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Final Dissertation Cheryl Stewart.pdf (3.23 MB)

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Evaluating Organizational Success of an AI-Based Recommender System at a Two-Year Higher Education Institution

Stewart, Cheryl P
http://orcid.org/0009-0005-5512-6035
http://rave.ohiolink.edu/etdc/view?acc_num=frank1694787730418467

Abstract Details

2023, Doctorate of Business Administration, Franklin University, Business Administration.
This study will evaluate the organizational effectiveness of an artificial intelligence (AI)/machine learning (ML) recommender system at a higher education institution. It will determine the positive or negative net benefits (i.e., organizational effectiveness) of utilizing the D&M ISSM. Identifying the value and efficacy of information systems (IS) management actions and investments requires evaluating their success or effectiveness. A system's effectiveness is evaluated from the organizational perspective based on the degree to which it meets the goals of the organization. Although the pandemic has negatively impacted numerous lives and business activities, more leaders considered it an opportunity because it offered new opportunities for business innovation and entrepreneurship, despite it being viewed as the most significant crisis in the modern world. Considering the significant changes caused by the COVID-19 pandemic and the response to it, it is no longer simply considered an option to adopt and use AI/ML systems, but rather an obligation. There is a lack of understanding of the factors contributing to the success of recommendation systems, therefore, these systems are rarely used to their full potential. An analysis of the relationship between information quality, system quality, use/intention to use, and user satisfaction in recommender systems was conducted using a mixed-method approach based on the DeLone and McLean IS success model. Students enrolled in a two-year college who used a portal as part of their academic journey were the target population of this study, and a total of 8,559 participants were contacted to participate, and 305 of them completed the survey. The results of this study indicate that quality factors relate closely to the success of the recommender system as measured by organizational effectiveness. The results indicate that there are statistically significant relationships between the independent variables, Information Quality, System Quality, and User Satisfaction, and the dependent variable, Organizational Effectiveness. However, inconclusive results were found in use and intention to use. The study demonstrated that the IS success model developed by Delone and McLean provides an effective foundation for understanding recommender system success. A strong relationship exists between organizational effectiveness, measured by information quality, system quality, and user satisfaction, as demonstrated by this study.
Brock Schroeder (Committee Chair)
Tim Reymann (Committee Member)
Rachel Tate (Committee Member)
190 p.
Artificial Intelligence; Computer Science; Higher Education; Information Systems; Information Technology; Organization Theory; Organizational Behavior
Organizational Effectiveness; Organizational Success; Artificial Intelligence; Machine Learning; Recommender Systems; Collaborative Filtering; DeLone & McLearn Information Systems Success Model; Competitive Advantage; Net Benefits; Higher Education Institutions; Higher Education Leadership

Recommended Citations

Citations

  • Stewart, C. P. (2023). Evaluating Organizational Success of an AI-Based Recommender System at a Two-Year Higher Education Institution [Doctoral dissertation, Franklin University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=frank1694787730418467

    APA Style (7th edition)

  • Stewart, Cheryl. Evaluating Organizational Success of an AI-Based Recommender System at a Two-Year Higher Education Institution. 2023. Franklin University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=frank1694787730418467.

    MLA Style (8th edition)

  • Stewart, Cheryl. "Evaluating Organizational Success of an AI-Based Recommender System at a Two-Year Higher Education Institution." Doctoral dissertation, Franklin University, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=frank1694787730418467

    Chicago Manual of Style (17th edition)

frank1694787730418467
160
© 2023, all rights reserved.
This open access ETD is published by Franklin University and OhioLINK.