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1 Factors Affecting Managers Willingness to Adopt AI to Project Management | Page i | Page ACKNOWLEDGEMENTS i | Page ii | P a g e ABSTRACT ii | P a g e | Page TABLE OF CONTENTS ACKNOWLEDGEMENTS i ABSTRACT .ii TABLE OF CONTENTS LIST OF TABLES .4 LIST OF FIGURES ABBREVIATION Chapter Introduction .6 1.1 Background of the study 1.2 Research question 1.3 Significance of the study 1.4 Scope .9 Chapter Literature review and Hypothesis development 11 2.1 Definition and current implication of AI in project mangement 11 2.2 Factors that influence the adaption process .11 2.3 Theoretical models of managers’ willingness to adapt with new technology 12 2.3.1 Theory of Perceived risk 12 2.3.2 Theory of Technology Acceptance Model-TAM .14 2.3.3 Combined theoretical model TAM-TPB (C-TAM-TPB) 17 | Page | Page 2.4 Empirical studies 18 2.5 Analytical framework 18 2.5.1 Basis for proposing research model 18 2.5.2 Proposed research framework .19 2.6 Conclusion 19 Chapter Data and Methodology 19 3.1 Research approach and data .20 3.2 Inductive and deductive research .20 3.3 Qualitative and quantitative research 20 3.4 Data collection .21 3.4.1 Research process 21 3.4.2 Scale .22 3.5 Ethical considerations 22 3.6 Quantitative procedure 22 3.7 Conclusion 25 Chapter Empirical findings 26 4.1 Descriptive analysis .26 4.2 Reliability test 26 4.3 Exploratory Factors Analysis .26 4.4 Hypothesis testing .26 | Page | Page 4.4.1 Pearson correlation 26 4.4.2 Regression 26 4.5 Implications 26 4.6 Conclusion 26 Chapter Conclusion, Recommendations, and Limitation .27 5.1 Conclusion 27 5.2 Recommendations 27 5.3 Limitations and future directions for further studies 27 Reference 28 | Page | Page LIST OF TABLES No table of figures entries found LIST OF FIGURES Figure 2-1: TAM model 16 Figure 2-2: C-TAM-TPB model 18 Figure 2-3: Analytical framework 19 | Page | Page ABBREVIATION | Page | Page Chapter Introduction This section briefly describes the background of the study as well as the rationale behind the choice of this research question It also clarifies the main objective and following research questions that the author urges to fulfill Moreover, scope of the research and presumable limitations are also discussed 1.1 Background of the study The implementation of Artificial Intelligence (AI) in Project Management (PM) has become a popular area of research in recent years (Kehoe et al., 2020) Even though the application of AI software to project management dates back as far as 1987, it is only now that it is really taking off With the advances in machine learning and data analytics, AI has the potential to transform the way projects are managed, from planning and scheduling to risk management and decision-making (Huang et al., 2021; Wang et al., 2022) However, the impact of AI on the working personnel in project management remains a topic of debate On the one hand, AI can augment the capabilities of project managers and team members, enabling them to make more informed decisions and optimize project performance (Bao et al., 2020) AI algorithms can analyze substantial volumes of data, identify patterns, and generate valuable insights, providing project personnel with valuable decision support (Zhang et al., 2021; Shtub et al., 2022) This can lead to improved | Page | Page project outcomes, increased efficiency, and better resource allocation (Bannerman et al., 2020; Yu et al., 2021) On the other hand, there are concerns about the displacement of human workers and the potential for AI to replace certain job roles altogether (Kang et al., 2019; Bryde et al., 2020) The automation capabilities of AI may lead to changes in the roles and responsibilities of project personnel, requiring them to adapt their skill sets and acquire new competencies (Laplante et al., 2021; Shen et al., 2022) There is a need to understand the potential impact of AI adoption on job security, career paths, and the overall workforce structure in project management (Cao et al., 2022; Liu et al., 2023) This thesis will attempt to examine the implementation of AI in project management with a focus on the effect that this will have on the working personnel and the power shift that will be created Specifically, the paper will explore the benefits and challenges of AI adoption in project management, the potential impact on the roles and responsibilities of project personnel, and the implications for workforce development and training The research will be guided by a review of the literature on AI in project management (Zeng et al., 2020; Chen et al., 2021) and case studies of AI implementation in real-world project settings (Li et al., 2022; Liu et al., 2023), along with interviews that will allow individuals to express their perspectives and opinions on this matter The findings of this study can ,potentially, contribute to the ongoing discourse on AI in | Page 13 | P a g e Chapter Literature review and Hypothesis development This chapter is to review existing literature regarding the interested issue of this paper and thus provide the basic theoretical framework leading the research flow By thoroughly reviewing antecedent literatures, the key hypotheses and its components will be formed 2.1 Definition and current implication of AI in project mangement 2.2 Theoretical models of managers’ willingness to adapt with new technology In recent years, there have been many researches to determine consumer behavior in the fields of information technology, marketing and ecommerce with proven theories and experiments in many places In the world Thereby, the author in turn re-study some popular models including the theory of risk perception (TPR) of Bauer, R.A (1960), Ajzen and Fishbein's theory of rational action (TRA) (1975), Ajzen and Fishbein's theory of planned buying behavior (TPB) (1975), and technology acceptance model (TAM) of Ajzen and Fishbein (1960) Davis and Arbor (1989) and two development models of the above theories are the combined model of TAM and TPB proposed by Taylor and Todd (1995) and the electronic commerce acceptance model (e-CAM) by Joongho Ahn, Jinsoo Park & Dongwon Lee (2001) 13 | P a g e 14 | P a g e 2.2.1 Theory of Perceived risk Bauer (1960) proposed the theory of Perceived Risk (TPR), which posits that risk perception during the online buying process comprises two distinct factors: (1) perceived risks associated with items and services, and (2) perceived risks associated with online transactions Bauer (1960) posited that the perception of risk has a significant role in customer behavior, perhaps serving as a crucial determinant in the transition from online browsing to real purchasing According to Cox and Rich (1964), the concept of perceived risk encompasses the collective uncertainty that customers experience when faced with a specific purchase scenario According to the categorization proposed by Jacoby and Kaplan (1972), consumers' perception of risk may be classified into five distinct categories, namely physical, psychological, social, financial, and performance The concept of Perceived Risk in the Context of Online Transaction (PRT) refers to the subjective evaluation individuals make about the potential negative outcomes associated with engaging in online transactions Multiple studies conducted in the realm of online transactions indicate that enhancing the transparency of the transaction process, including the disclosure of pertinent information such as supplier details, product specifications, and contractual obligations, contributes to the augmentation of customer trust and credibility Additionally, minimizing the collection of personal data during user interactions further supports this objective The present study examines the significance of 14 | P a g e 15 | P a g e consumption in establishing and validating the validity of knowledge, as shown via a series of case studies Bhimani (1996) highlights that the viability of e-commerce may be compromised by illicit activities, including the unauthorized disclosure of passwords, data manipulation, fraudulent practices, and failure to meet financial obligations punctually According to Swaminathan, LepkowskaWhite, and Rao (1999), it has been argued that customers exhibit a significant level of interest in evaluating online sellers prior to engaging in online transactions This interest stems from the recognition that the attributes of the seller have considerable significance in aiding the transaction process In summary, the concept of perceived risk in the context of online transactions (PRT) is considered a potential risk factor that customers may encounter Within the realm of online transactions, there are four distinct categories of risks: privacy, security- authentication, non-repudiation, and the overall perception of transaction risk The overarching perception of danger associated with internet transactions Conclusion: The risk perception model pertaining to e-commerce transactions, which influences purchasing behavior, has three key elements: perceived risk associated with online transactions, perceived risk associated with goods and services, and the resultant buying behavior The re-examination of the hypothesized association between components influencing e-commerce buying behavior is influenced by two 15 | P a g e 16 | P a g e factors: perceived risk associated with online transactions and perceived risk associated with items or services This influence is shown to be positive This implies that the capacity to discern the many hazards linked to electronic commerce might either augment or diminish, hence influencing purchasing behavior in a corresponding manner 2.2.2 Theory of Technology Acceptance Model-TAM According to Davis and Arbor (1989), D Fred and Ann, The Technology adoption Model (TAM), derived from the Theory of Reasoned Action (TRA), is generally acknowledged as a robust and foundational framework for studying user adoption of information technology (IT) There are five primary variables External variables, also known as external variables, refer to factors that are beyond the scope of a particular study or analysis These variables are not directly influenced or controlled by the researcher and may have an impact on The factors that influence the perception of utility and ease of use are as follows Perceived usefulness: It is often observed that users experience enhanced efficiency and productivity when using distinct application systems for certain tasks Perceived ease of use refers to the users' subjective perception of the level of convenience associated with using the system The attitude towards system use is based on assumptions about its utility and usability 16 | P a g e 17 | P a g e User's choice: The decision made by the user to use the system The choice to employ is intricately linked to the subsequent use The Technology Acceptance Model (TAM) is widely recognized as a standard framework for investigating the adoption and utilization of a system TAM serves as a model for assessing and forecasting the use patterns of an information system Therefore, the emergence of ecommerce may be attributed to advancements in information technology Consequently, the use of the survey methodology to examine the elements influencing the acceptability of information technology is also deemed suitable for investigating comparable concerns The order in ecommerce is structured as follows: Perceived usefulness is the degree to which a person believes that using a particular system will improve his or her performance” Constituents of the perceived usefulness variable Communication: The importance of communication in the operation of an information system has been recognized by previous researchers Indeed, if there is information, it is not possible to link the actors together System quality: Continuously improving system quality will help exploit information systems more effectively Information quality: It is the output quality of the information system: reliable, complete, and timely Service quality: insurance, reliability, responsiveness 17 | P a g e