Brooks Williams, L C (2024) Artificial intelligence/machine learning as a tool for project management enhancement: A qualitative study on the future of technology in increasing the effectiveness of project managers. DIT thesis, Capella University, USA.
Abstract
Projects are critical for all organizations. Over the years, a large number of literatures have highlighted that most projects fail due to inefficiencies in the current process. Technology has always been seen as a solution that could help in tackling the inefficiencies in the system. Machine learning-based systems have been used increasingly in different activities across multiple industries with the aim of increasing the efficiency of these activities while reducing errors. Previous literature has also shown the capability and use of ML in project management activities. At the same time, the literature on the topic is sparse, and there are no findings that highlight the use and effectiveness of implementing these tools for project management. Though the studies have highlighted the benefits and capabilities of its use in project management and its potential, the lack of data in guidelines regarding its adoption is a critical issue. The studies in the field have largely been aimed at exploring the possible benefits or barriers to adopting the new technology within the realm of project management but lack the required guidelines that would help adopt the tool and the critical issues that impact this adoption. The research question for the study explores the perceived usefulness of AI/ML across industries within project management. To find the answer to this, the study approach consisted of a generic qualitative research design that makes use of structured interviews conducted with ten experts with prior experience in using the tool for project management to express their perceived view of the tool and its need. From the responses and thematic analysis, we found that ML benefits the organization in risk management, resources management, communication, and planning and helps improve the overall decision-making in different activities. That said, many factors have been identified as responsible for the limited adoption. Some issues are the poor quality of data, concerns about regulations on data use and security threats, lack of awareness, and concerns regarding the cost, integration of technology with existing systems, and return on investment. The responses also highlight the perception that collaboration and PMI developing guidelines with the help of experts in the field would be critical for reducing the barriers. The research helps highlight and provide more information regarding ML's role in PM activities and also helps understand the limitations. It allows future researchers to further the development in the field, providing specific areas that could be focused on.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Mostafa, Ahmad and Litchmore, Kondo |
| Index terms: | qualitative study, new technology, project manager, critical issue, efficiency, artificial intelligence, project management, risk management, return on investment, effectiveness, perceived usefulness, interview, machine learning, regulation, thematic analysis, collaboration, qualitative research, decision-making, integration |
| Subjects: | economic analysis, innovation and technology management, behavioral psychology, artificial intelligence, research design and methodology, profession, risk assessment, data collection methods, decision analysis, organizational analysis, political science, performance management, management, project management theory and practice, methods and analysis |
| Topics: | Organizational Design, Digital Applications, Governance, Roles and Professions, Research Practice, Business Strategy, Quality Management, Risk Management, Project Management, Engineering Principles |
| Descriptive scope: | 5 PCTEA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here