=Paper=
{{Paper
|id=Vol-1388/PATH2015-paper5
|storemode=property
|title=Acceptance of Mobile Apps for Health Self-management: Regulatory Fit Perspective
|pdfUrl=https://ceur-ws.org/Vol-1388/PATH2015-paper5.pdf
|volume=Vol-1388
|dblpUrl=https://dblp.org/rec/conf/um/NierodaKK15
}}
==Acceptance of Mobile Apps for Health Self-management: Regulatory Fit Perspective==
https://ceur-ws.org/Vol-1388/PATH2015-paper5.pdf
Acceptance of Mobile Apps for Health Self-management:
Regulatory Fit Perspective.
Marzena Nieroda1, Kathleen Keeling1, Debbie Keeling2
1 Manchester Business School, University of Manchester, Manchester, M15 6PB
2 School of Business and Economics, Loughborough University, Leicestershire, LE11 3TU
Marzena.nieroda@mbs.ac.uk, Kathy.keeling@manchester.ac.uk
d.i.keeling@lboro.ac.uk
Abstract. This study addresses (non)acceptance by individuals of mobile
applications (apps) for health self-management (e.g., apps for running).
Regulatory Focus Theory (RFT) and Regulatory Fit (RF) principles are used to
facilitate understanding of acceptance of such apps within a goal pursuit process.
First, RFT was deployed to position different apps as strategies aligned with
promotion/prevention goal orientation (supporting pursuit of
achievement/safety). The Promotion-Prevention (PM-PV) scale was developed
to allow differentiation between such apps. Second, through experimentation it
was established that RF principles can be used to understand m-health adoption
where promotion/prevention oriented apps can be (mis)matched to individuals’
congruent goal orientation (promotion/prevention). The experiment was a first
study confirming fit effects resulting from product antecedents in combination
with a chronic (individual long-term) goal orientation; this condition was
necessary to understand m-health tools adoption in “real-life” situations.
Implications for healthcare practitioners are outlined.
Keywords: Regulatory Fit, Regulatory Focus, mobile apps for wellness, health
promotion
1 Introduction
Poor health around the world and low individual involvement in health self-manage-
ment are a major threat to healthcare system sustainability [1]. Some perceive technol-
ogy, particularly mobile health applications (m-health apps), as a transformation factor
facilitating individual engagement with health [2], e.g., mobile tracking provides a 40%
advantage for retention of weight-monitoring behavior over pen-and-paper methods
[3]. Despite the promise of m-Health, evidence indicates low acceptance and adoption
of such initiatives especially when individuals do not feel that tool use is compatible
with their health goals [4]. Thus, understanding the role of technology in relation to
individual goals may facilitate adoption of these tools and provide practical guidance
for healthcare practitioners to successfully recommend use.
Technology acceptance models are traditionally used to explain technology adoption
[5]. Those models predict behaviors based on individual beliefs and attitudes relating
to a given behavior or technology – not on individual preferences for goal pursuit. A
growing body of literature criticizes these models for failing to recognize individual
differences for taking an action, e.g., preferred ways of goal pursuit [6].
We propose a goal orientation framework for understanding m-health adoption
guided by principles of Regulatory Focus (RFT) and Regulatory Fit (RF) theories [7],
which focus on individual preferences for prevention or promotion oriented strategies
of goal pursuit. We further propose that prospective users perceive m-health apps as
promotion or prevention oriented and that a fit between user and app orientation will
increase uptake. To this end, we developed the Promotion-Prevention (PM-PV) scale
to differentiate between m-health tools and then conducted an experiment to test this
proposal.
2 Conceptual Foundations
2.1 Mobile Apps: Promotion/Prevention Focused Strategies of Goal Pursuit?
RFT distinguishes between two individual motivational orientations dictating different
concerns during goal pursuit [7]. Promotion-oriented individuals want their chosen
strategy for goal pursuit (means) to help them satisfy their needs for accomplishments
(gains), striving for positive outcomes from the goal pursuit. Promotion-oriented indi-
viduals see their goals as dreams or aspirations. Prevention-oriented individuals want
their chosen goal pursuit strategy to help them meet their needs for safety, tending to
use vigilant strategies to meet their goals believing that such strategies will help them
avoid negative outcomes (losses). Prevention-oriented individuals see their goals as du-
ties, responsibilities, and obligations [8]. RF posits that when individuals pursue their
goals with a matching goal pursuit strategy, they tend to be more engaged in their goal
pursuit and are more likely to progress with their tasks at hand [7].
This research proposes positioning mobile apps as promotion/prevention oriented
strategies of goal pursuit, which when matched with promotion/prevention oriented in-
dividuals are more likely to be adopted. However, the evidence that products have their
own focus is limited. A few scholars have implied (but not reliably measured) that dif-
ferent products have their own inherent promotion/prevention characteristics [10].
However, most of the studies highlight promotion/prevention attributes of a given prod-
uct, [e.g., 9], concentrating on added product attributes, not inherent characteristics of
the product. Products and their inherent characteristics have been verified as goal pur-
suit strategies appropriate for promotion- and prevention-oriented individuals, though
the products were not differentiated on their promotion/prevention dimensions but ra-
ther on categories such as hedonic and utilitarian [11]. Therefore, our first objective
was to demonstrate that m-health applications can be (reliably) differentiated by con-
sumers as promotion- or prevention-oriented strategies for health self-management.
2.2 m-Health Tool + Individual (Mis)match: Regulatory Fit in Action
To understand apps acceptance in “real world” situations we need to make sure that the
fit conditions can result from individual chronic (long-term) goal orientation rather than
a temporary, primed (short-term) goal orientation (predominantly used in previous
studies). Knowing how people with chronic predispositions react to different tools en-
ables provision of appropriate guidance for health professionals for successful app rec-
ommendation.
Research using behaviours or messages (not products) differing on strategies
aligned with promotion/prevention goal orientation confirms that RF can have varying
participative outcomes, for example, that RF correlates with individuals “feeling right”
about goal pursuit [12], favorable attitudes toward the tasks at hand [13, 14] and will-
ingness to expend effort on such goal pursuit [15]. While most of these effects resulted
from primed goal orientation, Higgins [7] states that the same effects should be ob-
served when chronic goal orientation is used as a fit antecedent. Hence:
H1a: A (mis)match (nonfit/fit) between an individual user regulatory orientation and
a mobile app leads to a (weaker)stronger sense of “feeling right” about using the
tool.
H1b: A (mis)match (nonfit/fit) between an individual user regulatory orientation and
a mobile app leads to (lessor)greater input of effort to use the tool.
3 Methodology and Results
Research included a scale development process and an experiment. Scale development
involved 7 studies following Churchill [16] and DeVellis [17] recommended steps.
Study 1a was a health support tool categorization task validating the concept. Study 1b
collected data for scale item generation; Studies 2 and 3 were two rounds of evaluation
of item face and content validity and purification, Study 4 (n = 210) comprised the
initial scale evaluation including exploratory and confirmatory factor analysis and eval-
uation of convergent and predictive validity, resulting in item reduction, Study 5 (n=86)
validated the reduced scale using the same analyses and evaluation of predictive and
nomological validity. Study 6 (n=242), the final validation, used different tools but the
same range of analyses and range of validity checks.
The result, apart from the actual PM-PV scale (see Table 1), was support for our
proposition that mobile health apps can be reliably differentiated as aligned with pro-
motion or prevention-oriented goal pursuit strategies. An experiment, using a 2 (pro-
motion, prevention chronic) by 2 (promotion, prevention tool) factorial design appro-
priate for tool manipulation, tested H1. (US respondents n =126, from Amazon Me-
chanical Turk online panel [18]). Experimental treatment involved promotion/preven-
tion-oriented individuals being exposed to description and photographs of either (a) a
promotion-oriented tool, e.g., a running app, or (b) a prevention-oriented tool, e.g., a
health information app. The outcome variables were expected invested effort in using
the app [15] and “feeling right” about app use [19].
Table 1. Final items in the PM-PV scale
PM-PV scale items
Promotion (PM) items
1. Improve their health
2. Fulfill needs for their ideal health
3. See themselves as striving to fulfill their health plans and goals
4. Focus on achieving desired health outcomes
5. Be successful in attaining future health goals
6. Achieve hopes and aspirations for their health
Prevention (PV) items
1. Take precautions to lead a safe and healthy life
2. Focus on protecting themselves from unwanted health outcomes
3. Safeguard against mistakes that might impact their health
4. Prevent health failures
5. Stop unwanted health crises
Individual respondent focus was assessed using the Regulatory Focus Questionnaire
(RFQ) [20]. The questionnaire inquires about strength of chronic promotion and pre-
vention focus. Summated scales of prevention foci are subtracted from summated scales
of promotion foci and scores of the differences above median value indicate promotion
focus, below indicate prevention focus. After data screening/manipulation checks, the
results supported H1a, with higher perceptions of “feeling right” (M=.33, SD .74) in
the case of a match (fit) between individual orientation and tool orientation than in a
mismatch (non-fit) (M=-.06, SD=.97, F (1: 124) = 4.18, p=.04). In a test of H1b, a 2 x
2 ANOVA of participants’ effort in using the tool showed a significant individual goal
orientation x tool orientation interaction (F (1,122) = 4.57, p=.035). Effort under fit
(match) conditions (M=.21, SD=.89) was significantly higher than effort in non-fit
(mismatch) conditions (M=-.19, SD=.96).
4 Discussion
The main contributions are: (1) The development of the PM-PV scale for tool differen-
tiation as promotion or prevention orientated. The scale is an important practical tool
and also a contribution to RFT theory; 2) Tool-individual matching possibilities based
on chronic goal orientation contributes to RF theory as the first to evaluate product
acceptance when matched/mismatched to chronic goal orientation. This is important
for understanding “real-world” situations in which individuals are encouraged to use
self-management tools.
Recommendations for different industry stakeholders are as follows. First, different
parties involved in the development and distribution of m-health tools can use the scale
development research findings to design and customize m-health tools for various con-
sumer groups. The PM-PV scale helps in the differentiation of existing tools and
whether newly developed tools have an intended promotion or prevention appeal. Sec-
ond, health service providers can use the match/mismatch principles to improve tool
acceptance and consequently health outcomes. For instance, a test for individual goal
orientation might offer one approach for physicians and healthcare insurers [20]. Such
a customized approach should make those tools more relevant for different individuals,
thus making them more acceptable.
References
1. Kellermann, A. L., & Jones, S. S. (2013). What it will take to achieve the as-yet-unfulfilled
promises of health information technology. Health Affairs, 32, 63-68.
2. Mattke, S., Schnyer, C., & Van Busum, K. R. (2012). A review of the U.S. workplace well-
ness market. Santa Monica, CA: RAND Corporation.
3. Carter, M. C., Burley, V. J., Nykjaer, C., & Cade, J. E. (2013). Adherence to a smartphone
application for weight loss compared to website and paper diary: pilot randomized controlled
trial. Journal of Medical Internet Research, e15.
4. Ruder, F. (2013). mHealth Report: Ruder Finn.
5. Venkatesh, V., Morris, M. G., Gordon, B. D., & Davis, F. D. (2003). User acceptance of
information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
6. Bagozzi, R. P. (2007). The legacy of the Technology Acceptance Model and a proposal for
a paradigm shift. Journal of the Association for Information Systems, 8, 244-254.
7. Higgins, E. T. (2014). Beyond pleasure and pain. How motivation works. Oxford: Oxford
University Press.
8. Higgins, E. T. (2002). How self-regulation creates distinct values: the case of promotion and
prevention decision making. Journal of Consumer Psychology, 12, 177-191.
9. Wang, J., & Lee, A. Y. (2006). The role of regulatory focus in preference construction.
Journal of Marketing Research, 43, 28-38.
10. Keeling, D. I., Daryanto, A., de Ruyter, K., & Wetzels, M. (2013). Take it or leave it: Using
regulatory fit theory to understand reward redemption in channel reward programs.
Industrial Marketing Management, 42, 1345–1356.
11. Micu, C. C., & Chowdhury, T. G. (2010). The effect of message's regulatory focus and
product type on persuasion. Journal of Marketing Theory & Practice, 18, 181-190.
12. Cesario, J., Grant, H., & Higgins, E. T. (2004). Regulatory fit and persuasion: transfer from
"Feeling Right.". Journal of Personality and Social Psychology, 86, 388-404.
13. Daryanto, A., de Ruyter, K., Wetzels, M., & Patterson, P. (2010). Service firms and customer
loyalty programs: a regulatory fit perspective of reward preferences in a health club setting.
Journal of the Academy of Marketing Science, 38, 604-616.
14. Lee, A. Y., Punam Anand, K., & Sternthal, B. (2010). Value from regulatory construal fit:
The persuasive impact of fit between consumer goals and message concreteness. Journal of
Consumer Research, 36, 735-747.
15. Pham, M. T., & Chang, H. H. (2010). Regulatory focus, regulatory fit, and the search and
consideration of choice alternatives. Journal of Consumer Research, 37, 626-640.
16. Churchill, Gilbert A., Jr. (1979), "A Paradigm for Developing Better Measures of Marketing
Constructs," Journal of Marketing Research, 16 (1), 64-73.
17. DeVellis, Robert F. (2011). Scale Development: Theory and Applications (Applied Social
Research Methods). SAGE Publications. Kindle Edition.
18. Peer, E., Vosgerau, J., & Acquisti, A. (2013). Reputation as a sufficient condition for data
quality on Amazon Mechanical Turk. Behavior Research Methods, 1-9.
19. Camacho, C. J., Higgins, E. T., & Luger, L. (2003). Moral value transfer from regulatory
fit: what feels right is right and what feels wrong is wrong. Journal of Personality and
Social Psychology, 84, 498-510.
20. Higgins, E. T., R. S. Friedman, R. E. Harlow, L. Chen Idson, O. N. Ayduk, & A. Taylor
(2001), "Achievement Orientations from Subjective Histories of Success: Promotion Pride
Versus Prevention Pride," European Journal of Social Psychology, 31 (1), 3-23.