Introduction

The wide spread of COVID-19 has fundamentally influenced American people. Besides physical harm, pandemics can cause serious mental health consequences such as stress, anxiety, depression, and insomnia [1]. America faces an unprecedented mental health crisis among all age groups. During the pandemic, about 4 out of 10 adults in the US reported anxiety or depression symptoms, showing a dramatic increase from 1 to 10 adults in the first half of 2019 [2]. Despite the serious negative consequences, this problem has not received adequate attention. The 2022 “State of Mental Health in America” report [3] suggests over half of adults in the US (over 27 million) with a mental illness do not receive treatment.

To address the mental health crisis, in 2022 President Biden announced a national mental health strategy to strengthen system capacity, connect more citizens to care, and create a continuum of support [4]. The proposed American Rescue plan laid out the groundwork to provide critical investments to expand Americans’ access to mental health services. Under the plan’s guidance, policy makers are striving to make mental health services accessible to everyone whenever and wherever they need it [5].

The mental health service infrastructure cannot solve the problem unless people start using it. However, the underutilization of mental health service is alarming. The latest report from the nonprofit Sapien Lab shows that 45% of US individuals with a clinical-level mental problem do not seek professional help [6]. The study’s over 45,000 respondents provided many reasons for not seeking professional help. The top barriers include: (1) lack of confidence in the mental health system (37%), (2) lack of knowledge of what kind of help to seek (34%), (3) unaffordability (25%), and (4) stigma for seeking mental health treatment (25%). These barriers call for solutions that can lower costs, improve access, and assist Americans to build confidence in mental health services.

During the pandemic, telehealth has demonstrated benefits of improving access to care, reducing disease exposure, and preserving scarce supplies [7, 8]. Telemental health service uses secure, real-time interactive multimedia technology to connect remote patients with mental health experts. Multiple studies have shown the effectiveness of telemental health and its significant impact on improving patient access, cost efficiency, convenience, and compliance with treatment [9,10,11]. Telemental health is a highly promising approach for alleviating the current American mental health crisis. Despite its huge potential, the wide adoption of telemental health has not taken place. Even during the pandemic, adoption of this technology has been piecemeal and fragmented [12]. Many technological, regulatory, cultural, ethical, and organizational barriers have been found to impede the adoption of telemental health services [7, 12,13,14,15,16] for both providers and patients.

This study focuses on how to improve telemental health adoption from the patient perspective. The aim of this study is to explore the impact of applying different education strategies on individuals’ attitude and intention towards using telemental health services. We synthesize the AIDA (Attention, Interest, Desire, and Action) model [17], the heuristic-systematic model of information processing [18], and social identity theory [19] to develop an integrated variance-process theoretical framework, under which we examine individuals’ mental health seeking behaviors. This study is one of the first to aim to improve individual adoption by exploring the efficacy of different education strategies. The findings of this study can be utilized to develop better strategies for promoting mental health service seeking among the public.

Health Seeking Behavior and Influencing Factors

Health seeking behavior refers to any action undertaken by individuals who perceive themselves to have a health problem for the purpose of finding an appropriate remedy [20]. Mental health seeking behavior is preceded by a decision-making process that is governed by factors that might encourage or discourage an individual from utilizing mental health services [21]. Previous studies have reported that people are likely to seek treatment when they believe the treatment will be effective and perceive few barriers to taking action [22].

Theory of Reasoned Action (TRA) has been widely applied to explain why individuals engage in a certain behavior based on their attitudes and behavioral intentions. Based on TRA, individuals’ behavior is determined by their intention to perform the behavior and this intention is, in turn, a function of their attitude towards the behavior and subjective norms [23, 24]. Attitude refers to individuals’ positive or negative feelings about performing the target behavior. Subjective norms refer to the belief about referent others approval or disapproval of the behavior. It relates to a person’s beliefs about whether peers and people of importance to the person (e.g., family and friends) think he or she should behave in a certain way [23].

Drawing from TRA, Fred Davis [25] created the technology acceptance model (TAM). In TAM, the success of the new technology adoption is based on positive attitudes towards two factors: perceived usefulness and perceived ease of use. Davis [25] defines perceived usefulness (abb: usefulness) as the degree to which an individual believes that using a particular system would enhance his or her job performance. While perceived ease of use (abb: ease of use) refers to the degree to which a person believes that using an innovation would be free of effort [24,25,26,27]. TAM has become a leading model in explaining users’ IT adoption behavior in many fields. Researchers have subsequently developed and extended TAM to create more complicated versions, but the central structure remains unchanged [28]. Therefore, we propose that ease of use, usefulness, and subjective norms are important antecedents of individuals’ attitude, which leads to their intention to use telemental health services.

Besides the factors derived from TRA and TAM, we compiled 10 other potential factors based on a review of the innovation adoption literature (experience and habit, face loss, facilitation conditions, fit with lifestyle, hedonic motivation, price value, relative advantage, security and privacy, stigma, trust) [27,28,29,30,31,32,33,34]. We provided the list to 10 experts (four family doctors, four telehealth adoption researchers, and two mental health counselors) who familiar with telemental health service. We specifically asked these experts to rank the top three potential factors that could influence individual telemental health service seeking behavior. After pooling their votes, trust, stigma, and relative advantage are identified as the top three potential influencing factors. These three factors were further confirmed by a different expert panel consisting of two professors in information systems, two psychiatrists, and one public health scholar.

Trust is “the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control that other party” [35]. As a motivator of health seeking [36], trust in telemental health services will likely lead to positive attitudes toward these services.

Stigma is a social phenomenon characterized by negative attitudes and beliefs towards individuals who are deemed to deviate from societal norms [37]. Stigma affects individuals by making them feel separated from the normalized social order. Such separation implies a process of devaluation and discrimination against the stigmatized group [38]. Considered as an insidious social force in mental health literature [39, 40], stigma is found to have a significant impact on the way individuals see themselves [41]. Mental illness is one of the most stigmatized conditions in our society [42]. Previous research highlights that people are reluctant to access mental health care because of stigma [43, 44].

Relative advantage refers to the degree to which an innovation is perceived to be better than its alternatives [45]. Comparing with traditional face-to-face mental health consultation, telemental health offers increased care access, reduced social stigma, enhanced efficiency, and reduced diagnosis-specific obstacles to treatment [46]. To the degree that individuals perform cost-benefit analysis when deciding whether to seek telemental health services, relative advantage is likely to play a significant role in their judgment and decision-making.

The AIDA Model and Information Processing Model

In the marketing literature, a classic hierarchy of effects model, the AIDA (attention, interest, desire, and action) model implies that consumers move through a series of linear, sequential stages when they make purchase decisions [17]. Since developed in 1898, the AIDA model has been applied in marketing for over a century. It provides a detailed understanding of how individuals move from stage to stage when making decisions. From the cognition (attention/awareness/learning) stage to the affect (feeling/interest/desire/attitude) stage to the behavior (action/usage/consumption) stage [47], the number of prospective customers gradually decreases. This phenomenon is also known as a “marketing funnel” as finally only the real customers go through the funnel [48]. Beyond its marketing value, the AIDA model can be extended to explain human behaviors in general. It could be used to guide the development of a telemental health education strategy to increase awareness, change attitude, and eventually enhance utilization of telemental health services.

While the AIDA model provides a staged view of consumers decision process, it assumes every consumer process information in the same way, which is not realistic. According to the heuristic-systematic model of information processing (HSM), individuals process messages in two ways: heuristically or systematically [18]. Heuristic information processing entails the fast and automatic use of simplifying decision rules to quickly assess the message content, employing experiential knowledge learned and stored in memory. In contrast, systematic information processing requires careful and deliberative processing of the message content, which involves holistic, comprehensive analysis and cognitive reflection [49]. Typically, when individuals believe their judgment will have significant impacts (high involvement) and they have sufficient cognitive capability, they tend to engage in a systematic rather than heuristic processing [18].

Social Identity Theory and Two Education Strategies

According to social identity theory (SIT), individuals’ self-concept derives from knowledge of their membership of a social group [19]. Individuals attach value and emotional significance to their group membership. Such in-group favoritism (also known as in-group bias) makes group members favor a person who is in-group while disfavor one who is out-group [50, 51], which influence their perceptions, attitudes, judgments, and behaviors.

Applying the idea of in-group favoritism, an educational strategy is to create an in-group context to implicitly influence individuals’ decision-making process. In this study, we develop two different telemental health education strategies based on social identity theory: peer (in-group) narrated video vs. professional (out-group) narrated video. These two strategies differ in three dimensions: audio (voice tone of a peer vs. a professional), perspective (patient vs. mental health provider), and graphics (more casual for peer perspective vs. more formal for professional perspective). The content of the two videos is semantically equivalent with wording differences to accommodate the different narration perspectives, which includes the definition, advantages, setting, and related procedures of the telemental health service. The voice of the peer narrated video was a student, while the voice of the professional narrated video was a mental health provider.

An Integrated Variance-process Theoretical Framework

Individual telemental health seeking behavior is a complicated phenomenon that can be best understood by integrating both a variance theory and process theory view. Variance theories focus on concepts, explain a dependent variable’s variances based on one or more independent variables (why something happens), while process theories focus on the relationships or sequences of events (how something happens) [51,52,53,54,55]. In this research, we apply TRA, TAM, and SIT as variance theories and the AIDA model and HSM as process theories. We integrate these theories to create an overarching theoretical framework to fully understand how individuals decide whether to adopt tele-mental health services and how to facilitate their adoption decision.

As Fig. 1 shows, taking the process-based view (AIDA), the telemental health adoption is a process moving from the cognition/education to affect/attitude to behavior stage. Taking the variance-based view (TRA, TAM), the variance of telemental health adoption can be explained by variables such as usefulness, ease of use, subjective norms, stigma, trust, and relative advantages. In addition, HSM and SIT can be applied to explain the mechanism underlying the influence of different education strategies, suggesting that individuals perform different information processing when they receive different educational stimuli. In the meantime, the process view provides a scheme that guides how factors in one stage influence factors in the next stage. Applying this theoretical framework, we can evaluate the impact of using different education strategies on individuals’ attitude toward and intention for using telemental health services and explore the potential information processing mechanisms.

Fig. 1
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An integrated variance-process theoretical framework

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Based on our research objective and the integrated theoretical framework, our overarching hypothesis is (1) that attitude is positively associated with intention to use telemental health services and (2) that attitude is influenced by a number of individual factors (usefulness, ease of use, subjective norms trust, relative advantage, stigma). Further, the strength of the effect of the individual factors is mediated by the form and method through which education about that factor is delivered. We hypothesize that factor knowledge delivered by an in-group peer will generally have a stronger effect on attitude than that delivered by an out-of-group expert, although this effect may vary by individual factor. The following specific hypotheses will be tested.

H1: (a) Ease of use, (b) usefulness, (c) subjective norms, (d) trust, and (e) relative advantage have a stronger positive effect on attitude towards telemental health services for individuals receiving the peer-narrated video education group than for those receiving the professional-narrated video education.

H2: Stigma has a stronger negative effect on attitude towards telemental health services for individuals receiving the peer-narrated video education group than for those receiving the professional-narrated video education.

H3: Attitude is positively associated with intention to use telemental health services.

Method

Procedure

We conducted a survey experiment study one month before a new telemental health service was launched at a major historically black university in a rural area of the southeast region in the US. The academic affairs office of the university facilitated the recruitment of both undergraduate and graduate students to participate in the survey. Invitations were sent via email to the student body, and on-campus events were organized to encourage participation. Survey barcode displays were also placed at various student activity events to raise awareness. Incentives (the chance to win an iPad or iWatch) were offered to participants. The University has an estimated student body of 4,000 individuals (both online and on campus), and our survey managed to gather 282 valid responses.

We developed two educational videos (peer-narrated vs. professional-narrated) based on the in-group and out-group contrast. We randomly assigned participants into one of two conditions: exposure to the peer-narrated video or exposure to the professional-narrated video). Once participants entered the survey site, they were greeted and requested to read and agree to the consent form. Anonymity and confidentiality were assured at the beginning of the survey experiment. They then watched the randomly selected educational video. After that, they needed to complete a survey to report their perceptions of the telemental health service and their attitude and intention to use it, as well as demographic and general mental health status information. Finally, they were asked to leave their contact information on a separate webpage that is not linked to their survey responses if they wanted to enter the prize drawing. Two randomly drawn Apple product awards were used as incentives for students’ participation.

Our study was conducted in March 2022 and lasted for 11 days. We collected 282 completed responses. The respondents’ ages ranged from 18 to 63 (mean: 25.28, SD: 9.442).Footnote 1 According to gender at birth, 84 were male (29.8%), 198 were female (70.2%). Most of them were Black/African Americans (201, 71.3%), 54 were White/Caucasian (19.1%), the rest included American Indian/Native American, Asian, Hispanic, Native Hawaiian/Pacific Island, and other race categories.

Video Development

Two educational videos were developed which contained the same content related to telemental health service. The videos briefly describe the necessity, process, and advantages of telemental health services. At the start of each video, the narrator made her perspective evident. In the peer-narrated video, the narrator explicitly stated, “…I am a college student…” On the other hand, in the professional-narrated video, the narrator clearly indicated, “…I am a mental health provider…” The peer-narrated video was narrated by a female university-system student,Footnote 2 while the professional-narrated video was produced by a female mental health professional. The graphics are different in two videos. We designed the peer-narrated video to have a more casual aesthetic, while the professional-narrated video in a more formal style. The two videos were of similar length, with the peer-narrated video lasting one minute 18 s and the professional-narrated video one minute 11 s. The scripts and screenshots of both videos can be found in Appendix A.

Variables and Measures

Except stigma, the measures for all the constructs were adapted from the literature. Each item was assessed by a five-point Likert scale anchored from 1 for “strongly disagree” to 5 for “strongly agree.” Some modifications were made to align the items with the telemental health context. Table 1 shows the detailed measures for the seven constructs (ease of use, usefulness, subjective norms, trust, relative advantage, attitude, and intention) and their sources. To keep our survey instrument concise, we self-developed a three-item scale to measure stigma by gauging different components of stigma [56]. The first item, “seeking telemental health service is considered strange by my peers,” evaluates the degree to which a person is labelled and separated into a special category. The second item, “seeking telemental health service is considered a spiritual or moral weakness by my peers,” assesses stereotyping induced by dominant cultural beliefs that link a person to undesirable stereotypes. The third item, “my peers think it’s shameful to seek telemental health service,” assesses status loss suffered by a person who is stigmatized. The face validity of the stigma items was approved by an expert panel before the data collection. Eleven control variables (age, gender, grade, GPA level, IT experience, work experience, student type, marital status, children, previous mental health experience, and depression) were collected in the survey.

Table 1 Variable measurements
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Results

Assessment of Biases

We assessed potential nonresponse bias associated with our data. Following Armstrong and Overton [61], we compared the demographic variables for respondents between the first three days (day 1–3) and last three days (day 9–11). ANOVA show that the two groups do not differ in age (p = .088). Chi-square tests show that the two groups do not differ in gender (p = .104) and race (p = .835). These results suggest that nonresponse bias is not likely to exist.

In addition, common method variance (CMV) is often associated with self-reported survey data and could lead to biased findings [62]. We took two approaches to evaluate the influence of CMV. First, we carried out the Harmon’s one factor test by following Podsakoff et al. [62]. The items of the eight theoretical constructs were entered into a principal component analysis. Eight factors were extracted and the first factor of the unrotated solution explains only 38.49% of the total variance, showing no indication of CMV. Second, we employed the correlational marker variable technique to assess CMV. Following Lindell and Whitney [63] the second smallest positive correlation amongst measurement items (r = .003) was selected as a conservative estimate of CMV. All the between-item correlations were adjusted by partialling out the CMV estimate. Results revealed that the correlations only changed slightly in magnitude and remained unchanged in significance, suggesting that CMV is unlikely a concern.

Measurement Evaluation

We evaluated the validity and reliability of the measures. The validity of the measures was tested using principal component analysis. The loading of each item on its substantive construct is over 0.45 (see Table 2), greater than the threshold (0.35) recommended by Hair et al. [64] when sample size exceeds 250, suggesting sufficient convergent validity [65, 66]. In addition, each item’s factor loading is much higher than its cross-loadings on other constructs, confirming the sufficiency of discriminant validity [64]. The reliability of the measurements was examined using Cronbach’s alpha coefficients. As Table 3 shows, all reliability scores exceed Nunnally’s [67] recommended cutoff of 0.70. Our measurement evaluation results indicate the measurements have adequate reliability and validity.

Table 2 Factor loadings
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Table 3 Reliability analysis results
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Group Comparison

We conducted ANOVA to compare the means of attitude and use intention between the two groups (peer- vs. professional-narrated video groups) and found no significant differences (Attitude: peer video group mean = 4.119; professional video group mean = 4.056; p = .517. Intention: peer video group mean = 4.017; professional video group mean = 3.838; p = .113). This suggests that the two educational strategies are equally effective in influencing respondents’ attitude and intention.

Hypothesis Testing

We conducted ordinary least squares (OLS) regression on the entire sample first. The results (Table 4) show that attitude towards telemental health is significantly influenced by four factors: subjective norms, trust, relative advantage over physical visit, and stigma. We then split the sample based on the group assignment: peer-narrated video or professional-narrated video. A regression was conducted on each subsample. As the results show, for the peer video group, subjective norms, trust, relative advantage, and stigma continue to be significant determinants of attitude. In addition, ease of use becomes significant. In contrast, for the professional video group, only trust and relative advantage are significant. Subjective norms, stigma, and ease of use are all insignificant. These results provide support for H1a, H1c, and H2, but not for H1b, H1d, and H1e.

Table 4 Regression results (DVs: attitude and intention)
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We ran another regression with behavioral intention as the dependent variable. As Table 4 shows, attitude is significantly related to intention, controlling for all other variables. These results suggest that attitude is the most important predictor of students’ intention to use telemental health services, supporting H3. This is consistent with TRA [23] and the AIDA model [47], both positing that attitude leads to behavior. Attitude possibly mediates the effects of subjective norms, trust, relative advantage, and stigma. While the effect of trust is partially mediated, the other three’s effects are fully mediated. Among all 11 control variables, only gender has a positive significant impact on intention to use telemental health service, suggesting that females (coded as 1) have higher adoption intention than males (coded as 0).

Discussion

The findings show that after students watched the peer-narrated education video, their attitude was influenced by five variables: relative advantage, trust, subjective norms, stigma, and ease of use. In contrast, only two variables (relative advantage and trust) significantly influenced their attitude after they watched the professional video. A plausible explanation for the group difference based on SIT [50, 51] is that when the video was narrated by a peer voice from the students’ own perspective, the students are more identified with the narrator and more likely to absorb the information conveyed by the video, think about the social ramifications of telemental health use, and realize that their telemental health use could be noticed by their peers who could show prejudice against it. As a result, they tend to agree that the telemental health service is easy to use, subjective norms are important, and stigma will be a concern. However, when the video was narrated by a professional voice from the health provider’s perspective, the students would treat the presenter as an outsider from their social group. As a result, they are less likely to believe the claims of telemental health’s ease of use, more likely to think about telemental health’s scientific value rather than its social ramifications, and less likely to realize the potential stigma because there are no cues to trigger such thoughts. Our results suggest that the students considered a broader set of information on making judgment after watching the peer-narrated video than after watching the professional-narrated video. Although our data analysis cannot provide direct evidence, it seems that HSM [18] can offer a reasonable interpretation, that is, systematic information processing was associated with the peer-narrated video whereas heuristic information process was associated with the professional-narrated video.

Individuals using heuristic information processing are likely to agree with a message without fully analyzing the message’s semantic content [68]. The marketing literature suggests that encouraging consumers to use heuristic information processing helps to improve their attitude and increase their purchase intention. However, this wisdom may not be applicable in the telemental health context. When selecting persuasive strategies for patients to adopt telemental health, the priority should be given to the one that enables patients to take all relevant information into consideration in a comprehensive manner to avoid biases and misunderstanding. Because of the significant impact on personal well-being, individuals have the rights to be informed to the maximum extent so that a mindful decision can be made that is the most suitable for their specific situation [69, 70].

Study Limitations

This study has a few limitations. First, to keep our survey concise, we self-developed a short scale for stigma. Stigma is a complicated concept, and the well-adopted Perceived Devaluation and Discrimination Questionnaire for stigma consisting of 12 items maybe more suitable to gauge stigma [71]. Second, we explored two different education strategies from the social identity perspective. More perspectives can be considered to design innovative education strategies, and more sophisticated experiments such as factorial design can be conducted in future studies. Third, the nature of the survey experiment makes it difficult to evaluate whether the education strategy can actually change human behaviors. A longitudinal design is needed if actual behavior change is of interest. Fourth, given that only respondents who agreed to participate in the study can be included, a sampling bias is inevitable. A possible remedy is to apply a stratified sampling strategy based on major demographic factors of the students. Respondents’ personability traits such as openness to innovation could also introduce biases. Future research should measure relevant personality traits and control for their influences. Finally, our study population are college students whose higher education could influence their acceptance of medical innovations. Caution should be exercised when attempting to generalize our findings to a general population with lower educational levels.

Conclusion

We conducted a survey experiment study to explore the impact of using different education strategies on individuals’ attitude towards and intention to use telemental health services. The findings suggest that individuals’ attitude was influenced by a broader set of variables after watching the peer-narrated education video, but by a narrower set of variables after watching the professional-narrated education video. Though the results cannot conclude which education strategy is optimal, it demonstrates the importance of understanding the impact of using different education strategies and calls for future research on developing appropriate telemental health education strategies. As one of the early studies on understanding the importance of telemental health education strategy, this study draws attention to the design of educational and marketing materials as they are essential to improving the utilization of telemental health services. It builds a theoretical foundation for illustrating the nuanced differences in individuals’ information processing when exposed to different educational materials, thus facilitating further exploration of the complicated health seeking behavior for telemental health. Moreover, this study was conducted at a historically black university. Most of our participants are African American students. Previous studies indicate that there are disparities in the US that disadvantage racial minorities in mental health services [72, 73]. The results of this study can be applied to implementing appropriate advocating strategies to address such disparities.

Appendix A. Educational Video Scripts and Screenshots

Peer-narrative Video Scripts

Hi, I am a college student who experiences environmental, financial/economic, social, political, academic, and social media related problems. These are the main contributing factors to mental health issues. Traditionally, at ECSU, students consult with an on-campus counselor to determine the level of support they need. Depending on this level, ECSU will oftentimes need to consult with a hospital to provide the student with a mental health provider or psychiatrist. Unfortunately, this process takes a lot of time. The newer approach shortens the treatment time through telemental health service. A behavioral health provider, or BHP for short, will be connected with a patient via secure, real-time technology. The BHP will then establish a diagnosis and treatment process to communicate with the primary care provider. Not only does the new approach shorten the treatment time, but it also helps to improve access to behavioral health care, reduces the need for trips to the emergency room and inpatient admissions, results in improved compliance with treatment, reduces delays in care, reduces the barrier of stigma, and improves continuity of care and follow-up.

Fig. A1
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Screenshot of the peer-narrated video

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Professional-narrated Video Scripts

Dear ECSU student, I am a mental health provider at East Carolina University (ECU), and ECSU is collaborating with ECU to bring telemental health services to your campus, and I am your mental health provider. College students’ mental health is affected by many factors, including environmental, financial, social, political, and academic pressures, and social media. The decline in mental health can affect students’ functioning. Traditionally, at ECSU, students are referred to an outside hospital to see a mental health provider or psychiatrist. Unfortunately, this process takes time, which can delay the much-required treatment. This new approach shortens the time to see a mental health provider or psychiatrist through telemental health. You can connect with a mental health provider via secure real-time audio technology. The telepmental health provider performs a thorough mental health evaluation, establishes the diagnosis, and creates a treatment plan, which will be communicated with your primary provider. There are several advantages to this new approach; it improves access, shortens treatment time and delays in care, reduces the need for trips to the emergency room and inpatient admissions, results in improved compliance with treatment, reduces the barrier in stigma, and improves continuity and follow-up.

Fig. A2
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Screenshot of the professional-narrated video

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