The Role of Information Technology Mindfulness in the Postadoption Stage of Using Personal Health Devices: Cross-Sectional Questionnaire Study in Mobile Health
Notice bibliographique
Résumé
BACKGROUND: Although personal health devices (for example, smartwatches, fitness trackers and intelligent bracelets) offer great potential to monitor personal fitness and health parameters, many users discontinue using them after a few months. Thus, it is critical to study the postadoption behaviors of current users to enhance their engagement with personal health devices and use behaviors. However, there is little empirical research on the factors affecting users' engagement in beneficial use behaviors. Mindfulness and identity are not new topics, but the applications of these concepts in the field of information systems are emerging themes. Information technology (IT) mindfulness has been conceptualized in previous studies; however, little is known about the antecedents and consequences of IT mindfulness in the mobile health (mHealth) context. OBJECTIVE: The main aim of this study is to explore both IT identity and IT mindfulness to develop a new ground for research in the domain of mHealth postadoption. Thus, we aim to explain why users should be fully mindful of their engagement with PHDs and what could be the consequences and implications. METHODS: This study proposes that IT mindfulness can play an important role in improving the use behaviors of users. Through a web-based survey with 450 current users of a personal health device, this paper tests the relationship between IT identity and IT mindfulness in the postadoption stage of using personal health devices. RESULTS: We found that IT identity significantly shapes IT mindfulness associated with PHDs. Moreover, the IT identity-IT mindfulness relationship is negatively moderated by individuals' perceived health status (P=.003). Finally, the results of this study show that IT mindfulness can significantly predict automatic use behaviors (eg, continued intention to use), active use behaviors (eg, feature use and enhanced use behaviors), and commitment behaviors in using personal health devices (eg, positive word-of-mouth intention). CONCLUSIONS: The findings of this study provide implications for both research and practice. This study can contribute to our current understanding of IT mindfulness by developing and empirically testing a research model that explains the determinants and outcomes of the IT mindfulness construct in the context of personal health devices. The results imply that IT mindfulness significantly helps individuals express their alertness, awareness, openness, and orientation in the present in their postadoption interactions with smart devices used for health care purposes. Finally, our findings may assist practitioners and IT developers in designing mindfulness-supporting PHDs. Owing to the impact of IT mindfulness on postadoption behaviors, its 4 dimensions could be used for developing PHD technologies. Moreover, PHD developers may need to direct their efforts toward increasing IT mindfulness by reinforcing IT identity to serve and retain a wide range of target users.
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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
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