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    Key Decision-Making Factors Influencing Bundling Strategies: Analysis of Bundled Infrastructure Projects

    Source: Journal of Infrastructure Systems:;2023:;Volume ( 029 ):;issue: 002::page 04023006-1
    Author:
    Ghiwa Assaf
    ,
    Rayan H. Assaad
    DOI: 10.1061/JITSE4.ISENG-2225
    Publisher: American Society of Civil Engineers
    Abstract: Project bundling is the process of awarding a single contract to several infrastructure projects to address construction, rehabilitation, replacement, or maintenance needs. This novel approach has presented several benefits to the infrastructure industry in terms of time and cost savings as well as enhanced efficiencies in project deliveries. However, due to the relative recency of, and interest in, the project bundling approach, the associated factors impacting the decision-making process are still not well understood. Moreover, little-to-no previous research efforts have particularly focused on the decision-making factors that need to be considered when implementing bundling strategies. To that extent, this paper addresses this knowledge gap through studying the decision-making factors affecting project bundling based on actual case studies that have used bundling strategies. In relation to that, this paper followed an analytical approach that is based on the implementation of network analysis and clustering analysis to explore data collected from bundled projects in the US. First, data was gathered from 30 case studies that relied on bundling strategies to deliver their projects. Based on the collected data, 23 decision-making factors related to project bundling have been identified. Second, network analysis was conducted to quantify the co-occurrences or dependencies among the identified factors. Finally, cluster analysis was used to group or prioritize the factors into highly connected clusters. The findings of this paper showed that the central and most critical decision-making factors that need to be considered when determining project bundling strategies are: geographic proximity; similarity in project types; homogeneity of work types; and condition rating of projects. In addition, the outcomes of this paper highlighted the importance of different decision-making factors in ensuring effective bundling practices. Ultimately, this research adds to the body of knowledge by providing a better understanding of the decision-making factors related to project bundling and determining or prioritizing the factors that agencies need to take into consideration when bundling their projects. To this end, this paper equips project stakeholders with the needed guidance to help them make optimal bundling decisions and capitalize on the benefits of their project bundling strategies.
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      Key Decision-Making Factors Influencing Bundling Strategies: Analysis of Bundled Infrastructure Projects

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    contributor authorGhiwa Assaf
    contributor authorRayan H. Assaad
    date accessioned2023-08-16T19:09:52Z
    date available2023-08-16T19:09:52Z
    date issued2023/06/01
    identifier otherJITSE4.ISENG-2225.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4292857
    description abstractProject bundling is the process of awarding a single contract to several infrastructure projects to address construction, rehabilitation, replacement, or maintenance needs. This novel approach has presented several benefits to the infrastructure industry in terms of time and cost savings as well as enhanced efficiencies in project deliveries. However, due to the relative recency of, and interest in, the project bundling approach, the associated factors impacting the decision-making process are still not well understood. Moreover, little-to-no previous research efforts have particularly focused on the decision-making factors that need to be considered when implementing bundling strategies. To that extent, this paper addresses this knowledge gap through studying the decision-making factors affecting project bundling based on actual case studies that have used bundling strategies. In relation to that, this paper followed an analytical approach that is based on the implementation of network analysis and clustering analysis to explore data collected from bundled projects in the US. First, data was gathered from 30 case studies that relied on bundling strategies to deliver their projects. Based on the collected data, 23 decision-making factors related to project bundling have been identified. Second, network analysis was conducted to quantify the co-occurrences or dependencies among the identified factors. Finally, cluster analysis was used to group or prioritize the factors into highly connected clusters. The findings of this paper showed that the central and most critical decision-making factors that need to be considered when determining project bundling strategies are: geographic proximity; similarity in project types; homogeneity of work types; and condition rating of projects. In addition, the outcomes of this paper highlighted the importance of different decision-making factors in ensuring effective bundling practices. Ultimately, this research adds to the body of knowledge by providing a better understanding of the decision-making factors related to project bundling and determining or prioritizing the factors that agencies need to take into consideration when bundling their projects. To this end, this paper equips project stakeholders with the needed guidance to help them make optimal bundling decisions and capitalize on the benefits of their project bundling strategies.
    publisherAmerican Society of Civil Engineers
    titleKey Decision-Making Factors Influencing Bundling Strategies: Analysis of Bundled Infrastructure Projects
    typeJournal Article
    journal volume29
    journal issue2
    journal titleJournal of Infrastructure Systems
    identifier doi10.1061/JITSE4.ISENG-2225
    journal fristpage04023006-1
    journal lastpage04023006-13
    page13
    treeJournal of Infrastructure Systems:;2023:;Volume ( 029 ):;issue: 002
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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