model for the internationalization of companies operating under uncertainty, seeking to address the shortcomings of existing one-dimensional approaches that ignore the dynamic and behavioral complexities of stakeholders. This research is applied and developmental in terms ...
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This research aims to design a model for the internationalization of companies operating under uncertainty, seeking to address the shortcomings of existing one-dimensional approaches that ignore the dynamic and behavioral complexities of stakeholders. This research is applied and developmental in terms of its purpose, and mixed and exploratory in terms of its type. Data were collected through interviews and questionnaires from managers and experts involved in the internationalization process of small and medium-sized companies, who were selected through purposive sampling. The final model was developed using fuzzy soft systems methodology and fuzzy structural interpretive modeling. The final model identifies key dimensions for the internationalization of companies under uncertainty. These dimensions include: monitoring macro trends, developing a technology ecosystem, financial and advisory support, developing standards, managing stakeholder relationships, developing specialized training centers, advanced intelligent systems for forecasting, developing flexible entry strategies, networking and strategic alliances, optimizing product quality and processes, and managing supply chain complexity. The presented model serves as an analytical tool for a deeper understanding of the complexities of internationalization in small and medium-sized enterprises and for more effective policymaking in the face of uncertainties in the business environment.IntroductionTo maintain and improve their competitive standing in the dynamic global marketplace, many businesses are adopting an internationalization strategy. The increasing volume of international trade and investment, coupled with the central role of firms in this arena, has made corporate internationalization a crucial topic in international business studies. The internationalization of a company is the process through which a business expands its operations beyond national borders and enters foreign markets. This process encompasses a wide range of activities, from establishing subsidiaries abroad to exporting goods and services internationally. By adopting an internationalization approach, companies can mitigate their business risks and achieve potentially greater returns compared to operating solely in domestic markets. However, the successful implementation of this strategy is influenced by numerous factors that must be carefully considered. When a company decides to enter international markets, it encounters distinct institutional and cultural characteristics that vary from country to country. Consequently, to succeed in this arena and owing to the inherent complexity and uncertainty of the process, managers require sufficient knowledge and awareness to efficiently process and analyze information and make sound decisions. This knowledge base enables them to effectively address the challenges arising from cultural and institutional differences.In theoretical literature, the process of company internationalization is usually explained through two main perspectives. The first perspective suggests that the internationalization of companies occurs via an incremental process involving stages, beginning in the domestic market and gradually expanding to international ones. Conversely, the second perspective, derived from international entrepreneurship, posits that a company can access several global markets from the outset. Considering the diverse perspectives in international business research, Glaum & Oesterle contend that establishing a unified definition for company internationalization has remained difficult and challenging, even after several years of dedicated research in the field.Johanson & Vahlne categorize company internationalization into four main stages: start-up, full launch, independent international activities, and joint international activities. Their viewpoint is predicated on the idea that internationalization involves creating and expanding connections with other actors within a foreign network. They assert that a company’s internationalization process initiates with interactions in local networks, subsequently extending to other countries as the business relationships within these networks mature. This development is accomplished through establishing new connections within networks new to the company, strengthening existing relationships in these networks, and ultimately integrating networks across different countries. Johanson & Vahlne define internationalization as the process of developing business relationship networks in other countries through expansion, penetration, and integration. The focus of this definition is clearly on relationships and networks, which subsequently facilitate companies’ entry into foreign market networks.Liu & Ko view internationalization as the process of organizing and deploying global economic resources, such as capital, raw materials, human resources, information, markets, and management capabilities. Costa et al. believe that internationalization enables companies to access new markets to create novel business activities and generate profit. Finally, Dominguez & Mayrhofer state that, according to the born global model, certain companies rapidly enter a significant number of markets from inception. In fact, born global companies are those that have achieved a considerable degree of internationalization right from their inception or early operational stages. Therefore, considering the presence of diverse and influential stakeholders and actors, dynamic and behavioral complexities, and inherent uncertainties in the internationalization process of SMEs, coupled with the significant share of SMEs, the main research question is as follows:What is the internationalization model for companies in small and medium-sized industries (SMEs) under conditions of uncertainty?MethodologyThis research employs a mixed-methods approach (both qualitative and quantitative) with an applied-developmental purpose and a descriptive-exploratory strategy. The primary aim is to provide solutions for the challenges of SME internationalization and to provide decision-making support to relevant stakeholders. Furthermore, due to a scarcity of sufficient information, the study also seeks to develop a comprehensive model for SME internationalization under conditions of uncertainty. The qualitative phase commenced with a literature review encompassing articles, books, projects, and documents related to SME internationalization. Subsequently, expert opinions, gathered through a Soft Systems Methodology (SSM) framework and Fuzzy Interpretive Structural Modeling (FISM), were integrated to develop a final model for SME internationalization under uncertainty.ResultsThis research utilized the Soft Systems Methodology (SSM) to investigate the internationalization of SMEs. In the initial stage, dedicated to encountering and understanding the problematic situation, in-depth interviews with experts and stakeholders were conducted to extract actors from both the distant and near environments, leading to the development of a Rich Picture. The findings of this research confirm that the internationalization of SMEs is a multifaceted and dynamic process fundamentally dependent on the coordination and synergy between various factors across different levels. The conceptual model developed illustrates that success hinges upon a systematic approach that simultaneously integrates internal company factors, environmental dynamics, and institutional frameworks. Effective implementation of this model is contingent upon continuous monitoring of global activities and trends, rigorous assurance of methodological compatibility, proactive prediction and consideration of inherent uncertainties, and the committed participation of all key stakeholders, including SMEs, governmental bodies (such as the Industry, Mining, and Trade Organization), supporting entities (like the Chamber of Commerce and Industrial Estates Company), and regulatory agencies (Customs and the Standards Organization).
supply chain management
Maryam Hosinie; davood andalib ardakani; Alireza Naser Sadrabadi; Seyed Mojtaba Hosseini Bamkan
Abstract
Despite organizations with Industry 4.0 technologies holding potential for enhancing supply chain resilience, the systematic integration of these two domains has faced delays. The present study aims to analyze the causal relationships among factors influencing the implementation of Industry 4.0 in resilient ...
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Despite organizations with Industry 4.0 technologies holding potential for enhancing supply chain resilience, the systematic integration of these two domains has faced delays. The present study aims to analyze the causal relationships among factors influencing the implementation of Industry 4.0 in resilient supply chain management within the Yazd steel industry. In the first phase, factors were identified and categorized using the qualitative meta-synthesis method, resulting in the identification of 21 factors across five dimensions: managerial-institutional, technological-structural, organizational, operational, and cultural. In the second phase, the fuzzy DEMATEL method was employed to determine the causal relationships among the identified factors. The results revealed that the managerial-institutional dimension is the most influential, while the operational dimension is the most influenced in the implementation of Industry 4.0 in resilient supply chain management. This study analyzes the causal relationships among factors and provides a framework for evaluating the link between the resilient supply chain and Industry 4.0.
Introduction
In recent years, global supply chains have faced unprecedented challenges. Supply chain resilience can be defined as an organization's ability to respond effectively to various disruptions, which can be assessed through two key components: “resistance” and “recovery speed.” These capabilities enable organizations to continue competing in today's dynamic and highly uncertain environments. Consequently, understanding the mechanism through which supply chains can enhance their resilience has recently garnered significant attention in the fields of operations and supply chain management. One mechanism that has gained attention in recent years is the concept of Industry 4.0 as a transformative strategy. This new paradigm, based on digitalization and process integration, enables the creation of a “Resilient Supply Chain 4.0.”
Despite the growing attention to both resilience and Industry 4.0 concepts, a review of the existing literature reveals significant research gaps. First, previous studies have primarily examined these two areas in isolation, and their systematic integration has been delayed. Second, the main focus has been on the technologies themselves, with less attention given to the systemic and network analysis of interactions between different dimensions. Third, this field of study has been particularly neglected in the context of Iran's strategic industries, such as the steel industry.
These gaps increasingly highlight the need for research that can explain the relationships between influencing factors using an integrated approach. Aiming to fill these gaps, the present study provides a model of factors affecting the implementation of Industry 4.0 in resilient supply chain management within Iran's steel industry. To achieve this research objective, a mixed-methods approach was used. In the first step, a comprehensive set of influencing factors and dimensions was identified using the qualitative meta-synthesis method. Then, in the subsequent stage, the causal relationships among the identified factors were measured and analyzed using the Fuzzy DEMATEL method.
Methodology
This research is considered an applied-developmental study in terms of its outcome, as it seeks to develop a novel scientific model of the factors affecting the implementation of Industry 4.0 in resilient supply chain management. For this purpose, in the first stage, the factors affecting the implementation of Industry 4.0 in resilient supply chain management were identified through the meta-synthesis method. In the second stage of the research, the authors analyzed the cause-and-effect relationships between these factors using the Fuzzy DEMATEL method.
Meta-synthesis is a qualitative method that provides researchers with a systematic perspective by synthesizing various types of studies, generating new and fundamental themes. In the first stage of this study, the statistical population consisted of all studies published in reputable domestic and international scientific databases related to factors affecting the implementation of Industry 4.0 in resilient supply chain management up to the time of the research. In the second stage, the statistical population of the study comprised professors and managers specializing in digital technologies and familiar with supply chain management in the steel industries of Yazd. Using purposive sampling, 10 of these individuals were selected to participate in the stage of analyzing the causal relationships between the factors.
Findings
The findings of the meta-synthesis method led to the identification of 21 factors influencing the implementation of Industry 4.0 in resilient supply chain management. Based on thematic similarity, these factors were classified into five new components. These components are: Managerial-Institutional, Technological-Structural, Organizational, Operational, and Cultural. Furthermore, at the conclusion of the meta-synthesis method, Cohen's Kappa coefficient was employed to analyze and assess the quality control and reliability of the model. The resulting Kappa value at this stage was 0.49, indicating a suitable level of agreement between the researcher and the expert. A survey of 10 experts and the analysis of their opinions using the Fuzzy DEMATEL method revealed the following:
Within the Managerial-Institutional dimension, “senior management's commitment and support for implementing Industry 4.0 technologies” has the greatest influence on other factors, while the indicator “development of supply chain planning” is the most influenced.
Within the Technological-Structural dimension, among all factors, the indicator “investment in Industry 4.0 technologies” has the greatest influence, and the indicator “capability to provide changes in supply chain design or mapping” is the most influenced.
Within the Organizational dimension, the indicator “effective collaboration and communication among supply chain partners and stakeholders” has the greatest influence, while the indicator “establishing security and safety assurance in creating a resilient supply chain” is the most influenced compared to other indicators in this dimension.
Within the Operational dimension, among all factors, the indicator “developing speed capability for a resilient supply chain” has the greatest influence, and the indicator “utilizing maximum capacity in the supply chain” is the most influenced.
Within the Cultural dimension, the indicator “government support for the adoption of Industry 4.0 technologies” has the greatest influence, while the indicator “developing a resilient supply chain culture within the organization” is the most influenced compared to other indicators in this dimension.
Conclusion
This study employed a mixed-methodology (meta-synthesis and Fuzzy DEMATEL) to propose a causal model of factors affecting Industry 4.0 implementation for resilient supply chain management in Yazd's steel industry. Findings reveal that digital transformation and achieving resilience constitute a systemic, multi-dimensional process. Managerial-Institutional, Technological-Structural, and Cultural dimensions function as causal factors, while Organizational and Operational dimensions act as effect-dependent, mediating variables.
“Senior management commitment and support” was the most influential factor, aligning with Özkan-Özen et al. (2020) and Agarwal et al. (2022), underscoring the critical role of leadership and strategic resource allocation for such transformative, high-risk projects. “Investment in Industry 4.0 technologies” was the key Technological-Structural driver, confirming findings by Raja Santhi & Muthuswamy (2022) and Marinagi et al. (2023), as it enables real-time monitoring and transparency. Culturally, “government support” was paramount, consistent with Al-Akilly et al. (2024) and Gade et al. (2020), highlighting the need for national strategies and incentives in emerging adoption contexts like Iran.
Consequently, resilience is not achieved through technology alone but through the dynamic interplay of soft (culture, leadership) and hard (technology, investment) factors. This provides managers with a strategic roadmap: begin by securing top-management commitment, followed by targeted technological investments, and then focus on enhancing organizational collaboration and human resource training. However, these findings should be interpreted in consideration of limitations, including reliance on expert judgment, a limited sample size, and the model's static nature, suggesting that future research should explore the impact of specific technologies like AI and blockchain on resilience.
multiple-criteria decision-making
Mojtaba Farrokh
Abstract
Nowadays, despite the growing awareness of evaluating suppliers based on sustainability aspects, there are still limitations in selecting suppliers according to sustainable performance due to the lack of a comprehensive list of sustainability criteria and well-developed methods for assessing them. Given ...
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Nowadays, despite the growing awareness of evaluating suppliers based on sustainability aspects, there are still limitations in selecting suppliers according to sustainable performance due to the lack of a comprehensive list of sustainability criteria and well-developed methods for assessing them. Given the complexity and uncertainty surrounding these criteria and the supplier evaluation process, along with the need for high precision and sensitivity, this study aims to apply a rough-fuzzy DEMATEL-TOPSIS approach to rank meat and livestock suppliers. This hybrid method is designed to manage both internal and external uncertainties as well as the complexity of sustainability criteria. In the first phase, the rough-fuzzy DEMATEL method is used to determine the weights and interrelationships among the criteria. In the second phase, the rough-fuzzy TOPSIS method is employed to rank the suppliers. The applicability of this approach is examined through a case study in the meat supply chain. The results reveal that five criteria—cost, livestock health and meat freshness, impact on the local community, delivery reliability, and technological capability—are the most influential factors in selecting sustainable suppliers.
Introduction
The selection of sustainable suppliers for meat and livestock has become a central topic within the food supply chain (Mohammed, 2020; Islam et al., 2024). Given the increasing global concerns regarding climate change, improving animal welfare conditions, and ensuring food quality and safety, the need for suppliers adhering to sustainability principles is becoming more urgent. Sustainable suppliers must not only comply with environmental requirements but also pay special attention to social and economic aspects to meet customer and community needs (Masudin et al., 2024; Singh et al., 2025). This paper examines the criteria and challenges associated with selecting sustainable suppliers in the meat and livestock industry and discusses innovative methods for evaluating and improving their performance.
This study proposes a methodology for selecting sustainable suppliers by developing a rough-fuzzy DEMATEL-TOPSIS approach that considers internal and external uncertainties. This approach offers several advantages: first, it combines fuzzy and rough sets—merging internal and external uncertainty management (Chen et al., 2019). Second, the proposed rough-fuzzy method simplifies the understanding of uncertainty using convex polygons. Third, the integration of fuzzy sets and rough sets provides a clear approach to managing various types of uncertainties, reducing distortions that could lead to incorrect outcomes (Chen et al., 2019; Stević et al., 2025). In this research, the Shahrvand Company was selected as a case study.
Literature review
Regarding the selection of meat and livestock suppliers, although selection criteria have been well developed, there is no consensus on the number of criteria or the overarching theory that defines the sustainability criteria chosen. Alikhani and colleagues (2019) show that strategic meat supplier selection is a multifaceted process that requires considering various factors, including sustainability and risk, yet no comprehensive research has simultaneously addressed both factors. According to them, traditional decision models in this area cannot distinguish sufficiently between different candidates, especially under conditions involving subjective judgments and separate criteria for each supplier. This study presents a multi-criteria approach utilizing fuzzy sets and a Data Envelopment Analysis (DEA) model that considers risk and sustainability simultaneously in supplier evaluation. Mohammed (2020) indicates that evaluating and selecting meat suppliers based on sustainability, involving environmental, social, and economic criteria, requires multi-criteria decision-making methods and integrated fuzzy multi-criteria techniques. This research develops an integrated approach based on fuzzy multi-criteria techniques for assessing, selecting, and optimally allocating suppliers in the meat supply chain, contributing to more comprehensive and sustainable decision-making processes. Khan and Ali (2021) demonstrate that selecting a sustainable supplier in the meat distribution chain involves analyzing multiple factors, including environmental, economic, and social dimensions. Furthermore, innovative methods such as interpretive structural modeling (ISM) and fuzzy VIKOR were employed to identify key factors and evaluate suppliers in the Pakistani meat chain.
Developing a systematic methodology that simultaneously considers these two types of internal and external uncertainties is essential. To address this issue, Chen et al. (2019) used rough-fuzzy sets within a DEMATEL-ANP framework to evaluate the needs for sustainable value in product service systems, providing a valuable reference for managing internal and external uncertainties concurrently.
Methodology
In this study, after gathering the effective criteria for supplier evaluation from a review of the literature in reputable databases and through interaction with the planning, commercial, production, research and development, and quality control departments of the studied company, a total of 14 sustainability indicators were selected. Additionally, five supplier companies were evaluated as decision-making model options. One of the objectives of this research is to examine the interrelationships among sustainability criteria across economic, social, and environmental dimensions. The proposed approach introduces a new framework for evaluating and selecting suppliers based on sustainability criteria. In the first stage, the rough-fuzzy DEMATEL method is used to determine the internal relationships among these criteria and their weights. In the second stage, the rough-fuzzy TOPSIS method is employed to rank the suppliers. The use of fuzzy numbers allows for consideration of external and internal impacts in selecting a sustainable supplier, providing more precise information for decision-making regarding the criteria and improving the accuracy of the ranking results (Chen et al., 2019).
Discussion and conclusion
Based on the rankings derived from the rough-fuzzy DEMATEL method, the five top criteria are cost, livestock health and meat freshness, impact on the local community, delivery reliability, and technological capability, in that order. The analysis and ranking of the factors influencing the selection of sustainable suppliers for meat supply show that cost, livestock health, and meat freshness are the highest priority criteria. These findings suggest that organizational managers should primarily focus on controlling and improving factors related to cost and product quality, namely livestock health and meat freshness, as they directly affect customer satisfaction, supply process effectiveness, and organizational credibility. The criteria related to impact on the local community and delivery reliability also hold significant importance but are ranked lower; this indicates that, alongside quality and cost, special attention should be given to social interactions, especially considering the requirements for development and long-term sustainability.
Therefore, organizations should consider strategies that not only focus on economic criteria but also emphasize social aspects, enabling them to identify and develop leading and sustainable suppliers in competitive markets. This ranking also provides suppliers with insights to recognize their weaknesses and areas needing improvement, guiding them toward performance enhancement and alignment with sustainability goals.
multiple-criteria decision-making
mahdi mashhadikhani; alireza poorebrahimi; mostafa moballeghi
Abstract
Artificial intelligence, through process optimization, productivity enhancement, and cost reduction, has created a significant transformation in production, management, and innovation. Accordingly, the present study aims to examine the mutual effects of parameters influencing the adoption of AI-based ...
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Artificial intelligence, through process optimization, productivity enhancement, and cost reduction, has created a significant transformation in production, management, and innovation. Accordingly, the present study aims to examine the mutual effects of parameters influencing the adoption of AI-based technologies: A case study of Kerman Motor Company. The research method is applied in terms of purpose and survey-based in terms of data collection. Data were gathered through the distribution of 130 questionnaires among the employees of Kerman Motor Automotive Company, selected by simple random sampling using Cochran’s formula. The measurement instruments were the technology adoption questionnaires developed by Chatterjee et al. (2021) and Shon & Vawn (2020). For data analysis, structural equation modeling (SEM) was employed. The findings indicated that employees’ subjective norms have a positive and significant effect on perceived usefulness and perceived ease of use of AI-based technologies in the automotive company. Perceived usefulness positively and significantly affects employees’ behavioral intention and attitudes toward the use of AI-based technologies. Perceived ease of use positively and significantly influences employees’ attitudes toward the use of AI-based technologies, and attitudes positively and significantly influence behavioral intention. Finally, behavioral intention to use AI-based technologies in the automotive company has a positive and significant effect on actual use. Overall, the results revealed that the effect of all research variables was positive and significant, and all research hypotheses were supported.
