Sustainable development is the core agenda item of the 21st century to be addressed simultaneously by societies, businesses, and academia. Likewise, sustainability research in the project context is fragmented and still at a nascent stage with less attention directed towards the key antecedents particularly in developing countries. Using institutional theory, this paper analyzes the role of normative and mimetic isomorphic pressures as external enablers for integrating sustainability in project processes. Additionally, it aims to empirically validate a structural model for predictors of sustainable project management (SPM). Data were collected from 146 large construction firms in Pakistan which were then analyzed using the partial least square structural equation modeling (PLS-SEM) technique. The results show that mimetic isomorphic pressures assert more influence than normative pressures in predicting the sustainability performance of construction projects. The inferred implications suggest that large construction firms will tend to improve their sustainable performance under isomorphic pressure from professional bodies and from those competitors who are early adopters of sustainable project practices. This paper contributes to the literature by explaining the role of non-coercive isomorphism as an important enabler of SPM in developing countries. The model presented will enrich our current understanding of SPM by studying its juxtaposition with institutional theory and sustainable development research.

The governance of public sector infrastructure projects became an important topic of interest in the project, program, and portfolio management literature during the last decade. Today, it is becoming a central focus for policymakers seeking to ensure success in selecting, designing, and implementing government-sponsored programs of multi-projects. Due to the multiple underlying risks and complexities, the governance of infrastructure programs constitutes a critical element in strategic planning in developing countries. This paper has analyzed the infrastructure development program in Gilgit-Baltistan (Northern Pakistan), and revealed major shortcomings in the areas of decision-making, stakeholder management, and role ambiguity. Approaches to remedy these shortcomings have, thus, been proposed.

This study aimed to evaluate the effects of low-intensity blood flow restriction (BFR) training and high-intensity resistance training (HI-RT) on the leaping performance of long-jumpers. Long jump players were divided into two groups; one group (group A) receiving HI-RT (n = 8) and the other group (group B) receiving combined low-intensity BFR training plus HI-RT (n = 8). Muscle power and knee muscle strength was assessed at baseline, 3 weeks and 6 weeks of intervention. 1-RM was found to be significantly different between Group A and Group B at 3 and 6 weeks. Further, IKDQR, IKDHR and IKDQL was significantly improved in group B as compared to group A both at 3 and 6 weeks. There was significant time effect, group effect and time-group interaction in the strength of quadriceps and hamstring of both left and right leg measured through isokinetic device. Post-hoc analysis for 1-RM in group B showed a significant improvement at baseline and 6 weeks and the broad jump was significant at baseline and 3 weeks and at baseline and 6 weeks. The combined effects of low-intensity BFR training and HI-RT is effective in improving the muscle strength and power of lower limbs in long jumpers.

Demographic and socio-economic (SE) factors are associated with modifiable, clinical factors and cardiovascular disease (CVDs). However, the inclusion of mediators in these relationships creates complex pathways which extend the roles of factors within the model. The traditional hierarchal logistic regression (HLR) is unable to estimate the transmittal effects of factors through different layers of factors of CVDs. The study aims to simultaneously estimate and validate the probable linear and non-linear relationships among factors of CVDs considering potential mediators. Four hundred sixty participants (312 males and 148 females) were selected through systematic sampling in this sex-matched case control (1:1 ratio) study conducted in the largest Cardiac Center of Pakistan. The information on demographic, SE, modifiable and clinical factors of CVDs was recorded. Warp partial least squares (PLS) based on warp 3 algorithm was used to estimate the simultaneous linear and non-linear path coefficients of the proposed model of study. The study found that demographic and SE factors played a significant role in shaping the modifiable factors which further transmit their impact to CVDs. However, this transmitted impact of modifiable factors on CVDs was mediated through metabolic syndrome abnormalities (MSA) except self-reported subjective stress (SSS). Sleep satisfaction and negative dietary habits were the mediators between the relationship of SSS and MSA. Physical activity is the strongest factor associated with CVDs status. The proposed path analyses, verifying the mediation role of MSA in the pathways of relationships which would help in identifying the risky group of population and guide in formulating the health promotion policies for the reduction of CVDs burden. Further, Warp 3 algorithm is the better option to estimate complex models containing linear and non-linear relationship in the same model.

Criticism of the implementation of existing risk prediction models (RPMs) for cardiovascular diseases (CVDs) in new populations motivates researchers to develop regional models. The predominant usage of laboratory features in these RPMs is also causing reproducibility issues in low–middle-income countries (LMICs). Further, conventional logistic regression analysis (LRA) does not consider non-linear associations and interaction terms in developing these RPMs, which might oversimplify the phenomenon. This study aims to develop alternative machine learning (ML)-based RPMs that may perform better at predicting CVD status using nonlaboratory features in comparison to conventional RPMs. The data was based on a case–control study conducted at the Punjab Institute of Cardiology, Pakistan. Data from 460 subjects, aged between 30 and 76 years, with (1:1) gender-based matching, was collected. We tested various ML models to identify the best model/models considering LRA as a baseline RPM. An artificial neural network and a linear support vector machine outperformed the conventional RPM in the majority of performance matrices. The predictive accuracies of the best performed ML-based RPMs were between 80.86 and 81.09% and were found to be higher than 79.56% for the baseline RPM. The discriminating capabilities of the ML-based RPMs were also comparable to baseline RPMs. Further, ML-based RPMs identified substantially different orders of features as compared to baseline RPM. This study concludes that nonlaboratory feature-based RPMs can be a good choice for early risk assessment of CVDs in LMICs. ML-based RPMs can identify better order of features as compared to the conventional approach, which subsequently provided models with improved prognostic capabilities.

The governance of public sector infrastructure projects became an important area of interest in the literature on project management. Today, it is a focal point for policymakers to ensure successful appraisal and implementation of government‐sponsored programs. This paper aims to investigate the current practices of project governance (PG) for steering the public sector infrastructure program in Pakistan. An empirical investigation was carried out among professionals of public sector organizations involved in different infrastructure development projects. Latent construct of PG was validated through second‐order confirmatory factor analysis (CFA) and quantified the three dimensions of PG, i.e., portfolio direction (PD), sponsorship, effectiveness, and efficiency (SEE), and disclosure and reporting (DR) through the relative importance index (RII) method. The result showed that DR is among the least practicing dimension having RII = 0.55, while PD and SEE have shown similar prevalence with RII = 0.70 and 0.69, respectively. Overall, the most practicing item in the PG was “the alignment of portfolios with objectives and strategy” whereas the lowest practicing item relates to the “completeness of project information distribution due to the multi‐layered bureaucratic system.” The findings of this study will guide the decision makers to take appropriate measures for enhancing the effectiveness of PG in Pakistan.