Vertical Integration Decision Making in Information Technology Management
Vertical integration, also known as make-or-buy, defines whether activities are conducted by company or provided by external parties. There are different models to support decision making for vertical integration in the literature. However, they ignore the uncertainty aspect of vertical integration....
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
|
| Series: | Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2078-2489/13/7/341 |
| _version_ | 1827618917902712832 |
|---|---|
| author | Menekse Gizem Gorgun Seckin Polat Umut Asan |
| author_facet | Menekse Gizem Gorgun Seckin Polat Umut Asan |
| author_sort | Menekse Gizem Gorgun |
| collection | DOAJ |
| description | Vertical integration, also known as make-or-buy, defines whether activities are conducted by company or provided by external parties. There are different models to support decision making for vertical integration in the literature. However, they ignore the uncertainty aspect of vertical integration. As a strategic decision, vertical integration is multidimensional and less frequent. This study contributes a new data-driven model that includes all these characteristics of vertical integration decisions. In this study, a methodology is suggested that benefits from the models in the literature and assesses the results with data obtained from real IT cases. Different methodologies were followed to reach a model that accurately predicts make-or-buy decisions in IT projects at a retail company. Firstly, three different knowledge-based generic models derived from the literature were applied to predict decisions for twenty-one different make-or-buy cases in IT. The highest accuracy rate reached among these knowledge-based models was 76%. Secondly, the same cases were also analyzed with Naïve Bayes using factors originally introduced by these generic models. The Naïve Bayes algorithm can represent the uncertainty inherent in the decision model. The highest accuracy rate obtained was 67%. Thirdly, a new data-driven model based on Naïve Bayes using IT-related factors was proposed for the decision problem of vertical integration. The data-driven model correctly classified 86% of the decisions. |
| first_indexed | 2024-03-09T10:17:03Z |
| format | Article |
| id | doaj.art-19559c35a5b1443982ceac3c458fdca4 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-09T10:17:03Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-19559c35a5b1443982ceac3c458fdca42023-12-01T22:16:56ZengMDPI AGInformation2078-24892022-07-0113734110.3390/info13070341Vertical Integration Decision Making in Information Technology ManagementMenekse Gizem Gorgun0Seckin Polat1Umut Asan2Department of Industrial Engineering, Istanbul Technical University, Macka, Istanbul 34367, TurkeyDepartment of Industrial Engineering, Istanbul Technical University, Macka, Istanbul 34367, TurkeyDepartment of Industrial Engineering, Istanbul Technical University, Macka, Istanbul 34367, TurkeyVertical integration, also known as make-or-buy, defines whether activities are conducted by company or provided by external parties. There are different models to support decision making for vertical integration in the literature. However, they ignore the uncertainty aspect of vertical integration. As a strategic decision, vertical integration is multidimensional and less frequent. This study contributes a new data-driven model that includes all these characteristics of vertical integration decisions. In this study, a methodology is suggested that benefits from the models in the literature and assesses the results with data obtained from real IT cases. Different methodologies were followed to reach a model that accurately predicts make-or-buy decisions in IT projects at a retail company. Firstly, three different knowledge-based generic models derived from the literature were applied to predict decisions for twenty-one different make-or-buy cases in IT. The highest accuracy rate reached among these knowledge-based models was 76%. Secondly, the same cases were also analyzed with Naïve Bayes using factors originally introduced by these generic models. The Naïve Bayes algorithm can represent the uncertainty inherent in the decision model. The highest accuracy rate obtained was 67%. Thirdly, a new data-driven model based on Naïve Bayes using IT-related factors was proposed for the decision problem of vertical integration. The data-driven model correctly classified 86% of the decisions.https://www.mdpi.com/2078-2489/13/7/341make-or-buy decisioninformation technologyvertical-integration modelsNaïve Bayes |
| spellingShingle | Menekse Gizem Gorgun Seckin Polat Umut Asan Vertical Integration Decision Making in Information Technology Management Information make-or-buy decision information technology vertical-integration models Naïve Bayes |
| title | Vertical Integration Decision Making in Information Technology Management |
| title_full | Vertical Integration Decision Making in Information Technology Management |
| title_fullStr | Vertical Integration Decision Making in Information Technology Management |
| title_full_unstemmed | Vertical Integration Decision Making in Information Technology Management |
| title_short | Vertical Integration Decision Making in Information Technology Management |
| title_sort | vertical integration decision making in information technology management |
| topic | make-or-buy decision information technology vertical-integration models Naïve Bayes |
| url | https://www.mdpi.com/2078-2489/13/7/341 |
| work_keys_str_mv | AT meneksegizemgorgun verticalintegrationdecisionmakingininformationtechnologymanagement AT seckinpolat verticalintegrationdecisionmakingininformationtechnologymanagement AT umutasan verticalintegrationdecisionmakingininformationtechnologymanagement |