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This paper covers the development of a multidimensional contract administration performance model (CAPM) for construction projects. The proposed CAPM is intended to be used by the industry stakeholders to measure the construction contract administration (CCA) performance and identify the strengths and weaknesses of the CCA system for running or completed projects.

The research design follows a sequential mixed methodology of qualitative and quantitative data collection and analysis. In the first phase, contract administration indicators were collected from relevant literature. In the second phase, an online questionnaire was prepared, and data were collected and analyzed using the crisp value of fuzzy membership function, and structural equation modeling (SEM). The fuzzy set was chosen for this study due to the presence of uncertainty and fuzziness associated with the importance of several key indicators affecting the CCA performance. Finally, SEM was used to test and analyze interrelationships among constructs of CCA performance.

The data collected from 336 construction professionals worldwide through an online survey was utilized to develop the fuzzy structural equation model. The goodness-of-fit and reliability tests validated the model. The study concluded a significant correlation between CCA performance, CCA operational indicators, and the process groups.

The contribution of this paper to the existing knowledge is the development of a fuzzy structural equation model that serves as a measurement tool for the contract administration performance. This is the first quantitative structural equation model to capture contract administration performance. The model consists of 93 Construction Contract Administration(CCA) performance indicators categorized into 11 project management process groups namely: project governance and start-up; team management; communication and relationship management; quality and acceptance management; performance monitoring and reporting management; document and record management; financial management; changes and control management; claims and dispute resolution management; contract risk management and contract closeout management.

This study investigates an under-researched yet fundamental question of how a developed country multinational enterprises (DMNE) perceives and manages political risks when undertaking infrastructure projects in the emerging markets (EMs).

The authors use an abduction-based qualitative research approach to analyze six international project operations of a multinational enterprise originating from Finland in five EMs.

The findings suggest that the overall nature of political risks in EMs is not the same, except few political risk factors that are visible in most EMs. Consequently, the applied risk management mechanisms vary between EMs, except with few common mechanisms. The authors develop an integrative analytical framework of political risk management based on the findings.

This paper is one of the first studies to identify political risk factors for western MNEs while undertaking international project operations and link them to reduction mechanisms used by them. The authors go beyond the notion of risk being conceptualized at a general level and evaluate 20 specific political risk factors referred to in extant literature. The authors further link these political risk factors with both social exchange and transaction cost theories conceptually as well as empirically. Finally, the authors develop a relatively comprehensive analytical framework of political risk management based on the case projects' findings that combine several strands of literature, including the social exchange theory, transaction cost theory, international market entry, project management and finance literature streams.

The purpose of this article is to identify the critical success factors (CSFs) of information technology (IT) projects using cloud computing. CSFs are variables that can influence the success of projects and therefore need to be identified and managed appropriately.

This is an exploratory qualitative study with 23 experts in cloud computing projects through semi-structured interviews. The data was analyzed using content analysis.

The results present a list of CSFs for projects in a cloud computing environment. The study demonstrated that the CSF with greater relevance to cloud projects is the team’s technical capacity, followed by the support of senior management and the team’s soft skills. In addition, results demonstrated that contract item management is a limiting factor for cloud projects.

The sample comprised only Brazilian experts, so it may not represent the same scenario as in other locations. The CSF ratio for cloud computing projects may vary depending on the company’s maturity in projects of this nature.

The CSF relationship can guide managers in properly conducting cloud computing projects, contributing to minimizing the risks and challenges that may interfere with the project.

The relationship of the CSFs in cloud computing projects proposed fills a gap in studies specifically related to this context and tries to minimize project managers’ stress.

Contract items for the cloud context are added to the CSF literature in IT projects, which have not been addressed so far.

The variance between the winning bid and the owner's estimated cost (OEC) is one of the construction management risks in the pre-tendering phase. The study aims to enhance the quality of the owner's estimation for predicting precisely the contract cost at the pre-tendering phase and avoiding future issues that arise through the construction phase.

This paper integrated artificial neural networks (ANN), deep neural networks (DNN) and time series (TS) techniques to estimate the ratio of a low bid to the OEC (R) for different size contracts and three types of contracts (building, electric and mechanic) accurately based on 94 contracts from King Saud University. The ANN and DNN models were evaluated using mean absolute percentage error (MAPE), mean sum square error (MSSE) and root mean sums square error (RMSSE).

The main finding is that the ANN provides high accuracy with MAPE, MSSE and RMSSE a 2.94%, 0.0015 and 0.039, respectively. The DNN's precision was high, with an RMSSE of 0.15 on average.

The owner and consultant are expected to use the study's findings to create more accuracy of the owner's estimate and decrease the difference between the owner's estimate and the lowest submitted offer for better decision-making.

This study fills the knowledge gap by developing an ANN model to handle missing TS data and forecasting the difference between a low bid and an OEC at the pre-tendering phase.

This study attempts to examine the factors that influence user intention to adopt e-procurement in the Nigerian public sector.

A well-structured questionnaire was used to collect primary data from 278 procurement and information technology (IT) departments’ officials of key federal government ministries and agencies. The technology acceptance model (TAM) model was adopted and extended with security-related factors, namely perceived trust and perceived security. A partial least squares-structural equation modelling (PLS-SEM) approach was used to test and validate the model.

The results indicated that perceived usefulness is the best predictor of users’ intention to adopt e-procurement, followed by perceived security and perceived trust. In contrast, however, perceived ease of use was found to have a significant negative effect on the intention to adopt e-procurement.

This study is among the first in the Nigerian public sector context to evaluate users’ perceptions on e-procurement adoption with the use of a distinctive research model (TAM). The study's findings contribute to a better understanding of the factors influencing the adoption of e-procurement in the Nigerian public sector.

The aim of this chapter is to describe a four-type model of organisational structures and to discuss two cases, Embrapa and the Brazilian Agricultural Research Corporation, as well as additional cases at SAM-Research and the centre for shared medical support services established at the University of Bologna.

These cases should help readers understand the importance of designing distinctive, tailored-made support services while keeping these structures flexible for further adaptation under unforeseen changes.

The chapter concludes by stressing the role of institutions to steadily invest in the design of these tailored support structures and in personalised training for their support staff.