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 (0.867), PE (0.890), EE (0.886), and BI (0.986) confirmed that the reliability measurement is greater than the recommended value of 0.70. 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 0.539 to the R[superscript 2]; by contrast, PE. contributes only 0.051 to the R[superscript 2], and the independent variable EE only contributes 0.019 to the R[superscript 2]. 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. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]