Nucleus Journal Jurnal Sains dan Teknologi e-ISSN: 2964-4100 p-ISSN: 2964-4097 Website: https://ejournal.
id/index.
php/Nucleus Effects of Low Comment Engagement on StudentsAo Information Processing in Instagram Reviews Herlina, 2Soraya Asnusa.
Bandarsyah Prodi Bisnis Digital.
Institut Informatika dan Bisnis Darmajaya1,2 Prodi Pariwisata.
Institut Informatika dan Bisnis Darmajaya3 herlina@darmajaya.
id, 2aya@darmajaya.
id, 3bandarsyah@darmajaya.
Article Info
ABSTRACT
Article history:
Received 24 Maret, 2026 Revised 14 May, 2026 Accepted 07 June, 2026 Keyword:
Perceived Information Reviews Credibility Trust Attitude Intention University Choice Decision The objective of this study is to analyze the influence of online reviews on college Instagram comments on prospective studentsAo decision- making processes through the integration of the Elaboration Likelihood Model (ELM) and Engel-Kollat-Blackwell (EKB) with an implicit issue: the low number of comments on Instagram posts is often viewed merely as a technical weakness in promotion, whereas this condition can influence digital persuasion The research gap lies in the limited number of studies specifically examining prospective studentsAo decisions within the context of low comment engagement, while the novelty of this study positions the low number of comments as a theoretical context that drives a shift from peripheral cues toward central processing.
The research sample consisted of 250 respondents, comprising potential prospective students and followers of the official IIB Darmajaya Instagram account, selected via purposive sampling.
Data were analyzed using SEM-PLS with the assistance of SmartPLS 3.
The results indicate that perceived information and review credibility have a significant positive effect on trust.
trust has a positive effect on attitude.
attitude has a positive effect on intention.
and intention has a positive effect on deciding on a university.
Conversely, low comment engagement was found to negatively moderate all primary relationships, thereby weakening the decision-making process.
Theoretically, this study expands the ELM and enriches the EKB within the context of digital higher education marketing.
Practically, these results underscore the importance of informative, credible content and active comment interaction.
The limitations of this study lie in its focus on a single institution, a single platform, and a cross-sectional design.
therefore, future studies should expand the scope of the subjects, platforms, and research variables.
This is an open access article under the CC BY-SA license DOI: https://doi.
org/10.
32492/nucleus.
Corresponding Author:
Herlina Digital Business Program.
Darmajaya Institute of Informatics and Business.
Bandar Lampung herlina@darmajaya.
INTRODUCTION
In the digital age.
Generation Z prospective students obtain information about higher education institutions more quickly due to their high familiarity with technology and digital media.
This situation differs from the millennial era, when information about higher education was still primarily obtained through conventional media, such as brochures and direct distribution by marketing teams .
Currently, prospective students tend to search for information online, read online reviews, pay attention to comments from current students and alumni, and evaluate how the institution is represented on social media .
Instagram has Nucleus Journal Journal of Science and Technology Vol.
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become one of the platforms widely utilized by higher education institutions because it effectively conveys messages in a visual and engaging manner .
For private universities, online reviews play a crucial role in building institutional reputation and attracting prospective students amid increasingly fierce competition.
However, in the context of limited digital social interaction, the influence of digital information is largely determined by individual information processing mechanisms, particularly through assessments of message clarity, source credibility, and content relevance.
This process subsequently contributes to the formation of the intention to choose a university.
The phenomenon of low comment engagement on university Instagram posts highlights a complex issue in digital higher education marketing.
On one hand, universities have made efforts to create engaging, informative, and persuasive content.
on the other hand, user comment responses remain minimal, meaning promotional messages risk losing their social validation.
In this context, previous studies generally treat comments, likes, and shares as indicators of successful digital communication and assume that audience interaction is at a sufficiently high level.
In practice, however particularly at private universities low comment engagement is actually quite common and has not yet been sufficiently explored as an empirical situation with its own theoretical implications .
The Elaboration Likelihood Model (ELM), which postulates that people digest information through central and peripheral channels, explains this digital communication scenario, which has undergone scientific advancement initially described by Cacioppo & Petty .
When potential students thoroughly comprehend and carefully analyze the message's content, they take the core route .
In the meantime, the peripheral path is created when potential students decide more swiftly based only on the popularity of the account, the quantity of comments, or the message's sender .
The Darmajaya Institute of Information and Business has relatively few comments in the context of online reviews on Instagram.
As a result, prospective students will give priority to the core path, where the message's content becomes the main determinant of their choice.
In these circumstances, the message's content quality becomes crucial.
Even with few comments on posts, the message can still have an impact on prospective students' decisions if all universities are able to provide pertinent and educational content that satisfies their demands.
On the other hand, the promotion could become ineffective if it only uses visually appealing information.
This is where ELM sheds light on the fact that people's interpretation of the communication itself influences the information processing pathway in addition to social media .
This research gap lies in the limited number of studies examining how prospective students process information and make decisions when comments on an institutionAos social media are scarce.
Most previous studies on online reviews and digital marketing in higher education have emphasized the influence of reputation, information quality, institutional image, or high user engagement, but have not specifically tested conditions where social validation in the comment section is weakened .
A more profound critical gap lies in the absence of a robust conceptual explanation regarding the relationship between the weakening of peripheral cues in the Elaboration Likelihood Model (ELM) and the alternative evaluation process in the Engel-Kollat-Blackwell (EKB) model.
In other words, previous studies tend to separate discussions of how messages are processed from how decisions are made, leaving a gap in explaining the decision-making mechanisms of prospective students amidst low levels of digital social interaction .
Given these conditions, the novelty of this study lies in the integration of ELM and EKB to explain that a low number of comments does not necessarily mean digital promotion is ineffective, but can be understood as a boundary condition that drives prospective students to shift from reliance on peripheral cues toward deeper message processing via the central route.
This study offers a new perspective that, in the context of higher education on Instagram, the effectiveness of online reviews is not only determined by the volume of comments but also by the quality of message content, source credibility, perceived informativeness, trust, and the attitudes formed among prospective students.
Thus, low comment engagement is positioned not merely as a technical weakness in promotion, but as a new theoretical context that can explain shifts in persuasion mechanisms and decision-making in digital higher education marketing .
The objective is to analyze how low comment engagement on higher education Instagram posts influences prospective studentsAo information processing and decision-making through the integration of ELM and EKB perspectives.
More specifically, this study aims to elucidate the roles of information quality, source credibility, trust, attitudes, and intentions in shaping prospective studentsAo decisions when comment interaction is low.
The theoretical contribution of this study is to expand the development of the ELM by demonstrating that weak peripheral cues can trigger central elaboration, while enriching the EKB model by Nucleus Journal Vol.
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Journal of Science and Technology positioning digital communication as a key trigger in the information search and alternative evaluation stages.
Practically, this study provides a foundation for universities to design more substantive, credible, and effective digital marketing strategies without having to rely entirely on a high number of comments on social media .
Literature Review and Hypothesis Development A more in-depth psychological understanding of how people interpret marketing messages is provided by the Elaboration Likelihood Model (ELM) .
The persuasive process is separated into core and peripheral channels by ELM.
In the context of higher education, prospective students who directly seek information from the universities they are interested in are part of the peripheral channel, while information processing in the core channel is active and highly engaged .
As a result, the peripheral channel is regarded as straightforward and is motivated by the popularity of the message or the appeal of the source.
Depending on the degree of participation.
ELM concentrates more on the processing of online reviews or consumer-to-consumer comment interactions in the context of social media and encourages potential students to thoroughly digest information .
In the meantime, a well-known paradigm in consumer behavior research that methodically explains the decision-making process is the Engel-Kollat-Blackwell (EKB) Model .
This model outlines the steps prospective students take in the decision-making process by first determining their needs and then starting to look for information to build alternative options that will be examined once they have selected a college .
The EKB model in this study focuses primarily on how consumer attitudes, motives, and experiences, as well as external elements like the social, cultural, and marketing environments, influence decision-making .
This model is applicable in a digital setting to comprehend how potential students interpret information from a variety of online sources, such as social media evaluations and comments, which consequently affect their decision-making behavior .
In order to analyze how prospective students create perceptions and make judgments through complicated digital stimuli, it is imperative to integrate the interaction between EKB theory and ELM theory.
Table 1.
Relationship Between the ELM and EKB Perspectives Stages of the EKB Model (Engel.
Kollatt, & Blackwel.
Recognition of Needs Elaboration Likelihood Model (ELM) Perspectives Prospective students begin to realize the need for information about campuses that align with their interests and goals.
Information Search The ELM central channel is used more frequently because .
omments, like.
are insufficient, shifting the focus to the message content.
Evaluation of Alternatives The cognitive understanding process becomes more in-depth when prospective students compare information across universities based on the quality of the message content The Phenomenon of Few Comments on Instagram Prospective students continue to look for information directly from the content itself rather than through encounters with the marketing personnel of the universities they want to attend, despite the dearth of Prospective students are more cautious while reading postings to get the information they require when there are no The absence of comments does students, as long as the promotional content provides a clear comparison of value.
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in online reviews within the comment section.
Decision-Making in Choosing a College Post-Decision Feedback The core route process's outcomes affect attitudes and intentions, which eventually result in the decision to choose a specific university.
After individual experiences can either confirm or change their beliefs going forward.
In addition to comments, prospective students' decisions might also be impacted by the reliability and applicability of the material they are given.
Because experiencesAirather than the impact of social mediaAiare the main source of validation when making decisions.
Prospective students' opinions of the quality, applicability, and comprehensiveness of the information they obtain from different sourcesAiincluding internet reviewsAidetermine the information they receive.
The quality of information has a crucial role in influencing people's opinions and perceptions of the goods and services provided in the context of consumer decision-making in a digital setting .
Information that is pertinent, accurate, and clear increases perceived credibility, which promotes trust in both the evaluated object and the information source.
Prospective students with high levels of participation analyze information profoundly through the core pathway, where information quality is the main factor in establishing trust, according to the Elaboration Likelihood Model (ELM) .
On the other hand, information that is unclear or irrelevant may be viewed as not satisfying information-seeking demands, which will reduce the degree of trust .
When selecting a university, prospective students have access to comprehensive information on facilities, accreditation, and testimonials from alumni and current students, which tends to increase the university's credibility.
The following theory can be put out in light of this material:
H1: The information received has a positive effect on trust.
The degree to which readers believe online reviews as reliable, truthful, and devoid of bias is known as review credibility.
In the internet sphere, prospective students' perceptions of their preferred university are significantly influenced by review credibility .
Credible online reviews usually show signs like information clarity, content consistency, source validity, and congruence with other prospective students' experiences .
According to the Elaboration Likelihood Model (ELM), reviews' trustworthiness has a minor impact on beliefs, especially for prospective students who are not very engaged, but it also makes a compelling case for those who are.
Reviews from reliable sources, such alumni or current students, are thought to boost potential students' confidence in the college when choosing one.
As a result, the following theories are put forth:
H2: Reviewing credibility has a positive influence on trust.
A prospective student's confidence in the dependability, honesty, and legitimacy of a product, service, or higher education institution is known as trust, and it is an essential basis for forming attitudes.
From the standpoint of customer behavior, good views that result in more favorable attitudes are fostered by strong confidence in a higher education institution .
In the context of prospective students' decision-making in higher education, where risks and uncertainties regarding the information collected frequently exert a bigger influence on their decisions, prior research has demonstrated that trust plays a crucial role in influencing Nucleus Journal Journal of Science and Technology Vol.
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According to the Elaboration Likelihood Model (ELM), prospective students who have faith in information sources are more likely to interpret messages favorably and develop more positive attitudes toward their choices of higher education institutions .
When choosing a college, prospective students who have a high level of trust in an institutionAiwhether due to knowledge or the experiences of othersAitend to have a favorable opinion of that higher education establishment.
In light of this conversation, the suggested theory is H3: Trust has a positive influence on prospective studentsAo attitudes toward higher education.
The attitudes of prospective students are the outcome of an individual's favorable or negative assessment of an item, which directly affects their desire to select a university.
In the context of digital marketing, it has been demonstrated that prospective students' desire to take more action, like making a purchase or choice, is influenced by a favorable opinion toward a university that is created from perceived experiences, knowledge, and credibility .
The idea that prospective students' attitudes, which are developed via the processing of solid and reliable information, will lead to greater intentions within the individual is further supported by the Elaboration Likelihood Model (ELM) .
When choosing a university, prospective students who have a favorable opinion of the institution are more likely to decide to attend and continue their education there.
Thus, the hypothesis that has been put out is H4: Attitude has a positive influence on prospective studentsAo intention to choose a university.
One of the behavioral markers that most closely resembles actual decisions is intention.
a strong intention usually results in the actualization of action .
Prospective students who have favorable attitudes, trust, and reliable information greatly influence decision-making in a university setting, including when choosing a university .
The idea that prospective students' intentions arising from a robust elaboration process are more stable and have a greater potential to be realized in the form of actual decisions is further supported by the Elaboration Likelihood Model (ELM) .
In this sense, prospective students are more likely to decide to apply to and continue their education at a certain university if they have a strong intention to do so .
light of this justification, the suggested theory is H5: Intention has a positive influence on prospective studentsAo decisions in choosing a university.
Conceptually, perceived information that is deemed informative, relevant, clear, and valuable will increase prospective studentsAo trust, as such information serves as a cognitive foundation for evaluating an institutionAos quality and credibility.
However, this relationship does not hold uniformly but is influenced by the level of engagement in the comment section.
In conditions of low comment engagement, minimal user interaction, low institutional responsiveness, and limited follow-up discussion weaken the process of social validation of the received information .
Consequently, information that is actually perceived positively may not necessarily be fully converted into strong trust, as prospective students do not receive adequate social reinforcement from the digital environment .
Thus, low comment engagement tends to weaken the influence of perceived information on trust.
consequently, the lower the comment engagement, the weaker the positive relationship between perceived information and trust.
The proposed hypothesis is H6: Low comment engagement moderates the effect of perceived information on trust Low comment engagement acts as a moderator on the relationship between review credibility and trust, leading to a negative effect.
because even though prospective students evaluate a review of a university as honest, logical, valid, and trustworthy, the influence of that credible review on trust formation is not always equally strong when the post exhibits low comment engagement .
The literature on online review credibility explains that review credibility relates to consumersAo perceptions of a reviewAos truthfulness, validity, and trustworthiness .
Meanwhile, studies on trust in digital environments indicate that reviews and consumer feedback serve as key cues in forming trustworthiness assessments.
On the other hand, social media research based on the MAIN model indicates that users not only evaluate the content of a message but also utilize non content cues or bandwagon cues to assess credibility.
thus, interaction metrics such as comments can be interpreted as signals of public attention, social validation, and message resonance.
Therefore, when comment engagement is low, prospective students may interpret that a review that is actually credible has not yet received sufficient social support, thereby weakening the ability of review credibility to foster trust .
Thus, the hypothesis that can be formulated is that low comment engagement negatively moderates the effect of review credibility on trust.
consequently, the lower the comment engagement, the weaker the positive Nucleus Journal Journal of Science and Technology Vol.
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effect of review credibility on trust.
H7: Low comment engagement moderates the effect of review credibility on trust Theoretically, trust is a psychological evaluation reflecting an individualAos belief in an institutionAos credibility, reliability, and integrity.
thus, the higher the trust formed, the more positive prospective studentsAo attitudes toward the university.
However, the strength of this relationship can be influenced by the level of comment engagement on social media or online reviews .
In conditions of low comment engagement, limited user interaction, minimal social feedback, and low discussion participation cause the digital environment to be less capable of reinforcing established beliefs.
Consequently, the trust held by prospective students does not always develop optimally into a more positive attitude, due to a lack of social support and interactive confirmation from the digital community .
Thus, low comment engagement tends to weaken the influence of trust on attitude.
consequently, the lower the comment engagement, the weaker the positive relationship between trust and attitude.
The proposed hypothesis is H8: Low comment engagement moderates the influence of trust on attitude Low comment engagement is expected to moderate the influence of attitude on intention in the context of college selection.
Conceptually, attitude reflects prospective studentsAo affective evaluations and overall assessments of the college.
thus, the more positive the attitude formed, the greater the intention to seek further information, apply, or choose that institution .
However, this positive relationship does not always hold consistently, as it can be influenced by the level of comment engagement on social media or online review platforms.
In conditions of low comment engagement, the low number of responses, limited user discussions, and minimal audience participation result in a digital environment that is less capable of providing the social support needed to strengthen positive attitudes into tangible behavioral motivation.
Consequently, even though prospective students may hold positive attitudes, their intention to make a decision or take action may not be firmly established due to a lack of social validation and interactive reinforcement from the digital community .
Thus, low comment engagement tends to weaken the influence of attitude on intention.
the lower the comment engagement, the weaker the positive relationship between attitude and intention.
The hypothesis is H9: Low comment engagement moderates the influence of attitude on intention Intention reflects prospective studentsAo behavioral tendencies to take concrete actions, such as seeking further information, considering alternatives more seriously, and ultimately selecting a specific university.
The stronger the intention, the greater the likelihood that an individual will make the actual decision to choose that institution .
However, the relationship between intention and deciding on a university is not always linear, as it can be influenced by the level of comment engagement on social media or online review In conditions of low comment engagement characterized by low interaction intensity, limited responses from other users or the institution, and minimal social discussion the digital environment is less capable of providing the final reinforcement needed to solidify the intention that has already formed.
Consequently, an initially high intention may not fully convert into an actual decision, as prospective students still face limitations in social validation and lack collective confirmation to support the decision-making Thus, low comment engagement tends to weaken the influence of intention on deciding on a consequently, the lower the comment engagement, the weaker the positive relationship between intention and the decision to choose a university .
The proposed hypothesis is H10: Low comment engagement moderates the influence of intention on deciding on a university Nucleus Journal Journal of Science and Technology Vol.
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Figure 1.
Review model for choosing a university II.
METHODS
This study uses the Elaboration Likelihood Model (ELM) and the Engel-Kollatt & Blackwell (EKB) decision-making model to examine how online evaluations in the Instagram comments area affect prospective students' decision-making process while selecting an institution.
This study's scope is restricted to Darmajaya Institute of Informatics and Business, with an emphasis on prospective students' interactions on Instagram as the main platform for the school's online marketing and promotion initiatives.
The population consists of all followers of DarmajayaAos official Instagram account active social media users who are potentially exposed to promotional content, read reviews, and pay attention to comments appearing on the institutionAos posts.
This population is deemed relevant because account followers serve not only as a passive audience but also as individuals who can be influenced by digital information and social interactions unfolding in the comment section .
Sampling was conducted using non-probability sampling techniques through a purposive sampling approach, which involves selecting respondents based on specific criteria aligned with the objectives to be achieved .
The respondent criteria include following the Darmajaya Instagram account, having viewed promotional posts or institutional information, having read comments or reviews on those posts, and being part of the group of potential prospective students, such as 12th-grade high school/vocational school or madrasah students, current year graduates, or graduates from the past two years who are currently considering their college choices.
Based on these criteria, the sample size was set at 250 respondents.
This sample size is considered adequate because it focuses on a specific population and uses respondents who truly match the characteristics, thereby making it more likely to obtain relevant, in-depth data that aligns with the analysis requirements .
An online survey created with Google Forms was used to collect data.
The questionnaire was distributed online through several channels, namely Instagram direct messages (DM.
Instagram Stories posts, and distribution via WhatsApp groups comprising prospective students, students from partner schools, and the new student recruitment promotion network.
Distribution was targeted to ensure the instrument reached respondents who had experience viewing DarmajayaAos Instagram content and reading comments on the institutionAos postsTo maintain data quality, screening questions were included at the beginning of the questionnaire to ensure only respondents meeting the purposive sampling criteria could proceed with the The survey was conducted over sixteen weeks, from April to July 2025, as this timeframe coincides with the peak period when prospective students actively seek information about higher education institutions.
Respondent profiles in this study are described based on demographic and digital behavior characteristics, such as gender, age, residence, school of origin, highest level of education, frequency of Instagram use, duration of following DarmajayaAos Instagram account, and frequency of reading comments on the institutionAos posts.
This description of respondent profiles is crucial for providing an understanding of the characteristics of prospective students targeted by the universityAos digital promotion efforts.
This study uses perceived information, review credibility, trust, attitude, and intention as independent variables and deciding on a university as the dependent variable, with low comment engagement serving as the moderating variable.
Each variable is measured using indicators adapted from previous studies.
Measurements were conducted using a 5-point Likert scale, ranging from 1 = strongly disagree to 5 = strongly agree, to capture respondentsAo perceptions in greater detail and with greater accuracy .
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Operational Definitions of Variables Variable Perceived Information Review Credibility Operational Definition Prospective studentsAo perceptions of information obtained from online reviews regarding higher education institutions, assessed based on the extent to which such information is considered clear, complete, relevant, and useful in assisting with the evaluation of higher education institutions.
Prospective studentsAo perceptions of the level of trustworthiness of online reviews, as demonstrated by their belief that the reviews are honest, objective, accurate, and reliable as a source of information about higher education institutions.
Trust The level of prospective studentsAo confidence that the institution possesses integrity, competence, and reliability, making it worthy of trust based on the online information and reviews received.
Attitude Prospective studentsAo overall attitude or evaluation, whether positive or negative, toward the university after receiving and assessing available online reviews.
Intention Prospective studentsAo tendency to take further action regarding the university, such as seeking additional information, contacting the university, or applying.
Deciding on a University The level of tendency among prospective students to decide on a specific university as their primary choice based on the evaluation of information and digital experiences Low Comment Engagement Conditions of low interaction engagement in the comment section of university reviews or content on social media, as indicated by a small number of comments, minimal discussion, low responsiveness, and limited interaction among users.
This variable acts as a moderating variabel Indicator .
Clarity of information, .
Completeness of information, .
Relevance of information, .
Ease of understanding, .
Usefulness of information .
Honesty of reviews, .
Objectivity of reviews, .
Accuracy of review content, .
Consistency of information, .
Reliability .
Trust in the institutionAos integrity, .
Trust in the institutionAos competence, .
Trust in the institutionAos reliability, .
Belief that the institution keeps its .
Sense of security in considering the institution .
Positive evaluation of the university, .
Liking of the institution, .
Interest in the institution, .
Favorable impression of the .
Perception that the institution is a worthy choice.
Intention to seek further information, .
Intention to visit the universityAos official .
Intention to consult or ask questions, .
Intention to apply, .
Intention to choose the university .
Confidence in choosing a university, .
Firmness of decision, .
Making the university their top choice, .
Readiness to make a decision, .
Final tendency to choose that university .
Low number of comments, .
Minimal discussion among users, .
Low institutional response, .
Little feedback from other users, .
Weak two-way interaction .
The structural equation modeling (SEM) method based on partial least squares (PLS) was used to analyze the data.
This approach was chosen on the basis of formative and reflective model creation, as well as predetermined features involving intricate interactions between variables.
SmartPLS 3.
0 software, which enables thorough model testing even when the data shows incomplete normality, was used for the analysis Testing the measurement model .
xternal mode.
, which includes testing construct validity and reliability by testing convergent validity, discriminant validity, and composite reliability, and testing the structural model .
nternal mode.
to analyze the relationships among variables within the model by measuring t-statistics and p-values to determine the significance of the effects between constructs, comprised the two main steps of the data analysis stages .
According to the ELM viewpoint, this investigation seeks Nucleus Journal Vol.
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RESULTS AND DISCUSSION
By comparing each indication to the variable constructs, external model testing was done to gauge reliability, discriminant validity, and convergent validity.
These three elements contribute to proving the measurement instruments' accuracy and dependability, which guarantees the validity of the processed data The table below displays the outcomes of data processing for the three measurement components in the external model:
Table 3.
Factor Loading Test Results Build Perceived Information Review Credibility Trust Attitude Meaning Choose A University Low Comment Engagement Indicator PI.
PI.
PI.
PI.
PI.
RC.
RC.
RC.
RC.
RC.
DOU1
DOU2
DOU3
DOU4
DOU5
LCE1
LCE2
LCE3
LCE4
LC35
Load Conclusion Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Legitimate Note: Ou 0.
According to the outer loading results, every indication in the constructs of perceived information, review credibility, trust, attitude, intention, and choosing a university, and low comment engagement have values ranging from 0.
739 to thus, all are deemed valid as they exceed the 0.
70 threshold.
These findings confirm that each indicator adequately reflects the latent construct it measures, meets convergent validity criteria, and reflects the accuracy of the variablesAo operationalization in this study.
The absence of indicators with low loadings also indicates that the instrument is well-constructed and does not require item elimination.
Thus, the measurement model is deemed appropriate, reliable, and robust enough to proceed to the next stage of structural equation modeling.
Table 4.
Results of Convergent Validity and Reliability Tests Nucleus Journal Vol.
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Journal of Science and Technology Composite Reliability (CR) CronbachAos Alpha Review Credibility (CU) Trust (T) Attitude (A) Intention (I) Deciding on a University (DOU) Low Comment Engagement (LCE) Variable AVE Perceived Information (PI) Conclusion Valid and Reliable Valid and Reliable Valid and Reliable Valid and Reliable Valid and Reliable Valid and Reliable Valid and Reliable Note: Ou 0.
Based on that test results, all construction meetthat criteria for convergent validity and internal reliability.
The AVE value for each variable is above 0.
50, namely perceived information 0.
612, review credibility 593, trust 0.
648, attitude 0.
672, intention 0.
701, choosing a university 0.
685, and low comment engagement That is, the value overall from AVE value Ou 0.
5 indicates every build enough explain that variance of their respective indicators.
Furthermore, composite reliability values of 0.
811Ae0.
889 and cronbach's alpha of 736Ae0.
825 confirmed good internal consistency.
Thus, all instruments were considered valid and reliable, making them suitable for structural equation modeling and subsequent empirical hypothesis testing in a comprehensive, systematic, accurate, and convincing manner.
Table 5.
Results of the Discriminant Validity Test Variable LCE Low Comment Engagement (LCE) Perceived Information (PI) Review Credibility (RC) Trust (T) Intention (I) Deciding on a University (DOU) DUO Note: HeterotraitAeMonotrait Ratio (HTMT) The results of the discriminant validity test indicate that the measurement model has adequate construct discriminant power.
The HTMT values for each variable such as PI at 0.
CU at 0.
T at 0.
A at 820.
I at 0.
837, and DOU at 0.
828 are higher than the correlations between the related constructs.
This indicates that each construct explains its own indicators more strongly than it shares variance with other Substantively, the highest relationship is observed between Intention and Deciding on a University at 0.
682, though this is still acceptable.
Thus, the entire instrument is deemed to meet discriminant validity criteria and is suitable for further structural equation modeling.
Table 6.
R Test Results Variable Trust Attitude Intention Choosing a University RA Category Moderate Moderate Moderate Moderate Note: Percentage (%) The results of the coefficient of determination test indicate that the modelAos ability to explain the endogenous variables falls into the moderate category.
The RA value for trust of 0.
622 indicates that 62.
2% of the variation in trust can be explained by its predictor constructs, while the remainder is influenced by other Nucleus Journal Vol.
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Attitude has an RA of 0.
504, intention 0.
561, and deciding on a university 0.
indicating explanatory power of 50.
4%, 56.
1%, and 48.
7%, respectively.
Scientifically, these findings indicate that the model has fairly good predictive ability, though it is not yet fully robust, leaving room to include other variables to make the modelAos explanation more comprehensive.
Table 7.
Results of the f2 Test Relationships Between Variables Perceived Information Ie Trust Review Credibility Ie Trust Trust Ie Attitude Attitude Ie Intention Intention Ie Choosing a University Perceived Information y Low Comment Engagement Ie Trust Review Credibility y Low Comment Engagement Ie Trust Trust y Low Comment Engagement Ie Attitude Attitude y Low Comment Engagement Ie Intention Intention y Low Comment Engagement Ie Deciding on a University Category Moderate Moderate High Large High Small Small Small Small Small Note: 3 categories The results of the effect size test .
A) show that the direct effects in the model have varying contributions.
That effect from perceived information on trust .
and review credibility on trust .
falls into the moderate category, indicating that both play an important role in shaping trust.
Meanwhile, the influence of trust on attitude .
, attitude on intention .
, and intention on the decision to choose a university .
is classified as large, so the main path of the model has substantial strength.
In contrast, the results obtained the moderating effect in this study needs to be interpreted with caution because the effect size value fA is in the range of 0.
031Ae0.
Where, this value indicates the presence of the contribution of moderating variables in strengthening or weakening the relationship between exogenous and endogenous variables is still relatively small.
In other words, although statistically the moderating effect can be detected, seen from side its practical show that the moderating role of the low comment involvement variable is not strong enough to produce major changes in the relationship between variable.
This shows that the existence of moderating variables does provide additional influence, but this influence is still limited and cannot be considered as the main determining factor in this study model, although remains theoretically relevant in explaining the context of digital interactions.
Table 8.
Q2Test Results Variable QA Description Trust 412 Predictive relevance Attitude 358 Predictive relevance Intention 401 Predictive relevance Choosing a University 345 Predictive relevance Note: 3 categories The results of the predictive relevance test show that all endogenous variables have a QA value above zero, indicating that the model has good predictive power.
The QA, trust value of 0.
412 indicates that the model is capable of strongly predicting variations in trust.
Attitude obtained a value of 0.
358, intention 0.
401, and deciding on a university 0.
345, all of which indicate moderate to good predictive relevance.
Scientifically, these findings show that the structural model is not only capable of explaining the relationships between variables but also has adequate accuracy in predicting endogenous constructs.
Thus, this model is suitable for further analysis and interpretation in the context of prospective studentsAo decision-making.
Table 9.
Direct Effect Test Results Hypothesis Relationships between Perceived Information Ie Trust Review Credibility Ie Trust Trust Ie Attitude p-value Conclusion .
Supported Supported Supported Nucleus Journal Vol.
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Journal of Science and Technology H10 Attitude Ie Intention Intention Ie Deciding on a University Perceived Information y Low Comment Engagement Ie Trust Review Credibility y Low Comment Engagement Ie Trust Trust y Low Comment Engagement Ie Attitude Attitude y Low Comment Engagement Ie Intention Intention y Low Comment Engagement Ie Deciding on a University .
[-0.
[-0.
[-0.
[-0.
[-0.
Supported Supported Supported Supported Supported Supported Supported Note: Sig > 0.
The first hypothesis indicates that Perceived Information has a positive and significant effect on Trust ( = t = 5.
p = 0.
CI .
The positive coefficient value indicates that the better the information perceived by prospective students, the higher the level of trust formed toward the university.
The confidence interval, which lies entirely within the positive range, reinforces that this effect is stable and statistically significant.
These findings indicate that clear, relevant, and easily understandable information reduces prospective studentsAo uncertainty in evaluating the institution, thereby increasing their confidence in the universityAos quality and credibility.
The second hypothesis demonstrates that Review Credibility has a positive and significant effect on Trust ( = 0.
t = 4.
p = 0.
CI .
This means that the more credible the reviews received by prospective students, the higher the level of trust that emerges.
The confidence interval (CI) range, which does not cross zero, indicates that this effect is consistent and reliable.
Scientifically, this finding confirms that trust is shaped not only by the content of the information but also by perceptions of the honesty, objectivity, and authenticity of the review source.
In the context of higher education marketing , reviews deemed convincing serve as social signals that reinforce prospective studentsAo confidence in the institution.
The third hypothesis indicates that Trust has a positive and significant effect on Attitude ( = 0.
p = 0.
CI .
The magnitude of this coefficient indicates that trust is a strong determinant in shaping prospective studentsAo attitudes.
When prospective students trust the information and reputation of a university, they tend to develop a more positive evaluation of that institution.
The entirely positive confidence interval further underscores the strength of this relationship.
Thus, trust does not stop at the cognitive level but evolves into an affective attitude reflecting interest, acceptance, and positive evaluation of the university under consideration.
The fourth hypothesis demonstrates that Attitude has a positive and significant effect on Intention ( = 0.
t = 9.
p = 0.
CI .
This indicates that prospective studentsAo positive attitudes toward a university will increase their intention to choose that institution.
The entirely positive CI range confirms that this influence is strong and consistent.
Theoretically, attitude is a form of internal evaluation that serves as a bridge to behavioral tendencies.
Therefore, when prospective students have a favorable perception, liking, and positive evaluation of the campus, their intention to apply or choose that university will strengthen.
The fifth hypothesis indicates that Intention has a positive and significant effect on Deciding on a University ( = 0.
t = 10.
p = 0.
CI .
This coefficient is one of the largest in the model, indicating that intention is the primary predictor of the actual decision to choose a university.
The positive and relatively narrow confidence interval indicates the stability of this influence.
Scientifically, this finding confirms that the stronger a prospective studentAos intention, the greater the likelihood that intention will be realized as an actual decision.
In other words, intention is the psychological stage closest to the act of choosing a university.
The sixth hypothesis shows that Low Comment Engagement negatively and significantly moderates the effect of Perceived Information on Trust ( = -0.
t = 2.
p = 0.
CI [-0.
The negative moderation value indicates that when comment engagement is low, the positive influence of perceived information on trust weakens.
The confidence interval (CI) falling entirely within the negative range Nucleus Journal Journal of Science and Technology Vol.
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reinforces that this weakening effect is statistically significant.
These findings suggest that good information is not necessarily effective in building trust if it is not supported by active social interaction.
In the context of social media, low comment engagement can reduce social validation, thereby diminishing the effectiveness of the information.
The seventh hypothesis indicates that Low Comment Engagement negatively and significantly moderates the effect of Review Credibility on Trust ( = -0.
t = 2.
p = 0.
CI [-0.
- 0.
These results imply that review credibility remains important, but its influence on trust diminishes when comment engagement is low.
The confidence interval (CI) that does not cross zero confirms the validity of this moderating effect.
Scientifically, this finding suggests that credible reviews require the support of a vibrant digital interaction environment to be more convincing.
When the comment section appears inactive, prospective students may doubt whether the reviews truly reflect a broad collective experience.
The eighth hypothesis demonstrates that Low Comment Engagement negatively and significantly moderates the effect of Trust on Attitude ( = -0.
t = 2.
p = 0.
CI [-0.
This means that the influence of trust on the formation of positive attitudes becomes weaker when comment engagement is low.
The entirely negative confidence interval supports the consistency of this effect.
These findings indicate that even though prospective students already possess a certain level of trust, positive attitudes toward the university may not develop to their fullest potential if the digital environment is not interactive.
practice, a lack of comments can create the impression thatthe campus is less popular or less responsive, thereby hindering the conversion of trust into positive attitudes.
The ninth hypothesis indicates that Low Comment Engagement negatively and significantly moderates the effect of Attitude on Intention ( = -0.
t = 2.
p = 0.
CI [-0.
This means that prospective studentsAo positive attitudes toward the university are not fully capable of generating strong intentions when comment engagement is low.
The negative CI range underscores the presence of a tangible weakening effect.
Theoretically, a positive attitude should drive intention, but in a digital context, intention is also influenced by social proof from the online communication environment.
Therefore, when there are few comments and low interaction, prospective students may become less confident in converting their positive attitude into the intention to choose a university.
The tenth hypothesis demonstrates that Low Comment Engagement negatively and significantly moderates the effect of Intention on Deciding on a University ( = -0.
t = 2.
p = 0.
CI [- 0.
These results indicate that even though prospective students have strong intentions, the actual decision to choose a university may weaken in situations of low comment engagement.
The entirely negative confidence interval confirms that this moderating effect is statistically significant.
Substantively, this condition indicates that the final decision is not only influenced by internal factors such as intention, but also by the digital interaction environment that provides social cues.
Low engagement can lead to doubt and delay the realization of the decision.
IV.
CONCLUSIONS
Overall, the results show that all main pathways are positive, significant, and consistent, as reinforced by confidence intervals that do not cross zero.
This means that the decision-making process of prospective students occurs in stages, beginning with the evaluation of information and reviews, then developing into trust, attitude, intention, and finally the actual decision.
However, all these relationships are proven to be weakened by low comment engagement.
This implies that the quality of information and the credibility of reviews alone are insufficient.
universities also need to foster active, responsive, and convincing comment interactions to optimize the impact of digital persuasion.
In theory, this study supports the applicability of combining the Engel-Kollat-Blackwell (EKB) model and the Elaboration Likelihood Model (ELM) to describe the social media behavior of potential students.
The study shows that prospective students tend to rely on the center route by more thoroughly assessing message substance, information relevance, and source reliability as peripheral indicators, including the quantity of comments and exchanges, diminish .
Thus, low comment engagement is not merely understood as poor social media performance but also as a theoretical context explaining shifts in digital persuasion mechanisms .
These findings expand the development of the ELM while enriching the EKB by incorporating digital Nucleus Journal Journal of Science and Technology Vol.
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communicationdynamics into the stages of information search, evaluation of alternatives, and the decision to choose a university.
Practically, these results indicate that universities need to prioritize social media content that is informative, clear, relevant, and credible, rather than merely visually appealing.
Information regarding accreditation, facilities, tuition fees, scholarships, career prospects, and student experiences must be consistently communicated to build trust and foster positive attitudes among prospective students.
Additionally, campus social media managers must strengthen two-way interaction through prompt comment responses.
Q&A sessions, authentic testimonials, and active discussion forums.
This strategy is crucial because high-quality messaging becomes more effective when supported by sufficient social validation, thereby strengthening the transition from trust to intent and decision- making.
This study has limitations as it focuses solely on one institution.
Darmajaya Institute of Informatics and Business, and uses only the Instagram platform.
therefore, generalizing the results to other institutions or social media platforms should be done with caution.
Furthermore, the cross-sectional research design and self-report questionnaire approach do not fully capture the dynamic changes in prospective studentsAo Therefore, future studies are recommended to expand the scope to include multiple universities and various digital platforms, such as TikTok or YouTube, and to incorporate additional variables, such as institutional image, parental influence, tuition costs, and academic reputation.
Future studies may also employ longitudinal, mixed-methods, or experimental approaches to provide a more in-depth explanation of the mechanisms through which low comment engagement influences the decision to choose a university.
ACKNOWLEDGMENT
Author Contributions: Each author contributed to this work, and their roles were clearly stated using the CRediT taxonomy.
As the principal author, [Herlin.
was in charge of conceptualization, methodology, writingAidrafting the original manuscript, writingAireviewing and editing, and supervision.
[Soraya Asnus.
: took part in writing the original draft, investigated and curated data, and contributed to software [Bandarsya.
: was in charge of data investigation and curation tasks that aided in the analysis The final published version of the work has been reviewed and approved by all authors.
Acknowledgments: The authors would like to thank the respondents and the Darmajaya Institute of Informatics and Business academic community for their volunteer involvement and assistance, which allowed this study to be completed.
Conflict of Interest: There is no conflict of interest disclosed by the writers.
No personal or financial ties that might have an impact on the research presented in this study have been disclosed.
Data Availability: The corresponding author can provide the datasets created and examined in this study upon reasonable request.
Since responses were gathered through a survey involving identifiable members of the academic community, these data cannot be made public in order to safeguard the privacy and confidentiality of study participants.
Informed Consent: Clinical or medical research did not use human beings.
Participation in the survey was optional in this study, and implicit informed consent was acquired by agreement before the questionnaire was submitted.
Animal Subjects: This study does not use any animal subjects.
VI.
REFERENCES