Determinants of the adoption of AI wearables - practical implications for marketing

Sułkowski, Łukasz; Kaczorowska–Spychalska, Dominika

doi:10.14254/1795-6889.2021.17-3.6

Cited by 13 publications

(8 citation statements)

References 57 publications

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“…UTAUT2 reflects a thorough theoretical framework that has been applied in various settings, thus proving its applicability and adaptability in various contexts that address consumers' adoption and use of technologies [32]. For instance, such research was examined in the context of NFC mobile payments in hotels [33], mobile banking [17,34,35], mobile tourism shopping frameworks [36], tourists' intention to use travel apps [37], gaming [38], or the impact on consumers' intents to use mobile payments [39].…”

Section: Unified Theory Of Acceptance and Use Of Technology (Utaut2)mentioning

confidence: 99%

“…As a standalone concept, social influence is grounded on (a) subjective norms, (b) social factors, and (c) image. Customers are more inclined to revert to their "social system" [26] to learn more and broaden their understanding on the use of new technology, or to receive social acceptance for their decision to utilize a new system [32]. Extending this idea to m-commerce, consumers who rely on shopping apps tend to seek "approval from friends, family, colleagues and others while engaging with them" [48].…”

Section: Antecedents Of Behavioral Intention In M-commercementioning

confidence: 99%

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Assessing the Effects of the COVID-19 Pandemic on M-Commerce Adoption: An Adapted UTAUT2 Approach

et al. 2022

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The COVID-19 pandemic has impacted consumers’ lives and their shopping patterns, leading them towards mobile commerce. To address current shopping circumstances, an adapted UTAUT2 model aims to integrate trust and perceptions of the effects of the COVID-19 pandemic on consumers’ behavioral intention to rely on m-commerce during this period. The study proposes a research model which is investigated with the help of structural equation modelling in AMOS. Using the framework of a cross-sectional study, data were collected from an emerging market in Romania, where internet speed ranks 4th worldwide and where the yearly increase in internet and social media users is approximately 10%. By using confirmatory factor analysis and structural equation modelling, the research validates the applicability of UTAUT2 in examining consumers’ behavioral intent of using m-commerce during a pandemic. Hedonic motivation is the strongest predictor of consumers’ behavioral intentions to continue using m-commerce. The findings contribute to understandings of consumers’ behavior with m-commerce in an emerging market, extending knowledge based on the adapted UTAUT2 model and allowing for comprehension of the key role of trust and social influences in affecting consumers’ perceptions of the COVID-19 outbreak in relation to shopping patterns.

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Section: Unified Theory Of Acceptance and Use Of Technology (Utaut2)mentioning

confidence: 99%

Section: Antecedents Of Behavioral Intention In M-commercementioning

confidence: 99%

Assessing the Effects of the COVID-19 Pandemic on M-Commerce Adoption: An Adapted UTAUT2 Approach

et al. 2022

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“…Our study shows that hedonic motivation is a significant factor in behavioural intention. Therefore, making the process fun and pleasurable is important towards patient adoption [ 47 ]. Facilitating conditions and hedonic motivation had a strong correlation ( r = 0.992, p -value < 0.001) in our study.…”

Section: Discussionmentioning

confidence: 99%

Applying the UTAUT2 framework to patients’ attitudes toward healthcare task shifting with artificial intelligence

Huang,

Ong,

Wong

et al. 2024

BMC Health Serv Res

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Background Increasing patient loads, healthcare inflation and ageing population have put pressure on the healthcare system. Artificial intelligence and machine learning innovations can aid in task shifting to help healthcare systems remain efficient and cost effective. To gain an understanding of patients’ acceptance toward such task shifting with the aid of AI, this study adapted the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), looking at performance and effort expectancy, facilitating conditions, social influence, hedonic motivation and behavioural intention. Methods This was a cross-sectional study which took place between September 2021 to June 2022 at the National Heart Centre, Singapore. One hundred patients, aged ≥ 21 years with at least one heart failure symptom (pedal oedema, New York Heart Association II-III effort limitation, orthopnoea, breathlessness), who presented to the cardiac imaging laboratory for physician-ordered clinical echocardiogram, underwent both echocardiogram by skilled sonographers and the experience of echocardiogram by a novice guided by AI technologies. They were then given a survey which looked at the above-mentioned constructs using the UTAUT2 framework. Results Significant, direct, and positive effects of all constructs on the behavioral intention of accepting the AI-novice combination were found. Facilitating conditions, hedonic motivation and performance expectancy were the top 3 constructs. The analysis of the moderating variables, age, gender and education levels, found no impact on behavioral intention. Conclusions These results are important for stakeholders and changemakers such as policymakers, governments, physicians, and insurance companies, as they design adoption strategies to ensure successful patient engagement by focusing on factors affecting the facilitating conditions, hedonic motivation and performance expectancy for AI technologies used in healthcare task shifting.

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“…“Artificial intelligence” (AI) term is often used with diverse meanings across various scientific disciplines [ 15 ]. Current scholarly debate in technical contexts navigates between limited and domain-specific forms of “narrow intelligence”, more sophisticated forms of human-like “general AI”, and “super AI” that go significantly beyond human cognitive abilities [ 16 ], while its meanings go more on to casual aspects [ 17 ]. The range of AI debate is extending with process automation concepts [ 18 ].…”

Section: Literature Reviewmentioning

confidence: 99%

The Aspects of Artificial Intelligence in Different Phases of the Food Value and Supply Chain

Bačiulienė

Bilan

Navickas

et al. 2023

Foods

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The types of artificial intelligence, artificial intelligence integration to the food value and supply chain, other technologies embedded with artificial intelligence, artificial intelligence adoption barriers in the food value and supply chain, and solutions to overcome these barriers were analyzed by the authors. It was demonstrated by the analysis that artificial intelligence can be integrated vertically into the entire food supply and value chain, owing to its wide range of functions. Different phases of the chain are affected by developed technologies such as robotics, drones, and smart machines. Different capabilities are provided for different phases by the interaction of artificial intelligence with other technologies such as big data mining, machine learning, the Internet of services, agribots, industrial robots, sensors and drones, digital platforms, driverless vehicles and machinery, and nanotechnology, as revealed by a systematic literature analysis. However, the application of artificial intelligence is hindered by social, technological, and economic barriers. These barriers can be overcome by developing the financial and digital literacy of farmers and by disseminating good practices among the participants of the food supply and value chain.

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Determinants of the adoption of AI wearables - practical implications for marketing

Cited by 13 publications

References 57 publications

Assessing the Effects of the COVID-19 Pandemic on M-Commerce Adoption: An Adapted UTAUT2 Approach

Assessing the Effects of the COVID-19 Pandemic on M-Commerce Adoption: An Adapted UTAUT2 Approach

Applying the UTAUT2 framework to patients’ attitudes toward healthcare task shifting with artificial intelligence

The Aspects of Artificial Intelligence in Different Phases of the Food Value and Supply Chain

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