Business intelligence: Applying the unified theory of acceptance and use of technology

by Pope, Angela D., Ph.D., CAPELLA UNIVERSITY, 2014, 127 pages; 3616064


The purpose of this study was to explore the variables that affect an individual’s intention to use business intelligence technology in organizations. Constructs in the study were social influence, performance expectancy, effort expectancy, and behavioral intention. Social influence refers to verbal comments from executives and coworkers that inspire employees and executives to adopt and use technology. Performance expectancy construct is the extent to which a person thinks that using new technology helps them gain recognition by management and adds value to their organization by increasing productivity. Effort expectancy is the degree to which a user finds technology easy to learn and use. Behavioral intention is based on a person’s decision to use or not use new technology in an organization. The adapted unified theory of user acceptance and technology instrument measured the impact that SI, PE, and EE (independent variables) has on BI (dependent variable). A field test with five subject matter experts confirmed that the modified UTAUT instrument met the requirements for face validity and content validity. A pilot test was conducted prior to administering the online survey hosted by SurveyMonkey and the Cronbach’s alpha values were computed using IBM SPSS Statistics Premium Grad Pack Version 21.0 for Mac OS and Microsoft Windows SPSS. The reliability test results for SI (.867), PE (.890), EE (.886), and BI (.986) confirmed that the reliability measurement is greater than the recommended value of .70. Four research questions and hypotheses were developed for this study. Multiple linear regression analysis procedures using a stepwise method examined the influence that SI, PE, and EE has on BI. Social influence makes a significant contribution to the explanation of the variance of BI; SI contributes .539 to the R2; by contrast, PE

contributes only .051 to the R2, and the independent variable EE only contributes .019 to the R2. Therefore, while all three independent variables demonstrate significant contributions in explaining the variation in BI, SI plays a much stronger role than do PE and EE. Effort expectancy plays the weakest role of the three independent variables in explaining the variance in BI. This finding is consistent with findings from the original UTAUT model because EE becomes less important as employees learn to use business intelligence technology. The present study advances the theoretical perspective in the academic literature to explain the attitudes and beliefs of employees regarding the acceptance and use of business intelligence technology throughout the organization. Overall, the study provides ways for executives to implement training, policies, or processes prior to implementing business intelligence technology.

AdviserBernard J. Sharum
Source TypeDissertation
SubjectsBusiness administration; Management; Information technology
Publication Number3616064

About ProQuest Dissertations & Theses
With nearly 4 million records, the ProQuest Dissertations & Theses (PQDT) Global database is the most comprehensive collection of dissertations and theses in the world. It is the database of record for graduate research.

PQDT Global combines content from a range of the world's premier universities - from the Ivy League to the Russell Group. Of the nearly 4 million graduate works included in the database, ProQuest offers more than 2.5 million in full text formats. Of those, over 1.7 million are available in PDF format. More than 90,000 dissertations and theses are added to the database each year.

If you have questions, please feel free to visit the ProQuest Web site - - or contact ProQuest Support.