Toward Project Complexity Evaluation: A Structural Perspective

Ellinas, Christos; Allan, Neil; Johansson, Anders

doi:10.1109/jsyst.2016.2562358

Cited by 28 publications

(28 citation statements)

References 64 publications

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“…Insight of this sort has both theoretical (context agnostic approaches are increasingly relevant within the domain of Systems Engineering, yet are rarely used) and practical (transferability of knowledge) implications. C⃝ 2016 Wiley Periodicals, Inc. Syst Eng 19: 179-192, 2016…”

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confidence: 99%

Exploring Structural Patterns Across Evolved and Designed Systems: A Network Perspective

Ellinas¹,

Allan²,

Johansson

2016

Systems Engineering

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms Abstract -Our desire to deliver increased functionality while setting tighter operational and regulative boundaries has fueled a recent influx of highly-coupled systems. Nonetheless, our current capacity to successfully deliver them is still in its infancy. Understanding how such Designed systems are structured, along with how they compare to their naturally Evolved counterparts, can play an important role in bettering our capacity to do so. Based on this premise, the structural patterns underlying a wide range of seemingly unrelated systems is uncovered using tools from network science. By doing so, structural patterns emerge and are subsequently used to highlight both similarities and differences between the two classes of systems. With a focus on the Designed class, and assuming that increased structural variety fuels design uncertainty, it is shown that their adherence to statistical normality (i.e. expected vs. encountered patterns and statistical correlations between combinations of such patterns) is rather limited. Insight of this sort has both theoretical (context agnostic approaches are increasingly relevant within the domain of Systems Engineering, yet are rarely used) and practical (transferability of knowledge) implications.

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mentioning

confidence: 99%

Exploring Structural Patterns Across Evolved and Designed Systems: A Network Perspective

Ellinas¹,

Allan²,

Johansson

2016

Systems Engineering

Self Cite

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“…Although earlier studies that included project complexity, i.e., (Horwitz & Horwitz, 2007), found no significant moderating effect, the concept and measures of complexity have progressed significantly since then. Recent studies on various topics have highlighted the importance of project complexity as a contextual variable (e.g., (Ellinas, Allan & Johansson, 2018;Gao, Chen, Wang & Wang, 2018;Hartono, 2018;Maylor & Turner, 2017). Furthermore, from a social identity standpoint, as mentioned earlier, the impacts of cultural dimensions on diversity also deserve further investigation, as proposed by Roberson et al (2017).…”

Section: Discussionmentioning

confidence: 92%

Impact of team diversity and conflict on project performance in Indonesian start-ups

Hartono

Dzulfikar

Damayanti

2020

JIEM

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Purpose: Grounded to social identity and information decision theories, the study develops and empirically verifies a fresh theoretical model on the possible mediating effect of emotional and task conflicts toward the associations between diversity (work, social, and ideology) and project performance.Design/methodology/approach: A survey was performed, and 68 Indonesian start-ups provided positive responses accounting for a 57.6% response rate. In total, 102 Indonesian project teams participated, and since multiple team members were inquired for respective teams, 395 individual respondents were involved. An aggregation protocol was utilized to compute team-level datasets. The partial least square (PLS) method was utilized for the main analysis.Findings: The findings suggest a detrimental effect of heightened ideology polarization towards team performance as mediated by emotional conflict. Moreover, work diversity positively affects task conflict, but it does not positively affect team performance. Possible moderating variables such as cultural aspects may lessen the impact.Research limitations/implications: The study offers an updated theoretical model which pertains to diversity, conflict and performance. It also offers unique empirical evidence to examine the theoretical propositions within the setting of Indonesian start-ups. The sample size is rather limited even though it is substantially larger than similar past studies. This could result in a relatively low statistical power. Practical implications: It offers useful practical insights for start-ups to improve their project performance by looking at the intricate association between aspects of team diversity, conflict, and project success.Originality/value: This study extends the contemporary knowledge on diversity studies, by focusing on the project context of Indonesian start-ups.

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“…For instance, Sinha et al (2006, Xia and Chan (2012), Lessard et al (2014), Maylor et al (2008), Gransberg et al (2012), Vidal and Marle (2008), Owens et al (2012), Jarkas (2017), and Luo et al (2016) mainly focus on the framework of project complexity from different views. In addition, several attempts have been made to propose methods to measure the project complexity quantitatively such as Vidal et al (2011b), Nguyen et al (2015), He et al (2015), Shafiei-Monfared and Jenab (2012), Lu et al (2015), Qureshi and Kang (2015), and Ellinas et al (2018).…”

Section: Literature Review On Project Complexity Measurementmentioning

confidence: 99%

Bayesian Belief Network-Based Project Complexity Measurement Considering Causal Relationships

Luo

Zhang

2020

Journal of Civil Engineering and Management

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This research proposes a Bayesian belief network-based approach to measure the project complexity in the construction industry. Firstly, project complexity nodes are identified for model development based on the literature review. Secondly, the project complexity measurement model is developed with 225 training samples and validated with 20 test samples. Thirdly, the developed measurement model is utilized to conduct model analytics for sequential decision making, which includes predictive, diagnostic, sensitivity, and influence chain analysis. Finally, EXPO 2010 is used to testify the effectiveness and applicability of the proposed approach. Results indicate that (1) more attention should be paid on technological complexity, information complexity, and task complexity in the process of complexity management; (2) the proposed measurement model can be applied into practice to predict the complexity level for a specific project. The uniqueness of this study lies in developing project complexity measurement model (PCMM) with the cause-effect relationships taken into account. This research contributes to (a) the state of knowledge by proposing a method that is capable of measuring the complexity level under what-if scenarios for complexity management, and (b) the state of practice by providing insights into a better understanding of causal relationships among influencing factors of complexity in construction projects.

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Toward Project Complexity Evaluation: A Structural Perspective

Cited by 28 publications

References 64 publications

Exploring Structural Patterns Across Evolved and Designed Systems: A Network Perspective

Exploring Structural Patterns Across Evolved and Designed Systems: A Network Perspective

Impact of team diversity and conflict on project performance in Indonesian start-ups

Bayesian Belief Network-Based Project Complexity Measurement Considering Causal Relationships

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