Reinforcing inspiration for technology acceptance: Improving memory and software training results through neuro-physiological performance

•Predict inspiration utilizing an upward spiraling inspiration model for TAM.•Monitor cortisol levels to measure errors and time for training tasks.•Tested fuzzy logic method and compared results to cortisol levels.•Increase in cortisol levels leads to decreases in errors and decreases in time. This paper investigates the phenomenon of reinforcing inspiration for technology acceptance by improving memory and software training results Neuro-physiological performance. Monitoring of cortisol levels provided feedback for a decision support system that measured errors and elapsed time for training tasks completed by end-users of a health care application. The training success was measured utilizing statistics, SEM and a Fuzzy approach. The predictive model was implemented by comparing the regression, fuzzy logic and SEM results. Data collected from 338 health care workers were used to test a proposed model that inspiration, memory, and inspirational memory affect end user intention to adopt a digitized patient record software application. Structural equation modeling showed that, as expected, inspiration affected the individual behavior of the end users. Inspiration had an interactive impact through memory on collective acceptance of the technology, thereby affecting subsequent evaluations and behavior. The proposed model was nomologically validated through the use of a portable platform loaded with software for the electronic collection of operational-level health care data. Embedded metrics measured participants’ memory as operationalized by task completion time, number of errors, and completeness of the data. In order to triangulate the results, salivary cortisol levels collected from 74 health care workers were used to measure whether inspiration improves memory and affects end user intention to adopt the application through reduced errors and decreased completion times. This paper contributes to the literature by introducing inspiration as a key driver that improves memory to affect end user intention to use digitized patient record technology.