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Identifying inter-project relationships with recurrent neural networks: towards an AI framework of project success prediction

Identifying inter-project relationships with recurrent neural networks: towards an AI framework of project success prediction
Identifying inter-project relationships with recurrent neural networks: towards an AI framework of project success prediction
A growing number of emerging studies have been undertaken to examine the mediating dynamics between intelligent agents, activities, and cost within allocated budgets to predict the outcomes of complex projects in dint of their significant uncertain nature in achieving a successful outcome. Emerging studies have used machine learning models to perform predictions, and artificial neural networks are the most frequently used machine learning model. However, most machine learning algorithms used in prior studies generally assume that input features, such as project complexity, team size and strategic importance, and prediction outputs, are independent. That is, a project’s success is assumed to be independent of other projects. As the datasets used to train in prior studies often contain projects from different clients across industries, this theoretical assumption remains tenable. However, in practice, projects are often interrelated across several dimensions, such as distributed overlapping teams. Therefore, we argue that the inter-project relationships should be taken into consideration to improve prediction performance. Furthermore, an ongoing ethnographic study at a leading project management artificial intelligence consultancy, referred to in this research as Company Alpha, suggests that projects within the same portfolio frequently share overlapping characteristics. To capture the emergent inter-project relationships, this study aims to compare two specific types of artificial neural network prediction performances; (i) multilayer perceptron and; (ii) recurrent neural networks. The multilayer perceptron is one of the most widely used artificial neural networks in the project management literature, and recurrent networks are distinguished by the memory they take from prior inputs to influence input and output. Through this comparison, this research will examine whether recurrent neural networks can capture the potential inter-project relationship towards achieving improved performance in contrast to multilayer perceptron. Our empirical investigation using ethnographic practice-based exploration at Company Alpha will contribute to project management knowledge and support developing an intelligent project prediction AI framework with future applications for project practice.
Hsu, Ming-Wei
76421de9-c403-4d48-940a-6ec69a50df58
Dacre, Nicholas
90ea8d3e-d0b1-4a5a-bead-f95ab32afbd1
Senyo, PK
b2150f66-8ef9-48f7-af32-3b055d4fa691
Hsu, Ming-Wei
76421de9-c403-4d48-940a-6ec69a50df58
Dacre, Nicholas
90ea8d3e-d0b1-4a5a-bead-f95ab32afbd1
Senyo, PK
b2150f66-8ef9-48f7-af32-3b055d4fa691

Hsu, Ming-Wei, Dacre, Nicholas and Senyo, PK (2021) Identifying inter-project relationships with recurrent neural networks: towards an AI framework of project success prediction. In British Academy of Management. 8 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

A growing number of emerging studies have been undertaken to examine the mediating dynamics between intelligent agents, activities, and cost within allocated budgets to predict the outcomes of complex projects in dint of their significant uncertain nature in achieving a successful outcome. Emerging studies have used machine learning models to perform predictions, and artificial neural networks are the most frequently used machine learning model. However, most machine learning algorithms used in prior studies generally assume that input features, such as project complexity, team size and strategic importance, and prediction outputs, are independent. That is, a project’s success is assumed to be independent of other projects. As the datasets used to train in prior studies often contain projects from different clients across industries, this theoretical assumption remains tenable. However, in practice, projects are often interrelated across several dimensions, such as distributed overlapping teams. Therefore, we argue that the inter-project relationships should be taken into consideration to improve prediction performance. Furthermore, an ongoing ethnographic study at a leading project management artificial intelligence consultancy, referred to in this research as Company Alpha, suggests that projects within the same portfolio frequently share overlapping characteristics. To capture the emergent inter-project relationships, this study aims to compare two specific types of artificial neural network prediction performances; (i) multilayer perceptron and; (ii) recurrent neural networks. The multilayer perceptron is one of the most widely used artificial neural networks in the project management literature, and recurrent networks are distinguished by the memory they take from prior inputs to influence input and output. Through this comparison, this research will examine whether recurrent neural networks can capture the potential inter-project relationship towards achieving improved performance in contrast to multilayer perceptron. Our empirical investigation using ethnographic practice-based exploration at Company Alpha will contribute to project management knowledge and support developing an intelligent project prediction AI framework with future applications for project practice.

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2021-Hsu_Dacre_Senyo-Identifying_Inter-Project_Relationships_with_Recurrent_Neural_Networks-BAM - Accepted Manuscript
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Accepted/In Press date: 2021
Published date: 9 June 2021

Identifiers

Local EPrints ID: 450055
URI: http://eprints.soton.ac.uk/id/eprint/450055
PURE UUID: c9e97798-86c8-4317-9f80-a1e5e2b4ce9a
ORCID for PK Senyo: ORCID iD orcid.org/0000-0001-7126-3826

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Date deposited: 07 Jul 2021 16:30
Last modified: 13 Dec 2021 03:37

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Contributors

Author: Ming-Wei Hsu
Author: Nicholas Dacre
Author: PK Senyo ORCID iD

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