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Saggi

No. 134 (2025)

Wearable health devices: The role of perceived complexity and effectiveness in shaping positive Word-of-Mouth (WOM). A study on Automated Insulin Delivery (AID) systems

DOI
https://doi.org/10.3280/mesa2025-134oa22030
Submitted
febbraio 10, 2026
Published
2026-04-24

Abstract

Wearable health devices are transforming the healthcare sector through the continuous expansion of their functionalities. In this context, this research analyzes the role of perceived complexity and effectiveness of wearable health devices in generating positive word of mouth (WOM). A survey-based quantitative study has been conducted among automated insulin delivery (AID) systems users. The results show that perceived complexity does not have a direct effect on WOM, but rather an indirect effect mediated by effectiveness perception.
These findings have important implications for the design and promotion of devices, suggesting the need for a user-centered approach.

References

  1. Abouzeid D. Y. (2025). Investigating the factors affecting users’ and non-users’ behavioral intention to use smartwatches for health-and fitness-specific purposes. Universidade do Minho Escola de Economia e Gestão.
  2. Adiyatma W., Oktavia T., Adhikara C. T., Gaol F. L., & Hosoda T. (2022). Evaluation Review on Wearable Technology Adoption for Sport Science. In: Proceedings of 11th International Congress (Vol. 81, pp. 464-473).
  3. Adjei M. T., Noble S. M., & Noble C. H. (2010). The influence of C2C communications in online brand communities on customer purchase behavior. Journal of the Academy of Marketing Science, 38: 634-653.
  4. Ahn J., Yang Y., & Park G. (2024). Advancing elderly diabetes care: exploring the usability and acceptance of continuous glucose monitoring (CGM). Geriatric Nursing, 59: 15-25.
  5. Ajzen I., & Fishbein M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs: Prentice-Hall.
  6. Alexandrov A., Lilly B., & Babakus E. (2013). The effects of social-and self-motives on the intentions to share positive and negative word of mouth. Journal of the Academy of Marketing Science, 41: 531-546.
  7. Aljawawdeh H., D’Auria A., Cavallone M., & Ferraris A. (2024b). eWOM in digital health innovation: The role of patient experience and online trust in the diffusion of wearable devices. Journal of Business Research, 171, 114266.
  8. Aljawawdeh H., Dughmosh R., Almashaqbeh D., Alazzeh H., & Aldroubi A. (2024a). Smart Insulin Calculator: Technological Innovation for Type 1 Diabetes Management Revolutionising Insulin Management with Intelligent Health Technology. In: 2024 Global Digital Health Knowledge Exchange & Empowerment Conference (gDigi-Health. KEE) (pp. 1-6). IEEE.
  9. Associazione Medici Diabetologi (AMD) (2023). Annali AMD 2023. Profili assistenziali nei pazienti con diabete di tipo 1 e con diabete di tipo 2 in relazione al genere (Monografie). Fondazione AMD. -- Retrieved from https://aemmedi.it/nuova-monografia-annali-amd-2023-profili-di-assistenza-al-diabete-tipo-1-e-al-diabete-tipo-2-in-base-al-genere/.
  10. Avilés-Santa M. L., Monroig-Rivera A., Soto-Soto A., & Lindberg N. M. (2020). Current state of diabetes mellitus prevalence, awareness, treatment, and control in Latin America: challenges and innovative solutions to improve health outcomes across the continent. Current Diabetes Reports, 20: 1-44.
  11. Babu M., Lautman Z., Lin X., Sobota M. H., & Snyder M. P. (2024). Wearable devices: implications for precision medicine and the future of health care. Annual Review of Medicine, 75(1): 401-415.
  12. Benbunan-Fich R. (2020). User satisfaction with wearables. AIS Transactions on Human-Computer Interaction, 12(1): 1-27.
  13. Bowman D., & Narayandas D. (2001). Managing customer-initiated contacts with manufacturers: The impact on share of category requirements and word-of-mouth behavior. Journal of Marketing Research, 38(3): 281-297.
  14. Cain M. K., Zhang Z., & Yuan K.H. (2017). Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation. Behavior Research Methods, 49(5): 1716-1735.
  15. Calderón D., Ortí A. S., & Kuric S. (2022). Self-confidence and digital proficiency: Determinants of digital skills perceptions among young people in Spain. First Monday, 27(4). DOI: 10.5210/fm.v27i4.12566.
  16. Chandrasekaran R., Katthula V., & Moustakas E. (2020). Patterns of use and key predictors for the use of wearable healthcare devices by US adults: insights from a national survey. Journal of Medical Internet Research, 22(10), e22443.
  17. Chandrasekaran R., Katthula V., & Moustakas E. (2021). Too old for technology? Use of wearable healthcare devices by older adults and their willingness to share health data with providers. Health Informatics Journal, 27(4), 14604582211058073.
  18. Chen X., & Li S. (2022, June). Research on wearable smart products for elderly users based on Kano model. In: International Conference on Human-Computer Interaction (pp. 160-174). Cham: Springer International Publishing.
  19. Cheng Y., Mukhopadhyay A., & Schrift R. Y. (2017). Do costly options lead to better outcomes? How the protestant work ethic influences the cost-benefit heuristic in goal pursuit. Journal of Marketing Research, 54(4): 636-649.
  20. Chong K. P., Guo J. Z., Deng X., & Woo B. K. (2020). Consumer perceptions of wearable technology devices: retrospective review and analysis. JMIR mHealth and uHealth, 8(4), e17544.
  21. Ciabattoni L., Foresi G., Monteriù A., Pagnotta D.P., Romeo L., Spalazzi L., De Cesare A. (2018). Complex activity recognition system based on cascade classifiers and wearable device data. In: 2018 IEEE Int Conf Consum Electron (ICCE) IEEE 1-2.
  22. Cilliers L. (2020). Wearable devices in healthcare: Privacy and information security issues. Health information management journal, 49(2-3): 150-156.
  23. Cnop M., Welsh N., Jonas J. C., Jorns A., Lenzen S., & Eizirik D. L. (2005). Mechanisms of pancreatic β-cell death in type 1 and type 2 diabetes: many differences, few similarities. Diabetes, 54(suppl_2): S97-S107.
  24. Dellaert B. G., & Stremersch S. (2005). Marketing mass-customized products: Striking a balance between utility and complexity. Journal of Marketing Research, 42(2): 219-227.
  25. Diamantopoulos A., Sarstedt M., Fuchs C., Wilczynski P., & Kaiser S. (2012). Guidelines for choosing between multi-item and single-item scales for construct measurement: A predictive validity perspective. Journal of the Academy of Marketing Science, 40(3): 434-449.
  26. Douglas S.P., Craig C.S., 2007. Collaborative and iterative translation: an alternative approach to back translation. Journal of International Marketing, 15(1): 30-43.
  27. El-Gayar O., & Elnoshokaty A. (2023). Factors and design features influencing the continued use of wearable devices. Journal of Healthcare Informatics Research, 7(3): 359-385.
  28. Erdem A., Eksin E., Senturk H., Yildiz E., & Maral M. (2024). Recent developments in wearable biosensors for healthcare and biomedical applications. TrAC Trends in Analytical Chemistry, 171, 117510.
  29. Ferreira J. J., Fernandes C. I., Rammal H. G., & Veiga P. M. (2021). Wearable technology and consumer interaction: A systematic review and research agenda. Computers in human behavior, 118, 106710.
  30. Fornell C., & Larcker D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1): 39-50.
  31. Goodyear V. A., Armour K. M., & Wood H. (2019). Young people learning about health: the role of apps and wearable devices. Learning, Media and Technology, 44(2): 193-210.
  32. Graffigna G., Barello S., Bonanomi A., & Lozza E. (2020). Measuring patient engagement: Development and psychometric properties of the Patient Health Engagement (PHE) Scale. Frontiers in Psychology, 11, 1253.
  33. Grewal R., Cote J. A., & Baumgartner H. (2004). Multicollinearity and measurement error in structural equation models: Implications for theory testing. Marketing Science, 23(4): 519-529.
  34. Gross M., Roberts C., Stinson K., & Wiltman S. (2023). Improving medical device usability by reducing complexity using a novel predictive models-based user interface assessment tool. Human Factors in Healthcare, 3, 100041.
  35. Gu D., Yang X., Li X., Jain H. K., & Liang C. (2018). Understanding the role of mobile internet-based health services on patient satisfaction and word-of-mouth. International journal of environmental research and public health, 15(9), 1972.
  36. Guillén-Gámez F. D., & Mayorga-Fernández M. J. (2019). Empirical study based on the perceptions of patients and relatives about the acceptance of wearable devices to improve their health and prevent possible diseases. Mobile Information Systems, (1), 4731048.
  37. Hair J. F., Sarstedt M., Ringle C. M., & Mena J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3): 414-433.
  38. Hasan M. N. U., & Stannard C. R. (2023). Exploring online consumer reviews of wearable technology: the owlet smart sock. Research Journal of Textile and Apparel, 27(2): 157-173.
  39. Hayes A. F. (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (3rd ed.). New York: Guilford Publications.
  40. Huedo-Martínez S., Molina-Carmona R., & Llorens-Largo F. (2018, May). Study on the attitude of young people towards technology. In International Conference on Learning and Collaboration Technologies (pp. 26-43). Cham: Springer International Publishing.
  41. ISS (Istituto Superiore di Sanitá) (2022). -- Available on: https://www.epicentro.iss.it/diabete/epidemiologia-italia.
  42. ISTAT (2024). -- https://www.epicentro.iss.it/diabete/epidemiologia-italia.
  43. Jafleh E. A., Alnaqbi F. A., Almaeeni H. A., Faqeeh S., Alzaabi M. A., Al Zaman K., ... & Alzaabi M. (2024). The role of wearable devices in chronic disease monitoring and patient care: a comprehensive review. Cureus, 16(9).
  44. Jeong J., Kim Y., & Roh T. (2021). Do consumers care about aesthetics and compatibility? The intention to use wearable devices in health care. SAGE Open, 11(3), 21582440211040070.
  45. Kano N., Seraku N., Takahashi F., & Tsuji S. (1984). Attractive quality and must-be quality. Hinshitsu: The Journal of the Japanese Society for Quality Control, 41: 39-48.
  46. Karim M. Z. A., Thamrin N. M., Shauri R. L. A., Jailani R., Manaf M. H. A., & Mustapa N. A. (2024). Evaluating Telemedicine Diabetes Mellitus: A Mobile Health App for Type-2 Diabetes. International Journal of Advances in Applied Sciences, 13: 787-795.
  47. Kim T. B., & Ho C. T. B. (2021). Validating the moderating role of age in multi-perspective acceptance model of wearable healthcare technology. Telematics and Informatics, 61, 101603.
  48. Kim T. Y., De R., Choi I., Kim H., & Hahn S. K. (2024b). Multifunctional nanomaterials for smart wearable diabetic healthcare devices. Biomaterials, 122630.
  49. Kim Y., Godino J. G., Cheung F. L. T., Multhaup M., Chan D. K. C. K., Chen Z., ... & Griffin S. (2024a). Effect of communicating genetic risk of type 2 diabetes and wearable technologies on wearable device-measured behavioural outcomes in East Asians: protocol of a randomised controlled trial. BMJ open, 14(12), e082635.
  50. Kyytsönen M., Vehko T., Anttila H., & Ikonen J. (2023). Factors associated with use of wearable technology to support activity, well-being, or a healthy lifestyle in the adult population and among older adults. PLOS Digital Health, 2(5), e0000245.
  51. Lee S. M., & Lee D. (2020). Healthcare wearable devices: an analysis of key factors for continuous use intention. Service Business, 14(4): 503-531.
  52. Li H., Wu J., Gao Y., & Shi Y. (2016). Examining individuals’ adoption of healthcare wearable devices: An empirical study from privacy calculus perspective. International Journal of Medical Informatics, 88: 8-17.
  53. Liang B., & Scammon D. L. (2011). E‐Word‐of‐Mouth on health social networking sites: An opportunity for tailored health communication. Journal of Consumer Behaviour, 10(6): 322-331.
  54. Lu L., Zhang J., Xie Y., Gao F., Xu S., Wu X., & Ye Z. (2020). Wearable health devices in health care: narrative systematic review. JMIR mHealth and uHealth, 8(11), e18907.
  55. Luo J., Zhang K., Xu Y., Tao Y., & Zhang Q. (2022). Effectiveness of wearable device-based intervention on glycemic control in patients with type 2 diabetes: a system review and meta-analysis. Journal of Medical Systems, 46(1), 11.
  56. Lupton D. (2021). Wearable devices: Sociocultural and ethical implications of self-tracking. The Lancet Digital Health, 3(11): e635-e636.
  57. Mao J., Xie L., Zhao Q., Xiao M., Tu S., Sun W., & Zhou T. (2022). Demand analysis of an intelligent medication administration system for older adults with chronic diseases based on the Kano model. International Journal of Nursing Sciences, 9(1): 63-70.
  58. Mehrotra S., Rai P., Saxena A., Priya S., & Sharma S. K. (2024). Advancements in enzyme-based wearable sensors for health monitoring. Microchemical Journal, 200, 110250.
  59. Moore K., O’Shea E., Kenny L., Barton J., Tedesco S., Sica M., ... & Timmons S. (2021). Older adults’ experiences with using wearable devices: qualitative systematic review and meta-synthesis. JMIR mHealth and uHealth, 9(6), e23832.
  60. Moulaei K., Malek M., & Sheikhtaheri A. (2021). A smart wearable device for monitoring and self-management of diabetic foot: A proof of concept study. International Journal of Medical Informatics, 146, 104343.
  61. Ntumba D., & Budree A. (2021). The Effect of Social Media Based Electronic Word of Mouth on Propensity o Buy Wearable Devices. In: HCI International 2021-Late Breaking Papers: Design and User Experience: 23rd HCI International Conference, HCII 2021, Virtual Event, July 24-29, 2021, Proceedings 23 (pp. 310-325). Springer International Publishing.
  62. Nunnally J. C., & Bernstein I. H. (1994). Psychometric Theory, third ed., New York: McGraw-Hill.
  63. Nutbeam D. (2008). The evolving concept of health literacy. Social Science & Medicine, 67(12): 2072-2078.
  64. Oh S. S., Kim K. A., Kim M., Oh J., Chu S. H., & Choi J. (2021). Measurement of digital literacy among older adults: systematic review. Journal of medical Internet research, 23(2), e26145.
  65. Podsakoff P.M., MacKenzie S.B., Lee J.Y., Podsakoff N.P., 2003. Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5): 879-903.
  66. Renard E. (2023). Automated insulin delivery systems: from early research to routine care of type 1 diabetes. Acta Diabetologica, 60(2): 151-161.
  67. Rodriguez-León C., Villalonga C., Munoz-Torres M., Ruiz J. R., & Banos O. (2021). Mobile and wearable technology for the monitoring of diabetes-related parameters: Systematic review. JMIR mHealth and uHealth, 9(6), e25138.
  68. Rodríguez-Rodríguez I., Campo-Valera M., Rodríguez J. V., & Woo W. L. (2024). IoMT innovations in diabetes management: Predictive models using wearable data. Expert Systems with Applications, 238, 121994.
  69. Rogers E. M. (2003). Diffusion of Innovations. New York: The Free Pree.
  70. Rousseva R. (2008). Identifying technological capabilities with different degrees of coherence: The challenge to achieve high technological sophistication in latecomer software companies (based on the Bulgarian case). Technological Forecasting and Social Change, 75(7): 1007-1031.
  71. Rupp M. A., Michaelis J. R., McConnell D. S., & Smither J. A. (2018). The role of individual differences on perceptions of wearable fitness device trust, usability, and motivational impact. Applied Ergonomics, 70: 77-87.
  72. Šabić J., Baranović B., & Rogošić S. (2022). Teachers’ self-efficacy for using information and communication technology: The interaction effect of gender and age. Informatics in education, 21(2): 353-373.
  73. Sedgwick P. (2013). Convenience sampling. BMJ, 347, f6304.
  74. Segura Anaya L. H., Alsadoon A., Costadopoulos N., & Prasad P. W. C. (2018). Ethical implications of user perceptions of wearable devices. Science and Engineering Ethics, 24: 1-28.
  75. Sempere-Bigorra M., Julián-Rochina I., & Cauli O. (2021). Differences and similarities in neuropathy in type 1 and 2 diabetes: a systematic review. Journal of Personalized Medicine, 11(3), 230.
  76. Senders J. W. (2006). On the complexity of medical devices and systems. BMJ Quality & Safety, 15(suppl 1): i41-i43.
  77. Seneviratne S., Hu Y., Nguyen T., Lan G., Khalifa S., Thilakarathna K., ... & Seneviratne A. (2017). A survey of wearable devices and challenges. IEEE Communications Surveys & Tutorials, 19(4): 2573-2620.
  78. Sherr J. L., Heinemann L., Fleming G. A., Bergenstal R. M., Bruttomesso D., Hanaire H., ... & Evans M. (2022). Automated insulin delivery: benefits, challenges, and recommendations. A Consensus Report of the Joint Diabetes Technology Working Group of the European Association for the Study of Diabetes and the American Diabetes Association. Diabetes Care, 45(12): 3058-3074.
  79. Soliño-Fernandez D., Ding A., Bayro-Kaiser E., & Ding E. L. (2019). Willingness to adopt wearable devices with behavioral and economic incentives by health insurance wellness programs: results of a US cross-sectional survey with multiple consumer health vignettes. BMC Public Health, 19(1), 1649.
  80. Staddon R. V. (2020). Bringing technology to the mature classroom: age differences in use and attitudes. International Journal of Educational Technology in Higher Education, 17(1), 11.
  81. Tran V. T., Riveros C., & Ravaud P. (2019). Patients’ views of wearable devices and AI in healthcare: findings from the ComPaRe e-cohort. NPJ Digital Medicine, 2(1), 53.
  82. Turan A., & Bozaykut-Bük T. (2016). Analyzing perceived healthcare service quality on patient related outcomes. International Journal of Quality and Service Sciences, 8(4): 478-497.
  83. Uzir M. U. H., Al Halbusi H., & Thurasamy R. (2023b). Why do users spread eWOM on wearable health devices? The role of utilitarian and hedonic values. Technology in Society, 72, 102169.
  84. Uzir M. U. H., Bukari Z., Al Halbusi H., Lim R., Wahab S. N., Rasul T., ... & Eneizan B. (2023a). Applied artificial intelligence: Acceptance-intention-purchase and satisfaction on smartwatch usage in a Ghanaian context. Heliyon, 9(8).
  85. Veinot T. C., Mitchell H., & Ancker J. S. (2018). Good intentions are not enough: How informatics interventions can worsen inequality. Journal of the American Medical Informatics Association, 25(8): 1080-1088.
  86. Wah J. N. K. (2025). Wear the Future of Healthcare: Revolutionizing Healthcare with AI-Driven Wearables for Enhanced Health and Wellness. Wear, 70(02).
  87. Wang H., Tao D., Yu N., & Qu X. (2020). Understanding consumer acceptance of healthcare wearable devices: An integrated model of UTAUT and TTF. International Journal of Medical Informatics, 139, 104156.
  88. Wang X., Tang P., Jiang Y., Zhao Y., Tang L., Qiao S., ... & Chen D. (2024). The application value of Kano Model in quality of healthcare: a scoping review. medRxiv, 2024-03.
  89. Wen D., Zhang X., & Lei J. (2017). Consumers’ perceived attitudes to wearable devices in health monitoring in China: A survey study. Computer Methods and Programs in Biomedicine, 140: 131-137.
  90. Wong E. L., Ho K., Wong S. Y., Cheung A. W., Yau P. S., Dong D. and Yeoh E. (2022). Views on Workplace Policies and its Impact on Health-Related Quality of Life During Coronavirus Disease (COVID-19) Pandemic: Cross-Sectional Survey of Employees. International Journal of Health Policy and Management, 11(3): 344-353.
  91. Xie Z., Yadav S., & Jo A. (2021). The association between electronic wearable devices and self-efficacy for managing health: a cross sectional study using 2019 HINTS data. Health and Technology, 11(2): 331-339.
  92. Yang H., Yu J., Zo H., & Choi M. (2016). User acceptance of wearable devices: An extended perspective of perceived value. Telematics and Informatics, 33(2): 256-269.
  93. Yang Q., Al Mamun A., Wu M., & Naznen F. (2024). Strengthening health monitoring: Intention and adoption of Internet of Things-enabled wearable healthcare devices. Digital Health, 10, 20552076241279199.
  94. Yang Q., Li P., Liu X., & Wei C. (2025). Exploring the functional quality attributes of smart home for older adults based on qualitative research and Kano model. Frontiers in Public Health, 13, 1541571.
  95. Zhang Z., Xia E., & Huang J. (2022). Impact of the moderating effect of national culture on adoption intention in wearable health care devices: meta-analysis. JMIR mHealth and uHealth, 10(6), e30960.
  96. Zhao Z., Haikel-Elsabeh M., Baudier P., Renard D., & Brem A. (2024). Functional, hedonic, and social motivated consumer innovativeness as a driver of word-of-mouth in smart object early adoptions: an empirical examination in two product categories. International Journal of Technology Management, 95(1-2): 226-252.