Effect of user characteristics on artificial intelligence acceptability and intention to use artificial intelligence-based products

2021 ◽  
Vol 40 (4) ◽  
pp. 487-509
Author(s):  
Namhee Kim ◽  
Jong-An Choi
Keyword(s):  
Artificial Intelligence ◽  
Intention To Use ◽  
User Characteristics

Intention to use analytical artificial intelligence (AI) in services – the effect of technology readiness and awareness

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Carlos Flavián ◽  
Alfredo Pérez-Rueda ◽  
Daniel Belanche ◽  
Luis V. Casaló
Keyword(s):  
Artificial Intelligence ◽  
Technology Adoption ◽  
Design Methodology ◽  
Technology Readiness ◽  
Intention To Use ◽  
Content Type ◽  
Financial Investment ◽  
Managerial Implications ◽  
Barriers To Technology

PurposeThe automation of services is rapidly growing, led by sectors such as banking and financial investment. The growing number of investments managed by artificial intelligence (AI) suggests that this technology-based service will become increasingly popular. This study examines how customers' technology readiness and service awareness affect their intention to use analytical AI investment services.Design/methodology/approachThe automation of services is rapidly growing, led by sectors such as banking and financial investment. The growing number of investments managed by AI suggests that this technology-based service will become increasingly popular. This study examines how customers' technology readiness and service awareness affect their intention to use analytical AI investment services.FindingsThe results indicated that customers' technological optimism increases, and insecurity decreases, their intention to use robo-advisors. Surprisingly, feelings of technological discomfort positively influenced robo-advisor adoption. This interesting finding challenges previous insights into technology adoption and value co-creation as analytical AI puts customers into a very passive role and reduces barriers to technology adoption. The research also analyzes how consumers become aware of robo-advisors, and how this influences their acceptance.Originality/valueThis is the first study to analyze the role of customers' technology readiness in the adoption of analytical AI. The authors link the findings to previous technology adoption and automated services' literature and provide specific managerial implications and avenues for further research.

Impact of AI regulation on intention to use robots

2019 ◽  
Vol 8 (2) ◽  
pp. 97-114
Author(s):  
Sheshadri Chatterjee
Keyword(s):  
Quality Of Life ◽  
Artificial Intelligence ◽  
Design Methodology ◽  
Regulatory Control ◽  
Control Beliefs ◽  
Intention To Use ◽  
Content Type ◽  
And Control ◽  
The Impact

Purpose The purpose of this paper is to identify the factors influencing the citizens to use robots that would improve the quality of life of the citizens. Design/methodology/approach With the help of different adoption theories and models and with the support of background studies, some hypotheses have been formulated and a conceptual model has been developed with the consideration of the impact of artificial intelligence regulation (IAR) that controls the use of robots as a moderator. The model has been validated and the hypotheses have been tested by statistical analysis with the help of survey works involving consideration of feedbacks from 503 usable respondents. Findings The study reveals that the use of robots by the citizens would appreciably increase if government imposes strict artificial intelligence (AI) regulatory control concerning the use of robots, and in that case, it appears that the use of robots would improve the quality of life of the citizens. Research limitations/implications The duly validated model would help the authority to appropriately nurse and nurture the factors such as ethical dilemma, perceived risks and control beliefs for enhancing the intention of the citizens to use robots for many purposes including domestic usage in the context of appropriate restrictions imposed through AI regulation. Such use of robots would eventually improve the quality of life. Originality/value There are a few studies covering analysis of IAR as a moderator on the linkages of the predictors with the intention of the citizens to use robots. In this context, this study is claimed to have offered a novel contribution.

Weight Loss in a Digital Diabetes Prevention Program Powered by Artificial Intelligence: Retrospective Cohort Study (Preprint)

2021 ◽  
Author(s):  
Sarah A Graham ◽  
Jonathan H Hori ◽  
Fjori Shemaj ◽  
Natalie Stein ◽  
OraLee H Branch
Keyword(s):  
Artificial Intelligence ◽  
Weight Loss ◽  
Prevention Program ◽  
Diabetes Prevention ◽  
Diabetes Prevention Program ◽  
Average Weight ◽  
User Characteristics ◽  
Educational Mission ◽  
Effective Manner ◽  
Control And Prevention

BACKGROUND The National Diabetes Prevention Program (DPP), governed by the Centers for Disease Control and Prevention (CDC), reduces the incidence of diabetes and diabetes-associated medical costs. Typically, providing this program is staffing-intensive, limiting the ability to scale the DPP and keep pace with the growing incidence of prediabetes. OBJECTIVE We investigated the average weight loss of users of a program called Lark DPP that has full CDC recognition and is powered by conversational artificial intelligence (AI). METHODS We analyzed weight loss of 674 users who met CDC qualifications (completed ≥3 lessons in months 1-6 with ≥9 months between first and last lessons). In addition to the weight loss expected from the CDC curriculum, we investigated whether user characteristics and engagement with AI coaching increased the likelihood of achieving the CDC’s benchmark of ≥5% weight loss at 12 months using logistic regression. RESULTS We observed that 279 users met CDC qualifications and achieved an average of 5.2% (SE=.4) weight loss at 12 months (46% achieved ≥5%). CDC qualifiers completed an average of 20.7 (SE=.4) of 26 available educational missions/lessons. The number of weeks with >2 weigh-ins (standardized coefficient β=.39; P<.001); days with an exchange with the AI coach (β=.24; P=.016); and days since last coaching exchange at final weigh-in (β=-.45; P<.001) were significantly associated with the likelihood of achieving ≥5% weight loss. CONCLUSIONS The Lark DPP resulted in weight loss and sustained engagement for 12 months that was equal to or greater than in-person or hybrid-digital DPPs. Beyond the association between educational mission completion and weight loss, the synchronous personalized feedback and exchanges with the AI coach increased the likelihood of achieving ≥5% weight loss. An AI-powered program is one method to deliver DPPs in a scalable and resource-effective manner to keep pace with the prediabetes epidemic.

scholarly journals Artificial Intelligence in Human Resources Information Systems: Investigating its Trust and Adoption Determinants

2020 ◽  
Vol 5 (1) ◽  
pp. 749-765
Author(s):  
Bilal Ibrahim Hmoud ◽  
László Várallyai
Keyword(s):  
Artificial Intelligence ◽  
Human Resources ◽  
Significant Influence ◽  
Behavioral Intention ◽  
Intention To Use ◽  
Technological Readiness ◽  
Performance Expectancy ◽  
Human Resources Information Systems ◽  
And Performance ◽  
Behavioral Intention To Use

With the rapidly emerging trend of employing Artificial Intelligence technologies within modern economics. This study is an attempt to fill the research gap associated with the factors that have influence with the adoption of artificial intelligence in human resources information systems on HR-leaders intention to use it. It empirically investigates the influences that trust, technological readiness, facilitating condition and performance expectancy on HR-professional’s behavioral intention to use AI in HRM. Besides, examine the moderating effect of age and experience on the proposed associations. Data were collected from by online questionnaire from 185 HR managers. A structural framework was introduced to test the relationship between study latent variables. Result exhibited that trust and performance expectancy has a significant influence on HR-professionals behavioral intention to use AI-HRIS. Trust and technological readiness showed a significant influence on HR-professionals performance expectancy of using AI-HRIS. While facilitating condition, organizational size and technological readiness did not show a significant influence on HR-professionals behavioral intention toward using AI-HRIS. Lastly, Age and Experience did not have a moderating effect on trust and performance expectancy association with the behavioral intention toward using AI-HRIS. The findings of this study contribute to the theory development of information technology diffusion in HRM.

Enabling organizational use of artificial intelligence: an employee perspective

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Amal Dabbous ◽  
Karine Aoun Barakat ◽  
May Merhej Sayegh
Keyword(s):  
Artificial Intelligence ◽  
Technology Acceptance ◽  
New Technologies ◽  
Negative Impact ◽  
Job Insecurity ◽  
Perceived Usefulness ◽  
Equation Modeling ◽  
Intention To Use ◽  
Content Type ◽  
Self Image

Purpose As artificial intelligence (AI) has become increasingly popular and accessible, most companies have recognized its far-reaching potential. However, despite numerous research papers on organizational adoption of new technologies including AI, little is known about individual employees’ intentions to use them. Given that organizational innovations are of limited value if they are not adopted by employees, the purpose of this study is to understand the underlying factors that push employees to make use of these new technologies in the workplace. Design/methodology/approach This study builds on previously developed technology acceptance models to provide a new theoretical model. The model is then tested using data collected from a survey of 203 employees and analyzed through structural equation modeling. Findings Findings show that five factors affect employees’ intention to use AI either directly or as mediators. Organizational culture and habit exert a positive impact on employees’ intention to use AI, whereas job insecurity has a negative impact. Perceived self-image and perceived usefulness fully mediate the relation between job insecurity and intention to use. Moreover, perceived self-image and perceived usefulness partially mediate the relationship between habit and intention to use. Originality/value To the best of the authors’ knowledge, this study is among the first to determine the factors that influence employees’ intention to use AI in general and more particularly chatbots within the workplace.

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