Social comparison and continuance intention of smart fitness wearables: an extended expectation confirmation theory perspective

Gupta, Anil; Dhiman, Neeraj; Yousaf, Anish; Arora, Neelika

doi:10.1080/0144929x.2020.1748715

Cited by 99 publications

(93 citation statements)

References 88 publications

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“…Thus, we once more show the applicability of the ECM to the smartwatch context. In contrast to the results obtained by Bölen (2020) and Gupta et al (2020), but in line with the ECM, we provide evidence that perceived usefulness significantly increases continuance intention. These results underline that continuous smartwatch use is not only driven by hedonic (Dehghani et al 2018), but also by utilitarian aspects.…”

Section: Discussion Of Resultscontrasting

confidence: 89%

“…The expectation-confirmation model In line with the assumptions of the ECM and prior research building upon the ECM to explain continuous smartwatch usage (e.g., Nascimento et al 2018;Pal et al 2018;Gupta et al 2020), we derive our first five hypotheses: H1: Satisfaction positively affects continuance intention. H2-3: Confirmation positively affects satisfaction (H2) and perceived usefulness (H3).…”

Section: Model Developmentmentioning

confidence: 92%

“…Moreover, Gupta et al (2020) investigated the indirect and direct impact of perceived health outcomes, the perceived benefits fitness wearables had on the own health, on the continuous intention to use smartwatches. The authors show that the backward-looking assessment of perceived health benefits increases satisfaction and continuance intention (Gupta et al 2020). Overall, previous literature on continuance intention disregards health and fitness factors as motivational variables.…”

Section: Research On Continuous Use Of Smartwatchesmentioning

confidence: 99%

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Understanding continued smartwatch usage: the role of emotional as well as health and fitness factors

Siepmann

Kowalczuk

2021

Electron Markets

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Smartwatches are the most popular wearable device and increasingly subject to empirical research. In recent years, the focus has shifted from revealing determinants of smartwatch adoption to understanding factors that cause long-term usage. Despite their importance for personal fitness, health monitoring, and for achieving health and fitness goals, extant research on the continuous use intention of smartwatches mostly disregards health and fitness factors. Grounding on self-determination theory, this study addresses this gap and investigates the impact of health and fitness as well as positive and negative emotional factors encouraging or impeding consumers to continuously use smartwatches. We build upon the expectation-confirmation model (ECM) and extend it with emotional (device annoyance and enjoyment) as well as health and fitness factors (goal pursuit motivation and self-quantification behavior). We use structural equation modeling to validate our model based on 335 responses from actual smartwatch users. Results prove the applicability of the ECM to the smartwatch context and highlight the importance of self-quantification as a focal construct for explaining goal pursuit motivation, perceived usefulness, confirmation and device annoyance. Further, we identify device annoyance as an important barrier to continuous smartwatch use. Based on our results, we finally derive implications for researchers and practitioners alike.

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Section: Discussion Of Resultscontrasting

confidence: 89%

Section: Model Developmentmentioning

confidence: 92%

Section: Research On Continuous Use Of Smartwatchesmentioning

confidence: 99%

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Understanding continued smartwatch usage: the role of emotional as well as health and fitness factors

Siepmann

Kowalczuk

2021

Electron Markets

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“…Digital health wearable devices are supported by electronic technology and can be worn directly on the human body [11]. It is expected that, by the end of 2020, this industry will generate revenue of around $22.9 billion [12].…”

Section: Digital Health Wearablesmentioning

confidence: 99%

Understanding Factors Influencing Elderly Diabetic Patients’ Continuance Intention to Use Digital Health Wearables: Extending the Technology Acceptance Model (TAM)

Ahmad

Rasul

Yousaf

et al. 2020

Journal of Open Innovation: Technology, Market, and Complexity

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Elderly diabetic patients in developed countries have been widely using digital health wearables for many years to manage their diabetes-related health data accurately. To encourage the increased adoption of digital health wearables among elderly diabetic patients in a developing country, Bangladesh, this study investigated the factors that influenced the existing elderly users’ continuance intention to use this technology. The Technology Acceptance Model (TAM) has been used here as a theoretical basis. A model using structural equation modelling was developed for the elderly diabetic patients’ continuance intention to use digital health wearables. Survey-based data were collected in Bangladesh from 223 diabetic patients aged sixty years and older. This study found that all six constructs, namely, perceived usefulness (β = 0.183), perceived ease of use (β = 0.165), perceived irreplaceability (β = 0.138), perceived credibility (β = 0.165), compatibility (β = 0.285) and social influence (β = 0.226) had a positive influence on elderly diabetic patients’ continuance intention to use digital health wearables. Along with the theoretical contributions, the findings of this study can be used by developers of digital health wearables, manufacturers, marketers and health practitioners in developing better strategies to increase the elderly diabetic patients’ continuance intention to use this technology.

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“…Partial least squares (PLS) regression was the statistical method used for data analysis. The main advantage of PLS regression is that it does not need data to be normally distributed, and it backs a smaller sample size for the study [76]. PLS regression is suitable here as the Jarque-Bera test of normality was carried out prior to data analysis, which showed that all key variables in the hypotheses were not normally distributed, thus enabling PLS to give more consistent outcomes than other covariance-based structural equation modeling methods.…”

Section: Structural Model and Hypothesis Test Resultsmentioning

confidence: 99%

Enhancing User Experiences with Cloud Computing via Improving Utilitarian and Hedonic Factors

et al. 2021

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This study provides insights into the initial and post-adoption of cloud computing services by integrating information technology adoption, social influence, trust, security, and information systems quality theories. Social influence, hedonicity, and automaticity are hedonic predictors of user satisfaction with cloud computing services. Perceived risks, trust in the provider, and system quality are utilitarian predictors of user satisfaction with cloud computing services. The Partial Least Squares (PLS) was employed to test eight hypotheses on the causal relationships between the variables. Six out of eight hypotheses were supported. Hedonic factors appear to have more influence than the utilitarian factor of increasing user satisfaction with cloud computing services in the school setting. The findings lead to both theoretical and practical implications for improving the initial and post-adoption of cloud computing services.

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Social comparison and continuance intention of smart fitness wearables: an extended expectation confirmation theory perspective

Cited by 99 publications

References 88 publications

Understanding continued smartwatch usage: the role of emotional as well as health and fitness factors

Understanding continued smartwatch usage: the role of emotional as well as health and fitness factors

Understanding Factors Influencing Elderly Diabetic Patients’ Continuance Intention to Use Digital Health Wearables: Extending the Technology Acceptance Model (TAM)

Enhancing User Experiences with Cloud Computing via Improving Utilitarian and Hedonic Factors

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