Insolvency of Small Civil Engineering Firms: Critical Strategic Factors

Alaka, Hafiz; Oyedele, Lukumon O.; Owolabi, Hakeem A.; Bilal, Muhammad; Ajayi, Saheed O.; Akinadé, Olúgbénga O.

doi:10.1061/(asce)ei.1943-5541.0000321

Cited by 9 publications

(9 citation statements)

References 42 publications

(37 reference statements)

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“…This analysis technique was chosen as it thrives in identifying the unexplained variables that impact the covariation between many data. These variables describe underlying ideas that are difficult to capture with a single variable (Doloi et al , 2012; Kline, 2014; Alaka et al , 2017). The research methodology flowchart is as shown in Figure 1 below:…”

Section: Methodsmentioning

confidence: 99%

Extraction of underlying factors causing construction projects delay in Nigeria

Egwim

Alaka

Toriola-Coker

et al. 2021

JEDT

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Purpose This paper aims to establish the most underlying factors causing construction projects delay from the most applicable. Design/methodology/approach The paper conducted survey of experts using systematic review of vast body of literature which revealed 23 common factors affecting construction delay. Consequently, this study carried out reliability analysis, ranking using the significance index measurement of delay parameters (SIDP), correlation analysis and factor analysis. From the result of factor analysis, this study grouped a specific underlying factor into three of the six applicable factors that correlated strongly with construction project delay. Findings The paper finds all factors from the reliability test to be consistent. It suggests project quality control, project schedule/program of work, contractors’ financial difficulties, political influence, site conditions and price fluctuation to be the six most applicable factors for construction project delay, which are in the top 25% according to the SIDP score and at the same time are strongly associated with construction project delay. Research limitations/implications This paper is recommending that prospective research should use a qualitative and inductive approach to investigate whether any new, not previously identified, underlying factors that impact construction projects delay can be discovered as it followed an inductive research approach. Practical implications The paper includes implications for the policymakers in the construction industry in Nigeria to focus on measuring the key suppliers’ delivery performance as late delivery of materials by supplier can result in rescheduling of work activities and extra time or waiting time for construction workers as well as for the management team at site. Also, construction stakeholders in Nigeria are encouraged to leverage the amount of data produced from backlog of project schedules, as-built drawings and models, computer-aided designs (CAD), costs, invoices and employee details, among many others through the aid of state-of-the-art data driven technologies such as artificial intelligence or machine learning to make key business decisions that will help drive further profitability. Furthermore, this study suggests that these stakeholders use climatological data that can be obtained from weather observations to minimize impact of bad weather during construction. Originality/value This paper establishes the three underlying factors (late delivery of materials by supplier, poor decision-making and Inclement or bad weather) causing construction projects delay from the most applicable.

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Section: Methodsmentioning

confidence: 99%

Extraction of underlying factors causing construction projects delay in Nigeria

Egwim

Alaka

Toriola-Coker

et al. 2021

JEDT

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“…It is not surprising to see working capital/ total assets as the chief contributor since it has to do with liquidity of the firm. Construction firms are known to always need high liquidity if they are to keep their projects running [29,48,68,69]. Poor liquidity is what led to the recent failure of Carilion construction firm in the United Kingdom.…”

Section: The Models and The Resultsmentioning

confidence: 99%

A framework for big data analytics approach to failure prediction of construction firms

Alaka

Oyedele

Owolabi

et al. 2018

ACI

Self Cite

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This study explored use of big data analytics (BDA) to analyse data of a large number of construction firms to develop a construction business failure prediction model (CB-FPM). Careful analysis of literature revealed financial ratios as the best form of variable for this problem. Because of MapReduce’s unsuitability for iteration problems involved in developing CB-FPMs, various BDA initiatives for iteration problems were identified. A BDA framework for developing CB-FPM was proposed. It was validated by using 150,000 datacells of 30,000 construction firms, artificial neural network, Amazon Elastic Compute Cloud, Apache Spark and the R software. The BDA CB-FPM was developed in eight seconds while the same process without BDA was aborted after nine hours without success. This shows the issue of not wanting to use large dataset to develop CB-FPM due to tedious duration is resolvable by applying BDA technique. The BDA CB-FPM largely outperformed an ordinary CB-FPM developed with a dataset of 200 construction firms, proving that use of larger sample size with the aid of BDA, leads to better performing CB-FPMs. The high financial and social cost associated with misclassifications (i.e. model error) thus makes adoption of BDA CB-FPMs very important for, among others, financiers, clients and policy makers.

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“…Construction management can be complex, as it involves multiple contracts, and mistakes can have costly implications. AI can diminish the risk of human errors by automating the efforts of contract managers to ensure that processes are faster and more accurate [59].…”

Section: Construction Contractsmentioning

confidence: 99%

“…Clash detection: Clash detection is essential in the design phase and needs to be simplified and accelerated by AI. Technologies such as BuildingSP and GenMEP are in development, which detects clashes in real-time without the need to export files to other platforms [59].…”

Section: Design Phasementioning

confidence: 99%

Opportunities and Adoption Challenges of AI in the Construction Industry: A PRISMA Review

Regona

Yiğitcanlar

Xia

et al. 2022

Journal of Open Innovation: Technology, Market, and Complexity

175

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Artificial intelligence (AI) is a powerful technology with a range of capabilities, which are beginning to become apparent in all industries nowadays. The increased popularity of AI in the construction industry, however, is rather limited in comparison to other industry sectors. Moreover, despite AI being a hot topic in built environment research, there are limited review studies that investigate the reasons for the low-level AI adoption in the construction industry. This study aims to reduce this gap by identifying the adoption challenges of AI, along with the opportunities offered, for the construction industry. To achieve the aim, the study adopts a systematic literature review approach using the PRISMA protocol. In addition, the systematic review of the literature focuses on the planning, design, and construction stages of the construction project lifecycle. The results of the review reveal that (a) AI is particularly beneficial in the planning stage as the success of construction projects depends on accurate events, risks, and cost forecasting; (b) the major opportunity in adopting AI is to reduce the time spent on repetitive tasks by using big data analytics and improving the work processes; and (c) the biggest challenge to incorporate AI on a construction site is the fragmented nature of the industry, which has resulted in issues of data acquisition and retention. The findings of the study inform a range of parties that operate in the construction industry concerning the opportunities and challenges of AI adaptability and help increase the market acceptance of AI practices.

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Insolvency of Small Civil Engineering Firms: Critical Strategic Factors

Cited by 9 publications

References 42 publications

Extraction of underlying factors causing construction projects delay in Nigeria

Extraction of underlying factors causing construction projects delay in Nigeria

A framework for big data analytics approach to failure prediction of construction firms

Opportunities and Adoption Challenges of AI in the Construction Industry: A PRISMA Review

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