Intention to use digital-based psychosocial counseling and its predictors among students in University of Gondar,Northwest Ethiopia,2023: Using modified unified theory of acceptance and use of technology model

Abstract

Background:

Psychosocial problems are widely affecting the population around the world and worsening in university students. Timely access to psychosocial counseling and support remained challenging. Hence, implementing a digital-based approach to psychosocial counseling services could potentially improve access and efficiency for students in need. However, limited evidence on the intention to use digital-based psychosocial counseling and its predictors among university students in Ethiopia. This study aimed to assess the intention to use digital-based psychosocial counseling and its predictors among students at the University of Gondar, Northwest Ethiopia.

Methods:

A cross-sectional study was conducted on 759 students from 28 March until 28 April 2023 at the University of Gondar, Northwest, Ethiopia. Proportional allocation was done, and the participants were selected using stratified random sampling. The median score was used as a cutoff point. The student who scores median and above on intention to use construct was considered as intended to use digital-based psychosocial counseling otherwise unintended. The interviewer-administered questionnaire, adapted from the unified theory of acceptance and use of technology model and kobo collect app, was employed to collect the data. Data were exported to SPSS 20 for descriptive analysis and Analysis of Moment and Structure version 23 for structural equation modeling analysis to assess the significance of relationships between variables using a path coefficient and a p-value of <0.05.

Results:

A total of 750 (98.8%) responded to complete all the questionnaires. The proportion of intention to use digital-based psychosocial counseling was 54.9%, (95% CI: 51.3%–58.5%). A structural equation modeling shows that performance expectancy (β = 0.510, p-value < 0.001), effort expectancy (β = 0.082, p-value = 0.003), and social influence (β = 0.307, p-value < 0.001) had a direct relationship with intention to use digital-based psychosocial counseling. Gender (β = 0.545, p-value < 0.001) was moderate performance expectancy.

Conclusion and recommendation:

The study has shown a promising intention to use digital-based psychosocial counseling among university students. Performance expectancy, effort expectancy, and social influence had a positive effect on the intention to use digital-based psychosocial counseling. Thus, the implementers need to give priority to enhancing and creating awareness for students related to how to use digital-based counseling.

Keywords

Digital counseling psychosocial support UTAUT intention to use student Ethiopia

Introduction

Psychosocial well-being is the state of the physical, mental, emotional, social, cultural, and spiritual determinants of health.¹ Currently, mental illness is a major public health issue in Africa.² In Ethiopia, the number of students experiencing psychosocial issues on campus has increased recently, yet mental health remains one of the least prioritized health programs at higher education institutions.³ According to a study conducted by universities in Ethiopia prevalence of mental distress among medical students ranged between 30% and 40.9%.^4–6

Nearly students who have psychosocial health problems do not obtain psychological guidance and counseling services due to the frustration of social stigma and discrimination.^7,8 Hence, implementing a digital-based approach to psychosocial counseling services could potentially improve access and efficiency for students in need. Digital counseling is not only feasible but also provides a special elasticity of communication that takes into account several aspects, such as flexibility of location and time as well as the flexibility to engage through email, SMS, live instant chat, video, or telephone depending on your needs and with what you feel most comfortable.^9–11 The primary focus of these services is on the psychosocial needs of the students, particularly those with academic, social, economic, psychological, psychiatric, and other related problems linked with major substance misuse, stress, anxiety disorders, depression disorder, attempt to suicide, suicide, comorbidity, and high healthcare expenditures.¹²

From low-and middle-income nations, Ethiopia is among the highly burdened countries with stigma and discrimination related to mental health.¹³ On the other hand in Ethiopia, there are a limited number of healthcare workers available to provide health services, mental disorders are not considered to be life-threatening problems, mental health services are not given attention, and the needs of people for mental health care are not met.¹⁴ In addition to this mental health services are included in the national health policy of Ethiopia, interventions against the problems are very limited. Furthermore, the lack of evidence-based information about mental health issues contributes to the insufficient provision of mental health services.¹⁵ It leads to physical and psychological impairment in university students.

As a result of recent advancements in information and communication technology, new healthcare innovations have emerged in Africa. Nowadays, more and more young people turn to the Internet for information, entertainment, and social connection.¹⁶ So, employing digital-based psychosocial counseling is very crucial as, a teletherapy approach. It connects counselors and students at any time and anywhere.¹⁷ Despite the benefits of digital-based counseling, the student’s intention to use online counseling services varied by country.^18,19 Evidence showed that the gap between the intention of students to seek online therapy for academic-related issues is determined by factors of their views regarding asking for professional assistance, their prior counseling experiences, and their level of academic stress.²⁰ Currently, the utilization of digital-based psychosocial counseling is not yet widespread, and it is limited to early adopters.²¹

Digital-based psychosocial counseling is used to solve some of the healthcare challenges experienced in developing countries including Ethiopia, which range from a high prevalence of mental illness, fragmented healthcare infrastructure, unattractive counseling centers, and a limited number of healthcare workers. Similarly, providing counseling for students by healthcare providers is very time-consuming and needs high cost, and scheduling conflicts may make it difficult for students to meet face-to-face counseling sessions in a university setting where they are highly busy. They might also find that digital-based therapy better fits their busy lives.²² Young people or students face numerous stressors, from academic pressures to social relationships and career uncertainties. Good psychosocial health equips them with coping mechanisms, reducing the risk of mental health issues like anxiety, depression, and burnout. Youths who might not seek out traditional therapy can be reached by digital counseling, increasing access, and normalizing mental health care. This strategy can lessen the strain on healthcare services and improve long-term mental health as a preventive step.

A successful digital counseling implementation can have a major positive economic impact, especially by lowering healthcare expenses and raising student academic performance. Before mild-to-moderate psychological problems deteriorate and necessitate more involved, expensive therapies, digital therapy can assist manage them by providing early support and prompt intervention. The views of Ethiopian students regarding digital counseling can be influenced by a number of cultural and societal issues, which can also affect their intention to use it. However, the implementation and continual usage depend on end-user behavior intention, age, sex, performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC) are the main predictors affecting behavioral intention to use digital-based psychosocial counseling.^23,24

In Ethiopia, digital-based psychosocial counseling is one of the major information communication technology (ICT) initiatives planned by the Ministry of Health.²⁵ To achieve national digital health system integration and implementation, the Ethiopian Federal Minister of Health is planning to create and form a national digital health plan, which includes digital-based psychosocial counseling to provide accessible psychosocial health services. However, there is limited scientific evidence that could inform digital-based psychosocial counseling system implementation and scale-up in higher education. Therefore, creating a good awareness among the students about digital counseling could overcome the challenges emanating from the face-to-face counseling system.

As much as my literature’s searching capacity, there is limited study on students’ intention to use digital-based psychosocial counseling to manage psychosocial health problems in Ethiopia using the unified theory of acceptance and use of technology (UTAUT) model. This study will also try to provide evidence of individual perceptions and reasons for intention to use digital-based psychosocial counseling in Ethiopia.

The theoretical background of the model and hypothesis

The UTAUT is one of the well-known theoretical frameworks that are widely and practically implemented in numerous ICT applications.²⁴ This model was extracted from the eight previous theoretical models that include the theory of reasoned action (TRA), Social Cognitive Theory, Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), Motivational model, Model of Personal Computer Utilization, Combined TAM and TPB (C-TAM-TPB) and innovation diffusion theory.^23,24 The reason chosen UTAUT model has been shown to explain up to 70% of the variance in the behavioral intention to use technology, making it more powerful than its predecessors.

The UTAUT model predicts that the behavioral intentions of users will influence how they accept and use technology. It is vital to select the appropriate model as a theoretical base to better describe user behavior toward the technology. This study proposes a theoretical framework based on the UTAUT to examine the intention to use digital-based psychosocial counseling because of the higher explanatory power.^24,26

PE, SI, EE, and FC, as well as individual factors like age, the voluntariness of user experience, and gender were presented as four constructs to influence users behavioral intention to adopt new technology.²⁴

We need an empirically verified model in Ethiopia to determine the essential predictors of implementation and enhance users’ behavioral intention to use digital health systems such as digital-based psychosocial counseling. The conceptual research model has three parts. Four exogenous variables from the UTAUT are included in the first part: PE, EE, SI, and FC.

The second part of the model is composed of the endogenous behavioral intention variable. User behavior, which was considered a dependent variable in the original UTAUT model, was not measured in this study because the suggested technology is not present in Ethiopia. Because of this, user behavior was not included as a dependent variable in the adapted UTAUT model.²⁴ The third part thus consists of moderators, which have an effect on both exogenous and endogenous variables including age, gender, the voluntariness of use, and experience. Voluntariness of use and experience, which was considered a moderator effect in the original UTAUT model, was not measured in this study because the suggested technology is not present in Ethiopia.^24,27

Moderating effect of intention to use digital-based psychosocial counseling

Moderator is the ability to change the strength, direction, and other aspects of the relationship between exogenous and endogenous variables.²⁸ The variables of sex and age have an impact on the direction or strength of the link between the exogenous and endogenous variables in the UTAUT model of context.²⁹

Moderating effect of age

Social norm and attitudes’ behavioral intention to use psychological cybercounseling was moderated by age in a study done in China.¹⁸ In another study in Chinese, different age groups have distinct moderating effects on effort expectancy and behavioral intention to use technology.³⁰ In another study in the USA³¹ and Indonesia,²⁴ age influences the effects of PE, EE, and SI on behavioral intention to use health information technology.

Moderating effects of gender

According to a study performed in China using the E-TPB model, gender has moderating effects on the intention to seek online counseling.³² In another study, the Chinese discovered that sex influences the relationship between EE and behavioral intention to use the healthcare system.³³ Another Malaysian study discovered that gender had moderating effects on behavioral intention to use online counseling.³⁴ Another study in Australia discovered gender differences in online emotional expressiveness.³⁵ In another study conducted in Asia, men and women exhibited varied views toward getting psychological aid, with women having a more positive attitude.³⁶ Comparison research conducted in Indonesia revealed that gender had no effect on the results at all.³¹

Factors affecting intention to use digital-based psychosocial counseling

The intention to use digital-based psychosocial counseling is affected by a variety of variables, including PE, EE, FC, and SI.^24,37

PE: Measures how much a person believes that using the system would help them improve their job performance. It is one of several factors that influence the intention to use digital-based psychosocial counseling.^24,38 Behavioral intention to use digital-based psychosocial counseling is positively correlated with PE in the USA.²⁹ In another study in Ghana,²³ and Indonesia³⁹ PE positively influence the intention to use digital-based counseling. Therefore, to test the effect of PE on behavioral intention, the following hypothesis is proposed:

H1a: PE will have a positive influence on student intentions to use digital-based counseling.

H1b: Sex will moderate the influence of PE on student behavioral intention to use digital-based counseling.

H1c: Age will moderate the effect of PE on student behavioral intention to use digital-based counseling.

EE: Measures how simple a system is to use.²⁴ Studies conducted in the Netherlands,⁴⁰ China,¹⁸ the USA,²⁹ and Ethiopia⁴¹ all found that EE positively influences students’ behavioral intention to use digital-based counseling. Comparable studies were done in Ghana,³⁷ and Indonesia³¹ EE does not influence people’s intentions to use online therapy. To test the effect of EE on behavioral intention, the following hypothesis is proposed:

H2a: EE will positively influence students toward the intention to use digital-based counseling.

H2b: The influence of EE on students toward intention to use digital-based psychosocial counseling was moderated by sex.

H2c: Age will moderate the effect of EE on student intentions to use digital-based counseling.

SI: The degree to which a person believes other people must believe they should use the new system.²⁴ According to studies done in the Philippines,³⁴ the USA,²⁹ the Netherlands,⁴² China,¹⁸ in Ghana³⁷ SI has a positive influence on the intention to use digital-based counseling. In a comparable study done in Indonesia, using digital-based psychosocial counseling is not influenced by SI.³¹

To test the effect of SI on behavioral intention, the following hypothesis is proposed:

H3a: SI will have a positive influence on student intentions to use digital-based counseling.

H3b: Sex will influence the SI on student intentions to use digital-based counseling.

H3c: Age will moderate the effect of SI on student intentions to use digital-based counseling.

FC: The degree to which a person believes that the system’s organizational and technical infrastructure is enough to support the use of the system.²⁴ According to a study done in China¹⁰ and the Netherlands⁴² FC are positively related to the intention to use digital-based counseling. According to a comparative study conducted in Ghana²³ and Indonesia,³¹ facilitating conditions do not influence the intention to use digital-based counseling. To test the effect of FC on behavioral intention, the following hypothesis is proposed:

H4a: FC will positively affect students toward intention to use digital-based psychosocial counseling.

H4b: Sex will influence the impact of FC on students’ intentions to use digital-based counseling.

H4c: Age will moderate the effect of FC on students’ intentions to use digital-based counseling.

So, the final proposed model of this study is presented as follows in Figure 1.

Figure 1.

Adapted UTAUT model source.^{23,37,41,43,44}

Methods

Study design

In this study, we employed a cross-sectional study design to assess students’ intention to use digital-based psychosocial counseling and its predictors at the University of Gondar.

Study area and period

The study was carried out at the University of Gondar which is located in the historic city of Gondar in the Amhara region. UOG is one of the oldest and most well-established higher education institutions in the country and is located 737 km far from Addis Ababa, which is the capital city of Ethiopia. It has five campuses: Maraki, Atse Tewodros, Ate Fasil, Teda, and College of Medicine and Health Sciences. Currently, 11 academic units: the College of Medicine and Health Science, College of Natural and Computational Science, College of Informatics, College of Social Science and Humanities, College of Business and Economics, College of Veterinary Medicine and Animal Science, College of Agriculture and Environmental Science, College of Education, Institute of Technology, Institute of Biotechnology, and School of Law. Nine thousand and eighty students were registered at the University of Gondar for the 2023 academic year, according to the registrar’s office report. This study was conducted from 28 March until 28 April 2023.

Source and study population

Source population

The source population of the study was all regular undergraduate students of the University of Gondar.

Study population

The study population was all students enrolled as regular undergraduate students at the University of Gondar during the data collection period.

Inclusion and exclusion criteria

Inclusion criteria

The study included all students who were active in their academics and were present on campus at the time when the data was collected.

Exclusion criteria

Students with special needs were excluded from the study, as it needs to establish a disability-tailored digital counseling system for the various disability types.

Sample size determination and sampling procedure

Sample size determination

Structural equation modeling (SEM) involves a number of activities, such as model identification, model estimation, model evaluation, model modification, and model specification.⁴⁵

Model specification

The model specification uses graphic conceptual models to communicate the assumptions. These models give a graphical representation of the essential theoretical variables and the expected relationships among them.⁴⁶ Before the hypothesis is established, executing the estimate depends on the model specification, which starts with construct selection. After reviewing the literature on the UTAUT models the construct an intention to use, PE, SI, EE, and FC were found essential constructs to answer the research objective. However, in setting the number of items that measure the construct it should fulfill the assumption of multiple measurements of the structural model. From the literature, we found and set the number of items for PE, EE, and FC with four items each. Whereas an intention to use, and SI were established with three items each. These constructs were used to develop the model and the causal pathways in the proposed model, which were based on prior theoretical findings.^{23,37,41,43,44}

Identification of the model

The aim of model identification is to solve the model or estimate unknown parameters. Based on calculations using AMOS version 23, the suggested model contains 70 parameters in total (46 free parameters and 24 fixed parameters). The free parameter is made up of 46 free parameters in total: 23 fixed values, 6 covariances between independent variables, 13 load factors between latent and its indicator, and 4 regression coefficients between exogenous and endogenous. Eighteen fixed error terms, one fixed disturbance, and five fixed factor loadings are examples of fixed parameters. The model needs to be over-identified in SEM in order to do an estimate. A degree of freedom more than zero is required to declare the model overidentified. The result of the AMOS software or manual computation can both yield a degree of freedom. By reducing the number of free parameters from the sample moment, one can manually obtain the degree of freedom. But the sample moment was determined by using all of the model’s components or indicators. In our study, there are 18 items (3 items from BI, 3 items from SI, 4 items from PE, 4 items from FC, and 4 items from EE).

\begin{array}{l} S a m p l e m o m e n t = (\begin{array}{l} n u m b e r o f i t e m s \\ (n u m b e r o f i t e m s + 1) / 2 \end{array}) \\ = 18 (18 + 1) / 2 = 171 \end{array}

\begin{array}{l} D e g r e e o f f r e e d o m = s a m p l e m o m e n t \\ - f r e e p a r a m e t e r s = 171 - 46 = 125 \end{array}

The suggested model is therefore overidentified. Determining up the path’s direction was the next stage. In this paradigm, the path has a single direction. Recursive is the unidirectional model. The study’s sample size was determined by applying the SEM’s thumb rule. For one free parameter, the most popular heuristic is that it takes ten observations.^45,47

The minimal sample size is determined by the number of free parameters in the hypothetical model; a ratio of 1:10 for participants to free parameters to be estimated has been proposed.⁴⁶ Therefore, with participants at a free parameter ratio of 10 and 46 parameters to be estimated using the proposed model, a minimum sample size of 759 is required. It accounts for the 1.5 design effect and the 10% non-response rate. The ultimate sample size then rises to 759. Mathematically: Fn = ((FP * R) + nr) * d, Fn = ((46 * 10) + 10%) * 1.5, Fn = (506) * 1.5), Fn = 759

where Fn: final sample size, FP: free parameter to be estimated, R: ratio of respondents to free parameters to be estimated and nr: non-response rate using 10%, d: design effect using 1.5.

Sampling procedure

Study participants were selected from out of 9080 students currently enrolled at the University of Gondar, 759 students from all 5 campuses were drawn by using a stratified random sampling technique. The required number of samples from each campus was determined by proportional allocation the ratio of the total number of samples times the total number of students in each campus to the number of students in the university (9080) with the final sample size (759). Then the sampling frame was prepared for each campus by having lists of students from the main registrar’s office. Finally, the study subjects of each campus were selected using OpenEpi random program version 3.

Measures

Endogenous variable

Intention to use the digital-based psychosocial counseling.

Exogenous variable

Socio-demographic characteristics of the students

Age, gender, education, prior residence, study year, the field of study, mobile device, social media use, and internet access, PE, EE, SI, and FC.

Intention to use means, in this study refers to the likelihood of students intended or not intended to use digital-based psychosocial counseling if they were offered the construct had 3 items and each was measured by a 5-point Likert scale response (1) strongly disagree, (2) disagree, (3) neutral, (4) agree, (5) strongly agree.^23,41 The median score was used as a cutoff point. The student who scores median and above on intention to use construct was considered as intended to use digital-based psychosocial counseling otherwise unintended.

Data collection tools and procedures

Data collection tools

In this study, we applied a common questionnaire, which is adapted from various studies on the UTAUT model.^24,31,48,49 A well-structured questionnaire was written in English version and changed to Amharic because it is the common language among the University of Gondar students. The questionnaire consists of two parts. The first part focuses on the socio-demographics of students and has 8 items, and part 2 contains 18 positive statements that symbolize the constructs included in the UTAUT model. There are a total of 26 items in the questionnaire, and all the items used to measure the constructs were measured by using a Likert scale ranging from 1 to 5 (1 = strongly disagree, and 5 = strongly agree).^24,35 A case scenario was prepared for students who might not know about digital-based psychosocial counseling during the data collection period (Supplemental Table 1).

Data collection procedures

In this study, data was collected by mobile-based kobo collect version 2022.4.4. The interviewer-administrated questionnaire technique was employed under the supervision of one MPH supervisor and data was collected by one BSC/HI professional, and two BSc/psychiatry nurses. Data collectors and supervisors received a 1-day training on the purpose of the research and how to gather data. Before the survey, trained investigators explained to the respondents about digital-based psychosocial counseling technology for psychosocial health services to help them understand the significance of the survey questions, and they either accepted or refused to participate in the study. Those who didn’t give their consent were thanked for their participation.

Data quality control

Supervisors received 1 day of training before the start of data collection on the study objectives, data gathering techniques, data collection tools, respondent approach, data confidentiality, and respondent rights. The correctness and comprehensiveness of the surveys were checked by the managers each day. Before analysis, the data was cleaned up and cross-checked. Although a questionnaire is a common tool, and back-translation method was utilized to translate from English into Amharic with the same meaning to original in both versions then translated again the questions back into English by language experts with professional. This step was important to ensure that the participants understood the survey questions.

To assure the quality of data, a pretest was done on 5%³⁸ of the sample size among students who were studying at Deberetabor University before actual data collection. The Cronbach’s alpha result value of the pretest on the PE, EE, SI, and FC were above 0.7 and the internal consistency of the construct was achieved. However, minimal modifications of the questionnaire for readability and understandability were made. To prevent data loss, data backup procedures were carried out, such as storing data in different locations and making hard and soft copies of the data.

Data processing and analysis

Prior to analysis, the data were manually coded and cleaned. They were then exported to the statistical package for social science (SPSS) version 20 program, and the measurement model and structural model assessment were examined using AMOS version 23. We used the Maximum Likelihood Estimator to estimate the measurement model and the structural model. The socio-demographic data and the magnitude of intention to use digital-based psychosocial counseling were analyzed descriptively using SPSS, and the result is presented using a frequency table. The proportion of intention to use digital-based psychosocial counseling for psychosocial health service among students was computed descriptively, and the finding was presented using a pie chart. The Kaiser-Meyer-Olkin (KMO) test is a measure of how suited your data is for factor analysis.⁵⁰ The test measures sampling adequacy for each variable in the model and for the complete model. KMO values less than 0.5 indicate the sampling is not adequate.⁵⁰ The KMO value of our model was 0.944. An additional assumption was that every latent variable in the structural equation model was measured using multiple measurements; three or more observable variables were to be employed. The assumption that at least three indicators were employed for each of our unobservable variables was met.

Normality analysis

Kurtosis and the critical ratio were used to verify multivariate normality. The data varied from the normal distribution when multivariate normality was computed. A kurtosis absolute value of less than 5, a critical ratio between −1.96 and +1.96, and a Mahalanobis distance of p1 larger than 0.001 were required in order to test multivariate normality.⁵¹ Nevertheless, multivariate normality was not achieved. This outcome means that the maximum likelihood estimate assumption is not met. The bootstrapping methodology, which is becoming more and more popular and a promising approach in several contexts, was therefore employed to manage multivariate none normality. This resampling method can be used to correct fit and standard errors for non-normality in SEM.

Multicollinearity analysis

With a tolerance of more than 0.1 and a cut-off point of less than 5, the variance inflation factor (VIF) was used to assess the occurrence of multicollinearity among independent variables. This study’s tolerance runs from 0.581 to 0.670, and its VIF ranges from 1.492 to 1.721 (Table 1). Additionally, we tested for multicollinearity between exogenous observable variables using the correlation coefficient approach; all Pearson’s correlations were less than 0.8, the recommended cutoff point for multicollinearity. All of the Pearson’s correlations in this study’s results were less than 0.8. The results show that there was no multicollinearity among exogenous variables.

Table 1.

Multicollinearity test for the proposed model.

Exogenous construct	Tolerance	Variance inflation factor
Performance expectancy (PE)	0.581	1.721
Effort expectancy (EE)	0.638	1.567
Social influence (SI)	0.670	1.492
Facilitating condition (FC)	0.598	1.672

Measurement model analysis

Using standardized values acquired from AMOS, confirmatory factor analysis (CFA) was used to investigate the measurement model. As part of the CFA error terms of indicators, a factor loading for every item was evaluated; the factor loading value for every item should be greater than 0.5.⁵² The goodness of fit of the model was evaluated using the Chi-square ratio (<3), the normal fit index (>0.9), the goodness of fit index (>0.9), the adjusted goodness of fit index (>0.8), the root mean square error approximation (<0.08), and the root mean square of standardized residual (<0.08). If there is model misspecification, we either removed the item below the cutoff point (0.5) or applied a large number of modification indices to improve the model fit index until the model was fitted with a threshold value of no more than four times.⁵² To determine the degree of consistency a variable or combination of variables has in what it seeks to measure, as well as the effectiveness with which the chosen construct item assesses the construct, the construct validity and reliability were assessed. Each research construct’s reliability was evaluated using Cronbach’s alpha, with composite reliability surpassing 0.7 and individual construct reliability above the required threshold of 0.7.⁵²

Using the square root of the Average Variance Extracted (AVE) method, we assessed the discriminant validity of the scale items. Based on the Heterotrait-Monotrait (HTMT < 0.9) ratio and the Fornell and Larcker criterion, the square root of AVE for a given construct was found to be greater than its correlation with the other constructs that were being examined.⁵² The value of the HTMT ratio was less than the required limit of 0.9.⁵³

Structural model analysis

The relationship between exogenous and endogenous variables was measured using path coefficient and squared multiple correlations (R²) to evaluate a structural model. A p-value < 0.05 was used to indicate statistical significance.⁵⁴ Multiple-group analysis was used by the moderator, a categorical variable, to change the relationship between the independent and dependent variables.⁵⁵ One additionally assumption was that in unconstrained models, there would be a statistical difference between the groups, and in restricted models, there wouldn’t be. The moderating impacts of predictors among the hypothesized paths inside the basic research model were investigated in this work. The moderator’s influence was determined by computing the p-value and Chi-square difference between the unconstrained and constrained models. p-value < 0.05 and Chi-square (χ²) >5 are the cutoff points. Additionally, the categorical variables of age (⩽21 and >21) and gender (male, female) were employed.

Results

Socio-demographic characteristics

A total of 759 study participants were included in this study with a response rate of 98.8% of the total participants, 495 (66%) of them were males, 475 (63.3%) participants were in the age group of >21 years, and the respondent’s median age was 22 (interquartile range (IQR; 21–23) years. About 734 (97.9%) of the participants had a mobile phone and 704 (93.9%) had access to the Internet. Table 2 provides detailed information about the socio-demographic characteristics of the study participants.

Table 2.

Socio-demographic characteristics of students at Gondar University in 2023 (n = 750).

Socio-demographic characteristics	Category	Frequency (n)	Percentage (%)
Gender	Male	495	66.0
Gender	Female	255	34.0
Age (year)	⩽21	275	36.7
Age (year)	>21	475	63.3
Previous residence	Rural	206	27.5
Previous residence	Urban	544	72.5
Study year	First	90	12.0
	Second	188	25.1
	Third	216	28.8
	Fourth and above	256	34.1
Field of study	Health	182	24.3
Field of study	Non-health	568	75.7
Having mobile phone	Yes	734	97.9
Having mobile phone	No	16	2.1
Social media use	Yes	678	90.4
Social media use	No	72	9.6
Internet access	Yes	704	93.9
Internet access	No	46	6.1

Behavioral intention to use digital-based psychosocial counseling

According to the result of this study among 750 students 412 (54.9%) (95% CI: 51.3%–58.5%) had an intention to use digital-based psychosocial counseling. The median score of intention to use digital-based psychosocial counseling was 11 with an IQR of (9–12), and the maximum and minimum scores for intention to use were 15 and 3 respectively. Figure 2 shows the proportion of intended and not intended use of digital-based psychosocial counseling.

Figure 2.

The proportion of behavioral intention to use digital-based psychosocial counseling in students at the University of Gondar in 2023.

Measurement model assessment

For the path analysis and hypothesis interpretation to be valid, the acceptability of the measurement model was evaluated. Evaluation of the measurement model involves testing the model finesses, internal consistency, convergent validity, and discriminant validity of indicators/items using CFA. To increase model fit, covariate error terms with high modification indices were used; based on their respective highest modification indices, we covariate e7 with e8 and e15 with e16. Figure 3. It illustrates the value of factor loading of the standardized estimate of the measurement model. All the item loading in the CFA have a value greater than 0.5.

Figure 3.

Shows the CFA of behavioral intention to use digital-based psychosocial counseling in students at the University of Gondar, 2023.

Reliability and convergent validity

The acceptance of the measurement model was evaluated for the reliability and validity of constructs and indicators to ensure the validity of the path analysis and hypothesis interpretation. To assess construct reliability, or the internal consistency of the variables, factor loading and AVE (>0.5), Cronbach’s alpha, and composite reliability (>0.7) tests were used. In this study, Cronbach’s alpha and composite reliability values were greater than 0.8. All the factor loading values were found in the range of 0.533 to 0.850 and the AVE value was found in the range of 0.518–0.716. Hence, construct reliability and convergent validity of the measurement model were achieved (Table 3).

Table 3.

Convergent validity of behavioral intention to use digital-based psychosocial counseling.

Constructs	Items	Factor loading	CR	AVE	CA
BI	BI1	0.841	0.883	0.716	0.882
	BI2	0.850
	BI3	0.839
PE	PE1	0.533	0.825	0.548	0.817
	PE2	0.752
	PE3	0.829
	PE4	0.808
EE	EE1	0.818	0.821	0.538	0.816
	EE2	0.837
	EE3	0.605
	EE4	0.620
SI	SI1	0.769	0.812	0.691	0. 813
	SI2	0.732
	SI3	0.752
FC	FC1	0.680	0.809	0.518	0.817
	FC2	0.880
	FC3	0.711
	FC4	0.545

CR: composite reliability; AVE: average variance extracted; CA: Cronbach’s alpha.

Discriminant validity of the construct

The result of this study indicates the square root of the AVE value was greater than the value of the inter-construct correlations. The heterotrait-monotrait ratio of correlation (HTMT) was an additional criterion used to ensure the discriminant validity of the models. Therefore, the discriminant validity of the measurement model is achieved. Table 4. Shows that all of the construct HTMT ratios are less than 0.9, indicating that all constructs are acceptable and useful for further analysis.

Table 4.

Discriminant validity result and root of average variance extracted of the students in the University of Gondar in 2023.

Constructs	PE	EE	SI	FC	BI
PE	0.740
EE	0.635	0.733
SI	0.626	0.509	0.769
FC	0.642	0.638	0.584	0.720
BI	0.722	0.530	0.657	0.650	0.846

The bolded value in the table is higher than other values in its column, and the row represents the square root of the AVE.

A model modification was made, to increase the model fitness by creating covariance of error terms based on the magnitude of modification indices. The model was retested after the changes were made, and sufficient overall goodness of fit values were obtained (Table 5).

Table 5.

Model fitness of intention to use digital-based psychosocial counseling in students at the University of Gondar in 2023.

Model fit indices	Threshold value	Authors	Result obtained value	Model fit decision
Chi-square divided by degrees of freedom	⩽3	Schumacker and Lomax (2004)	2.167	Excellent
Standardized root mean squared residual (SRMR)	<0.08	Hu and Bentler (1998)	0.041	Excellent
The goodness of the fit index (GFI)	>0.9	Chau (1997)	0.963	Excellent
Adjusted goodness of fit index (AGFI)	>0.8	Chau (1997)	0.948	Excellent
Normed fit index (NFI)	>0.9	Bentler and Bonett	0.958	Excellent
Comparative fit index (CFI)	>0.9	Bentler (1990)	0.977	Excellent
Root mean square error of approximation	<0.08	Byrne (2001)	0.039	Excellent

All of the fitting indices for the research model are above the normal average acceptance threshold, indicating that they closely match the gathered data.

Structural equation model assessment

To determine the relationships between the constructs in the research model, a structural model was developed. To assess the structural model which includes testing the theoretical hypothesis and the relationships between the latent constructs.

Predictors associated with the intention to use digital-based psychosocial counseling

The exogenous constructs such as PE, EE, SI, and facilitating condition explained 65.0% of the endogenous construct (intention to use digital-based psychosocial counseling construct). The proposed model indicates a 65% variance (R² = 0.65) in the intention of students to use digital-based psychosocial counseling. Figure 4 shows the model’s standardized estimate.

Figure 4.

SEM analysis of behavioral intention to use digital-based psychosocial counseling students at the University of Gondar in 2023.

The findings from the measurement model sections have conclusively shown that the data and the model proposed in the current study well fit each other. In another way, the model is suitable for additional investigation, such as hypothesis testing. The study finding shows that PE, EE, and SI had a direct positive and significant effect on students’ intention to use digital-based psychosocial counseling to receive psychosocial health support. H1, H2, and H3 were accepted and verified. H4, on the other hand, was rejected.

According to SEM analysis, the result of the study finding showed that PE had the most substantial effect on the student’s intention to use digital-based psychosocial counseling, which was larger than the effects of other predictors. Having PE (β = 0.510, 95% CI: 0.386, 0.625), p-value < 0.001), SI (β = 0. 307, 95% CI: 0.177, 0.444), p-value < 0.001) and EE (β = 0.082, 95% CI: 0.052, 0.135, p-value = 0.003) had a direct positive effect on the intention to use digital-based psychosocial counseling. In contrast, FC (β = 0.038, 95% CI: −0.036, −0.151, p-value = 0.252) had no direct effect on the intention to use digital-based psychosocial counseling (Table 6).

Table 6.

Structural model assessment and hypothesis test results of the proposed model.

Parameters	Hypothesis	Estimate	SE	CR	p-Value	Confidence interval 95%		Result
Parameters	Hypothesis	Estimate	SE	CR	p-Value	Lower	Upper	Result
PE → BI	H1	0.510	0.50	9.915	***	0.386	0.625	Accepted
EE→ BI	H2	0.082	0.040	1.009	0.003	0.052	0.135	Accepted
SI → BI	H3	0.307	0.053	5.841	***	0.177	0.444	Accepted
FC → BI	H4	0.038	0.045	1.146	0.252	0.036	1.051	Not accepted

CR: critical ratio; SE: standard error.

Variables with *** indicate statistically significant at p-value < 0.001.

Testing moderator’s effects

To examine the moderation effects of gender and age on students in the relationship between PE, EE, SI, and FC and to use digital-based psychosocial counseling multi-group analyses were performed.

To test moderators, two model comparisons were computed, including unconstrained and constrained (structural weight) models. The constrained model suggests the variable has a similar effect to influencing the relationship between the exogenous and endogenous variable, in contrast to the unconstrained model assumption that there is a moderator or statistical difference in the given variable to influence the exogenous and endogenous variable. The proposed moderator variable was confirmed as a moderator if the significant difference between the two models was determined to be significant (p-value < 0.05). According to the findings PE on intention to use digital-based psychosocial counseling was positively moderated by gender and significantly stronger for male respondents (β = 0.545, p-value < 0.001) compared to the relationship for female respondents (β = 0.485, p-value = 0.571) (Table 7).

Table 7.

Sshow moderating effects of gender in multi-group analysis of behavioral intention to use digital-based psychosocial counseling students in the University of Gondar in 2023.

Hypothesis	Moderator	Path coefficient	p-Value	Model test (unconstrained and constrained model)		Result
Hypothesis	Gender	Path coefficient	p-Value	∆X² p-value		Result
PE → BI	Male	0.545	***	5.314	0.033	Accepted
PE → BI	Female	0.485	0.571	5.314	0.033	Accepted
EE → BI	Male	0.601	0.005	1.299	0.254	Not accepted
EE → BI	Female	0.039	0.004	1.299	0.254	Not accepted
SI → BI	Male	0.270	***	1.175	0.278	Not accepted
SI → BI	Female	0.389	***	1.175	0.278	Not accepted
FC → BI	Male	0.040	0.459	0.238	0.626	Not accepted
FC → BI	Female	0.088	0.283	0.238	0.626	Not accepted

Variables with*** indicate statistically significant at p-value < 0.001.

According to the finding, the effects of PE, SI, FC, and EE on the intention to use digital-based psychosocial counseling are not significantly different between individuals by age because most of the participants had a little age difference (Table 8).

Table 8.

Moderating effects of age in the multi-group analysis of behavioral intention to use digital-based psychosocial counseling students in the University of Gondar in 2023.

Hypothesis	Moderator	Path coefficient	p-Value	Model test (unconstrained and constrained model)		Result
Hypothesis	Age	Path coefficient	p-Value	∆X² p-value		Result
PE → BI	⩽21	0.503	***	0.010	0.919	Not accepted
PE → BI	>21	0.491	***	0.010	0.919	Not accepted
EE → BI	⩽21	0.080	0.030	0.928	0.335	Not accepted
EE → BI	>21	0.003	0.035	0.928	0.335	Not accepted
SI → BI	⩽21	0.209	0.053	1.187	0.276	Not accepted
SI → BI	>21	0.346	***	1.187	0.276	Not accepted
FC → BI	⩽21	0.001	0.992	0.494	0.482	Not accepted
FC → BI	>21	0.066	0.271	0.494	0.482	Not accepted

Variables with*** indicate statistically significant at p-value < 0.001.

Discussion

This study has shown that more than half of the study participants have the intention to use digital-based psychosocial counseling. PE (β = 0.510), EE (β = 0.082), and SI ((β = 0.307) were significantly associated with students’ intention to use digital-based psychosocial counseling. Being male was found to positively moderate (β = 0.545) the relationship between PE and intention to use digital-based psychosocial counseling.

In this study, students’ intention to use digital-based psychosocial counseling was 54.9% (95% CI: 51.3%–58.5%). Despite the promising findings, still, about half of the students have no intention to use digital tools for remote psychosocial support. Students’ refusal could be related to digital literacy, access to digital devices, and perceived fear of information security. On the other hand, the findings could not adequately satisfy the global and national interest in the digitalization of public services including digital health. Ethiopia envisaged ambitious digitalization in all socio-economic sectors including the health information revolution strategy in the health sector.⁵⁶ All initiatives related to the digitalization of health care should be aligned with the digital advancement that improves the quality of life by providing equitable, affordable, and quality health services and continues to reshape health delivery. The implications of the national digital health strategy are to revolutionize the availability, accessibility, quality, and use of health information for decision-making processes, through the appropriate use of ICT. In addition, the United Nations has recommended using digitalization of health services, as it accelerates the implementation and achievement of the targets of Sustainable Development Goals and Universal Health Coverage in the health service.⁵⁶ In general, putting the global and national digital health plan into consideration, this study has shown a promising intention to use digital-based psychosocial counseling. However, the need to put much effort into the area, as digital tools are essential to improve access, high-quality, evidence-based, and affordable mental health care services.

Regarding predictors, PE, EE, and SI were found to significantly associate with the use of digital-based psychosocial counseling. In contrast, FC has no significant association with the intention to use digital-based psychosocial counseling.

According to our study, PE was revealed by SEM analysis to be the most motivating (predictive) factor, and it had a direct effect on student’s intention to use digital-based psychosocial counseling (β = 0.510, p < 0.001). This indicates that participants are motivated to employ digital-based psychosocial counseling when those technologies allow them to manage their psychosocial health more effectively and efficiently. The findings of this study are consistent with previous studies on intentions to use e-counseling in Ghana,²³ and this finding is consistent with studies on intentions to use tele counseling in Indonesia.³⁹

The possible reason might be students recognized the importance of digital-based psychosocial counseling for their academic development. The student’s ultimate finding might be the diversity in counseling methodologies through digital means.²³ Another possible reason might be that psychosocial problems faced time is unknown and students think new technologies are important for managing this condition at anytime and anywhere. Another possible reason for this could be the usefulness of a digital-based psychosocial counseling system is recognized by similar technologies like online counseling.

This study also found that EE was a statistically significant predictor of the intention to use digital-based psychosocial counseling on students (β = 0.082, p = 0.003), and EE had a direct effect on students’ intention to use digital-based psychosocial counseling. This finding is consistent with previous studies in different countries on the intent to seek online counseling in the Philippines³⁴ and the intention to use cybercounseling in China.¹⁸ This study indicated that students who believed that it is easy to learn how they use digital-based psychosocial counseling, have clear use of digital-based psychosocial counseling, and easily become skillful in using digital-based psychosocial counseling. So, the intention to use digital-based psychosocial counseling could be enhanced. This might be also due to their perception that using digital-based psychosocial counseling will simplify their tasks by getting the counseling at anytime and anywhere.

Another reasonable explanation for this finding is that digital-based psychosocial counseling requires the user to send a text, or phone call which is a simple task, and all users can easily learn how to use digital-based psychosocial counseling from social media.²³ Therefore, adopting digital-based psychosocial counseling might be easy to understand and operate for users for the sustainable adoption of the technologies in the future. When students were familiar with using social media, as a result using online counseling is currently not difficult to use. The other possible explanation might be the popularity of social media in the study area, it is reasonable to believe that students can easily learn how to use digital-based psychosocial counseling. Another possible explanation is related to the respondents' characteristics. The all of respondents in our study had higher educational students, which leads to understanding and they may have the ability to use digital-based psychosocial counseling.

The second most significant predictor of the intention to use digital-based psychosocial counseling is SI. It had a positive influence on the intention to use digital-based psychosocial counseling (β = 0.307, p < 0.001). This is consistent with studies conducted on the intent to seek online counseling in the Philippines,³⁴ on the intention to use psychological cyber counseling in China,¹⁸ and on the intention to use e-counseling in Ghana.³⁷ The possible reason could be due to students are more likely to establish peer pressure judgments and may be paying more respect to what other people think. This suggests that students perceived that close friends believe they should use digital-based psychosocial counseling to improve their academic and career development.³⁷ The people who influence their conduct such as parents, teachers, and friends would think that using digital-based psychosocial counseling may enhance their usual counseling services. In other words, the community surrounding a person such as a person’s family and friends is the greatest influencer to the person’s decision to adopt (or not) digital-based psychosocial counseling.

The possible reasons students might perceive external pressure toward using a new system from friends, family, and teachers. So, students might perceive pressure from an external body, which is important for them to increase their motivation and intention to use digital-based psychosocial counseling in their organization. Another factor is that regardless of what is important others may think all students may be focused on using digital-based psychosocial counseling by seeing other people’s views or the degree to which the results of digital-based psychosocial counseling are visible to others.

According to SEM analysis in this study, the relationship between PE and intention to use digital-based psychosocial counseling was positively moderated by gender (β = 0.545, p < 0.001). This shows that a significant difference existed in performance expectations between male and female students for those who intend to use digital-based psychosocial counseling. This finding showed that males were more influenced by the relation between PE and intention to use digital-based psychosocial counseling than females. It stated that females tend to find new information systems difficult to use and find them less useful when performing their tasks. This scenario may lead to lower PE among them and hence perceive the system as not useful and not satisfying. This result is supported by the original model and other findings, which are conducted on the intention to use psychological cyber counseling in China,¹⁸ and on the intent to seek online counseling in the Philippines.³⁴ A possible reason for this could be due to their interactions with digital health technology, men are more aware of online health services than women.⁵⁷ Another possible reason might be males need digital counseling because face-to-face counseling may create social stigma and digital counseling might create a sense of safety, the reduction of social stigma, and decrease frustration. Another possible reason might be males perceive digital counseling as a more appropriate method to address their issues because males deal with their problems individually.⁵⁸ As a result, males believe that using digital-based psychosocial counseling become easy.

Implications

The theoretical value of this finding is to expand the body of knowledge for interested readers regarding digital-based psychosocial counseling and identify the possible factors and determine the proportion of digital-based psychosocial counseling among students. Our findings may alleviate any concerns regarding the acceptance of digital-based psychosocial counseling in situations with low resources. For researchers, especially in places with low resources, it serves as a baseline to implement these systems because there is not much information on the intention to employ digital-based psychosocial counseling. The significance of the UTAUT model for assessing students’ intent to use digitally based psychological counseling is thus statistically confirmed by our study, and the results may apply to other nations. This study also advances our knowledge of the significance of the key predictors of intention to use digital-based psychosocial counseling for psychosocial health management.

The practical value of this finding is that the university may prepare an action plan for promoting digital-based psychosocial counseling through digitizing the counseling service, professionals give attention to digital-based psychosocial counseling and used it as an input for policy making and researchers use this finding as a baseline for future study.

Strengths and limitations of the study

Strength of the study

SEM analysis enables the simultaneous examination of multiple variables, accounting for error terms of observed variables and correlations between exogenous variables. This approach can effectively evaluate students’ intention to use digital technologies for psychosocial health support by applying a standardized instrument, the UTAUT model, which has been validated across both developed and developing countries to explain user behavior.²⁴

Limitation of the study

The study population included only a single university which could limit the generalizability of the findings. Future studies should aim to replicate the research in different public and private universities and colleges within Ethiopia and other low and middle-income countries. Large representative samples could provide insights into how digital counseling services might be adapted to the needs of various demographics people. The capacity to determine causal relationships between the variables is limited by the use of a cross-sectional design. Longitudinal designs should be used in future studies to examine how user intentions to use digital counseling change over time. This investigation did not include UTAUT2 predictors like habit and price value, self-reported data are subject to a social desirability bias or recall bias, and sampling bias for this study are some of the limitations of this study that need to be addressed in future studies. Lastly, future studies may use a mixed-method approach, in addition to including extraneous elements that could influence studntes’ behavioral intention to digital-based pschosocial counseling in order to improve perception and provide a greater generalization of the findings.

Conclusion and recommendation

Conclusion

Overall, this study has shown that intentions to use digital-based psychosocial counseling were promised by the respondents. PE, EE, and SI were statistically significant predictors of intention to use digital-based psychosocial counseling in the students. Among the three influencing predictors, PE had a more significant prediction power of students’ intention to use digital-based psychosocial counseling. The relationship between PE and intention to use digital-based psychosocial counseling was positively moderated by gender.

Supplemental Material

sj-docx-1-smo-10.1177_20503121241307136 – Supplemental material for Intention to use digital-based psychosocial counseling and its predictors among students in University of Gondar, Northwest Ethiopia, 2023: Using modified unified theory of acceptance and use of technology model

Supplemental material, sj-docx-1-smo-10.1177_20503121241307136 for Intention to use digital-based psychosocial counseling and its predictors among students in University of Gondar, Northwest Ethiopia, 2023: Using modified unified theory of acceptance and use of technology model by Deje Sendek Anteneh, Jenberu Mekurianew Kelkay, Henok Dessie Wubneh, Miftah Abdella Beshir and Kassahun Dessie Gashu in SAGE Open Medicine

Footnotes

Acknowledgements

The authors would like to thank University of Gondar institute of public health for the approval of ethical clearance, data collectors, supervisors, and study participants.

Authors’ contributions

DSA was responsible for a significant contribution to the conceptualization, study selection, data collection, formal analysis, funding acquisition, investigation, methodology, and original draft preparation. Project administration, resources, software, supervision, validation, visualization, and reviewing are all handled by JMK, MAB, and KDG. DSA, MAB, and KDG wrote the final draft of the manuscript, and the final draft of the work was read, edited, and approved by all writers.

Availability of data and materials

The datasets generated and/or analyzed during the current study will be available upon reasonable request from the corresponding author.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethical considerations

The University of Gondar Institutional Review Board (IRB), CMHS, and the Institute of Public Health provided the ethical approval (ethical clearance number SPH /2941/2023). For each campus, supportive and permission letters were obtained from the health informatics department. The data collection procedure was kept secret to protect the study participants’ privacy and the confidentiality of any information they provide. Written Informed consent was obtained from each participant on the questionnaire. The right to refuse was clearly stated in the letter if the respondent is not a volunteer to participate in the study. Written informed consent was obtained from these participants as well. This study was conducted according to the Helsinki. Declaration. This research is also not an experimental study.

Consent for publication

Not applicable.

Informed consent

Written informed consent was obtained from all subjects before the study.

Trial registration

Not applicable.

ORCID iDs

Deje Sendek Anteneh

Jenberu Mekurianew Kelkay

Supplemental material

Supplemental material for this article is available online.

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