Title

AI Transformative Influence: Extending the TRAM to Management Student's AI’s Machine Learning Adoption

Date of Award

2021

Document Type

Dissertation

Degree Name

Doctor of Business Administration (DBA)

Committee Chair

Jonathan McCombs

Committee Member

Susan Campbell

Committee Member

Alexander Heckman

Abstract

Industries worldwide have adopted Artificial Intelligence's (AI) Machine Learning (ML) cognitive business functions to gain performance, productivity, competitive advantage, and economic prosperity. World Economic Forum (2020) reported that ML is on top of technology adoption. In the light of ML redefining management functions, not much is known about the management students' ML technology adoption rates. Hence, this study was aimed to investigate the management students' technology readiness and ML technology adoption in their future managerial jobs using the Technology Readiness and Acceptance Model (TRAM). Using a non-experimental, quantitative approach, data were collected from the university management students using the Technology Readiness Index (TRI 2.0) and Technology Acceptance Model (TAM) instruments. Correlational statistical analysis performed on the online survey data revealed that management students' ML technology adoption in their future managerial jobs is positively influenced by technology readiness (TR), perceived ease of use (PEOU), and perceived usefulness (PU). The model has shown the adoption path as TRI → PEOU → PU → TA, with PU as the single strongest predictor (β=0.797). When it comes to TR and TA, students are generally categorized as laggards; therefore, by using the right mix of TRAM variables, there is a potential to increase the ML adoption propensity amongst future managers, especially in the wake of ML cognitive applications redrawing managerial functions. Ultimately, the study will contribute to the TRAM body of knowledge and propel ML technology adoption.

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