“…. ), smart shopping lists, and the smart home (Rashidi and Mihailidis, 2013), end-user perception and evaluation fo these technologies becomes increasingly important (Wilkowska et al, 2018;Kelly et al, 2023). This is also evident in professional contexts, where AI is used-for example-in medical diagnosis (Kulkarni et al, 2020), health care (Oden and Witt, 2020;Jovanovic et al, 2022), aviation (Klos et al, 2020;Kulida and Lebedev, 2020), and production control (Brauner et al, 2022).…”
Section: Studies On Human Perception Of Aimentioning
confidence: 99%
“…Second, we asked participants about numerous aspects and whether they thought the given development was likely to occur (i.e., Will this development happen? ), and, as a measure of acceptability (Kelly et al, 2023), how they personally evaluated this development (i.e., Do you think this development is good?). Overall, we asked about the expectation (likelihood) and evaluation (valence) of 38 different aspects, ranging from the influence on the personal and professional life, to the perceived impact of AI on the economy, healthcare, and culture, as well as wider societal implications.…”
Section: Surveymentioning
confidence: 99%
“…In this way, we identify areas where expectations and evaluations are aligned, as well as areas where there are greater differences and potential for conflict. Since areas of greater disparity can hinder social acceptance (Slovic, 1987;Kelly et al, 2023), they need to be publicly discussed. Based on accessible and transparent information about AI and a societal discourse about its risks and benefits, these discrepancies can either be reduced or regulatory guidelines for AI can be developed.…”
IntroductionArtificial Intelligence (AI) has become ubiquitous in medicine, business, manufacturing and transportation, and is entering our personal lives. Public perceptions of AI are often shaped either by admiration for its benefits and possibilities, or by uncertainties, potential threats and fears about this opaque and perceived as mysterious technology. Understanding the public perception of AI, as well as its requirements and attributions, is essential for responsible research and innovation and enables aligning the development and governance of future AI systems with individual and societal needs.MethodsTo contribute to this understanding, we asked 122 participants in Germany how they perceived 38 statements about artificial intelligence in different contexts (personal, economic, industrial, social, cultural, health). We assessed their personal evaluation and the perceived likelihood of these aspects becoming reality.ResultsWe visualized the responses in a criticality map that allows the identification of issues that require particular attention from research and policy-making. The results show that the perceived evaluation and the perceived expectations differ considerably between the domains. The aspect perceived as most critical is the fear of cybersecurity threats, which is seen as highly likely and least liked.DiscussionThe diversity of users influenced the evaluation: People with lower trust rated the impact of AI as more positive but less likely. Compared to people with higher trust, they consider certain features and consequences of AI to be more desirable, but they think the impact of AI will be smaller. We conclude that AI is still a “black box” for many. Neither the opportunities nor the risks can yet be adequately assessed, which can lead to biased and irrational control beliefs in the public perception of AI. The article concludes with guidelines for promoting AI literacy to facilitate informed decision-making.
“…. ), smart shopping lists, and the smart home (Rashidi and Mihailidis, 2013), end-user perception and evaluation fo these technologies becomes increasingly important (Wilkowska et al, 2018;Kelly et al, 2023). This is also evident in professional contexts, where AI is used-for example-in medical diagnosis (Kulkarni et al, 2020), health care (Oden and Witt, 2020;Jovanovic et al, 2022), aviation (Klos et al, 2020;Kulida and Lebedev, 2020), and production control (Brauner et al, 2022).…”
Section: Studies On Human Perception Of Aimentioning
confidence: 99%
“…Second, we asked participants about numerous aspects and whether they thought the given development was likely to occur (i.e., Will this development happen? ), and, as a measure of acceptability (Kelly et al, 2023), how they personally evaluated this development (i.e., Do you think this development is good?). Overall, we asked about the expectation (likelihood) and evaluation (valence) of 38 different aspects, ranging from the influence on the personal and professional life, to the perceived impact of AI on the economy, healthcare, and culture, as well as wider societal implications.…”
Section: Surveymentioning
confidence: 99%
“…In this way, we identify areas where expectations and evaluations are aligned, as well as areas where there are greater differences and potential for conflict. Since areas of greater disparity can hinder social acceptance (Slovic, 1987;Kelly et al, 2023), they need to be publicly discussed. Based on accessible and transparent information about AI and a societal discourse about its risks and benefits, these discrepancies can either be reduced or regulatory guidelines for AI can be developed.…”
IntroductionArtificial Intelligence (AI) has become ubiquitous in medicine, business, manufacturing and transportation, and is entering our personal lives. Public perceptions of AI are often shaped either by admiration for its benefits and possibilities, or by uncertainties, potential threats and fears about this opaque and perceived as mysterious technology. Understanding the public perception of AI, as well as its requirements and attributions, is essential for responsible research and innovation and enables aligning the development and governance of future AI systems with individual and societal needs.MethodsTo contribute to this understanding, we asked 122 participants in Germany how they perceived 38 statements about artificial intelligence in different contexts (personal, economic, industrial, social, cultural, health). We assessed their personal evaluation and the perceived likelihood of these aspects becoming reality.ResultsWe visualized the responses in a criticality map that allows the identification of issues that require particular attention from research and policy-making. The results show that the perceived evaluation and the perceived expectations differ considerably between the domains. The aspect perceived as most critical is the fear of cybersecurity threats, which is seen as highly likely and least liked.DiscussionThe diversity of users influenced the evaluation: People with lower trust rated the impact of AI as more positive but less likely. Compared to people with higher trust, they consider certain features and consequences of AI to be more desirable, but they think the impact of AI will be smaller. We conclude that AI is still a “black box” for many. Neither the opportunities nor the risks can yet be adequately assessed, which can lead to biased and irrational control beliefs in the public perception of AI. The article concludes with guidelines for promoting AI literacy to facilitate informed decision-making.
“…Furthermore, AI can be divided into artificial general intelligence (AGI), artificial narrow intelligence (ANI), and artificial super intelligence (ASI), as shown in Fig. 2 (Kelly et al, 2023 ). Fig.…”
As the application of artificial intelligence (AI) becomes more prevalent, it has attracted the attention of high-tech firms, which adopt AI applications in response to emerging societal, technological, and environmental challenges. In the AI application processes, big data analytical capacity has become increasingly important. Although AI may potentially revolutionise the markets, industries, and general business activities, the question remains how high-tech firms can implement AI in their operations effectively and efficiently, so as to enhance their operational performances. This study aims to explore whether high-tech firm employees’ intention to adopt AI applications and the firms’ big data analytical capability would affect the operational performance. Besides utilising the unified theory of acceptance and use of technology as a framework, this study also adopted the structural equation modelling (SEM) and related statistical analyses (using SPSS and LISREL). The results show that employees’ intention to adopt AI applications is positively related to integration capability and team collaboration, and big data analytical capability is positively related to integration capability but not to team collaboration. Moreover, both integration capability and team collaboration are positively correlated with operational performance.
Sobel t
test was employed to test the mediating effect, and found that integration capability is a significant mediator in the influence of big data analytical capability on operational performance. Employees’ intention to adopt AI applications and big data analytical capability can effectively enhance the goals associated with achieving high operational performances.
“…TAM is the most prevalent theory to evaluate user acceptance of new AI technologies [24] and was first proposed by Davis [25]. TAM is now widely used in different aspects of new technologies and confirmed that perceived usefulness (PU) and perceived ease of use (PEOU) have a significant impact on user acceptance.…”
Artificial intelligence (AI) applications in different fields are developing rapidly, among which AI painting technology, as an emerging technology, has received wide attention from users for its creativity and efficiency. This study aimed to investigate the factors that influence user acceptance of the use of AIBPS by proposing an extended model that combines the Extended Technology Acceptance Model (ETAM) with the AI-based Painting System (AIBPS).A questionnaire was administered to 528 Chinese participants, using validated factor analysis data and Structural Equation Modeling (SEM) was used to test the hypotheses. The findings showed that hedonic motivation (HM) and perceived trust (PE) had a positive effect (+) on users' perceived usefulness (PU) and perceived ease of use (PEOU), while previous experience (PE) and technical features (TF) had no effect (-) on users' perceived usefulness (PU). This study provides an important contribution to the literature on AIBPS and the evaluation of systems of the same type, which helps to promote the sustainable development of AI in different domains and provides a possible space for further extension of TAM, thus helping to improve the user experience of AIBPS. The results of the study provide insights for system developers and enterprises to better motivate users to use AIBPS.
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