Non-Linear Impact of Organizational Structure on Schedule Management for R&D Project Success: An ANN Approach
Journal Title: Mehran University Research Journal of Engineering and Technology - Year 2019, Vol 38, Issue 2
Abstract
To attain project success in R&D (Research & Development) public sector environment, the aspects of an organizational structure play a vital role in the successful implementation of R&D projects. Nevertheless, not meeting proposed timelines and inadequate management of schedules of R&D projects is the intrinsic hurdle in R&D projects. However, the study gauging the impact of organizational structure on the schedule management of R&D projects has never been discussed before. Therefore, this study aims to gauge the effectiveness of organizational structure and schedule management dimensions upon each other by employing predictive modelling technique i.e. ANN (Artificial Neural Network). This study is based on a quantitative approach and the model followed is non-linear regression. A simple random sampling technique is used to collect data from 285 respondents (in two rounds) from various R&D public sector set-ups. The robustness and homogeneity of data is checked by carrying out F and T tests. Subsequently, data is pre-processed; ANN model is trained and validated by choosing appropriate tuning parameters and quantitative performance measures like RMSE (Root Mean Square Error) and MAPE (Mean Absolute Percentage Error) are analyzed. The results clearly indicated that formalization, decentralization and authority of managers are strongly correlated; differentiation, specialization, coordination mechanism, departmentalization are positively but weakly correlated with some subconstructs and centralization also constitutes positive but weakly correlated among all. The results also imply that decentralized organizational structures are more preferable than centralized structures for the execution of R&D projects when proposed timelines are to be met timely.
Authors and Affiliations
H. Hanif, A. Hassan
Keywords
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- EP ID EP549766
- DOI 10.22581/muet1982.1902.04
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