Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
Details
Keywords
Jiaojiao Xu and Sijun Bai
The critical chain project buffer monitor process addresses uncertainty and variability in project duration. However, classical buffer monitor methods only consider buffer…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
A new project buffer monitoring method suitable for uncertain project environments is proposed.