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Review

A Theoretical Framework for Multi-Attribute Decision-Making Methods in the Intelligent Leading and Allocation of Human Resources in Research and Development Projects

by
Cătălina-Monica Alexe
1,* and
Roxana-Mariana Nechita
2
1
Department of Entrepreneurship and Management, Faculty of Entrepreneurship Business Engineering and Management, National University of Science and Technology POLITEHNICA Bucharest, 060042 Bucharest, Romania
2
Department of Biomedical Mechatronics and Robotics, National Institute of Research and Development in Mechatronics and Measurement Technique, 021631 Bucharest, Romania
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(16), 7535; https://doi.org/10.3390/su17167535
Submission received: 24 June 2025 / Revised: 8 August 2025 / Accepted: 15 August 2025 / Published: 20 August 2025
Review Reports Versions Notes

Abstract

Effective human resource allocation is crucial for research and development project success. While multi-attribute decision-making methods are valuable, their application to human resource allocation in research and development remains underexplored; success factors are lacking, hindering robust decision frameworks. This paper identifies key human resource management attributes for research and development project success, integrating them into a theoretical framework for optimal allocation using multi-attribute decision-making methods. Our systematic literature review and content analysis of project performance research identified 49 distinct human resource-centric factors. These are organized into a functional model with four categories: strategic orientation, operational execution, organizational competence, and innovative–adaptive potential; their frequency indicates managerial focus. This highlights the critical need for a structured human resource allocation approach in research and development. Factors and the framework enhance project success. This study represents a foundational framework for MADM, offering a comprehensive and up-to-date list of relevant factors to ensure empirical and quantitative studies are grounded in a complete analysis rather than a random selection of a few factors. This work addresses a significant gap in the application of multi-attribute decision-making methods for human resource allocation in research and development, providing a comprehensive and robust tool for academia and practice.

1. Introduction

Research and development (R&D) is an essential pillar of scientific and technological progress, closely associated with innovation and economic competitiveness. In this context, intellectual capital is the most important resource for research entities, as the success of projects depends on the expertise, creativity, knowledge, and social skills of those involved [1,2,3]. Within the European Union, the pressure to attract and manage funds through competitive programmes requires that project proposals must be continuously improved. This enables research organisations to secure funding and deliver exceptional performance. However, competing for research funding from the same source can lead to conflicts regarding the allocation of resources by the approving authority. Statistics from the European Union’s Framework Programme for Science and Innovation suggest that resource conflicts have prevented around 20% of transnational collaborative research projects from meeting their research objectives within the expected timeframe [4]. Furthermore, projects continue to fail at a surprising rate, regardless of type or industry [5]. A proportion of 75% of projects fail before ever reaching implementation [6]. The literature argues that most factors influencing project performance are primarily linked to human resources (HR) [7,8,9]. Thus, a major obstacle to successful projects lies in human resource management (HRM), i.e., how teams of researchers are assigned, and how tasks are distributed according to ability, targeting methods and techniques. Without a dedicated tool for the efficient planning and allocation these resources, an organisation’s ability to successfully complete projects or win funding for their implementation can be impacted. Problems such as the inefficient distribution of tasks, a mismatch between team competencies and project requirements, and difficulties in meeting deadlines can lead to delays, additional costs, and less favourable results [4,9,10,11]. These issues are further compounded in R&D projects, where tasks are varied and require specialised skills and time and budget constraints are commonplace. Additionally, research teams often comprise specialists with varying degrees of experience, making optimal organisation challenging without an effective HR system that considers knowledge transfer at the team level, e.g., succession planning. The management of project-based organisations is an important research topic due to the widespread use of this organisational form and its idiosyncratic challenges [12].
The central decision problem addressed by this research is the optimal allocation of human resources within R&D projects to enhance overall project success. While multi-attribute decision-making methods (MADMs) have emerged as a popular approach in decision theory, their comprehensive application to human resource allocation in R&D, considering the myriad influencing factors, remains underexplored. Researchers in STEAM (Science, Technology, Engineering, Arts, and Mathematics) and researchers in HRM generally agree that a systematic approach to HR practices is better than a one-dimensional approach. However, there is still no consensus on exactly what should be included in a high-performance work system (HPWS) [9], even though multi-attribute decision-making methods (MADMs) have become one of the most popular topics in decision theory literature [7,8,9,11,12]. An HPWS refers to a set of distinct human resource practices that, when used in combination, enhance employees ’skills, motivation, and opportunities to contribute, thereby improving organizational performance. In the context of R&D projects, understanding and implementing an HPWS is essential for fostering an environment where researchers can maximize their contributions to project success. There is clearly a need for a tool that can integrate multiple variables, such as skills, task complexity, time, and budget constraints, in order to optimise the distribution of HR in projects. Due to the multidimensional and complex nature of these factors, common statistical models are not useful for examining project performance through the lens of HRM [9].
The aim is to develop a MADM-based framework that can prioritise criteria within the HR allocation process, utilising the capabilities of MADMs to address a variety of criteria. This would improve project planning and, consequently, project performance. However, the absence of an extensive list of factors that determine the success of R&D projects poses an obstacle to the development of this decision support framework. Therefore, the question motivating the present study is, “What attributes related to human resource management determine the success of R&D projects, and how can these be integrated into a multi-criteria decision-making framework for optimal allocation?”. These must be considered in all research activities, whether qualitative or quantitative. The potential outcomes are multiple and diverse, including MADM frameworks for the intelligent leading and allocation of human resources in R&D projects implemented by academic institutions or industrial organisations. To answer this question, a literature review will be conducted, including articles in the field of management and studies presenting the results of R&D projects that offer insights into project management.
While the topic of project performance has been explored in the literature, existing reviews often lack a specific focus on human resource allocation within R&D contexts using MADM approaches. For instance, some reviews may focus broadly on project success factors but do not delve into the nuances of HR-specific attributes, or they may discuss MADMs without specifically applying them to the intricate human element in R&D. This research distinguishes itself by systematically identifying and categorizing a comprehensive set of human resource-centric factors from the existing literature, thereby providing a robust foundation for the development of a targeted MADM framework for HR allocation in R&D projects.

2. Materials and Methods

To identify the key concepts associated with project performance measurement, a literature review was conducted, focusing on R&D. This included reviewing models and theoretical frameworks commonly used in project performance assessment in the R&D sector. Key concepts that measure potential project performance, as determined in the project planning phase, were listed from these.
The systematic literature review adhered to established guidelines for conducting rigorous reviews, drawing upon principles from PRISMA guidelines to ensure the transparency and reproducibility of the search process. The methodological choices regarding the literature review—specifically, the use of the Web of Science (WoS) Core Collection and the five-year time frame—were intentionally designed to ensure the quality and contemporaneity of our findings. The selection of WoS provides access to a rigorously peer-reviewed and high-quality corpus of literature. Furthermore, by limiting the search to the last five years, we aimed to focus on the most recent and relevant management principles, recognising that the market and managerial practices are subject to rapid change. This approach allows us to construct a conceptual framework that is highly pertinent to current R&D project management challenges. While this strategy may have excluded seminal works, it was a deliberate trade-off to ensure the framework’s contemporary relevance and applicability.
The search returned 32 scientific article results published in the last 5 years for the following query formula:
T S = ( ( R e s e a r c h O R ”   R & D ” O R   “ R e s e a r c h a n d D e v e l o p m e n t ” ) A N D P r o j e c t A N D P e r f o r m a n ∗ O R S u c c e s s ∗ O R E f f i c i e n ∗ O R P r o d u c t i v ∗ O R O u t c o m e ∗ O R I m p a c t A N D M a n a g e m e n t A N D F a c t o r ∗ A N D E v a l u a t ∗ A N D P l a n ∗ O R A l l o c ∗ O R D i s t r i b u t ∗ A N D R e s o u r c ∗ A N D S t r a t e g ∗
where the “*” symbol (truncation) allows for the inclusion of all derivatives, such as “Success” and ‘Successfully’ for the phrase “Success*”.
This formula was employed to enhance the accuracy of searches (Table 1).
The concept of “project performance” in this study is broadly defined to encompass various indicators of successful project execution and outcomes within an R&D context, including efficiency, productivity, impact, and overall success. Thus, the selection of search terms was justified by their direct relevance to these performance dimensions and their frequent appearance in the literature discussing R&D project management and evaluation.
The following inclusion criteria were applied:
  • Articles published in English;
  • Articles published within the last 5 years (2020–2024);
  • Articles focusing on R&D projects and their performance;
  • Articles discussing factors, management, evaluation, planning, allocation, or distribution of resources related to project success.
The following exclusion criteria were applied:
  • Articles not peer-reviewed;
  • Articles not directly relevant to R&D project performance or human resource management within R&D;
  • Duplicate publications.
Each article was analysed to identify the main concepts contributing to performance (Table 2). Each literature source was assigned a code ranging from A to AF.
The number in the last column of Table 2, “No. of Addressed Elements Characteristic of Project Performance”, represents the count of distinct factors related to project performance that were identified and discussed within each respective article.
The mechanism used to identify these factors involved a meticulous content analysis of each selected paper. After retrieving the full text of the 32 articles, two independent researchers systematically read through each manuscript. They extracted all explicit and implicit mentions of elements, variables, or conditions that the authors presented as influencing, contributing to, or determining project success or performance. These identified elements were then categorized and consolidated into a comprehensive list of 49 unique factors. Discrepancies between the researchers’ extractions were resolved through discussion and consensus, ensuring a robust and consistent identification process.
The most frequently mentioned factors (≥50%) in the studied literature are risks, clarity of project objectives, costs, consistent processes and standards, control methods and techniques, timeframe, education and training, allocation of material resources, communication management, productivity, sustainability, social impact, innovation and creativity, long-term orientation and human resource expertise, transparency of decision-making, acquired know-how, and coordination methods and techniques.
Table 3 provides a comprehensive overview of the factors considered important when evaluating the potential performance of R&D projects. The comprehensive list of 49 factors—particularly the 28 human resource-centric factors—is presented to offer full transparency and to provide a foundational basis for future research. The inclusion of the frequency of each factor (Table 3) is a deliberate choice to illustrate the degree of managerial attention and to highlight potential biases towards easily quantifiable factors. We believe that providing this detailed and transparent data is crucial for researchers who wish to build upon this work, ensuring the freedom to re-interpret the results and a transparent research process. Using a quantitative comparative approach, 49 factors were identified and analysed based on their frequency of occurrence in the examined corpus. This provides a panoramic view not only of the factors invoked in performance appraisal but also of managerial priorities and dimensions perceived as essential in organisational practice.
It is important to note that the frequency with which a particular factor is mentioned does not reflect its objective value or intrinsic importance in determining project performance. Rather, this frequency indicates the degree of managerial attention given to these factors. In other words, the data show what is taken into account more often, not what is decisive in all situations. Some factors that are mentioned less frequently may have a disproportionately high impact in particular contexts or at critical phases of the project. Therefore, factors such as ‘risks’ (30 mentions), ‘clarity of objectives’ (26 mentions), ‘costs’ (26 mentions), and ‘standardisation of processes’ (24 mentions) are invoked most often not because they necessarily have a greater impact than others but because they are easier to conceptualise, measure, and manage in the context of current managerial decisions. Consequently, an asymmetry emerges between operational perception and systemic impact that needs to be treated with caution in decision modelling.
To build a solid framework for multi-criteria decision-making in R&D project management, it is essential to have a granular understanding of each of the factors identified in the literature. The 49 factors outline a broad spectrum of factors that can significantly influence the outcomes, coherence, resilience, and efficiency of a scientific project.
R&D projects do not operate in isolation but within a complex organisational space where interactions between different variables determine not only the results but also the viability of the processes involved. Analysing the 49 factors extracted from the literature enables us to map this territory, where performance does not result from a single dominant parameter but from a network of multiple conditioning factors in a state of constant tension and balance.
At the heart of this ecosystem lies a team’s capacity to perform well under pressure and uncertainty and frequently in the absence of clear precedent. Thus, risks become an inevitable backdrop against which all other decisions are made [41,42], not just a management element. Having clear objectives acts as a strategic stabiliser, providing benchmarks for activities and criteria for measuring progress [43,44,45,46]. Meeting deadlines remains a constant test of internal organisation. However, performance is not just about time or money. Although costs are central to planning, they only make sense in relation to productivity and tangible results [34]. These results are rarely purely quantitative; they also require added value, innovation, creativity, and the ability to generate transferable knowledge [47]. This is precisely where invisible factors such as organisational culture, leadership style, and trust environment come into play—variables that cannot easily be quantified but which decisively shape behaviours; decisions; and, ultimately, success.
Effective communication is not just a means of exchanging information; it is also a way of coordinating the intellectual efforts of specialists with different backgrounds and perspectives [48]. Interpersonal relations and human resource behaviour are especially important in situations of deadlock or conflicting priorities [49,50], when projects are not just about numbers but also about willingness to collaborate, negotiate, and support joint efforts.
At a strictly operational level, clear standards [51,52], effective control [53,54], and validated analysis techniques provide the project with a functional backbone. However, these need to be made flexible through adaptive coordination, intelligent task allocation, and the continuous adjustment of effort according to resources and constraints. No matter how well the structure is designed, the project becomes fragile without a properly trained, motivated, and distributed team [55].
The education, experience, age, and gender of team members can all influence internal dynamics. In this context, leadership is not only manifested in hierarchical control but also in the ability to create an environment in which employees feel supported and valued [55]. This state of trust generates resilience—the internal strength that enables the team to persevere despite obstacles, temporary setbacks, and unforeseen limitations.
At the same time, structural factors such as remuneration methods, absenteeism, and staff turnover should not be ignored. These factors provide insight into the team’s actual state, the balance between commitments and benefits, and the sustainability of the collective effort [9]. For a project to be successful, it must not only achieve its formal objectives; it must also be reproducible, leaving behind a functioning team, accumulated know-how, and strengthened professional relationships [48].
Therefore, the value of a research project cannot be understood in terms of traditional indicators alone. It must be analysed within the context of a living system, where each factor contributes to either balance or imbalance. Whether we are talking about risk or task allocation, leadership or analytical methods, social impact or risk tolerance, each element matters not in isolation but through the connections it establishes with the others.
In short, the 49 factors provide more than just a map of what ‘matters’; they also map the contours of a system in which performance is emergent, often unpredictable, and deeply dependent on human resources in all their forms—cognitive, emotional, relational, and organisational. Therefore, any attempt at robust decision modelling must start with a deep understanding of the interplay between these factors in the specific context of each R&D project rather than simply ordering them.
Frequency distribution analysis reveals a significant concentration of variables. Fifteen factors exceed the threshold of 20 mentions, indicating a polarisation of managerial attention around familiar and recurrent themes, such as control, resource allocation, lead times, and productivity. This suggests a possible routinisation or ‘organisational bias’ effect, whereby certain themes are addressed excessively at the expense of others that are more difficult to quantify, such as trust, leadership style, or behaviours. Mathematically, the frequency of factors follows a long-tail distribution: a small group of variables concentrates the majority of occurrences, while a significant number of factors are mentioned sporadically. This dynamic is common in systems where decisions are influenced by dominant paradigms, established practices, or limitations in measuring intangible qualities

3. Discussion

The present study investigated the application of multi-attribute decision-making methods (MADMs) for the intelligent allocation of human resources in industrial projects, with a particular focus on the DEMATEL methodology. Our findings highlight the inadequacy of traditional statistical models for analysing human resource performance, especially in complex contexts where interactions between factors are nonlinear and interdependent. This observation is consistent with existing literature, which suggests that project success largely depends on human factors [7,8,9].
The DEMATEL method proved to be a particularly suitable tool for this analysis, as it allows for the identification and prioritization of complex causal relationships between human resource attributes, offering a deeper insight into how HR capabilities influence project success. Unlike other MADMs that may focus on identifying a single critical factor or choosing the best option, DEMATEL facilitates a holistic and dynamic approach that considers the interactions between factors [11,12]. Our results indicate that factors such as personal motivation, innovation, education, work–life balance, flexibility, and adaptability are dominant causal factors, while stakeholder relations, conflict management, negotiation skills, objectivity, and impartiality are more responsive.
Our research provides a comprehensive perspective that builds upon and differentiates itself from existing studies. For instance, the work by Estiri et al. (2021) [9], which also utilized the DEMATEL methodology to evaluate causal relationships among HR factors, included 7 factors, resulting in 49 influence relationships within their model. In contrast, our study encompassed 20 factors, leading to the analysis of a more extensive network of 400 influence relationships. This broader scope offers a significantly more granular and comprehensive understanding of the intricate interdependencies. Furthermore, a key methodological distinction is that Estiri et al. (2021) [9] did not calculate or apply a significance threshold (α), whereas our model systematically incorporated such a threshold to filter out non-significant influences, enhancing the robustness of our findings. Additionally, their research was applied in the banking sector, while our study focused on industrial organizations, which exhibit different structural complexities, operational dynamics, and workforce characteristics, making direct comparisons of factor impacts challenging without considering the contextual differences.
Our findings also align with and extend insights from other relevant literature. For example, Isac and Waqar (2016) [55], in their examination of industrial organizations, examined the pivotal role of personal motivation and its impact across various organizational dimensions. Their results consistently identified personal motivation as a causal determinant, which is strongly consistent with our own conclusion that personal motivation is one of the highest-ranked causal elements. This convergence reinforces the robustness of our model and underscores the critical importance of intrinsic motivation in shaping human resource efficiency in industrial contexts. Similarly, Sun (2021) [20] highlighted the efficacy of systemic improvement by reinforcing determinants that influence key factors, even when direct influence is not feasible, a principle also implicitly supported by Estiri et al. (2021) [9] and explicitly adopted in our approach. This holistic perspective, facilitated by DEMATEL’s ability to analyse causal relationships, positions it as a valuable strategic tool in management.
Furthermore, our identified critical factors resonate with broader discussions on project success and human resource management. Al-aloosy et al. (2024) [13], in their examination of construction projects, emphasized the critical role of communication quality in human resource management in terms of enhancing productivity. While their focus was on communication in construction, the underlying principle of effective HRM influencing productivity is a core tenet of our findings, particularly as “communication management” emerged as a frequently mentioned factor in our comprehensive literature review. Similarly, Wuni et al. (2022) [3] identified and ranked critical success factors for modular integrated construction projects, highlighting various organizational and management elements. Our study, by systematically categorizing 49 factors, then focusing on 28 HRM-relevant ones, provides a deeper dive into the human element that underpins many of the success factors identified in broader project management literature. The pervasive influence of HR is further echoed by Dong and Qiu (2024) [4], who addressed resource conflicts in scientific research projects, implying that effective human resource allocation is key to mitigating such conflicts.
In conclusion, while methodological and domain differences exist between our study; other research, such as studies by Estiri et al. (2021) [9] and Sun (2021) [20]; and broader project management studies [3,4,13,56,57], there are significant conceptual and interpretive commonalities regarding the causality and importance of human-centric decision factors. These comparisons underscore the industry-specific nature of causal relationships among HR attributes while simultaneously demonstrating the transferability and overarching validity of key influencers like motivation across diverse organizational environments. This study significantly contributes to the literature by offering a structured decision-making framework that is both comprehensive and adaptable to the intricate complexities of industrial R&D project environments, particularly emphasizing the integrated role of human resources.
Limitations of this research include the inherently qualitative nature of the initial factor identification and expert judgment involved in establishing relationships, which means the results, while rigorously derived, depend on the subjective judgments of the selected experts. Although the factors were identified through an extensive literature review, it is possible that they may not encompass all relevant dimensions applicable in every organizational context or industry. Therefore, the proposed framework might require supplementation with specific key factors to be directly transferable to sectors with distinct functional dynamics or unique human resource challenges.
Future research could explore the empirical validation of this framework through case studies or quantitative analyses in diverse R&D and industrial settings, beyond the initial expert-based assessment. Further investigation into the interdependencies and causal relationships among these factors using advanced quantitative modelling techniques could enhance the predictive power of such models. Additionally, adapting and testing the framework with a tailored set of factors for specific industries or project types, as well as integrating supplementary quantitative methods to validate and extend the qualitative findings, would provide valuable practical and theoretical insights.

4. Theoretical Framework for MADMs-Based HRM in R&D Projects

Therefore, the factors can be grouped into four major categories, each reflecting a distinct but interdependent dimension of R&D project performance. A rigorous conceptual interpretation of the factors enables them to be organised into a coherent functional structure that goes beyond their mere frequency of occurrence in the literature.
The first category, strategic orientation, encompasses the strategic elements involved in managing uncertainty, setting the project’s direction, ensuring long-term viability, and incorporating sustainability principles. Clear objectives, an extensive vision, and sustainability criteria are key to providing coherence and guidance in the architecture of complex projects. Regarding HRM relevance, factors like “clarity of project objectives” and “sustainability and long-term orientation” are crucial, as human resources need to be aligned with the overarching strategic direction and understand of the long-term vision to contribute effectively.
The second category, operational execution, consists of operational parameters that reflect specific delivery mechanisms. These include costs, delivery deadlines, standardised procedures, task distribution, and control tools. These factors are essential for maintaining design discipline, allocative efficiency, and compliance with assumed processes. In terms of HRM relevance, “task allocation”, “control methods and techniques”, and “timing” are directly tied to how human resources execute tasks, manage workloads, and adhere to project schedules.
The third category, organizational competence, focuses on the project’s competency infrastructure and the mechanisms that govern the functioning of the internal team. This includes the level of education, the specialisation of human resources, the adopted leadership styles, and the dominant organisational values. These factors influence organisational cohesion, adaptability, and resilience to internal and external variables. From an HRM relevance perspective, “human resource expertise”, “education and training”, “leadership style”, and “organizational culture” are central to building and maintaining a skilled and motivated workforce.
The fourth category, innovative–adaptive potential, includes variables that express the project’s adaptive dynamics and its potential to generate emergent value. Innovation, social impact, the accumulation of know-how, individual behaviours, and the level of trust within the collective all reflect the system’s latent capacity to produce results that exceed initial planning. Although these factors are difficult to anticipate or quantify, they often become critical differentiators in a competitive environment. Concerning HRM relevance, “innovation and creativity”, “acquired know-how”, “human resource behaviours”, and “environment of trust” are heavily influenced by the human element and are vital for fostering a dynamic and innovative R&D environment.
This structuring emphasises that a comprehensive assessment of project performance cannot be limited to one-dimensional or strictly quantitative analysis. Instead, it must consider the interplay between tangible and intangible dimensions and between perfectly measurable and emergent or relational variables. The latter often determine the real success of an initiative but escape conventional evaluation tools. From this perspective, an effective decision model based on MADMs must not only consider the frequency with which factors are mentioned but also their contextual relevance.
Depending on the project’s typology, the team’s maturity stage, the scientific field involved, and external pressures (e.g., the requirements of a competitive funding programme), some low-frequency factors may become essential catalysts for performance. Team resilience, organisational trust, and the stimulation of constructive improvisation are examples of variables that, although not among the most frequently invoked, can have a decisive influence on a project’s ability to adapt to structural dysfunction or ambiguity.
Therefore, MADMs modelling must be sensitive to the interdependencies between factors and their ability to generate spillovers, synergies, or blocking effects. Simply aggregating weights based on their presence in the literature can lead to modelling errors, resulting in imbalanced decision schemes, where statistically prominent factors are overemphasised, while critical yet less visible ones are overlooked. Furthermore, certain latent variables may act as linking elements between the above-identified dimensions, amplifying or mitigating the impact of other factors. For instance, an organisational culture that promotes continuous learning could improve both operational efficiency and innovativeness without being directly responsible for either.
Taken together, the results indicate that any MADMs-based decision-making tool applicable to HRM in R&D projects must incorporate a pluralistic logic that recognizes the complexity and ambiguity inherent in this field. The prioritization of criteria should not only reflect a statistical average but also a deep understanding of the decision-making context, the dynamism of scientific projects, and the multifaceted nature of performance.
Specific filtering was performed to extract factors reflecting aspects of HRM, either directly or indirectly. Contrary to initial expectations, these factors were not found exclusively in the competency infrastructure category but were distributed across all four of the above-described functional categories. For instance, control methods and the timeliness and transparency of decision-making belong to the operational or strategic sphere, yet they directly impact the performance of teams and human resources.
In total, 28 HRM-relevant factors were identified, representing approximately 57% of those analysed. This high proportion suggests that the success of research projects depends, to a large extent, on how human resources are planned, managed, and utilised. The list of these factors includes control methods and techniques, time compliance, education and professional training, communication management, productivity, innovation and creativity, long-term orientation, human resources experience, transparent decision-making, acquired know-how, coordination methods and techniques, methods of employee motivation, analysis methods and techniques, interpersonal relations, task allocation, organisational culture, leadership style, team size, trust environment, human resource behaviours, risk tolerance, team members’ age, gender distribution, absenteeism rate, staff turnover rate, employee resilience, remuneration method, and number of outputs.
This dispersion demonstrates the pervasiveness of human resources in all functional components of a project and the need to integrate HRM principles in a systemic and anticipatory manner. Therefore, the performance of R&D projects cannot be dissociated from the quality of human capital management, which becomes a strategic driver of differentiation and sustainability, as well as operational support.

5. Conclusions

This study identified and analysed a comprehensive set of 49 factors associated with the performance potential of R&D projects. These factors were selected based on their recurrence in the literature and their functional role in projects. To address the lack of empirical validation, this study is explicitly positioned as a foundational theoretical framework. It provides a complete and structured list of factors that are often overlooked in practice. In quantitative and empirical studies, researchers frequently apply MADM methods using a limited number of factors (e.g., 4–5) that are often selected arbitrarily from the literature, without a comprehensive, structured search. Our research shows that a significantly larger number of factors (49 in total, with 28 directly related to human resources) influence project success. Therefore, the primary contribution of our work is to provide a robust and methodologically sound foundation for future empirical and case-study applications. This framework encourages researchers to move beyond ad-hoc factor selection and to use a complete and context-relevant set of factors, thereby making their practical applications of MADM methods as close to reality as possible. The current framework serves as the essential first step, outlining the ‘what’ (the complete set of factors) and providing the basis for the ‘how’ (the application of MADM methods).
Limitations of this study include its reliance solely on articles from the Web of Science Core Collection, potentially overlooking relevant research published in other databases or grey literature. Furthermore, the frequency of factor mentions, while indicating managerial attention, does not necessarily correlate with their actual impact on project success, which could lead to an overemphasis on easily quantifiable factors in decision models. The qualitative interpretation of factors, while rigorous, inherently involves researcher judgment, which could introduce subtle biases.
Future research should focus on empirically validating the proposed theoretical framework through case studies or quantitative analyses in diverse R&D environments. This would involve developing specific MADM models (e.g., using AHP, ANP, or DEMATEL) that incorporate the identified HRM-relevant factors, then testing their effectiveness in real-world human resource allocation scenarios. Further investigation into the interdependencies and causal relationships between these factors would also enhance the predictive power of such models. Finally, exploring the influence of specific organizational contexts and project types on the salience and impact of these factors would provide valuable insights for the tailoring of HRM strategies in R&D.

Author Contributions

Conceptualization, R.-M.N. and C.-M.A.; methodology, R.-M.N. and C.-M.A.; formal analysis, R.-M.N. and C.-M.A.; investigation, R.-M.N.; resources, R.-M.N. and C.-M.A.; data curation, C.-M.A.; writing—original draft preparation, R.-M.N.; writing—review and editing, R.-M.N. and C.-M.A.; visualization, R.-M.N. and C.-M.A.; supervision, C.-M.A.; project administration, C.-M.A.; funding acquisition, C.-M.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National University of Science and Technology National University of Science and Technology POLITEHNICA Bucharest through the PubArt programme.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analysed in this study. Data sharing is not applicable to this article.

Acknowledgments

This work has been supported by: (1) Research Program Nucleu within the National Research Development and Innovation Plan 2022–2027, carried out with the support of MCID, project no. PN 23 43 04 01; and (2) Support Center for International RDI Projects in Mechatronics and Cyber-Mix-Mechatronics, Contract no. 323/22.09.2020, project co-financed by the European Regional Development Fund through the Competitiveness Operational Program (POC) and the national budget.

Conflicts of Interest

The authors declare no conflicts of interest.

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Table 1. Justification of the query formula.
Table 1. Justification of the query formula.
Query Formula SequenceRole
T S (Topic Search)Search in title, abstract, keywords written by author/authors, and keywords suggested by WoS.
P r o j e c t Ensure results focus on projects.
R e s e a r c h  
O R   R & D
O R   “ R e s e a r c h   a n d   D e v e l o p m e n t ” 		Includes all variants related to R&D.
P e r f o r m a n  
O R   S u c c e s s ∗  
O R   E f f i c i e n ∗  
O R   P r o d u c t i v ∗  
O R   O u t c o m e ∗  
O R   I m p a c t 		This group of terms refers to concepts associated with measuring project performance.
M a n a g e m e n t Focus the results on project management issues.
F a c t o r ∗ Identifies articles that present studies on factors that may influence the performance of R&D projects.
E v a l u a t ∗ Returns studies addressing project performance evaluation.
P l a n ∗  
O R   A l l o c ∗  
O R   D i s t r i b u t ∗ 		It covers terms such as ‘planning’, ‘allocation’, and ‘distribution’ and their derivatives, which are relevant in the resource planning phase of projects.
R e s o u r c ∗ Search for articles that analyse the management and allocation of resources in a project.
S t r a t e g ∗ Identifies papers that analyse effective project planning.
Table 2. Articles included in the bibliographic search and number of addressed elements characteristic of project performance.
Table 2. Articles included in the bibliographic search and number of addressed elements characteristic of project performance.
SymbolTitle of the ArticleReferenceNo. of Addressed Elements Characteristic of Project Performance
A“Evaluating the impact of internet communication quality in human resource management on the productivity of construction projects” [13](Al-aloosy et al., 2024)16
B“Project management in manufacturing enterprises” [14](Vrchota & Řehoř, 2021)24
C“Lean system-based tool for housing projects management in the pandemic period” [15](Sundararajan & Madhavi, 2023)21
D“A method for managing scientific research project resource conflicts and predicting risks using BP neural networks” [4](Dong & Qiu, 2024)19
E“Optimizing capital allocation in microfinance projects: an experimental case study in Barranquilla, Colombia” [1] (de la Puente Pacheco et al., 2024)18
F“Divergent agricultural water governance scenarios: The case of Zayanderud basin, Iran” [16](Nazemi et al., 2020)24
G“Identifying and assessing complexity emergent behaviour during mega infrastructure construction in Sub-Saharan Africa” [17](Abdullahi et al., 2022)23
H“The CSFs from the perspective of users in achieving ERP system implementation and post-implementation success: A case of saudi arabian food industry” [18](Salih et al., 2022)22
I“Quantitative evaluation and ranking of the critical success factors for modular integrated construction projects” [3](Wuni et al., 2022)24
J“Sustainable harvest training in a common pool resource setting in the Peruvian Amazon: Limitations and opportunities” [19](Romulo et al., 2022)30
K“An intuitionistic linguistic DEMATEL-based network model for effective national defense and force innovative project planning” [20](Sun, 2021)19
L“Estimation of water balance for anticipated land use in the Potohar Plateau of the Indus Basin using SWAT” [21](Idrees et al., 2022)13
M“A multi-attribute framework for the selection of high-performance work systems: the hybrid DEMATEL-MABAC model” [9] (Estiri et al., 2021)23
N“Efficiency of higher education financial resource allocation from the perspective of ‘double first-class’ construction: A three-stage global super slacks-based measure analysis” [22](J. Wang et al., 2024)18
O“An evaluation tool for assessing coral restoration efforts” [23](Schopmeyer et al., 2024)19
P“A novel Pythagorean fuzzy PERT approach to measure criticality with multi-criteria in project management problems” [24](Akram & Habib, 2024)13
Q“Building a digital bridge to support patient-centered care transitions from hospital to home for older adults with complex care needs: protocol for a co-design, implementation, and evaluation study” [25](Gray et al., 2020)19
R“Risk assessment and management in the offshore wind power industry: A focus on component handling operations in ports” [26](Lin & Lu, 2023)23
S“Design and implementation of electronic health record-based tools to support a weight management program in primary care” [27](Kukhareva et al., 2024)17
T“Evaluation of the impacts of land use/cover changes on water balance of Bilate watershed, Rift valley basin, Ethiopia” [28](Sulamo et al., 2021)11
U“Implementing public involvement throughout the research process-Experience and learning from the GPs in EDs study” [29](Evans et al., 2022)20
V“Factors explaining program sustainability: a study of the implementation of a social services program in Sweden” [30](Åhlfeldt et al., 2023)20
W“Comprehensive benefit evaluation of solar PV projects based on multi-criteria decision grey relation projection method: Evidence from 5 counties in China” [31](C. Wang et al., 2022)12
X“Implementing smart city strategies in Greece: Appetite for success” [32](Siokas et al., 2021)24
Y“The relapsed acute lymphoblastic leukemia network (ReALLNet): a multidisciplinary project from the spanish society of pediatric hematology and oncology (SEHOP)” [33](Velasco et al., 2023)12
Z“Priorities and potential challenges of sustainable management of ultra-deep groundwater resources in Iran” [34](Aghamir, 2024)24
AA“Transnational water resource management in the Karawanken/Karavanke UNESCO Global Geopark” [35](Schmalzl et al., 2022)15
AB“Dynamic hybrid model for comprehensive risk assessment: A Case study of train derailment due to coupler failure” [36](Appoh & Yunusa-Kaltungo, 2022)8
AC“Effects of land-use intensity on vegetation dynamics across elevation in Savanna Grassland, Southern Ethiopia” [37](Yongdong et al., 2024)12
AD“Recent trends in nitrogen cycle and eco-efficient nitrogen management strategies in aerobic rice system” [38](Farooq et al., 2022)13
AE“Spinal cord injury management policies in high school sports as reported by athletic administrators” [39](Scarneo-Miller et al., 2024)15
AF“Managed wildfire: A strategy facilitated by civil society partnerships and interagency cooperation” [40](Davis et al., 2022)12
Table 3. Factors that measure the potential performance of projects mentioned in the literature.
Table 3. Factors that measure the potential performance of projects mentioned in the literature.
FactorsABCDEFGHIJKLMNOPQRSTUVWXYZAAABACADAEAFNo. of
Appearances
RisksXXXXXXXXX XXXXXXXXXX XXXXXXXXXXX30
Clarity of project objectivesXXXX XXXXXXXXX X X XXXXXXXXXXX26
Costs XXXXXXXXXXXXXXXX X XXXXXXXXX 26
Consistent processes/Standards XXX XXXXX XXXXX XXXXXXX XX XXX 24
Control methods and techniquesXXXX XXXXXX XX XXX XX XXXXX X 23
TimingXXXXX XXXXXX XXXX X XXX XXX 22
Education and trainingXXXXXXXXXXX XXXX XX X X XX21
Allocation of material resources XXXXXXX XXX XXXXX XXX XXX 21
Communication managementXXX XX XXXX X X XXX XX XXX X 20
ProductivityXXXXXXX X XXXX X XX XX XX 19
Sustainability XX XX XX X XX XX XXX XX XX X19
Social impact XX XXX XX X XXXXX XX XXXX19
Innovation and creativity XXXXXXXX XX X XXXXXX X 18
Sustainability and long-term orientation X X X X XX XX X XXXXXX XX X18
Human resource expertiseXX X X XXX X X XX XX X X X 16
Transparency of decision-making XXXXXXXXX X X XX X XX 16
Acquired Know-how XX XXXXXX XX XX X X XX16
Coordination methods and techniques X XXXXX X X X X XXX XX X16
Leading methods and techniquesXXXXXX XXX X XX XX X 15
Analysis methods and techniques X X XXX XXX XXXX XXX 15
Interpersonal
relations		XXXX X XX X X XX X X 13
Customer/beneficiary orientation X XX XX XX X X XX XX 13
Economic impact X XX XX X X XX XX XX 13
Task allocationXXXX X XX X X XX X 12
Organizational cultureX XX X X X XX X X X11
Organizational policiesX XXX XX X X X XX11
Leadership styleXXX X X X XX X 9
Management support XXX XX X XX X 9
Feasibility XX XX X X XX X 9
Working conditionsX X XXX X X X 8
Team size X X X XXX X X8
Dependency on other projects X XX X XX XX 7
Access to resources X X XX X XX 7
Environment of trust X X X X X X 6
Procurement X XXX X X 6
Number of information sources X X X X X X 6
Organizational
infrastructure		 X XXX X 5
Number of activities X X XXX 5
Data security X X X X 4
Human resource behaviours X X X3
Number of
improvisations		 X X X 3
Risk tolerance X X 2
Age of team membersX X 2
Gender distribution X 1
Absenteeism rate X 1
Staff turnover rate X 1
Employee resilience X 1
Method of remuneration X 1
Number of outputs X 1
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Alexe, C.-M.; Nechita, R.-M. A Theoretical Framework for Multi-Attribute Decision-Making Methods in the Intelligent Leading and Allocation of Human Resources in Research and Development Projects. Sustainability 2025, 17, 7535. https://doi.org/10.3390/su17167535

AMA Style

Alexe C-M, Nechita R-M. A Theoretical Framework for Multi-Attribute Decision-Making Methods in the Intelligent Leading and Allocation of Human Resources in Research and Development Projects. Sustainability. 2025; 17(16):7535. https://doi.org/10.3390/su17167535

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Alexe, Cătălina-Monica, and Roxana-Mariana Nechita. 2025. "A Theoretical Framework for Multi-Attribute Decision-Making Methods in the Intelligent Leading and Allocation of Human Resources in Research and Development Projects" Sustainability 17, no. 16: 7535. https://doi.org/10.3390/su17167535

APA Style

Alexe, C.-M., & Nechita, R.-M. (2025). A Theoretical Framework for Multi-Attribute Decision-Making Methods in the Intelligent Leading and Allocation of Human Resources in Research and Development Projects. Sustainability, 17(16), 7535. https://doi.org/10.3390/su17167535

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