Investigating key challenges in major public engineering projects by a network-theory based analysis of stakeholder concerns: A case study

Mok, Ka Yan; Shen, Geoffrey Qiping; Yang, Rebecca J.; Li, Clyde Zhengdao

doi:10.1016/j.ijproman.2016.10.017

Cited by 107 publications

(64 citation statements)

References 38 publications

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“…The other latest studies regarding the application of social network analysis in project environments, go from the development of models based on SNA to analyze communication, collaboration, and knowledge networks in project meta-networks [77], to the analysis of mega projects networks, from the perspectives of different important project stakeholders that help to develop proper long-term project governance policies [78,79]. In 2017, Mok et al (2017) [80] applied SNA basic network centrality measures to identify key challenges in major engineering projects (MEPs) based on interdependencies between stakeholder concerns, resulting in the identification of a set of key challenges that that occur in such MEPs, and helped to properly develop a set of recommendations to alleviate those challenges, which could be used in future MEPs. Yu et al (2017) [81], used social network analysis to investigate social risks related to housing demolition, from a stakeholder perspective in China.…”

Section: Application Of Social Network Analysis In Project Managementmentioning

confidence: 99%

Applying Social Network Analysis to Identify Project Critical Success Factors

Nunes

Abreu

2020

Sustainability

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A key challenge in project management is to understand to which extent the dynamic interactions between the different project people-through formal and informal networks of collaboration that temporarily emerge across a project´s lifecycle-throughout all the phases of a project lifecycle, influence a project's outcome. This challenge has been a growing concern to organizations that deliver projects, due their huge impact in economic, environmental, and social sustainability. In this work, a heuristic two-part model, supported with three scientific fields-project management, risk management, and social network analysis-is proposed, to uncover and measure the extent to which the dynamic interactions of project people-as they work through networks of collaboration-across all the phases of a project lifecycle, influence a project's outcome, by first identifying critical success factors regarding five general project collaboration types ((1) communication and insight, (2) internal and cross collaboration, (3) know-how and power sharing, (4) clustering, and (5) teamwork efficiency) by analyzing delivered projects, and second, using those identified critical success factors to provide guidance in upcoming projects regarding the five project collaboration types.Sustainability 2020, 12, 1503 2 of 32 competency and training are important factors, it is almost always the network factor that is the big key predictor of high performance in organizations and are usually characterized by having broader and diverse problem-solving networks fueled with positive energy [20]. But a broader network does not necessarily mean larger in size. Furthermore, some authors argue that when it comes to effective networks of collaboration, bigger does not always means better, but rather, superior quality means better [7,8]. However, creating a broad and diverse problem-solving network usually requires an extra mile from the employees of an organization, essentially because they need to be more flexible, accountable, and empowered, and proactively search and maintain such networks. This also means that more work will be done through informal networks of relationships, removing to a certain extent, the role of the formal organizational structure in several ways [8,21]. In fact, building a broader and more diverse problem-solving network requires a proper organizational structure that enables people to strategically create the necessary connections in an energized way. Usually, due its rigid nature, a formal organizational structure [6] is not able to provide for the needs of building such problem-solving networks [8,22]. However, either under the pressure or when highly motivated to get the work done, employees of an organization naturally engage in informal networks of collaboration, in order to overcome the natural constraints of the formal organizational structure to get the work done [8,22,23]. Very often these emerging informal networks are not ruled by the rational-legal authority system that the formal organizational structure provides ba...

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Section: Application Of Social Network Analysis In Project Managementmentioning

confidence: 99%

Applying Social Network Analysis to Identify Project Critical Success Factors

Nunes

Abreu

2020

Sustainability

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“…This study contributes to the cluster theory by enriching the cluster life-cycle literature in elaborating the reasons of the cluster declining in developing countries. Most of previous studies concentrate on advantages and growth of industrial clusters in growing as well as matured stages (Meng & Forman, 2016;Mok, Shen, Yang, & Li, 2017). Our research fills the gap on the cluster decline in Africa from the copycat perspective.…”

Section: Introductionmentioning

confidence: 72%

“…Then the cluster's survival would depend on its capability to adapt to the changing trends and renovation (Ingstrup, Jensen, & Christensen, 2017;Valdaliso, Elola, & Franco, 2016;Østergaard & Park, 2015). According to Spencer et al (2010), firms located in clusters do have a greater advantage with an efficient network distribution and are able to achieve higher returns than non-clustered ventures but in the absence of a collaborative network among stakeholders (Freeman & McVea, 2001;Mok et al, 2017), clusters become ineffective, experience a high cost of production and are not able to withstand business revolutions (Bankova, 2015;Kirankabeş & Arik, 2014;Weng, 2016). Currently, most literature on industry cluster focuses on the advantages and challenges but rarely discusses the copycat problem in industry clusters.…”

Section: Industry Clustermentioning

confidence: 99%

Prevalence of Copycat in Africa Textile Clusters: The Blame Game Among Stakeholders

Asare-Kyire

Essel

et al. 2019

Journal of Business Economics and Management

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In this paper we investigate the reasons behind the pirated textiles and try to address the questions of why copycats are rampant on the African textile market, their impact on textile clusters and why it is ineffectively being controlled. Taking Ghana as a sample, this study employed grounded theory methodology to explore the key factors that account for copycat prevalence in African textile industry. This study reveals that economic foundations, political factors and stakeholder interactions in the textile ecosystem have influenced copycat popularity in Africa. More specifically, the blame game among stakeholders with no one accepting responsibility for copycat prevalence gave space for perpetrators of copycat textiles to breed. The study extends the stakeholder and cluster theories particularly within the confines of developing regions, the interplay of actors and how their actions promote or revert the fight against copycats. This article implores governments should proactively lead in collaborative inter-agency actions to fight the copycat menace by repackaging and designing strategies/approaches through the employment and increasing of stakeholder consultations.

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“…Such projects also require an accounting of the project's social, economic, and environmental implications [38]. Life-cycle assessment (LCA) is the most widely used method to assess the environmental impact of construction projects, including mega projects.…”

Section: Evaluation Indicators For Mscpsmentioning

confidence: 99%

What are the Key Indicators of Mega Sustainable Construction Projects? —A Stakeholder-Network Perspective

Qiang

Zuo

et al. 2018

Sustainability

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Mega sustainable construction projects (MSCPs) require complex system engineering. There are various indicators available to evaluate sustainable construction, and it is difficult to determine which the key indicators are among them. Existing studies do not adequately consider the stakeholders associated with the indicators of sustainable construction, leading to key decision-makers' lack of targeted management strategies to improve the sustainability level of MSCPs. Using literature analysis and expert interviews, this study identified the key evaluation indicators of MSCPs from a stakeholder-network perspective. Social network analysis (SNA) was used to explore the relationships between the key evaluation indicators and corresponding stakeholders. The results showed that the government and designers significantly impacted other stakeholders and played as the key stakeholders in MSCPs. Regarding the indicators, applying energy-saving and intelligent technologies plays a key role in the MSCPs. This study links key indicators of MSCPs with the associated stakeholders, which helps decision-makers to develop targeted strategies to improve the sustainability level of MSCPs, thereby not only improving the efficiency and effectiveness of the intervention strategies, but also helping to save decision-makers' monetary and human resources which are usually limited.

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Investigating key challenges in major public engineering projects by a network-theory based analysis of stakeholder concerns: A case study

Cited by 107 publications

References 38 publications

Applying Social Network Analysis to Identify Project Critical Success Factors

Applying Social Network Analysis to Identify Project Critical Success Factors

Prevalence of Copycat in Africa Textile Clusters: The Blame Game Among Stakeholders

What are the Key Indicators of Mega Sustainable Construction Projects? —A Stakeholder-Network Perspective

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