Authors:This paper was jointly published by the MQ Global Research Lab in October 2024, authored by doctoral researchers Lucas Tsai (Ph.D.), Ling Shen (Ph.D.), and Professor Richard Suttie. It was submitted to the 2024 Symposium on Practical Artificial Intelligence Applications.
Abstract:
The Motivation Quotient (MQ) assessment is an AI-assisted tool designed to measure deep intrinsic motivations and learning preferences. It builds upon Reiss Motivation Science (RMP) by expanding its framework to evaluate 24 different motivational DNAs and six life indices, providing users with a detailed report on their intrinsic motivations. Through an interactive AI-powered advisor, these results are further explained to offer practical recommendations tailored to individuals' specific needs and circumstances.
Research Focus:
1. Reliability of MQ Assessment
The reliability of the MQ tool was measured using Cronbach's alpha, a method for evaluating internal consistency. The results indicate:
19 out of 30 indices demonstrated strong reliability, with alpha values ranging from 0.6 to 1.0.
11 indices showed moderate reliability, with alpha values between 0.4 and 0.6, suggesting they are still useful but could benefit from further refinement.
2. Validity of MQ Assessment
The MQ demonstrated high face validity and content validity, with participants reporting that the assessment accurately reflected their real motivations.
There was a positive correlation (r = 0.94) between participants' self-assessed understanding of the MQ report and their evaluation of the report’s accuracy.
Motivational traits with higher scores (above 70) exhibited the highest validity, indicating that strong motivational characteristics were accurately captured by the assessment.
Context-Based Scale Approach
A key feature of the MQ assessment is its use of real-life scenarios to assess motivations. This approach, which presents situational questions, helps capture the complexity of human behavior more effectively than the more abstract prompts of the RMP framework, leading to more authentic and relevant responses.
Integration of AI for Enhanced Interactive Reporting
The MQ integrates a generative AI tool known as the MQ Advisor to interpret results and provide interactive recommendations. Users can ask personalized questions such as, “How does my curiosity affect my learning?” to receive tailored guidance that connects their motivations to real-world outcomes.
Conclusion
The MQ assessment is a reliable and valid tool for measuring core motivations. Its innovative use of AI and scenario-based methods provides users with detailed insights into their intrinsic motivations, making it a valuable resource for personal and professional development. However, continuous tracking and improvements, along with integration with external indicators relevant to businesses, will enhance its precision and applicability in human resource management.
This research highlights the potential impact of the MQ assessment in areas such as education and human resources, where understanding motivations can improve performance, engagement, and personal fulfillment. Future studies will focus on further validation and exploring how AI-guided coaching through MQ affects organizational outcomes.
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