Understanding the Effects of a Modified Theory of Planned Behavior That Includes Privacy and Security on Continuance Intention of Telehealth Services

Abstract

Most studies in telehealth focus on telehealth availability or use by healthcare systems or providers. Only a few behavioral studies explore determinants of individuals' continuance intention for telehealth.

This study seeks to identify factors that encourage individuals to continue the intention of using telehealth. We extended the Theory of Planned Behavior (TPB) by examining constructs that could identify reasons individuals plan to continue using telehealth including security and privacy.

A cross-sectional survey evaluated the determinants that predicted the continuance intention of telehealth. Responses from 194 individuals were analyzed with Partial Least Squares (PLS) Structural Equation Modeling. Perceived usefulness, security, attitudes, privacy, and subjective norms were important predictors of the continuance intention of telehealth. Conversely, perceived behavioral control did not influence the continuance intention of telehealth.

The extended TPB model predicted an individual’s continuance intention of telehealth. Healthcare professionals can use these results to address individuals’ telehealth privacy and security concerns and improve their perceptions of the usefulness of telehealth. Privacy and security concerns create barriers to telehealth use that must be reduced to facilitate repeated telehealth usage in future healthcare settings.

Keywords: Telehealth; Theory of Planned Behavior (TPB); privacy; security, usefulness

Introduction

Initially, telehealth use was primarily limited to facilitating medical care in rural and underserved areas. Pre-COVID-19, telehealth use expanded with the shift to patient and quality outcomes as well as cutting costs.¹ During the COVID-19 pandemic, telehealth visits were often the choice for most routine physician visits. Bestsennyy, Gilbert, Harris and Rost ² indicated that telehealth use increased from 19 percent pre-COVID-19 to 46 percent during the pandemic. Kaiser Family Foundation ³ predicts that telehealth usage in the United States (US) will continue in the post-COVID-19 era.

According to Medicaid.Gov ⁴ telehealth provides a low-cost, convenient alternative to face-to-face office visits. Telehealth use increases provider access, reduces travel costs and wait times, and improves continuity of care.⁵ Other researchers projected a potential $200 billion reduction in the cost of healthcare from the use of telehealth to manage chronic disease via remote monitoring of medical devices.⁶

However, many barriers to telehealth adoption exist including those related to users’ concerns about their healthcare data privacy and security,⁷ and the usefulness of telehealth.⁸ Most research studies on telehealth have concentrated on availability, telehealth use by healthcare systems, or telehealth use by healthcare providers. Few behavioral studies explore determinants of the individual’s intention to use or continuance intention of telehealth. The purpose of this study was to explore factors that can encourage individuals to use telehealth by identifying individuals’ telehealth concerns and improving their perceptions of the usefulness of telehealth.

Background

Theory of Planned Behavior

TPB is based on the social cognitive Theory of Reasoned Action (TRA). Ajzen and Fishbein ⁹ introduced the TRA to assess what motivates an individual to behave in a certain manner based on their intention, attitudes, and subjective norms. According to TRA, an individual’s actions are influenced by their intention to act. The individual’s intentions are determined by their attitude, which is their belief that the outcome will be favorable or beneficial, and by the level of “social pressure” (subjective norm) compelling them to complete the task.

Ajzen ¹⁰ added perceived behavioral control to the TRA resulting in the TPB. The TPB has been used to explore the pathways among attitude, perceived behavior control, subjective norms, and the intention to use various healthcare systems. Bell ¹¹ predicted the intention to use a web-based medical appointment scheduling system at a primary care medical clinic with the TPB. During the COVID-19 pandemic, Ramírez-Correa, Ramírez-Rivas, Alfaro-Pérez and Melo-Mariano ¹² used the TPB to predict the intention to use telemedicine. TPB is suitable for this study because one purpose of the study is to explore the determinants of an individual’s continuance intention of telehealth. While individuals may adopt technology, their continuance intention is a better measure as it determines their decision to sustain that use of the technology rather than just using it once or only when required.¹³ For example, Wang, Wang, Liang, Nuo, Wen, Wei, Han and Lei ¹³ identified antecedents to the continuance intention of mHealth in their metanalysis/systematic review. We believe it is important to ensure that individuals are going to continue to use the tool (continuance intention) rather than a one- or two-time use as required by an event such as the COVID-19 pandemic.

Hypothesis Development

In a novel approach, we extended the TPB model by incorporating usefulness, privacy beliefs, and security concerns into the model to determine how these constructs impact the causal pathways shown in Figure 1. Few studies have included these constructs in theoretical models for the continuance intention of telehealth.

Figure 1. Model for Continuance Intention of Telehealth

Chau and Hu ⁸ found that usefulness positively affected attitudes when exploring the adoption of telehealth by healthcare professionals. Hsieh et. al ¹⁴ concluded that usefulness had a positive effect on attitudes toward the adoption of adoption electronic health record exchanges. Thus, we add the hypothesis:

H1: Usefulness positively influences an individual’s attitude toward using telehealth services.

Security concerns are the unease that one feels about whether their protected health information is vulnerable to risks that could disclose the information and whether protective actions are taken to guard against these threats. ¹⁵ Privacy is defined as an individual’s belief that their protected health information will only be accessed by those with a “need to know.” ^16,17 Several prior researchers determined that security directly impacted an individual’s intent to adopt m-payment systems. ¹⁸ Both Kisekka et al. ¹⁹ and  Moqbel, Hewitt, Nah and McLean ¹⁵ determined that security concerns negatively impacted an individual’s intention to use an e-health portal. Other researchers noted that security impacted privacy when exploring physicians’ willingness to adopt telehealth.²⁰ For example, Elkefi and Layeb ⁷ investigated the benefits and challenges of telemedicine adoption for patients and caregivers after the COVID-19 pandemic and determined that security and privacy were important when studying the usability of telehealth tools. Smith, Smith, Kennett and Vinod ²¹ found technology barriers, including security concerns, when evaluating the telehealth use of cancer patients. Houser, Flite and Foster ²² recognized security as a challenge using telehealth. This study measured security as a risk that would be perceived as having a negative influence on an individual’s continuance intention of telehealth.¹⁵

Thus, we propose the following hypothesis:

H2: Perceived security concerns negatively influence an individual’s privacy concerns about telehealth.

Attitude is present in the original TPB. Attitude refers to whether an individual feels that the outcome will favor them. Ramírez-Correa, Ramírez-Rivas, Alfaro-Pérez and Melo-Mariano ¹² established that the TPB significantly predicted behavioral intention to use telehealth, with attitude having the strongest influence on behavioral intentions. Using the TPB, Kisekka, Goel and Williams ¹⁹ determined that the strongest predictor of intent to use an e-health portal was attitude. Thus, we posit the following hypothesis:

H3: Attitude positively influences an individual’s continuance intention of telehealth services.

Privacy concerns negatively impact an individual’s intention to use an e-health portal.¹⁹ Pool, Akhlaghpour, Fatehi and Gray ²³ determined that elderly patients were less likely to adopt and use telehealth due to privacy concerns. Hirani, Rixon, Beynon, Cartwright, Cleanthous, Selva, Sanders and Newman ²⁴ found that expressed privacy concerns affected whether individuals with chronic conditions used telemedicine. Zhang, Guo, Guo and Lai ²⁵ found that privacy concerns positively impacted usefulness. While these studies explore how privacy impacts one’s use of telehealth, we believe that privacy beliefs will directly impact whether an individual will continue to use telehealth and will test the following hypothesis.

H4: Privacy beliefs positively influence an individual’s continuance intention of telehealth.

Subjective norms are the social pressures that induce individuals to take actions that meet with the approval of their peers or the approval of society. Thus, if an individual believes that society approves of their intended actions, they are more likely to perform those actions. Kisekka, Goel and Williams ¹⁹ used the TPB and determined that subjective norms strongly predicted the intention to use telehealth. Ramirez-Rivas, Alfaro-Perez, Ramirez-Correa and Mariano-Melo ²⁶ determined that subjective norms strongly influenced the intention to use telemedicine. Thus, we add this hypothesis:

H5: Subjective norm positively influences an individual’s continuance intention of telehealth services.

Ajzen ^10,Ajzen ²⁷ suggested that perceived behavioral control impacted the intent to perform different behaviors. Self-efficacy represents “beliefs in one’s capabilities to organize and execute the courses of action required to produce a given attainment.” ²⁸ Often used in the TPB, perceived behavioral control is defined as “a person’s perception of the ease or difficulty of performing the behavior of interest.” ⁹ Researchers have determined that self-efficacy influences perceived usefulness when examining the intention to use internet banking ³⁰ or mobile payment systems. We posit that perceived behavior control influences perceived usefulness.  ^30-32 Accordingly, we propose the following hypothesis:

H6: Perceived behavioral control positively influences an individual’s perceived usefulness of telehealth.

Methods

This cross-sectional survey study explores the factors influencing individuals' continuance intention of telehealth.

Measures

The measures for usefulness were modified from Davis ³³ Privacy and security constructs were altered from studies by Dinev and Hart ³⁴ and the remaining constructs were adapted from prior TPB studies including those by Ajzen ¹⁰

Data collection

After receiving approval from the Internal Review Board, Qualtrics Survey Service provided a panel of individuals who completed the survey utilizing our requirements. The data was collected by Qualtrics in mid-2022. Specifically, we requested survey respondents from a general population of US residents over the age of 18. We requested respondents from only one country (i.e., the US) because residents of other countries may have different perceptions of privacy. Using our specifications, Qualtrics distributed email links to a random sample of their pool who were over age 18 from the US. Qualtrics literature indicates they take steps to prevent selection bias by following up with non-responsive participants. No identifiable information was collected from the participants. The anticipated demographics were a random sample of US residents. We obtained informed consent by using a click online button within the Qualtrics survey.

Statistical Analysis

The survey construct data was analyzed using Smart PLS (Partial Least Squares) 4.0 because our predictive model was comprised of latent variables.³⁵ Demographic data was analyzed with R statistical software version 4.1.2.³⁶ The response rate is unknown. There were 194 valid responses from individuals over age 18 and living in the US that were analyzed. To ensure that our sample size was sufficient, we used Soper’s ³⁷ Post-Hoc Statistical Power for Multiple Regression calculator. Soper’s ³⁷ calculator indicated that our sample size was more than efficient based on our number of latent variables (n = 6), our R² of .50, and our sample size of 194. See Appendix A for the survey questions.

Results

Demographics

Survey respondents included 94 females (48 percent), 99 males (51 percent), and one other (1 percent). Approximately 23.7 percent of the respondents were represented by groups ages 18-34, and the 35-50 age group accounted for 34.5 percent. The largest age group was over age 50 (41.8 percent). The largest percentage of participants held a baccalaureate degree (31.4 percent), followed by those with a high school degree (17.5 percent). The majority were employed (60.3 percent). Most had a primary care physician (95.4 percent), and 88.1 percent reported having regular doctor’s visits. Table 1 presents demographic information about respondents.

Next, study demographics are considered in comparison to the US Census 2021 data ³⁸ and the US Census education table.³⁹  The demographic age groups and percentages for the study respondents were 20-34 (23.7 percent) and 35-50 (34.5 percent), in comparison with the most similar Census findings the group percentages are 20-34 (20.0%) and 35-54 (25.5%). Regarding education above high school, the US Census reports high school graduates (28.3 percent), some college (17.1 percent), associate degree (9.9 percent), 4-year degree (22.2 percent), master’s degree (9.6 percent), and doctorate (1.9 percent). As expected, the current study had more respondents with associate, bachelor, master, and doctorate degrees than the US Census.

When comparing gender to the US population, Census data showed that 54.9 percent of the population are females and 45.1 percent are males. By comparison, females made up 48.5 percent of the respondents in this study, which was lower than the population, but 51 percent were males. When examining the employment rate for study respondents, 75.3 percent were employed, and 24.7 percent were unemployed. Census results for over 16 years in the civilian labor force reported lower employment (59.6 percent). There were 65.4 percent reporting annual household income less than or equal to $89,999. In similar categories, the Census found 64.2 percent reported income less than or equal to $99,999.

Table 1. Demographic Information

Characteristics	Number	Percentage (%)
Age, years
18-34	46	23.7
35-50	67	34.5
Over 50	81	41.8
Education (highest level)
Less than High School	3	1.6
High school graduate	34	17.5
Some college	22	11.3
2-year degree	29	15.0
4-year degree	61	31.4
Master’s degree	33	17.0
Professional Degree	5	2.6
Doctorate	7	3.6
Gender
Female	94	48.5
Male	99	51.0
Other	1	0.5
Employment Status
Full-time	117	60.3
Part-time	29	15.0
Unemployed	48	24.7
Employment Industry
Education	11	5.7
Financial Services	15	7.7
Healthcare	15	7.7
IT/Computing	33	17.0
Professional and Business Services	10	5.2
Retail	17	8.8
Other	93	47.9
Household Income
Less than $10,000	7	3.6
$10,000 - $99,999	22	11.3
$30,000 - $49,999	41	21.1
$50,000 - $69,999	28	14.4
$70,000 - $89,999	29	15.0
$90,000 - $149,999	44	22.7
More than $150,000	23	11.9
Primary Care Physician
Yes	185	95.4
No	9	4.6
Regular Doctor Visits
Yes	171	88.1