Comparing “Individual Health” Message Framing to “Organizational Efficiency” Message Framing to Encourage Adoption of Wearable Health Technologies at Work

Design

This study utilized a 2 (framing: organizational efficiency vs individual health) × 2 (financial incentive: absent vs present) between-subjects posttest only factorial online experiment to test whether framing impacts behavioral intent associated with wearable technology adoption at work.

Sample

Data was collected in August 2018 using Qualtrics Research Services. Participants resided in the United States, were at least 18 years of age, and worked for their current company for at least 6 months. After providing informed consent, 310 participants were randomly assigned to one of the 4 conditions: organizational efficiency frame with incentive (O$, n = 78), individual health frame with incentive (I$, n = 78), organizational efficiency frame with no incentive (O, n = 77), and individual health frame with no incentive (I, n = 77). As indicated in Table 1, there were no significant differences in demographics, suggesting randomization was successful.

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Table 1.

Demographics, χ², and ANOVA Results by Condition.

	Organizational efficiency frame with incentive (O$), % or M (SD)	Individual health frame with incentive (I$), % or M (SD)	Organizational efficiency frame with no incentive (O), % or M (SD)	Individual health frame with no incentive (I), % or M (SD)	χ2	ANOVA
N	78	78	77	77
Age	42.18 (12.61)	42.99 (14.42)	46.32 (14.10)	44.38 (12.72)		1.40
Ethnicity					18.61
Non-Hispanic white	79.5%	78.2%	79.2%	75.3%
Hispanic	3.8%	6.4%	1.3%	2.6%
Black	7.7%	6.4%	9.1%	10.4%
Pacific Islander	0.0%	0.0%	0.0%	1.3%
Native American	1.3%	0.0%	1.3%	2.6%
Middle Eastern	0.0%	0.0%	0.0%	1.3%
Asian American	7.7%	9.0%	6.5%	6.5%
Other	0.0%	0.0%	2.6%	0.6%
Sex					1.56
Male	43.6%	46.2%	53.2%	48.1%
Female	56.4%	53.8%	46.8%	51.9%
Work experience (years)	9.37 (8.21)	7.78 (8.82)	9.01 (8.89)	7.93 (7.88)		.68
Hours worked (per week)	36.82 (9.26)	36.57 (11.68)	35.67 (9.50)	39.52 (12.51)		1.80
Political ideologya	3.88 (1.74)	3.87 (1.73)	3.95 (1.77)	4.17 (1.82)		.47
Household income (in thousands)	68.81 (54.45)	67.34 (56.55)	72.63 (53.09)	64.03 (48.90)		.34
Educationb						13.26

Abbreviation: ANOVA, analysis of variance.

^a Political ideology: The question asked “On a scale of 1 to 7, how would you identify your political ideology?” with Likert response options from conservative (1) to progressive (7).

^b Education: The question asked, “What is your highest level of education you have completed?” Response options were treated as ordinal variables ranging from “less than high school” to “doctoral degree (eg, MD, JD, PhD, etc).”

Manipulation

Participants were exposed to one of the 4 different versions of an article about the benefits of wearable technology at work. Information about wearable technology at work, its impact, and specific benefits (productivity, error reduction, savings, and job satisfaction) was identical. Differences involved attributing the benefits of wearable devices to either increased operational efficiency or employee health. For instance, when describing how wearables enhanced productivity at work, the organizational efficiency frames attributed improved productivity to the ability of managers to monitor participant’s activities at work resulting in better time management, fewer unnecessary breaks, and less downtime. Alternatively, the individual health frame attributed improved productivity to employees better understanding their behaviors at work, which led to better self-management of health resulting in less sick days, burnout, and stress.

The difference between financial incentive conditions was O$ stated the company kept the savings from insurance premiums, while I$ passed savings on directly to employees. No financial incentive information was included in conditions O and I.

Measures

The manipulation check consisted of 6 true/false items measuring participants’ perceived understanding and recall of information presented in their article and 1 multiple-choice question asking about the primary beneficiary of wearable technology at work.

The 3-item Unified Theory of Acceptance and Use of Technology (UTAUT) ⁶ subscale on behavioral intent (modified for wearable technology) was used to measure participants’ intent to adopt and use wearable devices at work. The scale ranged from 1 (strongly disagree) to 7 (strongly agree). Two additional items asking how participants intended to use devices were added, resulting in a 5-item scale. Examples include “I intend to use wearable devices at work if they were available to me,” “If available to me, I plan to sign up for programs that offer wearable devices to employees,” and “I would use wearable devices to monitor my productivity.” All items loaded onto a single factor (lowest loading = 0.92, total explained variance = 89.97%, ∝ = 0.97).

Analysis

Chi-square and analysis of variance (ANOVA) were used to check randomization among groups. Behavioral intent to adopt scores were entered into a 2 × 2 between-subject ANOVA.

Summary

This study examined the effect of framing and financial incentives on participant’s intention to adopt a wearable health device at work. Using a posttest only experiment, this study found support for the 2 main effects of frame and incentive on behavioral intent to adopt a wearable at work.

With respect to H1, findings suggest participants are less likely to report intention to adopt wearable technology when benefits to the organization are stressed versus individual health. This finding supports previous research that devices promoting cultures of health and safety at work are most likely to be adopted by employees. ^4,5 This is understandable since the health frame offered direct benefits to employees, whereas the organizational efficiency frame offered no direct, tangible benefit for individuals.

For RQ1, the study revealed a significant positive main effect for the presence of a financial incentive on individual adoption intention. In this case, both the individual health frame and presence of a financial incentive produced an independent and significant main effect that each increased intention to adopt wearable technology. Consequently, participants in the individual health frame with financial incentive (I$) were most likely to express intention to adopt a wearable activity tracker offered by their employer, whereas those in the organizational efficiency frame with no financial incentive (O) were the least. This finding corroborates existing survey research that found individuals were most willing to use devices that monitored activity or status “at work for safety” and least willing to use devices that monitored activity or status “in and out of work for productivity.” ⁴

From a practical standpoint, this suggests that companies should not overlook the importance of communicating with employees about new technology in the workplace. Organizational efficiency frameworks might be useful when persuading leadership to invest in wearables, but this study shows it is far less effective at convincing employees to adopt the technology. To maximize the likelihood of adoption, companies should develop strategies that stress individual health benefits of wearables while including incentives.

Future studies should continue to explore communication strategies utilized by organizations to encourage employee buy-in. This includes how factors like organizational culture, support, and trust impact employee decision-making as well as how privacy attitudes impact behavioral intention.

Limitations

Limitations include cross-sectional data rather than longitudinal, no-pretest data, and an online sample reacting to a hypothetical scenario. However, the posttest only design was beneficial in reducing test sensitization. This study minimized bias with randomization. Using Qualtrics, we recruited a sample that was representative of the US population. To ensure data integrity, we included screening criteria and quality checks and excluded cases with suspicious responses (ie, those who answered too fast, had over 25% missing data, and straight-line responses).

Significance

This study is one of the first experimental studies to test 2 common frames—individual health versus organizational benefits—on wearable adoption at work. Findings suggest individual health frames are more likely to motivate adoption of health-related behaviors at work particularly when combined with financial incentives.