RETRACTED: User acceptance of machine learning models – Integrating several important external variables with technology acceptance model

Abstract

Machine learning models enable data-based decision-making in many areas and have attracted extensive attention. By testing the factors that influence the adoption of machine learning models, this study expands the scope of machine learning models in information technology adoption research. Based on the machine learning background and Technology Acceptance Model, this study integrates the necessary external variables, proposes a research model, and further verifies the validity of the model through the survey of 192 users of machine learning models. The results showed that organizational factors, trust, perceived usefulness, and perceived ease of use are positively correlated with the attitude of machine learning models. Moreover, our findings show that the interpretability of the model has an important positive effect on trust. The factors examined in this study are the basis for the development and use of reliable machine learning models. And it has important practical significance for promoting user adoption of machine learning model. Meanwhile, these theoretical studies also provide a strong literature support for the adoption of machine learning models and fill the theoretical research gap in this field.

Investigation of a Web-Based Explainable AI Screening for Prolonged Grief Disorder

“…Finally, the results from the qualitative feedback session with grief researchers and practitioners on our explainable AI UI mockup had highlighted several interesting insights into the use of machine learning models in grief care. While earlier works which aims to developed models to diagnose conditions in mental health tended to emphasize on perfor-mance [99], our findings echoed those from more recent studies which sought to put such models into practice, showing how the explainability of the model could be equally essential in enhancing user acceptance of the system [88], [118]. In the context of PGD in particular, participants in the interview mentioned that in practice, diagnosing whether a user has a mental health condition is often subjective and while they do use the results from diagnostic tools, it is often used only as a reference and practitioners tend to look into other factors such as symptoms or risk factors as well.…”

“…Previous studies demonstrated that the accuracy of a AI model played a key role in the level of trust users had in the system (which in turn significantly influenced their acceptance of the system) [87], [88]. Hence, we were uncertain whether the performance of our model was sufficient for the practitioners to adopt our models in their practice in a clinically meaningful way.…”

School Teachers’ Behavior in Remote Learning During Covid-19 Pandemic: Indonesia Perspective

“…Furthermore, the third hypothesis is supported in line with Venkatesh and Davis (2000), Molobi, Kabiraj, and Siddik (2020), and Zhang, Wang, and Li (2021) who concluded that perceived ease of use has an important influence on shaping perceived usefulness. In other words, individuals who find it easy to use new technology will automatically form their perception of the benefits that will be received when adopting the system.…”

“…In other words, a person inserts these beliefs into his belief structure. This internalization is equivalent to what Deutsch and Gerard (1955) call informational social influence (as opposed to normative), which is defined as the influence to receive information from others as evidence of reality (Ali, Gongbing, & Mehreen, 2018;Molobi, Kabiraj, & Siddik, 2020;Tao et al, 2020;Zhang, Wang, & Li, 2021). In the current context, if a boss or coworker suggests that a particular system might be helpful, then someone might believe that the system is beneficial and thus form the intention to use it.…”

Evaluate the Consumer Acceptance of AIoT-Based Unmanned Convenience Stores Based on Perceived Risks and Technological Acceptance Models

“…erefore, numerous studies have applied the technology acceptance model (TAM) to the fields of information, finance, consumption, research and development, etc. and have received remarkable outcomes [22][23][24][25][26].…”