Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products

2020 ◽  
Vol 47 ◽  
pp. 101324 ◽  
Author(s):  
Kwonsang Sohn ◽  
Ohbyung Kwon
Keyword(s):  
Artificial Intelligence ◽  
Technology Acceptance ◽  
Factors Influencing ◽  
Intelligent Products

Why Do Physicians Reject the Electronic Health Record: A Theoretical Perspective? (Preprint)

2018 ◽  
Author(s):  
Weam Alfayez ◽  
Arwa Alumran ◽  
Dr Saja A. Al-Rayes
Keyword(s):  
Information Technology ◽  
Electronic Health Record ◽  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Health Record ◽  
Record System ◽  
Factors Influencing ◽  
Tam Model ◽  
Electronic Health ◽  
Acceptance Model

BACKGROUND Many theories/ models adopted from behavioral sciences literature or developed within the field of information technologies could help in understanding the technology acceptance, usage, and effective adoption. OBJECTIVE The main aim of this paper is to review the different theories/ models that can help in understanding information technology/system acceptance and use, and to choose the most appropriate theoretical framework that could be applied to understand the factors influencing physicians’ use of the Electronic Health Record system (EHR) at King Fahd Military Medical Complex (KFMMC) in Dhahran city, Saudi Arabia. METHODS The theories/ models were reviewed using scientific databases. The inclusion criteria were if the theories/ models used to explain individual behaviors toward accepting and using of information technology including the once conducted within the healthcare. RESULTS The review showed that there were five theories/ models were used within information technology studies to understand the technology acceptance and used. There were Theory of Reasoned Action, Theory of Planned Behaviour, Innovation Diffusion Theory, Unified theory of acceptance and use of technology, and Technology Acceptance Model. Each has different explanatory power of technology use. The most appropriate theoretical framework to understand the reason behind physician use of the EHR at KFMMC would be the Technology Acceptance Model (TAM). TAM model could explain up to 75% of the variation in the behavioral intention (acceptance), and up to 62% of the variation in the actual use. It is the gold standard for assessing the usage of health technologies and systems. In fact, the TAM model is one of the core models used to explore the physician’s perceptions of the Electronic Health Record system adoption. CONCLUSIONS This review showed that there are different theories available in the literature can be used to justify the reason behind electronic health record acceptance. TAM is one of the effective, simplest models used to understand the factors influencing physicians to use the EHR-system. Further studies need to apply the TAM model to check its ability in explaining the reason behind EHR within different hospitals in Saudi Arabia

Trust in Artificial Intelligence: Meta-Analytic Findings

2021 ◽  
pp. 001872082110139
Author(s):  
Alexandra D. Kaplan ◽  
Theresa T. Kessler ◽  
J. Christopher Brill ◽  
P. A. Hancock
Keyword(s):  
Artificial Intelligence ◽  
Empirical Research ◽  
Meta Analysis ◽  
Automated Vehicles ◽  
Factors Influencing ◽  
Moderating Factors ◽  
Shared Context ◽  
Build Systems ◽  
Significant Factors

Objective The present meta-analysis sought to determine significant factors that predict trust in artificial intelligence (AI). Such factors were divided into those relating to (a) the human trustor, (b) the AI trustee, and (c) the shared context of their interaction. Background There are many factors influencing trust in robots, automation, and technology in general, and there have been several meta-analytic attempts to understand the antecedents of trust in these areas. However, no targeted meta-analysis has been performed examining the antecedents of trust in AI. Method Data from 65 articles examined the three predicted categories, as well as the subcategories of human characteristics and abilities, AI performance and attributes, and contextual tasking. Lastly, four common uses for AI (i.e., chatbots, robots, automated vehicles, and nonembodied, plain algorithms) were examined as further potential moderating factors. Results Results showed that all of the examined categories were significant predictors of trust in AI as well as many individual antecedents such as AI reliability and anthropomorphism, among many others. Conclusion Overall, the results of this meta-analysis determined several factors that influence trust, including some that have no bearing on AI performance. Additionally, we highlight the areas where there is currently no empirical research. Application Findings from this analysis will allow designers to build systems that elicit higher or lower levels of trust, as they require.

scholarly journals Acceptance of artificial intelligence in German agriculture: an application of the technology acceptance model and the theory of planned behavior

2021 ◽  
Author(s):  
Svenja Mohr ◽  
Rainer Kühl
Keyword(s):  
Artificial Intelligence ◽  
Theory Of Planned Behavior ◽  
Technology Acceptance ◽  
Planned Behavior ◽  
Technology Acceptance Model ◽  
Structural Equation ◽  
Perceived Behavioral Control ◽  
Labor Costs ◽  
Online Questionnaire ◽  
Acceptance Model

AbstractThe use of Artificial Intelligence (AI) in agriculture is expected to yield advantages such as savings in production resources, labor costs, and working hours as well as a reduction in soil compaction. However, the economic and ecological benefits of AI systems for agriculture can only be realized if farmers are willing to use them. This study applies the technology acceptance model (TAM) of Davis (1989) and the theory of planned behavior (TPB) of Ajzen (1991) to investigate which behavioral factors are influencing the acceptance of AI in agriculture. The composite model is extended by two additional factors, expectation of property rights over business data and personal innovativeness. A structural equation analysis is used to determine the importance of factors influencing the acceptance of AI systems in agriculture. For this purpose, 84 farmers were surveyed with a letter or an online questionnaire. Results show that the perceived behavioral control has the greatest influence on acceptance, followed by farmers’ personal attitude towards AI systems in agriculture. The modelled relationships explain 59% of the total variance in acceptance. Several options and implications on how to increase the acceptance of AI systems in agriculture are discussed.

scholarly journals A Path Model of the Intention to Adopt Variable Rate Irrigation in Northeast Italy

2021 ◽  
Vol 13 (4) ◽  
pp. 1879
Author(s):  
Maurizio Canavari ◽  
Marco Medici ◽  
Rungsaran Wongprawmas ◽  
Vilma Xhakollari ◽  
Silvia Russo
Keyword(s):  
Technology Acceptance ◽  
Precision Agriculture ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Path Model ◽  
Irrigated Agriculture ◽  
Variable Rate ◽  
Intention To Adopt ◽  
Factors Influencing ◽  
Variable Rate Irrigation

Irrigated agriculture determines large blue water withdrawals, and it is considered a key intervention area to reach sustainable development objectives. Precision agriculture technologies have the potential to mitigate water resource depletion that often characterises conventional agricultural approaches. This study investigates the factors influencing farmers’ intentions to adopt variable rate irrigation (VRI) technology. The Technology Acceptance Model 3 (TAM-3) was employed as a theoretical framework to design a survey to identify the factors influencing farmers’ decision-making process when adopting VRI. Data were gathered through quantitative face-to-face interviews with a sample of 138 fruit and grapevine producers from the Northeast of Italy (Veneto, Emilia-Romagna, Trentino-Alto Adige, Friuli-Venezia Giulia). Data were analysed using partial least squares path modelling (PLS-PM). The results highlight that personal attitudes, such as perceived usefulness and subjective norm, positively influence the intention to adopt VRI. Additionally, the perceived ease of use positively affects intention, but it is moderated by subject experience.

Analysis of technological, individual and community factors influencing the use of popular Web 2.0 tools in LIS education

2017 ◽  
Vol 35 (5) ◽  
pp. 977-993 ◽  
Author(s):  
Chokri Barhoumi
Keyword(s):  
Standard Deviation ◽  
Web 2.0 ◽  
Technology Acceptance ◽  
Information Science ◽  
Instant Messaging ◽  
Research Approach ◽  
Web 2.0 Tools ◽  
Content Type ◽  
Community Factors ◽  
Factors Influencing

Purpose This research paper aims to explore the technological, individual and community factors influencing the use of popular Web 2.0 tools in library and information science (LIS) education to prepare LIS students for Library 2.0. The study was guided by the activity theory (AT) and technology acceptance model (TAM) of Davis as a lens. The study reveals a set of factors concerning the technical tools, subjective perceptions, goals of online discussion, social presence within a community, rules for participation and roles of the participants that affect their online engagement patterns. Design/methodology/approach This study was performed during the 2015 academic year; it used a descriptive analytical research approach for exploring and analysing technological, individual and community factors influencing the use of the popular Web 2.0 tools in LIS education. Findings The results show that at the technological level of the AT, educators in the sample found the WhatsApp instant messaging and Twitter to be the easiest tools to use, selecting those tools at, respectively, 73.2 per cent (standard deviation = 0.450) and 61.1 per cent (standard deviation = 0.490). WhatsApp and Twitter also lead at the individual level of the AT, as the most valuable platforms for sharing information and knowledge. Video, text and photo objects are the most commonly shared items, used by 90.7, 93.5 and 98.9 per cent, respectively. Originality/value This study may be useful to help information science educators to prepare graduates for the emerging Web 2.0 environments and to prepare students for Library 2.0.

Factors Influencing Trainees’ Interest in Breast Imaging

2021 ◽  
pp. 084653712110495
Author(s):  
Tong Wu ◽  
Wyanne Law ◽  
Nayaar Islam ◽  
Charlotte J. Yong-Hing ◽  
Supriya Kulkarni ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Students ◽  
Breast Imaging ◽  
Current Level ◽  
Common Reason ◽  
Training Level ◽  
Chi Square ◽  
Factors Influencing ◽  
Radiology Residents ◽  
The Impact

Purpose: To gauge the level of interest in breast imaging (BI) and determine factors impacting trainees’ decision to pursue this subspecialty. Methods: Canadian radiology residents and medical students were surveyed from November 2020 to February 2021. Training level, actual vs preferred timing of breast rotations, fellowship choices, perceptions of BI, and how artificial intelligence (AI) will impact BI were collected. Chi-square, Fisher’s exact tests and univariate logistic regression were performed to determine the impact of trainees’ perceptions on interest in pursuing BI/women’s imaging (WI) fellowships. Results: 157 responses from 80 radiology residents and 77 medical students were collected. The top 3 fellowship subspecialties desired by residents were BI/WI (36%), abdominal imaging (35%), and interventional radiology (25%). Twenty-five percent of the medical students were unsure due to lack of exposure. The most common reason that trainees found BI unappealing was repetitiveness (20%), which was associated with lack of interest in BI/WI fellowships (OR = 3.9, 95% CI: 1.6-9.5, P = .002). The most common reason residents found BI appealing was procedures (59%), which was associated with interest in BI/WI fellowships (OR, 3.2, 95% CI, 1.2-8.6, P = .02). Forty percent of residents reported an earlier start of their first breast rotation (PGY1-2) would affect their fellowship choice. Conclusion: This study assessed the current level of Canadian trainees’ interest in BI and identified factors that influenced their decisions to pursue BI. Solutions for increased interest include earlier exposure to breast radiology and addressing inadequacies in residency training.

Information Credibility Evaluation in Presence of Users’ Safety in New Retailing

2021 ◽  
Author(s):  
Dong Wang ◽  
Kehong Wang ◽  
Lemei Yan ◽  
Zeyu Yue ◽  
Jiewen Zhang
Keyword(s):  
Technology Acceptance ◽  
Information Acquisition ◽  
Structural Equation ◽  
Information Quality ◽  
Positive Impact ◽  
Reliability And Validity ◽  
Equation Modeling ◽  
Self Identity ◽  
Factors Influencing ◽  
Information Credibility

Understanding users’ safety perception of the credibility of web-based information has become increasingly important in the context of new retailing. This study extends the existing literature by exploring the factors influencing information credibility in the context of new retailing. Based on the technology acceptance model and the rational behavior theory, a theoretical model for the assessment of information credibility in new retailing was developed. We analyzed the factors influencing users’ safety preference toward information communication procedures and information credibility in new retailing based on two aspects: perceived information quality and user judgment motivation. The reliability and validity of the model measure were analyzed, and structural equation modeling was used to test the model hypotheses. The following results were obtained: (1) Authenticity, accuracy, and practicability positively affected the perceived information quality of new retailing information; (2) User judgment motivation had a positive impact on information users’ safety preference and information credibility; (3) Users’ safety preference positively affected information credibility; (4) Information acquisition, social interaction, and self-identity positively affected the perceived credibility of new retailing information.

Sign in / Sign up

Export Citation Format

Share Document