Technology Acceptance of Future Decision Makers - An Investigation of Behavioral Intention Toward Using Artificial Intelligence Tools in Decision Making

Abstract

Background & Purpose: Rapid technological advancements makes it difficult for organizations to keep up, this alongside with the world becoming more and more digital results in large amount of data being produced. The technological advancements and the large amount of data available results in that AI can be used as a decision making tool in organizations, enabling better decision making. This because humans are not rational or logical when making decisions. The concept of bounded rationality is introduced and it may be explained as that humans take shortcuts in the thought process which result in suboptimal decision making. AI enables data driven decisions to be taken and the restrictiveness of the human mind indicates that the old way businesses made decisions are about to change. This thesis focuses on the role of AI in decision making. The Technological Acceptance Model (TAM) is a theory that explains to what degree a user is intending to perform a certain behavior. Because students are likely to reach a decision maker position in the future working environment, the interest of this study is to examine students’ behavioral intent to use AI as a decision support system in their future workplace. This result in the following research question: What factors in TAM influence business students’ behavioral intent to use AI in decision making? IV Theory: The theory chapter is based upon four main sections; technological Advancements impact on the business environment, decision making theory, AI and TAM. The technological advancements is divided into three subcategories; organization, people and data & information. Decision making theory includes characteristics, definition and Simons decision making process with five different phases; intelligent, design, choice, implementation phase and lastly feedback. In the section about AI, historical aspects are highlighted and a definition used in this report is provided. Later, AI is connected to an organizational perspective and linked to decision support system (DSS) and automation of decision making. At last, the framework TAM is presented and its history alongside a description of the components of TAM is provided. Methodology: The methodology chapter is divided into four subcategorize; research strategy, research design, research method and data analysis. Firstly, the research strategy is described and this thesis is based upon a deductive approach, whereas the assumptions objectivism and positivism is the basis and this thesis takes upon a quantitative approach. In the section research design, the quality criteria of this thesis is described and the chosen research design cross-sectional is described. Later, in the research method section, the secondary and primary data collection is accounted for. At last, the data analysis is motivated. Result & Discussion: This study concluded that perceived usefulness (PU) is 2,43 times as important than perceived ease of use (PEU) in influencing behavioral intention (BI) among students of using AI as a decision making tool. This information can be valuable when constructing systems because it shows that time spent on improving PEU might be more useful for the user and lead toward a stronger behavioral intent of using the system if more time is spend on increasing the PEU of the system.