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This study explores entrepreneurial orientation (EO) on project portfolio success in new product development projects, with the moderating effects of digitalization capability and modularization process.

The sample data of 204 firms was used to analyze the research hypotheses. This study adopted hierarchical regression to test the theoretical conceptual model incorporating EO, digitalization capability, modularization process, and project portfolio success.

These results indicate that EO positively affects project portfolio success. More importantly, digitalization capability and modularization process positively moderate the relationship between EO and project portfolio success.

Prominent studies have focused on different antecedent and consequence factors of project portfolio success; however, the impacts of EO still need to be noticed. This study makes a pioneering effort to make up this gap and investigate the effects of EO on project portfolio success, digitalization capability, and modularization process as moderators, which can enrich the current literature on project portfolio management.

Risk response decisions (RRDs) are vital for project risk mitigation. Although past research has focused on RRDs for independent single projects, it has scarcely explored how to make RRDs for single projects in project portfolios (SPPPs). Consequently, this study aims to bridge the gap in extant literature by developing an integrated approach to select risk response strategies (RRSs) for SPPPs considering objective adjustments and project interdependencies (PIs).

An integrated quality function deployment (QFD) method was used throughout this study. More so, a balanced score card (BSC) and stratified-Z-numbers-full consistency method (SZFUCOM) was applied to identify SPPP success criteria (SP3SC) to determine their weights. In addition, a spherical fuzzy set-design structure matrix (SFDSM) was used to quantify the correlation between the risks and the relationship between the risks and the predecessor projects. Consequently, the relationships between the risks and SP3SC and RRSs were described by the spherical fuzzy set (SFS) and Z-numbers, respectively. Besides, the results are weaved into QFD to transform SP3SC into risks and then into RRSs, while a linear optimization model is used to obtain the optimal RRSs. Lastly, a construction project portfolio (PP) was used to test the veracity of the results to prove their validity.

The approach to RRDs for single projects is observed to be different from that of SPPPs. In addition, this study finds that project portfolio objective adjustments (PPOAs) and PIs have significant impacts on RRDs given that they influence the risk priorities of independent single projects and SPPPs. Moreover, the application of an integrated QFD effectively synthesized the results from the findings of this study, as well as enabled companies to determine robust RRSs. Finally, the consistency results of the SZFUCOM were better than those of the triangular fuzzy number-full consistency method.

The study innovatively explores the method of RRDs for SPPP, which has been ignored by past research. SP3SC highly compatible with PP success is determined. Z-numbers are first used to evaluate the effect of RRSs to enhance the robustness of RRDs. The study proposes a method of RRDs comprehensively considering PPOAs and PIs, which provides robust methodological guidance for SPPP managers to control risks.

The critical chain project buffer monitor process addresses uncertainty and variability in project duration. However, classical buffer monitor methods only consider buffer consumption, while the dynamic allocation of buffer zones and the buffer consumption trend of activities are ignored. This paper presents the innovative framework for dynamic monitoring of project buffer which covers the dynamic buffer allocation, predictive analytics of buffer utilization and a new monitoring technique based on control chart graph.

First, a dynamically buffer allocation model is framed, and buffer zones are given to the activities considering risks. Then, a predictive model amalgamating Bayesian Optimization, Convolutional Neural Networks, and Long Short-Term Memory networks (BO-CNN-LSTM) is framed. Finally, a new buffer monitor framework is constructed that takes into account historical information about buffer usage and utilizes two thresholds derived from control chart theory.

This approach is empirically tested on a representative agricultural website project in China. The results show that, first, the dynamic buffer allocation makes better use of the project buffer, reduces buffer waste and increases the possibility of timely completion of the project. Second, the BO-CNN-LSTM model predicts better than Long Short-Term Memory (LSTM) and Grey Neural Network Model (GNNM), providing project managers with new management insights and perspectives. Third, the novel monitoring procedure makes the leveraging of historical data possible in the control of the schedule deviations, allowing for more timely interventions in the course of the implementation of the project.

A new project buffer monitoring method suitable for uncertain project environments is proposed.

This paper aims to develop an algorithm to study the impact of dynamic resource disruption on project makespan and provide a suitable resource disruption ratio for various complex industrial and emergency projects.

This paper addresses the RCPSP in dynamic environments, which assumes resources will be disrupted randomly, that is, the information about resource disruption is not known in advance. To this end, a reactive scheduling model is proposed for the case of random dynamic disruptions of resources. To solve the reactive scheduling model, a hybrid genetic algorithm with a variable neighborhood search is proposed.

The results obtained on the PSLIB instances prove the performance advantage of the algorithm; through sensitivity analysis, it can be obtained, the project makespan increases exponentially as the number of disruptions increase. Furthermore, if more than 50% of the project's resources are randomly disrupted, the project makespan will be significantly impacted.

The paper focuses on the impact of dynamic resource disruptions on project makespan. Few studies have considered stochastic, dynamic resource uncertainty. In addition, this research proposes a reasonable scheduling algorithm for the research problem, and the conclusions drawn from the research provide decision support for project managers.