Effects of emotional attachment on mobile health-monitoring service usage: An affect transfer perspective

https://doi.org/10.1016/j.im.2020.103312Get rights and content

Highlights

  • Patients form emotional attachments to an MMS, which can induce their active usage of such services.

  • Patients’ satisfaction with service components influences their overall affective evaluation of using the MMS.

  • Patients’ reliance on service-component in developing emotional attachments to the service is contingent on decision rationality.

Abstract

Mobile health-monitoring services (MMSs) empower patients with chronic illnesses to self-manage their health concerns; however, in practice, many patients become inactive users after employing MMSs for a short time. The reasons for this usage pattern remain unclear. By extending the literature that focuses on the cognitive reasoning behind system usage, our study takes an affect transfer perspective to examine how these patients develop emotional attachment to MMSs that subsequently drives their usage. Drawing on affect transfer theory, we hypothesize that patients’ satisfaction with MMS components influences their emotional attachment to the service through both cognitive and misattribution routines, and that the combined effects of these two routines are contingent on patients’ health rationality. Our hypotheses are tested with survey data collected from 228 patients with chronic illnesses. This study contributes to the mobile health (mHealth) literature by investigating patients’ actual behavior based on their interactions with MMSs from an affect transfer perspective. It also informs service providers of MMSs regarding how to motivate service usage by patients with chronic illnesses by adopting a strategic design.

Introduction

The term “mobile health services” refers to the provision of healthcare support, delivery, and intervention via mobile technologies in the healthcare sector, such as smartphones, tablets, and wearable technology. It connects health professionals with patients and engages patients in the healthcare process, with the aim of enhancing health service quality and reducing costs [1]. One group of mobile health (mHealth) services enables patients to track their health conditions at home and self-manage their health concerns. We refer to this group as “mobile health-monitoring services” (MMSs), which are designed to help patients with chronic illnesses.

Chronic illnesses are long-lasting illness conditions; examples include heart disease, diabetes, and cancer. The World Health Organization (WHO) indicates that health services for chronic diseases are a vital “investment” for the healthcare industry, particularly given that ischemic heart disease and stroke are the world’s biggest killers, accounting for a combined 15.2 million deaths in 2016 [2]. In the United States (US), chronic diseases are among the most prevalent and costly health conditions, with around 133 million Americans suffering from at least one chronic disease [3]. Thus, this paper focuses on MMS usage by patients with chronic illnesses.

Over the past decade, the global healthcare industry has invested significantly in MMSs. The worldwide market of MMSs is projected to reach US$90 billion by the end of 2025, accounting for more than half of the total mHealth market [4]. However, prior findings in relation to MMS usage are mixed. Some studies have indicated that MMSs for chronic illness healthcare provide favorable clinical outcomes [5], streamlined clinical processes [[5], [6], [7]], reduced medical expenses [8,9], strong compliance behavior among patients [5,9], and health-related quality of life [7].

However, real users only seem to remain interested in MMS innovations for a short period of time, and active users comprise only a small percentage of the user population. In China, Chunyu Doctor—a leading mHealth technology developer—launched MMS products to 30 million patients; of these, only 900,000 patients (3%) became frequent users [10]. Helander et al. [11] collected the behavioral data of nearly 190,000 people who planned to regulate their diet in the long term by adopting a mobile app, called Eatery. The authors examined these people’s usage patterns and found that less than 3% of them remained active Eatery users six months after starting to use the app. Possibly, these mixed findings have arisen because most studies in the former group employed cross-sectional data, yet MMS usage does not seem to be sustainable over a longer time period. Given that the core value of MMSs derives from their ability to effectively monitor patients’ health conditions over time [12], patients’ inactive usage compromises the benefits of MMSs. Therefore, our study examines how the interplay of cognition and affect influences patients with chronic illnesses to continue their MMS usage.

We draw on two theories to guide our study. We use the affect transfer theory to examine how patients with chronic illnesses may transfer their affective response to two MMS components (device and feedback) to their perception of the entire MMS, and how this perception influences their continuance usage. Information Systems (IS) researchers have empirically demonstrated the dominant role of cognition in service usage (e.g., Bhattacherjee [13],Soongeun et al. [14],Venkatesh et al. [15]). However, we consider their findings less applicable to MMSs for patients with chronic illnesses for two reasons. First, using a cross-sectional cognitive view to predict user behavior fails to explain actual usage at the post-adoption stage—particularly usage frequency and intensity [15], which are our foci. Second, patients with chronic illnesses experience long-term suffering, such as pain, fatigue, and mood disorders. Therefore, they have a stronger emotional need than typical system users [16] and their cognitive evaluation of MMSs may not play an important role in technology use.

Affect transfer theory describes a phenomenon in which people’s favorable attitudes toward components of an object (i.e., device and feedback, in our case) can facilitate the development of their overall attitude toward the object [17,18]. The key concept of this theory is emotional attachment, which refers to the strength of the affective bond between a person and a specific object [19]. In our case, the specific object is MMSs. We argue that patients with chronic illnesses may experience negative emotions as a result of their conditions. However, because the two components of MMSs—device and feedback—relieve these negative emotions [16,20], these patients emotionally attach to MMSs, which alters their continuance usage. We examine how interactions between patients with chronic illnesses and MMSs may lead to such emotional attachment, which, in turn, facilitates their continuous usage of MMS. Thus, our first research question is as follows:

RQ1

How do patients’ interactions with MMSs influence their emotional attachment to and continuous usage of MMSs?

To examine the interplay between cognition and affect of patients with chronic illnesses, we propose two affect transfer routines—cognitive and misattribution routines. Cognitive routine is based on cognitive evaluations and is thus considered rational [21]. By contrast, misattribution routine concerns a misattributing affect derived from other objects and is thus considered irrational [22]. This dissension renders the underlying mechanism of affect transfer underexplored and has resulted in mixed findings [23,24]. For instance, individuals can hold different affect regarding a given object based on the same affect transferred from related objects. To uncover the mixed findings of affect transfer, we draw on contingency theory and identify health rationality as the boundary condition [25]. Health rationality is a personal characteristic of patients and defined as the extent to which individuals make rational health decisions. We anticipate that it plays a pivotal role in differentiating the effects of the two affect transfer routines on health decision-making. By examining the moderating role of health rationality, we seek to understand how patients with different characteristics form an emotional attachment to MMSs. Thus, our second research question is as follows:

RQ2

To what extent is the transfer process contingent on patients’ health rationality?

To test our six hypotheses which are to be elaborated in subsequent sections, we collaborated with a hospital in Beijing, which helped us disseminate our survey to their patients with chronic illnesses. We collected 228 usable responses and conducted a partial least square (PLS) analysis. Our findings have the potential to enhance researchers and practitioners of how patients with chronic illnesses may form affect toward MMS components and transfer this to their overall attitude toward MMSs, and whether patient characteristics (i.e., health rationality, in our case) influence this transfer process.

Section snippets

Two key MMS components: device and feedback

MMSs comprise two components that empower patients to self-manage their health conditions and obtain advice for critical health-related decisions [26,27]. The first component is a health condition tracker and analyzer. This regularly collects data related to patients’ health conditions, and then sends it to a centralized system or processes it in the local device in real time. The entire tracking and analyzing process is transparent to patients. We refer to this first component as “device.” The

Research model and hypotheses

Based on the theoretical foundation discussed above, we developed a research model to examine the effects of emotional attachment to the two MMS components (device and feedback) on actual usage. Fig. 1 depicts our research model.

We first discuss the effect of emotional attachment on MMS usage. Emotional attachment reflects an affective bond between patients and the MMS [47]. In general, a user who possesses an emotional bond with a service often perceives the interactions with the service as

Study context and data collection

For our data collection, we collaborated with a major hospital in Beijing that launched an mHealth service for patients with chronic illnesses in 2016. Before launching this health-awareness campaign, the hospital invited 500 patients with chronic illnesses to pilot-test the service. Our experiment was based on the results of this test. Patients who agreed to subscribe to the healthcare service could use their mobile devices to monitor their health indexes, such as exercise, blood pressure, and

Key findings

Our study investigated how patients with chronic illnesses develop emotional attachment to MMSs based on their affective responses to two MMS components: device and personalized feedback. We adopted an affect transfer lens to capture the transfer of affect from each of these components to the entire MMS through cognitive and affective responses. We theorized the moderation of health rationality on the above relationships and set the boundary condition for the influence of the two transfer

Conclusion

Through their rising popularity, MMSs have captured a large share of the mHealth market and acquired an increasing user population. However, the reality is that patients do not make active usage of these services. Thus, our study aimed to explore MMS usage through the theoretical lens of affect transfer. We developed and tested six hypotheses with tracked behavioral data and administered a survey distributed among MMS users who were patients with chronic illnesses. Our findings illustrate that

CRediT authorship contribution statement

Xiaofei Zhang: Conceptualization, Methodology, Writing - review & editing. Xitong Guo: Data curation, Project administration, Writing - review & editing. Shuk Ying Ho: Conceptualization, Writing - review & editing. Kee-hung Lai: Writing - review & editing, Supervision. Doug Vogel: Writing - review & editing, Supervision.

Acknowledgements

The authors wish to thank the Editors-in-Chief, Associate Editor, and four anonymous reviewers for their highly constructive comments. This study was partially funded by the National Natural Science Foundation of China (71901127, 71531007, 71622002, 71871074, 71871073, and 71971123) and the Key Projects of Philosophy and Social Sciences Research of Chinese Ministry of Education [grant number 19JZD021].

Xiaofei Zhang is an Assistant Professor at Business School, Nankai University, Tianjin, China. He completed his PhDs from The Hong Kong Polytechnic University and Harbin Institute of Technology. His research interest is in the area of Healthcare IT, Human–Computer Interaction, and Affective Response. His research has appeared in Information & Management, European Journal of Information Systems, International Journal of Production Economics, and others. Email: [email protected].

References (121)

  • D.B. Grisaffe et al.

    Antecedents of emotional attachment to brands

    J. Bus. Res.

    (2011)
  • S. Akter et al.

    Trustworthiness in mhealth information services: an assessment of a hierarchical model with mediating and moderating effects using partial least squares (Pls)

    J. Assoc. Inf. Sci. Technol.

    (2011)
  • WHO

    The Top 10 Causes of Death

    (2017)
  • E. Willis et al.

    Online health communities and chronic disease self-management

    Health Commun.

    (2017)
  • GVR

    Mhealth Market Analysis Report by Participants (Mobile Operators, Device Vendors, Healthcare Providers), by Service (Diagnosis, Monitoring, Healthcare Systems), and Segment Forecasts, 2018 - 2025

    (2018)
  • A.S. Shetty et al.

    Reinforcement of adherence to prescription recommendations in asian indian diabetes patients using short message service (Sms)–a pilot study

    J. Assoc. Physicians India

    (2011)
  • S.-M. Liew et al.

    Text messaging reminders to reduce non-attendance in chronic disease follow-up: a clinical trial

    Br. J. Gen. Pract.

    (2009)
  • W.-T. Liu et al.

    A mobile telephone-based interactive self-care system improves asthma control

    Eur. Respir. J.

    (2011)
  • K.C. Leong et al.

    The Use of Text Messaging to Improve Attendance in Primary Care: A Randomized Controlled Trial

    Fam. Pract.

    (2006)
  • V. Ostojic et al.

    Improving asthma control through telemedicine: a study of short-message service

    Telemed. J. E-Health

    (2005)
  • EnfoDesk

    Chinese Mhealth Market Research Report

    (2014)
  • E. Helander et al.

    Factors related to sustained use of a free mobile app for dietary self-monitoring with photography and peer feedback: retrospective cohort study

    J. Med. Internet Res.

    (2014)
  • V. Aceti et al.

    Exploring the effect of mhealth technologies on communication and information sharing in a pediatric critical care unit

  • A. Bhattacherjee

    Understanding information systems continuance: an expectation-confirmation model

    MIS Quarterly

    (2001)
  • H. Soongeun et al.

    Antecedents of Use-Continuance in information systems: toward an inegrative view

    J. Comput. Inf. Syst.

    (2008)
  • V. Venkatesh et al.

    Predicting different conceptualizations of system use: the competing roles of behavioral intention, facilitating conditions, and behavioral expectation

    MIS Quarterly

    (2008)
  • R. Maunder et al.

    Love, Fear, and Health: How Our Attachments to Others Shape Health and Health Care

    (2015)
  • D.A. Aaker et al.

    Consumer evaluations of brand extensions

    J. Mark.

    (1990)
  • K.S. Coulter

    The Effects of Affective Responses to Media Context on Advertising Evaluations

    J. Advert.

    (1998)
  • J. Bowlby

    The Making and Breaking of Affectional Bonds

    (2012)
  • G.H. Bower

    Mood and memory

    Am. Psychol.

    (1981)
  • M. Oikawa et al.

    There is a fire burning in my heart: the role of causal attribution in affect transfer

    Cogn. Emot.

    (2011)
  • I. Ajzen et al.

    The influence of attitudes on behavior

    The Handbook of Attitudes

    (2005)
  • K.A. Machleit et al.

    Emotional feelings and attitude toward the advertisement: the roles of brand familarity and repetition

    J. Advert.

    (1988)
  • H.L. Tosi et al.

    Contingency theory: some suggested directions

    J. Manage.

    (1984)
  • G. Giamouzis et al.

    Telemonitoring in chronic heart failure: a systematic review

    Cardiol. Res. Pract.

    (2013)
  • S. Agarwal et al.

    Remote health monitoring using mobile phones and web services

    Telemed. E-Health

    (2010)
  • M. Akhter et al.

    Predicting mobile health adoption behaviour: a demand side perspective

    J. Cust. Behav.

    (2014)
  • M. Cocosila et al.

    Adoption of mobile ict for health promotion: an empirical investigation

    Electron. Mark.

    (2010)
  • Y. Sun et al.

    Understanding the acceptance of mobile health services: a comparison and integration of alternative models

    J. Electron. Commerce Res.

    (2013)
  • L. Carter et al.

    Exploring user acceptance of a text-message base health intervention among young african americans

    Ais Trans. Hum. Interact.

    (2015)
  • A. Rai et al.

    Understanding determinants of consumer mobile health usage intentions, assimilation, and channel preferences

    J. Med. Internet Res.

    (2013)
  • S. Akter et al.

    Modelling the impact of mhealth service quality on satisfaction, continuance and quality of life

    Behav. Inf. Technol.

    (2013)
  • S. Akter et al.

    Service quality of mhealth platforms: development and validation of a hierarchical model using pls

    Electron. Mark.

    (2010)
  • J. Wu et al.

    Toward a better understanding of behavioral intention and system usage constructs

    Eur. J. Inf. Syst.

    (2012)
  • P.B. Lowry et al.

    Understanding patients’ compliance behavior in a Mobile healthcare system: the role of trust and planned behavior

  • S. Lim et al.

    A study on singaporean women’s acceptance of using mobile phones to seek health information

    Int. J. Med. Inform.

    (2011)
  • X. Guo et al.

    The dark side of elderly acceptance of preventive mobile health services in China

    Electron. Mark.

    (2013)
  • N. Kordzadeh

    Communicating personal health information in virtual health communities: an integration of privacy Calculus model and affective commitment

    J. Assoc. Inf. Syst.

    (2017)
  • B. Khatoon

    The use of a mobile app to motivate evidence-based oral hygiene behaviour

    Br. Dent. J.

    (2015)
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    Xiaofei Zhang is an Assistant Professor at Business School, Nankai University, Tianjin, China. He completed his PhDs from The Hong Kong Polytechnic University and Harbin Institute of Technology. His research interest is in the area of Healthcare IT, Human–Computer Interaction, and Affective Response. His research has appeared in Information & Management, European Journal of Information Systems, International Journal of Production Economics, and others. Email: [email protected].

    Xitong Guo is a Professor of Information Systems at the Harbin Institute of Technology. He received his PhD in Information Systems from the City University of Hong Kong and PhD in Management Science and Engineering from the University of Science and Technology of China. His current research focuses on Big Data and Business Analytics. His work has been published in peer-reviewed journals, including MIS Quarterly, Information Systems Research, Journal of Management Information Systems, ACM Transactions on Management Information Systems, Information & Management, and others. Email: [email protected].

    Shuk Ying Ho is currently a professor at the Australian National University. She completed her PhD in the Hong Kong University of Science and Technology in 2004. Her doctoral dissertation examined how web personalization influences the behavior of online users and her current research portfolio reflects a continuing interest in this area. Her research focuses on the area of human–computer interaction, electronic commerce, technology adoption, and electronic government. Her publications have appeared in MIS Quarterly, Information Systems Research, European Journal of Operational Research, The Journal of Organizational Computing and Electronic Commerce, The Journal of American Society Information Science and Technology, The Journal of E-Government, Electronic Markets, and International Journal of Human-Computer Interaction. Email: [email protected].

    Kee-hung Lai is a Professor in the Department of Logistics and Maritime Studies at the Hong Kong Polytechnic University. He obtained his PhD in Business from the same university. He has co-authored seven books and published over 100 papers in journals such as Production and Operations Management, Information & Management, Journal of Management Information Systems, International Journal of Production Economics, California Management Review, Communications of the ACM, Journal of Business Logistics, and others. He serves in the board of many journals, such as Information & Management and Journal of Shipping and Trade. Email: [email protected].

    Douglas R. Vogel is a Professor of Information Systems and is an Association for Information Systems (AIS) Fellow as well as previous AIS President. He received his PhD in Information Systems from the University of Minnesota in 1986. He has published widely and directed extensive research on eHealth, group support systems, knowledge management, and technology support for education. He has recently been recognized as the most cited IS author in Asia-Pacific. He is currently engaged in introducing mobile devices and virtual world support for collaborative applications in educational and health systems. Email: [email protected].

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