Evaluating barriers inhibiting investors participation in Public-Private Partnership project bidding process using structural equation model

Abstract

The study investigates the critical barriers restraining private investors from participating in the PPP construction project bidding process. Hence, three key potential inhibiting factors, which include political constraints, public sector poor PPP capacity, and undefined project scope, were investigated. Therefore, the study adopted a quantitative research technique through a questionnaire survey to obtain data from 384 PPP practitioners sample representative in Lagos and Abuja, Nigeria. The survey recorded 274 responses, and SmartPLS (v.3.2.3) was used for the data analysis. The study developed a conceptual model to formulate hypotheses which were validated with Smart PLS3. Findings from the study indicated that the three key factors have a significant effect on the private investor's decision not to participate in the PPP bidding process. The findings from this study can help the government understand how to attract investors under her PPP arrangement. The study is unique based on the research idea and approach.

Immersive Technology Implementation in the Construction Industry: Modeling Paths of Risk

“…The T-values of all significant path models were higher than 1.96, which is the threshold value proposed by Ojelabi et al [95]. Thus, these T-values confirm that the relationships corresponding to these paths are significant.…”

“…The conceptual model of the network of risk criticality path for the adoption of ImTs was developed using partial least squares-structural equation modeling (PLS-SEM). Previous studies [92][93][94][95] have stated the importance of PLS in carrying out path analyses. Vinzi et al [92] defined the PLS-SEM approach as a regression-based approach that applies the principal component factor analysis test in the path analysis.…”

“…The values obtained in the Fornell-Larcker criterion test and the factor cross-loadings (see Tables 6 and 7, respectively) the discriminant validity of the variable observed in the model. The model's predictive relevance (Q 2 ) was also determined by initiating blindfolding procedures and calculating the cross-validated redundancy [95]. The cross-validated redundancy measurement in Q 2 statistics is the preferred blindfolding output among various cross-validated measures because it focuses on the model fit of the PLS latent variable model [94].…”

“…Furthermore, the effect size-which indicates the effect of an independent construct on a dependent construct in a path model-was calculated for each significant relationship using SMART-PLS. It has also been stated that the effect size is evaluated by observing the change in the dependent construct when a predictor is omitted, which changes the coefficient of determination [95].…”

Factors inhibiting Sustainable Knowledge Management in Nigeria

“…This is because annually there is an urgent need of over 100 billion dollars to address the infrastructural gap in Nigeria [2]. Evidence of infrastructural deficit can be found in previous studies in [3][4][5][6][7]. This cuts across different sectors in housing, road network, aviation, ports, healthcare, and other public facilities.…”