Stakeholders Classification System Based on Clustering Techniques

In the Industry 5.0 era, companies are leveraging the potential of cutting-edge technologies such as artificial intelligence for more efficient and green human-centric production. In a similar approach, project management would benefit from artificial intelligence in order to achieve project goals by improving project performance, and consequently, reaching higher sustainable success. In this context, this paper examines the role of artificial intelligence in emerging project management through a systematic literature review; the applications of AI techniques in the project management performance domains are presented. The results show that the number of influential publications on artificial intelligence-enabled project management has increased significantly over the last decade. The findings indicate that artificial intelligence, predominantly machine learning, can be considerably useful in the management of construction and IT projects; it is notably encouraging for enhancing the planning, measurement, and uncertainty performance domains by providing promising forecasting and decision-making capabilities.

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“…Moreover, Pérez Vera and Bermudez Peña [26] provided a fuzzy inference system to classify stakeholders using two ML clustering algorithms.…”

Section: Stakeholder Pdmentioning

confidence: 99%

Artificial Intelligence Enabled Project Management: A Systematic Literature Review

et al. 2023

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“…Several studies have proposed different approaches to address these challenges. For instance, one study introduced a fuzzy inference system using a selective Bayesian classifier and a dynamic evolving neural-fuzzy inference system to improve stakeholder classification in projects [ 66 ]. Another study employed an embedded Markov framework and a mixture of Markov models to cluster career paths in the IT sector [ 67 ].…”

Section: Related Workmentioning

confidence: 99%

Role-Driven Clustering of Stakeholders: A Study of IoT Security Improvement

Almalki

Alnahdi

Albalawi

2023

Sensors

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This study aims to address the challenges of managing the vast amount of data generated by Internet of Things (IoT) devices by categorizing stakeholders based on their roles in IoT security. As the number of connected devices increases, so do the associated security risks, highlighting the need for skilled stakeholders to mitigate these risks and prevent potential attacks. The study proposes a two-part approach, which involves clustering stakeholders according to their responsibilities and identifying relevant features. The main contribution of this research lies in enhancing decision-making processes within IoT security management. The proposed stakeholder categorization provides valuable insights into the diverse roles and responsibilities of stakeholders in IoT ecosystems, enabling a better understanding of their interrelationships. This categorization facilitates more effective decision making by considering the specific context and responsibilities of each stakeholder group. Additionally, the study introduces the concept of weighted decision making, incorporating factors such as role and importance. This approach enhances the decision-making process, enabling stakeholders to make more informed and context-aware decisions in the realm of IoT security management. The insights gained from this research have far-reaching implications. Not only will they benefit stakeholders involved in IoT security, but they will also assist policymakers and regulators in developing effective strategies to address the evolving challenges of IoT security.

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“…These general methods can be used for specific case studies without precise stakeholder salience measurement. Furthermore, such classification of stakeholders might affect the accuracy of decision making in the planning process (Pérez Vera & Bermudez Peña, 2018). On the other hand, some methods are specific and depend on the temporal and spatial objective of stakeholders.…”

Section: Literature Review and Research Gapmentioning

confidence: 99%

Stakeholder salience and prioritization for port master planning, a case study of the multi-purpose Port of Isafjordur in Iceland

Eskafi

Fazeli

Dastgheib

et al. 2019

European Journal of Transport and Infrastructure Research

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The dynamic and ever-increasing complex nature of a port system involves a variety of stakeholders with a broad spectrum of involvement and objectives. In the port master planning, to fulfill the objectives of the various stakeholders and manage conflicts and controversies, a stakeholder analysis is carried out. However, effective and timely engagement of the key stakeholders in the planning process is not an easy task. This paper presents a framework of stakeholder analysis for the case study of the Multi-Purpose Port of Isafjordur in Iceland to underpin the master planning process. The framework deals with a systematic procedure of identification, grouping and then static mapping of stakeholders by means of the power-interest matrix. Further, the fuzzy logic 3-dimensional decision surface was adopted for dynamic salience mapping of the

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Stakeholders Classification System Based on Clustering Techniques

Cited by 4 publications

References 20 publications

Artificial Intelligence Enabled Project Management: A Systematic Literature Review

Artificial Intelligence Enabled Project Management: A Systematic Literature Review

Role-Driven Clustering of Stakeholders: A Study of IoT Security Improvement

Stakeholder salience and prioritization for port master planning, a case study of the multi-purpose Port of Isafjordur in Iceland

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