Digitization for fun or reward?

Rajanen, Dorina; Weng, Min

doi:10.1145/3131085.3131118

Cited by 13 publications

(4 citation statements)

References 67 publications

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“…Device trust is defined as “the degree to which individuals believe that they are confident in the quality and reliability of wearable devices” [ 39 ]. Although many studies have verified the accuracy and reliability of wearable devices from the perspective of precision medicine, there are relatively few studies on their technological acceptance [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

Factors Predicting Older People’s Acceptance of a Personalized Health Care Service App and the Effect of Chronic Disease: Cross-Sectional Questionnaire Study

Koo¹,

Park²,

Kang³

2023

JMIR Aging

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Background Mobile health (mHealth) services enable real-time measurement of information on individuals’ biosignals and environmental risk factors; accordingly, research on health management using mHealth is being actively conducted. Objective The study aims to identify the predictors of older people’s intention to use mHealth in South Korea and verify whether chronic disease moderates the effect of the identified predictors on behavioral intentions. Methods A cross-sectional questionnaire study was conducted among 500 participants aged 60 to 75 years. The research hypotheses were tested using structural equation modeling, and indirect effects were verified through bootstrapping. Bootstrapping was performed 10,000 times, and the significance of the indirect effects was confirmed through the bias-corrected percentile method. Results Of 477 participants, 278 (58.3%) had at least 1 chronic disease. Performance expectancy (β=.453; P=.003) and social influence (β=.693; P<.001) were significant predictors of behavioral intention. Bootstrapping results showed that facilitating conditions (β=.325; P=.006; 95% CI 0.115-0.759) were found to have a significant indirect effect on behavioral intention. Multigroup structural equation modeling testing the presence or absence of chronic disease revealed a significant difference in the path of device trust to performance expectancy (critical ratio=–2.165). Bootstrapping also confirmed that device trust (β=.122; P=.039; 95% CI 0.007-0.346) had a significant indirect effect on behavioral intention in people with chronic disease. Conclusions This study, which explored the predictors of the intention to use mHealth through a web-based survey of older people, suggests similar results to those of other studies that applied the unified theory of acceptance and use of technology model to the acceptance of mHealth. Performance expectancy, social influence, and facilitating conditions were revealed as predictors of accepting mHealth. In addition, trust in a wearable device for measuring biosignals was investigated as an additional predictor in people with chronic disease. This suggests that different strategies are needed, depending on the characteristics of users.

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Section: Methodsmentioning

confidence: 99%

Factors Predicting Older People’s Acceptance of a Personalized Health Care Service App and the Effect of Chronic Disease: Cross-Sectional Questionnaire Study

Koo¹,

Park²,

Kang³

2023

JMIR Aging

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“…[161] 534 0.770 [161] 534 0.352 [146] 274 0.580 [195] 156 0.180 [196] 129 0.454 [197] 313 0.386 [198] 600 0.690 [199] 210 0.370 [200] 519 0.691 [198] 436 0.320 [198] 436 0.340 [201] 927 0.280 [28] 227 0.380 [150] 164 0.504 [202] 273 0.80 [203] 225 0.750 [204] 196 0.125 [203] 225 0.3891 [147] 462 0.427 [147] 232 0.299 [147] 230 0.308 [205] 927 0.151 EE > BI [96] 195 0.020 [129] 318 −0.050 [33] 58 0.133 [27] 123 0.004 [27] 112 0.097 [90] 787 0.003 [152] 376 0.220 [206] 194 −0.050 [29] 256 0.004 [39] 406 0.281 [30] 325 0.206 [137] 185 0.140 [26] 660 0.194 [93] 217 0.145 [93] 109 0.072 [93] 108 0.169 [178] 263 0.126 [179] 311 0.150 [85] 296 −0.032 [148] 340 0.255 [181] 458 0.261 [131] 81 0.640 [182] 100 0.305 [183] 280 0.2...…”

Section: Relation Label N Original Correlationmentioning

confidence: 99%

“…[32] 392 0.130 [149] 324 0.235 [31] 927 0.300 [145] 146 0.235 [101] 280 0.258 [130] 130 0.040 [132] 386 0.510 [138] 363 −0.010 [77] 245 0.183 [138] 363 −0.111 [161] 534 0.126 [161] 534 0.230 [146] 274 0.131 [195] 156 0.174 [196] 129 −0.050 [201] 927 0.183 [150] 164 0.080 [203] 225 0.126 [203] 225 0.067 [203] 225 0.126 [205] 927 0.250 HM > BI [129] 318 −0.010 [33] 58 0.267 [98] 268 0.176 [29] 256 0.127 [30] 325 −0.030 [179] 311 0.065 [148] 340 0.410 [159] 322 0.107 [182] 100 0.239 [188] 410 0.098 [144] 271 0.420 [32] 392 0.071 [149] 324 0.111 [130] 130 0.015 [132] 386 0.050 [197] 313 −0.050 [150] 164 0.080 [204] 196 0.151 [203] 225 0.227 [202] 273 0.257 [203] 225 0.255 [147] 462 0.190 [147] 232 0.227 [147] 230 0.213 HB > BI …”

Section: Relation Label N Original Correlationmentioning

confidence: 99%

Getting Connected to M-Health Technologies through a Meta-Analysis

Calegari

Tortorella

Fettermann

2023

IJERPH

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The demand for mobile e-health technologies (m-health) continues with constant growth, stimulating the technological advancement of such devices. However, the customer needs to perceive the utility of these devices to incorporate them into their daily lives. Hence, this study aims to identify users’ perceptions regarding the acceptance of m-health technologies based on a synthesis of meta-analysis studies on the subject in the literature. Using the relations and constructs proposed in the UTAUT2 (Unified Theory of Acceptance and Use of Technology 2) technology acceptance model, the methodological approach utilized a meta-analysis to raise the effect of the main factors on the Behavioral Intention to Use m-health technologies. Furthermore, the model proposed also estimated the moderation effect of gender, age, and timeline variables on the UTAUT2 relations. In total, the meta-analysis utilized 84 different articles, which presented 376 estimations based on a sample of 31,609 respondents. The results indicate an overall compilation of the relations, as well as the primary factors and moderating variables that determine users’ acceptance of the studied m-health systems.

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“…(Landolfi, 2016) Pleasing others not only starts a new economy of the self that is sold online through Instagram, TikTok and other channels, but also, in addition, activates the reward system or a gratification system that lights up in the brain and transmits hormones with the effect of a feeling of well-being in the subject. This system produces a circularity as users seek to repeat te experiences that give gratification and cause satisfaction through the reward system (Rajanen & Weng, 2017) and, of course, the digital platforms are oriented to allow it. On the Internet, the relationship is based on judgement: the dominance of the image and the creation of the self as a "brand" let possible the growth of a judgemental approach to others.…”

Section: Judgementmentioning

confidence: 99%

An Inquiry into Psychic Dimensions of Digital: Consuming, Judgement, Affects and Awareness

Landolfi

2023

Proceedings of the 6th International Conference on Advanced Research in Social Sciences

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According to anthropology, humanity is not a "substance" but a process of permanent transformation of its features and potentialities. Digital wireless technology is a relevant and, at the same time, dangerous ally for the metamorphosis of humans and their natural and social environment. Indeed, new technology requires a co-creation of online activities and meanings as well as an active and deep involvement of users in everyday life. Such phenomenon can be defined as a 'process of subjectivation' since it is based on mediated interaction which produces a modification of that same individual. AI, therefore, provides a subjectivity shaping which implies several psychic dimensions such as consumption, judgement and affection. This work mainly focuses on the subjective implications of human-digital interaction in terms of the transformation of the way humans perceive and conceive their lives. In particular, does a digitally mediated environment guarantee a subjects' awareness? The work is conducted by interweaving interdisciplinary concepts and methodology to highlight the psychic emergence in the digital world and the processes of subjectivation determined by the constant online activity which is enriched by subjective, affective and cognitive human potentialities. Concepts such as consumption, judgment and affection are highlighted to analyze the psychic level of subjectivity and lead a reflection on the problem of awareness of subjects who establish a close correlation between their lives and the digital world.

show abstract

Digitization for fun or reward?

Cited by 13 publications

References 67 publications

Factors Predicting Older People’s Acceptance of a Personalized Health Care Service App and the Effect of Chronic Disease: Cross-Sectional Questionnaire Study

Factors Predicting Older People’s Acceptance of a Personalized Health Care Service App and the Effect of Chronic Disease: Cross-Sectional Questionnaire Study

Getting Connected to M-Health Technologies through a Meta-Analysis

An Inquiry into Psychic Dimensions of Digital: Consuming, Judgement, Affects and Awareness

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