Abstract
Purpose:
The purpose of this study is to determine if there are unique customer archetypes that can describe the motivations behind consumer buying choice (in person or online) for hearing aids in hearing health care.
Method:
A consumer survey was developed from themes that arose during 11 semistructured interviews with adults who had no previous hearing aid experience. Using Qualtrics research panels, a 28-item questionnaire was distributed online to U.S. residents above the age of 50 years with no previous hearing aid experience. A quota of 1,000 completed responses was set, with a maximum of 70% of respondents identifying as White. Completed surveys were obtained from 1,377 individuals. Three hundred forty responses were excluded due to ineligibility and/or poor response quality.
Results:
Two unique customer archetypes were developed using five factors identified in the data set: Physician Trust, Sociability, Comfort Buying Online, Verify Sources, and Reliance on Others. Eighty-four percent of respondents chose an in-person pathway for hearing health care. There was no association between customer archetype and pathway selection choice.
Conclusions:
The two archetypes reflect those with greater comfort with consuming health care online and in person, respectively. However, both archetypes are likely to use in-person models of hearing health care at the present time.
Supplemental Material:
Hearing loss impacts approximately 37.5 million adults across the United States (National Institute on Deafness and Other Communication Disorders, 2021). Unfortunately, less than 30% of those who could benefit from hearing aids use them (Goman et al., 2017). As a legislative response to the consistently gross underutilization of hearing aids by adults with age-related hearing loss, the Over-the-Counter (OTC) Hearing Aid Act was passed by the U.S. Congress in 2017. The Food and Drug Administration (FDA) created a new class of OTC hearing aids in response, and these devices have been available to consumers since October 2022. The advent of OTC hearing aids has introduced an alternative, direct-to-consumer (DTC) pathway for hearing health care delivery. Cost, stigma, and lack of perceived need are typically reported to be the principal barriers to hearing aid adoption in the traditional provider-driven model of hearing health care (Humes, 2021; Knudsen et al., 2010; McCormack & Fortnum, 2013; McMahon et al., 2021). The hope is that DTC models can help minimize barriers to hearing health care, especially those related to cost, to hearing aid use.
Several landmark studies have assessed the feasibility of aspects of DTC hearing health care. For example, Humes et al. (2017) investigated if consumers could self-select and self-fit hearing aids in a double-blind, placebo-controlled, clinical trial. Participants in the consumer-driven delivery branches obtained similar benefit from hearing aids as those in the provider-driven delivery branch (Humes et al., 2017). Similarly, Sabin et al. (2020) found participants could successfully self-select signal processing parameters via a smartphone application and had comparable outcomes (hearing aid benefit and hearing handicap reduction) to participants who were fit using audiology best practices. Participants who self-fit their hearing aids selected appropriate gain for their degree of hearing loss, with no differences in hearing aid benefit between the self-fit and clinician-fit groups, measured 6 weeks after initial fit (Sabin et al., 2020). Finally, Convery et al. (2011, 2017, 2019) completed a series of studies investigating if adults could successfully self-assemble and self-fit a hearing aid with and without personalized support. Results from these studies showed that most participants were able to perform basic hearing aid assembly tasks (e.g., put in battery, put on dome), but some required more personalized support for more advance hearing aid skills (e.g., pairing to a smartphone; Convery et al., 2011, 2017, 2019). The results from the aforementioned studies, and several other more recent studies (De Sousa et al., 2023; Vyas et al., 2022), have shown that DTC hearing health care models can result in efficacious outcomes, at least in the short term, with most individuals being able to demonstrate the necessary skills to self-fit hearing aids.
However, the efficacy of DTC hearing health care models cannot be judged solely on their technical feasibility in narrow domains such as aid selection and acoustic fitting. Rather, the evaluation of these models also needs to include the holistic perception of those who will use them to determine consumer confidence in all aspects of the care process (Convery et al., 2011). Thus far, such holistic evaluation has been limited, and the results have not been universally positive. For example, Chandra and Searchfield (2016) explored the perception of older hearing aid users toward online hearing health care delivery systems in Australia. Results showed that participants generally lacked awareness of online DTC models and expressed concerns over the viability of the DTC hearing aids without access to clinical services (Chandra & Searchfield, 2016). Thus, consumer familiarity with, and consistent exposure to, in-person models of health care may be anchors to current consumer opinions of DTC hearing health care. More recently, Singh and Dhar (2023) explored consumer attitudes toward DTC hearing health care models by surveying over 1,000 non–hearing aid users across the United States. Results showed that the majority (84%) of respondents were uncomfortable with pursuing hearing aids via DTC channels (Singh & Dhar, 2023). However, those who had previous experience with DTC health care (i.e., purchased eyeglasses online) and did not have or were uncertain about insurance coverage for hearing aids were more likely to pursue hearing aids online. These results suggest that although DTC hearing health care models may not have immediate acceptance, there are segments of the population who may be more inclined to explore these pathways. Said differently, the mere availability of OTC hearing aids may not automatically lead to a dramatic uptick in utilization. Historically, the transition of prescription medications to OTC status was not readily embraced at the outset by professionals and consumers (Jacobs, 1998). Stakeholders (i.e., manufactures, hearing health care providers, public health systems) would be well advised to invest strategically in improving consumer confidence in DTC hearing health care models.
A profitable pathway for improving consumer awareness and confidence would demand a nuanced strategy for differentially understanding the needs, motivations, and apprehensions of different segments of the potential consumer base. While Singh and Dhar (2023) attempted to segment potential consumers of hearing health care by using demographic and socioeconomic variables, this type of analysis is limited in its ability to describe the motivations behind respondent pathway choice (DTC vs. in person) and the psychological and social determinants of such choice. Psychological and social factors have been shown to impact the uptake and use of hearing aids in the provider-driven model of hearing health care and thus are likely to play a critical role in DTC models as well. One way to explore consumer motivations is to develop “customer archetypes,” a technique used in market research (Merlo et al., 2022). Customer archetypes allow for researchers to discover patterns that group individuals to help explain why consumers act in specific ways, which may not be captured in other traditional methods of evaluation (i.e., focus groups, regression of survey items). Furthermore, archetypes allow for the creation of distinct groups, which marketers need for a binary distinction rather than trying to understand where any given individual may be on a sliding/continuous scale. Although typically used in the context of consumer goods and services, market research does have the ability to inform clinical practice. For example, understanding consumer buying behaviors in hearing health care can aid in the optimization of care models by identifying limitations perceived by the intended user. Identifying limitations for a specific archetype can help in the development of tailored solutions. Thus, using the principles developed in market research, the purpose of this study was to determine if there are specific customer archetypes that are more inclined to consider DTC or provider-driven models of hearing health care.
Method
A consumer-research survey was developed from themes that arose during 11 semistructured interviews with adult, non–hearing aid users. Interviewees were provided with a description of potential hearing health care pathways (i.e., in person with a clinician, DTC with and without professional postfitting support) and asked to provide their opinions. Semistructured interviews were conducted by the research team, and the following topics were discussed: hearing loss and hearing aid treatment, interviewees' understanding of how to obtain hearing aids and their challenges, opinions of DTC health care, importance of clinicians, and comfort with shopping online. Themes that emerged from these interviews were hearing loss importance, shopping online versus in person, doctor trust, impact of COVID-19 on buying behaviors, and self-efficacy with DTC hearing aid treatment. Using the identified themes, a 28-item questionnaire was developed (see Supplemental Material S1). To address the theme of doctor trust, the Trust in Physician Scale was utilized (Anderson & Dedrick, 1990). Furthermore, the Multidimensional Health Locus of Control Scales were administered, but data collected from these items were not utilized for this analysis (Wallston et al., 1978).
The survey was then launched online via Qualtrics survey panels from March 22 to 25, 2022, to U.S. residents only. Qualtrics survey panels are targeted research panels that are prescreened to meet specific inclusion or exclusion criteria. All respondents in this study were 50 years old or older and not a hearing aid owner. Respondents were not asked to self-report their hearing status but were asked if they were interested in purchasing hearing aids at the time of the survey. A quota of a minimum of 1,000 completed responses was preset with a maximum of 70% of respondents identifying as “White only” to closely match U.S. census data (United States Census Bureau, 2021). Any respondent who completed the survey at one half the median response time (7 min), or less, was automatically excluded for quality control. All de-identified data were collected via the Qualtrics Experience Management System. All statistical analyses were performed using RStudio Version 4.1.2 using the factoextra package (R Core Team, 2021). The study was approved by the institutional review board at Northwestern University.
Results
Responses were obtained from 1,377 individuals. Three hundred forty (340) responses were excluded due to study ineligibility, partial completion of the survey, and/or poor-quality responses. Table 1 provides a summary of the demographic characteristics of the 1,037 respondents included in this study.
Table 1.
Overall and cluster-specific demographic characteristics.
| Factor | Overall (N = 1,037) |
Cluster 1 (n = 453) |
Cluster 2 (n = 584) |
|---|---|---|---|
| Age (years) | |||
| Mean (SD) | 61.4 (7.84) | ||
| Gender | |||
| Female | 714 (68.9%) | 309 (68.2%) | 406 (69.5%) |
| Male | 322 (31.0%) | 143 (31.6%) | 178 (30.5%) |
| Prefer not to say | 1 (< 0.00%) | 1 (< 0.00%) | 0 (0.00%) |
| Community | |||
| Rural | 287 (27.7%) | 138 (30.5%) | 149 (25.5%) |
| Suburban | 507 (48.9%) | 216 (47.7%) | 291 (49.8%) |
| Urban | 243 (23.4%) | 99 (21.8%) | 144 (24.7%) |
| Household income | |||
| > $15,000 | 119 (11.5%) | 59 (13.0%) | 60 (10.3%) |
| $15,000–$24,999 | 139 (13.4%) | 47 (10.4%) | 92 (15.8%) |
| $25,000–$34,999 | 142 (13.7%) | 58 (12.8%) | 84 (14.4%) |
| $35,000–$49,999 | 162 (15.6%) | 79 (17.4%) | 83 (14.2%) |
| $50,000–$74,999 | 215 (20.7%) | 94 (20.8%) | 121 (20.7%) |
| $75,000–$99,999 | 117 (11.3%) | 55 (12.1%) | 62 (10.6%) |
| $100,000–$124,999 | 62 (6.00%) | 32 (7.06%) | 30 (5.14%) |
| $125,000–$149,999 | 32 (3.08%) | 12 (2.65%) | 20 (3.42%) |
| $150,000+ | 49 (4.72%) | 17 (3.75%) | 32 (5.50%) |
| Race | |||
| White | 674 (65.0%) | 323 (71.3%) | 351 (60.1%) |
| Black or African American | 205 (19.8%) | 67 (14.8%) | 138 (23.6%) |
| Asian or Asian American | 48 (4.63%) | 17 (3.75%) | 31 (5.30%) |
| Hispanic, Latino, Latina, or Latinx | 48 (4.63%) | 21 (4.64%) | 27 (4.62%) |
| American Indian or Alaska Native | 16 (1.54%) | 5 (1.10%) | 11 (1.88%) |
| Middle Eastern or Northern African | 1 (< 0.00%) | 0 (0.00%) | 1 (< 0.00%) |
| Native Hawaiian or Pacific Islander | 4 (0.39%) | 0 (0.00%) | 4 (0.68%) |
| Multiple | 29 (2.80%) | 14 (3.09%) | 15 (2.57%) |
| Other | 12 (1.16%) | 6 (1.32%) | 6 (1.03%) |
| Employment | |||
| Retired | 444 (42.8%) | 189 (41.7%) | 255 (43.6%) |
| Self-employed | 87 (8.40%) | 49 (10.8%) | 38 (6.51%) |
| Unemployed, looking for work | 60 (5.79%) | 26 (5.74%) | 34 (5.82%) |
| Unemployed, not looking for work | 111 (10.7%) | 55 (12.1%) | 56 (9.59%) |
| Working full-time | 253 (24.4%) | 104 (23.0%) | 149 (25.5%) |
| Working part-time | 82 (7.91%) | 30 (6.62%) | 52 (8.90%) |
| Stage of hearing health care journey | |||
| No, I am not interested in HAs | 581 (56.0%) | 235 (51.9%) | 346 (59.2%) |
| Maybe, I plan to research HAs | 376 (36.3%) | 184 (40.6%) | 192 (32.9%) |
| Yes, I intend to buy HAs | 80 (7.70%) | 34 (7.50%) | 46 (7.88%) |
| Insurance coverage for HAs | |||
| Yes | 280 (27.0%) | 121 (26.7%) | 159 (27.2%) |
| No | 213 (20.5%) | 109 (24.1%) | 104 (17.8%) |
| Maybe | 111 (10.7%) | 39 (8.61%) | 72 (12.3%) |
| Don't know | 443 (41.8%) | 184 (40.6%) | 249 (42.6%) |
| Purchased eyeglasses online before | |||
| Yes | 174 (16.8%) | 79 (17.4%) | 96 (16.4%) |
| No | 863 (83.2%) | 374 (82.6%) | 489 (83.7%) |
Note. Means and standard deviations are reported for continuous variables. HAs = hearing aids.
Principal Component Analysis and K-Means Clustering
The first step in developing the customer archetypes was to explore underlying common characteristics from all dimensions of interest using principal component analysis (PCA). PCA is a data reduction technique used to describe as much of the variation in the data as possible using fewer dimensions. In this study, 17 questions were included in the PCA. Respondents' sentiments toward these 17 questions were captured using a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). Overall mean scores for each question are shown in Table 2. The data set was first evaluated using Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy (Kaiser & Rice, 1974) and Bartlett's test of sphericity (Bartlett, 1937). The KMO value was .79, and Bartlett's test of sphericity was significant with a p value of < .01. These results indicate that the data set is adequately sampled and that factor analysis of the data set is appropriate. The results showed five components with eigenvalues greater than 1, which accounted for 62% of the total variance. Table 2 provides each varimax rotated component and their contributive variance. Variables with a factor loading of 0.5 or higher were grouped on a common factor. Variables that loaded on multiple factors were assigned to the factor with the highest loading. Using the rotated component matrix in Table 2, each factor was labeled based on the common themes observed between correlated variables. The factors were labeled as the following: (a) Physician Trust, (b) Sociability (c) Comfort Buying Online, (d) Verify Sources, and (e) Reliance on Others.
Table 2.
Contributed variance (greater than or equal to 0.5) of each item using varimax rotation.
| Questionnaire item | 1 | 2 | 3 | 4 | 5 |
M
score (SD) |
|---|---|---|---|---|---|---|
| Satisfied with my social life | 0.786 | 4.80 (1.66) | ||||
| Integral part of my community | 0.822 | 4.05 (1.60) | ||||
| Spend quality time with others | 0.823 | 4.21 (1.66) | ||||
| I like to figure things out on my own | −0.526 | 5.56 (1.20) | ||||
| I prefer to ask others for help | 0.779 | 3.23 (1.51) | ||||
| Prefer to shop online vs. in person | 0.744 | 3.81 (1.86) | ||||
| Products sold online are lesser quality | 3.00 (1.54) | |||||
| Use online reviews to inform purchases | 0.500 | 0.544 | 4.67 (1.71) | |||
| Online customer service is sufficient | 0.696 | 4.67 (1.36) | ||||
| Compare different brands before purchase | 0.694 | 5.04 (1.37) | ||||
| Comfort purchasing health care products online | 0.758 | 4.46 (1.63) | ||||
| My doctor is considerate of my needs | 0.875 | 5.68 (1.28) | ||||
| I follow my doctor's advice | 0.827 | 5.75 (1.09) | ||||
| My doctor would tell me about a mistake with my treatment | 0.849 | 5.38 (1.47) | ||||
| My doctor cares about me as a person | 0.889 | 5.49 (1.32) | ||||
| I verify my doctor's advice | 0.728 | 4.78 (1.49) | ||||
| Cost is not a concern for my hearing health care decisions | 4.29 (1.74) |
Note. Overall mean scores and standard deviations for each item are also shown.
k-means clustering analysis was then applied to the reduced data set. k-means clustering analysis is an unsupervised machine learning algorithm, which aggregates data based on underlying similarities. In order to achieve the target number of clusters (n = k), each cluster serves as its own centroid in the data set. Every data point (i.e., respondent) is then allocated to the nearest cluster centroid. To determine the optimal number of clusters (k) in this study, data were visualized with a dendrogram (see Figure 1). A dendrogram shows the hierarchical relationship between clusters, with the y-axis representing the distance of dissimilarity between clusters. The number of clusters that have the largest vertical difference (i.e., greatest height) is the optimal choice. Using this metric, k-means clustering analysis was then performed with k = 2. Figure 2 shows the k-means cluster plot results, with each data point representing a respondent from our survey. Customer archetypes were developed using the five factor loadings from the PCA for each cluster using their respective z scores (see Figure 3). Factors with a mean value ± 0.2 SD from zero were used for the interpretation. The choice of ± 0.2 SD was driven by a desire to highlight subtle differences that were likely to be observed in the hearing health care market at the present time given the novelty of DTC hearing health care channels. Table 3 provides a description for the two customer archetypes identified. The Explorer archetype represents a customer group that is highly independent and will seek out multiple sources to verify information prior to making a buying decision. They are also very comfortable purchasing products online. The Entrusting archetype represents a customer group that relies heavily on the opinions of others and makes buying decisions based on a trusted source, rather than using multiple sources. This customer archetype also has low comfort with purchasing products online.
Figure 1.
Dendrogram from k-means clustering analysis.
Figure 2.
k-means cluster plots. Colored polygons represent an independent cluster. Each respondent is assigned to the closest centroid. Each number represents an individual respondent. Dim1 = physician trust, Dim2 = sociability.
Figure 3.
Factor loadings for each cluster. Positive values indicate agreement with a factor. Values are represented as z scores.
Table 3.
Description of customer archetypes and number of respondents in each group.
| Archetype | n | Description of customer archetype |
|---|---|---|
| Explorer | 453 | Highly independent, comfort buying online, and verifies sources |
| Entrusting | 584 | Heavily relies on others, low comfort buying online, and does not check multiple sources |
Logistic Regression Using Customer Archetypes
All respondents in the consumer survey were asked which of the following pathways of hearing health care they would most likely use for services: (a) through a hearing health care provider (in person), (b) online that requires a hearing test, or (c) online that does not require a hearing test. Eighty-four percent (n = 874) of respondents ranked the in-person pathway as their first choice, while the remaining 16% ranked pursuing hearing health care online as their first. There was an equivalent number of respondents who would purchase a hearing aid online with (n = 82) and without (n = 81) a hearing test. A logistic regression was performed with the independent variable being cluster assignment and the binomial dependent variable being the chosen pathway for hearing health care services (in person or online with/without a hearing test). Results of the model showed (see Table 4) no association between cluster type and selected health care model.
Table 4.
Logistical regression estimates for the association between customer archetype and chosen pathway for hearing health care.
| Variable | Beta coefficient | Standard error | p value |
|---|---|---|---|
| Intercept | −1.57 | 0.12 | < .01* |
| Customer Archetype 2 | −0.29 | 0.17 | .24 |
Intercept has a p-value that is significant. P-value is less than the cut off of 0.05.
Discussion
We sought to explore if there were unique customer archetypes that could describe the motivations behind respondent pathway choice in hearing health care, specifically hearing aid acquisition (i.e., in person or DTC). Social and psychological determinants that can influence choice of health care pathway have not been explored in the context of hearing health care. These perceptions are likely to play a critical role in understanding consumer behavior in hearing health care. To our knowledge, this is the first study exploring consumer motivation behind pathway selection choice in hearing health care.
Results from our consumer survey suggest that, presently, there are two unique customer archetypes in the hearing health care market: the Entrusting and Explorer archetypes. Five factors were identified in our consumer survey that were used to generate these customer archetypes. The two customer archetypes differed on three factors: Comfort Buying Online, Verify Sources, and Reliance on Others. The Explorer archetype was more comfortable with buying products online, more likely to verify sources of information, and less likely to rely on others when making purchasing decisions. In contrast, the Entrusting archetype was less comfortable with purchasing products online, less likely to verify sources of information, and more likely to rely on others when making purchasing decisions. Previous research has shown these three factors to be related to one another (Chen et al., 2022; Wang et al., 2022). The ability to research one's health care condition and potential treatments empowers the consumer to explore and develop personalized health care interventions, reducing their dependence on others. Similarly, those comfortable shopping online can avail product comparisons more conveniently. Thus, it is expected that those who are less comfortable buying products online would be less likely to use multiple sources to verify their product/service choice and would rely upon the opinions of others to guide their decisions.
While these customer archetypes differed on how they make buying decisions, there was no association between customer archetype and pathway choice. This is not surprising given that the vast majority (84%) of respondents chose in-person hearing health care. Thus, at the present time, consumers appear to have a strong preference for in-person hearing health care even when their archetypical preference would suggest otherwise. It is also possible that the comfort the Explorer archetype has with purchasing products online does not extend to health care products. While we sought to survey a sufficient number of potential DTC hearing health care consumers, the large preference for in-person pathways indicates that consumers who would choose DTC models either are not numerous at the present time or were not adequately sampled. Thus, the customer archetypes identified in this study represent groups who might display different consumer buying behaviors for more established OTC products and/or services but converge to choosing in-person hearing health care. We suspect that a lack of knowledge and/or trust in the OTC hearing health care delivery pathway, only available since October 2022, may be contributing to the low interest in DTC hearing health care. Interestingly, Singh and Dhar (2023) reported that consumers who used DTC models in other health care domains were more inclined to pursue DTC hearing health care. This would suggest that as consumer trust in other DTC health care domains improves, more consumers may view this novel hearing health care pathway as a viable treatment option. Relatedly, as interest and confidence in DTC hearing health care grow, consumer archetypes preferring different pathways of hearing health care may become better defined.
The two customer archetypes were similar on the remaining factors: Physician Trust and Sociability. Physician Trust was the largest component of our factor analysis. Previous research has shown that the more value consumers place on an information source, the more likely they are to incorporate new information into their decision-making process (O'Reilly, 1982). Furthermore, in the context of health information, the more importance a consumer places on the source of information, the more likely they are to perceive the information as relevant and trustworthy (Alexander et al., 2011). The Physician Trust factor was not markedly different between the two customer archetypes in this study. However, overall mean scores show that respondents trust the information provided to them by their physician. This would suggest that respondents are not opposed to discussing potential options for hearing aid treatment with their physician and/or clinician. These findings corroborate results reported in the MarkeTrak VIII, which found that individuals with mild-to-moderate hearing loss were likely to speak with their primary care physician about potential treatment options (Kochkin, 2012). This demonstrates that consumer buying behavior in the hearing health care market has yet to change. Specifically, consumers are likely to continue to seek out the opinions of their health care providers to determine if OTC hearing aids are an appropriate solution for their hearing loss. Thus, efforts to inform consumers of the effectiveness and safety of OTC hearing aids for mild-to-moderate sensorineural hearing loss are needed to engender consumer trust, thereby minimizing their reliance on their health care providers to make a decision on whether or not to purchase an OTC hearing aid.
Sociability was the second largest component identified in our consumer survey, and the two customer archetypes did not vary significantly on this factor either. Overall mean scores show that respondents were neutral about their social lives, which suggests that for this sample, sociability is likely a nonfactor with respect to consumer choices and/or buying behavior made in hearing health care. Traditionally, research exploring the relationship between sociability and consumer behavior has shown that those with richer social lives tend to make buying decisions that are beneficial to the entire social group (rather than the individual; Wood & Hayes, 2012). While the factor of sociability is commonly explored in consumer research, its application to hearing aids may be more complex. Much of the literature exploring the relationship between social richness and consumer buying behavior has attempted to understand how consumer choices may modify the social experience (Li et al., 2021; Umberson & Montez, 2010; Wood & Hayes, 2012). However, hearing loss is often the inhibiting factor for the fundamental social experience to occur. For example, a family with young children when deciding where to go out to eat may decide to go to a child-friendly restaurant. In this scenario, the fundamental experience of “going out to eat” is modified by a restaurant choice that will suit their family structure and needs. In contrast, those with hearing loss may choose to avoid a restaurant entirely, which alters the fundamental experience. Therefore, hearing loss acts as a gatekeeper rather than a modifier. Furthermore, this decision may have cascading effects, as those who are more socially connected are more likely to make better health care decisions, be healthier, and live longer than those who are less socially connected (Umberson & Montez, 2010). This relationship between the choice of purchasing a hearing aid and its influence on social richness (and vice versa) should be explored further.
There are limitations to this study. The administration of this survey predates the finalized FDA guidance for OTC hearing aids and their sale, which may play a role in consumer trust in these novel models of hearing health care. Furthermore, the survey was only provided online. Thus, this sample may not adequately represent consumers who primarily make purchasing decisions in person.
Conclusions
This study establishes two customer archetypes in hearing health care that are consistent with other consumer behavior research. The two archetypes reflect those with greater comfort with consuming health care online and in person, respectively. These results, however, indicate that those who might be favorably disposed to consume other health care products/services online are not yet comfortable to do so with hearing health care.
Data Availability Statement
Data collected for this study will be available on https://doi.org/10.5061/dryad.n5tb2rc2f.
Supplementary Material
Acknowledgments
This work and Jasleen Singh were supported by the American Hearing Research Foundation. Sumitrajit Dhar was supported by the Patient-Centered Outcomes Research Institute and the National Institutes of Health.
Funding Statement
This work and Jasleen Singh were supported by the American Hearing Research Foundation. Sumitrajit Dhar was supported by the Patient-Centered Outcomes Research Institute and the National Institutes of Health.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data collected for this study will be available on https://doi.org/10.5061/dryad.n5tb2rc2f.