In the current digital era, implementing an effective governance system is crucial for project-based organizations (PBOs). The scientific issue addressed in this study is the lack of understanding of how project governance interacts with digital maturity and organizational parameters in driving innovative project success in PBOs. This study aims to investigate the relationship between project governance and innovative project success, mediated by digital maturity, and moderated by transformational leadership and flexible culture. Drawing on the resource-based view (RBV) and dynamic capabilities theory (DCT), we develop a theoretical model and employ a field survey with 215 Pakistani PBOs, analyzed using PLS-SEM. RBV examines how project governance within an organization operates, while DCT demonstrates the adaptive capabilities of digital maturity through culture and leadership practices that are associated with success. The empirical findings demonstrate that project governance is positively associated with innovative project success both directly and indirectly through the mediation of digital maturity. Furthermore, the relationship between digital maturity and innovative project success is also positively associated with transformational leadership and flexible culture. The study findings show that effective governance in digitally mature organizations, in conjunction with leadership and culture, is essential to foster innovation-driven project success in PBOs. Future research may investigate other external conditions, including agile organizational structure and governance issues throughout the project life cycles, leadership and culture, and the competency of individual team members in connection with organizational digital maturity.
This article proposes a performance-oriented governance framework for strategic resource allocation in federal universities using the PROMETHEE II multicriteria method. The study addresses the challenge of unsystematic resource distribution in the final stage of the budgetary process, reframing budget allocation as a strategic decision-support mechanism and replacing discretionary practices with a transparent approach. The methodology is grounded in objective criteria reflecting the institutional mission of Higher Education Institutions (HEIs) across teaching, research, and outreach. The hierarchical framework includes quantitative indicators and quality criteria, while the weighting scheme is derived from real budgetary decentralization matrices to mitigate subjectivity. Application of the model at a Brazilian federal university campus demonstrated its capacity to generate a clear ranking of departments and an allocation flow proportional to their performance, embedding performance signals into internal financial dynamics. Sensitivity tests confirmed the robustness of the method under varying weighting scenarios. The results suggest that this multicriteria approach not only optimizes financial distribution but also serves as a strategic governance tool, promoting accountability and institutional efficiency in the public sector. This framework provides a scalable solution for enhancing resource management in complex educational environments.
Supplier selection in circular supply chain networks poses a multi-criteria decision-making challenge where environmental sustainability and operational responsiveness must be jointly optimized. Although agility and circularity are both essential for competitiveness, prior studies have typically treated them as independent constructs, leaving a methodological gap in their integrated evaluation. This study develops and empirically validates a structured decision framework that integrates agile and circular supplier evaluation. A context-specific set of criteria is established based on the literature and expert input, and an integrated fuzzy PIPRECIA-CODAS methodology is employed to quantify trade-offs under uncertainty. The framework is applied to a medium-sized technology manufacturer in the sustainable mobility sector, demonstrating its ability to identify suppliers that balance flexibility, recyclability, and resource efficiency. Results show that the approach improves decision transparency and consistency compared to conventional weighting-ranking models. The findings indicate that the framework enables a more integrated evaluation of agility and circularity, supporting consistent and strategically aligned supplier selection decisions under uncertainty. Beyond methodological integration, the study offers a replicable decision-support framework that provides both theoretical contribution and practical guidance for aligning sustainability and responsiveness in supplier selection.
This study distills sustainability into three core dimensions: Economic, Safety, and People Welfare (ESP) and demonstrates through the literature that sustainable maintenance management in the petrochemical industry must be assessed across these pillars. Building on this foundation, it introduces a novel multi-criteria decision-making (MCDM) framework for implementing Fourth Industrial Revolution (4IR) technologies in maintenance and bridging strategic objectives (ESP) with operational activities: Inspection, Repair, and Servicing (IRS). The framework was developed through four phases: (1) identifying existing frameworks, (2) conducting literature reviews and expert interviews, (3) developing a maturity assessment, and (4) outlining an implementation program. Application to a case-study company involved evaluating 4IR maturity across 16 dimensions and 5 levels, followed by designing a roadmap to address readiness gaps. Findings revealed the company to be at a "technology novice" stage, highlighting two key barriers: upgrading legacy systems for IoT integration and developing workforce competencies aligned with 4IR standards. Managerial recommendations emphasize phased implementation, workforce upskilling, and infrastructure modernization. The study contributes both theoretically and practically by aligning strategic sustainability goals (ESP) with operational maintenance execution (IRS), providing a replicable framework that enhances economic performance, safety, and people welfare and establishing a new model for sustainable industrial maintenance.
Exploring construction schedule risks in infrastructure projects has been highly topical. However, few studies have examined construction schedule risks from a dynamic perspective and mainly provided nongeneralizable single-case experiences. This article explores the phase variations of the construction schedule risks in three construction-related phases including construction readiness, construction for major units, and delivery readiness. With questionnaires and semi-structured interviews from China, a research methodology that combines grounded theory, fuzzy Bayesian network, and Noisy-or gate was conducted. The study grouped 22 risks into five categories: management, participant, technical, resource, and environmental risks, and identified the causal relationships among all risks. The results indicate that the key construction schedule risks vary in different phases. This study expands the knowledge domain of schedule risk management and provides practitioners with practical and efficient insights into infrastructure project management within the context of China.
This study develops a behaviorally informed evaluation model for Internet of Things (IoT) development platforms by integrating human-centered decision perspectives with a hybrid multi-criteria decision-making (MCDM) approach. Through an extensive literature review and expert consultation, this study identifies and structures the critical factors influencing users' (customer satisfaction) and developers' (strategy selection) perceptions of IoT platform performance. Three stages of purposive surveys were conducted to gather the judgments of experienced IoT specialists. The fuzzy Delphi method (FDM) synthesizes expert judgments to reduce 30 sub-criteria to 16 key factors through fuzzy-based consensus. The analytic hierarchy process (AHP) then converts subjective assessments into consistent and quantifiable weights of the sub-criteria to ensure hierarchical rigor. Finally, VIKOR identifies a compromise solution that balances conflicting criteria and yields robust platform rankings of four representative IoT platforms. This study finds that customer satisfaction outweighs strategic considerations in IoT platform evaluation, with Security and Privacy ranked as the top two critical criteria, followed by Technology, Implementation, Convenience, and Market. Operations optimization, Trust and verification, and Customization emerge as the most influential sub-criteria. Benchmarking four IoT platforms identifies performance gaps and provides a self-assessment tool to optimize resources, align technical performance with user perceptions, and support evidence-based policy.
Main path analysis (MPA) is a critical tool for exploring the development and evolution of technological innovation, effectively characterizing the linkage patterns of technology development and offering significant insights for enterprises seeking efficient innovation. However, existing research predominantly focuses on identifying main paths, often neglecting the inclusion of the latest technologies due to time lags in patent publication and citation. This results in main paths that are rooted in past or present technologies, failing to highlight potential innovation opportunities. To address this issue, this paper proposes a novel integrated framework that extends main paths by overcoming the limitations of single-path approaches and time lags in current patent citation network-based MPA. The framework uniquely integrates methods such as subject-action-object (SAO) semantic analysis and technology-function matrix, utilizing a function-oriented search (FOS) to extend multi-dimensional main paths. The improved method captures various types of potential technological innovation opportunities and provides advanced guidance for technological activities. The effectiveness of this approach is demonstrated through an evidence analysis in the all-solid-state lithium-ion batteries (ASSLIBs) technology domain. The resulting extended main path offers a more coherent view of technology development and flow, serving as a valuable reference for enterprises to identify potential innovation opportunities.
Lean Six Sigma (LSS) helps to enhance environmental performance (EP), which helps to adopt environmental sustainability-related practices and augment operational productivity by focusing on organizational waste elimination and defect reduction. Although the successful adoption of LSS has numerous benefits for small and medium-sized enterprises (SMEs), which ultimately help improve EP. This study examines Organizational Cultural Practices (OCP) and Sustainable Supply Chain Management (SSCM) as the mediating variables between LSS and EP. Our dataset is extracted from 382 respondents from Pakistani SMEs, whom we approached through LinkedIn, an industrial engineering consultancy, and professional training centers. We used three different software packages for data analysis, including Smart-PLS, Jamovi, and the Statistical Package for the Social Sciences. We executed Structural Equation Modeling, Confirmatory Factor Analysis, and an Artificial Neural Network Modeling (ANNM) to analyze relationships among LSS, OCP, SSCM, and EP. The results confirm that LSS positively influences EP at (beta=0.245, p<0.001), with OCP and SSCM acting as partial mediators at (beta=0.173, p<0.001), and (beta=0.201, p<0.001) respectively, and R-2 for EP is 0.649. Moreover, we found that the ANNM results have RMSE of 0.429 for training and 0.428 for testing. This study extends the literature of Goal Setting Theory.
Suppliers play a key role in the pharmaceutical industry to ensure the quality of the product, regulatory compliance, and safety of the patient. Traditional Multi-Criteria Decision-Making (MCDM) techniques struggle to handle a large dataset of factors involved in supplier selection and to adapt rapidly changing industry requirements. This study proposes a scalable, data-driven framework that integrates experts' knowledge with artificial intelligence (AI) algorithms and principal component analysis (PCA) to support effective and sustainable supplier selection. A total of 67 factors were considered, including seven innovative factors identified through expert opinion. PCA was used to identify the top 20 most critical factors, and Gradient Boosting emerged as a superior classifier in predictive accuracy. Traditional factors, such as location, delivery accuracy, and availability of supplies, consistently scored high across machine learning metrics, along with three innovative factors, i.e. bio-digital twin compatibility, quantum-resistant encryption, and psychedelic precursor ethics, indicating their growing relevance in modern, technology-sensitive pharmaceutical supply chains. This study contributes robust, explainable, and scalable factors for supplier selection in the pharmaceutical industry by integrating expert-driven insights with AI-powered evaluation techniques that account for both current industry standards and future technological challenges. A limitation is the geographic restrictions of expert opinion. Future research should explore the longitudinal studies to enhance applicability.
This study provides a comparative review of existing research on happiness at work before and after the onset of the COVID-19 pandemic. A systematic search was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines to ensure comprehensive retrieval of relevant literature. The study sourced articles from various academic databases, resulting in 27 publications from 2009 to 2019 (the decade preceding the pandemic) and 61 publications from 2020 to 2023 (after the onset of COVID-19). This was subsequently complemented by a narrative review to extract qualitative thematic insights pertaining to organizational, leadership, employee, and work-level factors. At the organizational level, research since the start of the pandemic has shifted focus toward creating a positive environment centered on human-centered ethics, organizational culture, practices, certification, and strategic approaches rooted in workplace happiness to benefit employees. Leadership styles have adapted in response to the pandemic, emphasizing human-centered and empathetic methods. At the employee level, the focus has been on individual customization, coping strategies, promoting psychological well-being, and mindfulness. At the work level, attention is given to issues such as flexible work arrangements, remote work, autonomy, job demands, and job crafting. Various theoretical frameworks exist within the literature on happiness at work; however, Fisher's Happiness at Work has gained significant prominence since the pandemic's onset, with 51% of articles focusing on this framework. Organizations must acknowledge that happiness at work is evolving alongside changes in work practices and arrangements and management must prioritize and actively support employees' well-being to foster a positive work environment. This paper offers valuable insights for engineering managers by highlighting human-centered leadership and employee well-being, which are often ignored as critical priorities in the post-pandemic era. Just as employees are integral components of an organizational system essential for its proper functioning, focusing on this system and enhancing employee happiness at work will improve productivity, quality, reduce absenteeism, and overall enhance work experience. Therefore, this review clears the way for future happiness at work research specifically focused on engineering management.
The complex knowledge flow relationships in R&D projects introduce high levels of uncertainty, making reasonable buffer sizing crucial for absorbing project uncertainties and ensuring successful delivery. Based on this, this paper considers the knowledge flow relationships in R&D projects and proposes a method for determining the project buffer that incorporates the characteristics of knowledge networks. First, the impact of knowledge network structure on project buffer is analyzed from three dimensions: node position, inter-node relationships, and overall network structure. Next, the influence of knowledge network resilience on the buffer is assessed from the perspectives of structural resilience and functional resilience, followed by a comprehensive calculation of the project buffer size. Finally, Monte Carlo simulation is used for validation. The results show that, compared to classical project buffer determination methods, the proposed approach achieves dual optimization of both project duration and cost.
Manufacturing organizations' survival has been threatened by international competition, changing customer demands, and resource scarcity. To ensure sustainable business growth, one of the most crucial approaches manufacturing companies should employ is to successfully integrate Lean Green and Six Sigma (LG&SS) strategies. This study identifies, evaluates and establishes a contextual relationship between LG&SS enablers within three sustainability dimensions: social, economic, and environmental and also investigates the causal relationships between them. A fuzzy decision-making trial and evaluation laboratory structural evaluation (DEMATEL) methodology is employed in conjunction with an analytical network process (ANP) for this purpose. The study provides a quantitative evaluation framework and a strategy map for implementing integrated LG&SS in the manufacturing SMEs. The findings reveal that employee empowerment is the most important enabler. Top management commitment and support, as well as consumer demand for Green products, drive the successful implementation of integrated LG&SS. The 'Environmental' perspective is the most important dimension of sustainability, while the 'Economic' dimension is the driving force behind implementing integrated LG&SS. The article contributes to the field of sustainable operations in two ways. First, it proposes a framework along with strategy map for assessing and quantifying the causal relationships between the three dimensions of sustainability and their associated enablers. The proposed approach can be seen as a proof-of-concept and be adjusted to the diverse set of industries and organizations to conciliate the characteristics of the organization Second, it conducts an empirical study to generate significant managerial insights and policy suggestions.
Procurement practices are often characterized by competitive tendering, which aims to ensure transparency, probity, and value for money in the process of acquiring goods and services. The lowest-price bidding approach, which considers price as the sole criterion for selecting the winning bidder, is one of the most popular bidding methods in the current bidding process. Because of the imperfect development level, legal environment, credit system, and other factors, abnormal behaviors, including bid rigging and collusion, still occur in the construction industry when applying the lowest-price bidding method. Despite this, practical and effective methods and tools for preventing these behaviors are still lacking. Thus, this research developed a theoretical model that considers the reserve price and the cost price of the project and proposed methods for setting these prices that are compatible with practice. Additionally, the proposed methods and models were applied to an actual construction project in order to demonstrate their feasibility. In addition to providing tenderers with a basis for selecting qualified contractors, this research will mitigate abnormal behavior, improve the lowest-price bidding mechanism, and purify the bidding process.
Implementing lean production principles in industrial processing units has a direct and significant impact on improving their competitive position and profitability. By reducing production time, achieved through minimizing hidden and apparent losses and streamlining workflows, these units gain valuable time and resources while indirectly lowering product costs. This study aims to identify opportunities for process improvement and optimization in the advancing section of a mineral processing facility using the value stream mapping (VSM) method. A VSM utilizing lean production tools was then designed to address these inefficiencies. This quantitative research collected data through field measurements, recording the time required for each action from start to finish within the advancing section. Through brainstorming sessions and group discussions with relevant experts, a process map was created and analyzed, and waste sources were identified. The results indicated that the average total cycle time for the advancing section was 852 minutes, with 390 minutes attributed to waiting waste, comprising over 45% of the total cycle time. After identifying the waste sources, appropriate solutions were proposed. The study concludes that lean production is a practical approach for analyzing processes, enhancing efficiency, reducing waiting times, and maximizing value to improve process flow.
Nowadays, the adoption of integrated product-service solutions has become imperative for companies dealing with ever-increasing, sophisticated customer requests. Advanced informatics tools can support companies in collecting and interpreting a large amount of information to provide more customer-tailored solutions. However, few studies have investigated the use of Artificial Intelligence (AI) tools in multiple criteria decision-making (MCDM) for the development of integrated product-service offerings. Using a case study from the green energy sector, this study explores the integration of AI tools with Quality Function Deployment for Product-Service Systems (QFDforPSS) to refine customer-centric PSS development by leveraging data from customer care services and customer relationship management systems. Results obtained by AI tools were compared with those of experts. Research findings underscore AI's capability to extract detailed insights from customer data, enabling a more holistic and concurrent development of PSS characteristics. This research introduces a novel data-driven framework for PSS design, demonstrating AI's potential to transform customer service data into actionable specifications and providing a more thorough analysis of PSS features. The study not only suggests several implications that can assist management in improving business offerings in the photovoltaic industry but also augments knowledge on the capabilities of QFD powered by AI tools.
In the era of the Fourth Industrial Revolution and digital transformation, start-ups driven by innovative technologies have become pivotal to the global economy. Among them, laboratory start-ups, based on intellectual property and experimental results from universities and government-funded research institutes, have garnered attention because of their superior success rates and job creation potential. This study focuses on founding team diversity and social capital as major factors affecting laboratory start-up performance, considering the start-up environment of increasing sociocultural diversity and the hyper-connected era. Therefore, this study selects and analyzes the I-Corps program, which has been evaluated as a representative laboratory start-up initiative launched by the US National Science Foundation in 2012 and introduced in Korea in 2015. The results indicate that gender and academic major diversity have a significant positive effect on start-up performance, educational background diversity has a significant negative effect, and cognitive social capital has a significant positive effect. These findings provide theoretical and practical implications for researchers and practitioners in laboratory start-ups and related fields.
