Building a Strong Brand: How Digital Marketing Shapes Students’ Online Engagement in Higher Education

 

Dr. K. C. Balaji ¹

 

¹ Department of Business Administration, Loyola College, Chennai, India

 

1. INTRODUCTION

1.1. Background of the Study

The digital revolution has made a lasting impression on the higher education world, changing the way institutions engage with prospective students. Digital marketing, including social media, SEO, email marketing, and online ads, has emerged as a critical method for schools to become more visible and acquire students Rutter et al. (2016). Traditional marketing methods such as brochures, mail shots, and campus tours have increasingly been replaced or supplemented by digital methods that facilitate immediate response and individualized contact Han and Hwang (2022).

Today's students increasingly rely on the internet for discovering learning opportunities. In line with Duffett (2017), students with positive attitudes towards online marketing are more inclined to search for online content when making learning decisions. A number of factors influence such dispositions, including the quality and credibility of information provided in online marketing content, usability, and perceived value Lim et al. (2020). Learning institutions offering interactive, personalized, and engaging online marketing content are generally capable of reaching more students Dwivedi et al. (2021).

Secondly, social influence, also known as subjective norms in Ajzen (1991) Theory of Planned Behavior, plays a crucial role in shaping the decision-making behavior of students. Students' trust in digital marketing channels is greatly influenced by parents, peers, teachers, and academic counselors Alalwan et al. (2017). Additionally, students' perceived behavioral control, or their self-efficacy in using digital platforms, influences their ability to engage with online marketing content effectively Davis et al. (2019). Several factors such as digital literacy, internet accessibility, and usability of university websites influence the willingness of students to rely on digital marketing for educational decisions Han and Hwang (2022).

Trust in digital information is an essential mediating factor influencing students' adoption of digital marketing Kim and Kim (2019). Learning institutions that offer transparent, correct, and authentic marketing information bring about higher levels of trust among students, which motivates them to use digital platforms in making academic choices Boateng and Narteh (2016).

The current research examines the effect of students' attitudes, subjective norms, and perceived behavioral control on their intention to apply digital marketing in higher education, with digital information trust as a mediating variable. The understanding of these relationships can help educational institutions develop more effective digital marketing strategies to enhance student engagement and enrollment levels.

 

1.2. Research Problem

Digital marketing has emerged as a vital tool for universities; however, its ability to impact students' decision-making processes is mixed. Some students are a great supporter of digital marketing campaigns, while others remain skeptical because of credibility issues, potential misinformation, or saturation of digital media Chatterjee and Kar (2020). The overall lack of trust in online information, combined with the complexity of navigating digital media, can prevent student engagement with digital marketing initiatives Hollebeek and Macky (2019). This research aims to fill the existing gap by examining the role of students' perceptions, social influences, and perceived behavioral control in influencing their trust in digital information and their willingness to engage with digital marketing in higher education.

 

1.3. Objectives of the Research

·        To study how students' attitudes towards digital marketing influence their willingness to use digital platforms to pursue higher studies.

·        To assess the impact of subjective norms (social pressure) on students' engagement in digital marketing content.

·        To determine the impact of perceived behavioral control on students' ability to use digital marketing platforms efficiently.

·        To examine the intermediary function of trust in online information within the context of students' use of online marketing for academic decision-making.

 

1.4. Research Questions

·        In what way does students' attitude towards digital marketing influence their intention to utilize digital platforms for further studies?

·        How does subjective norms contribute to students' engagement in digital marketing activities?

·        How does perceived behavioral control influence students' dependence on Internet marketing?

·        Does online information trust mediate the relationship between online marketing variables and students' decision-making?

 

1.5. Significance of the Study

This research adds to the literature on digital marketing within the educational context by exploring significant psychological and behavioral determinants of students' use of digital platforms. The results will be greatly valuable to institutions of higher learning that aim to optimize their digital marketing to enhance students' outreach, interaction, and enrollment. It will also be of great value to policymakers and educators regarding how to enhance digital literacy and foster trust in online learning resources.

 

1.6. Scope and Limitations

The population of interest in this research is students studying higher secondary, undergraduate, and postgraduate programs in Chennai, India. The quantitative survey method was employed to track the students while testing the students' attitudes, social influence, perceived behavior control, and trust in digital marketing. However, the analysis was bounded by self-report perceptions that are prone to bias. Moreover, the findings may not be generalizable to other places outside the geographical and learning context in Chennai.

 

2. Review of Literature

2.1. Theoretical Framework

Two theories underpinned this study: the Technology Acceptance Model (TAM) Davis et al. (1989) and Theory of Planned Behavior (TPB) Ajzen (1991). TAM postulates that perceived ease of use and usefulness drive people's attitudes towards technology, affecting adoption behavior Venkatesh & Bala (2008) . TPB describes how attitude, subjective norms, and perceived behavioral control (PBC) influence behavioral intentions, such as the adoption of digital marketing in higher education Ajzen (2002). These models assist in explaining how higher education students interact with digital marketing approaches.

 

2.2. Higher Education Digital Marketing

Digital marketing transformed student recruitment such that universities could reach out to potential students on social media, search engine optimization (SEO), and email targeting Chugh & Ruhi (2018). Constantinides and Stagno (2017) discovered that there is greater engagement with organized digital strategies like Facebook, Instagram, and LinkedIn campaigns. Rutter et al. (2016) observed that interactive digital information, including virtual campus tours and student reviews, has a significant impact on the enrollment decision of students.

 

2.3. Educational Attitude Towards Digital Marketing

Attitude plays a significant role in making students willing to interact with digital marketing Davis et al. (1989). Empirical evidence reveals that visually appealing and informative digital ads enhance the image of universities among students Duffett (2020). Alalwan (2018) discovered that students who view digital marketing as helpful and pertinent tend to investigate study opportunities using digital media. Additionally, Mulyanegara et al. (2018) indicate that personalized narratives in digital campaigns promote positive attitudes towards institutions among students.

 

2.4.  Subjective Norms and Social Influence

Subjective norms, or social pressures from family, peers, and teachers, influence students' decisions regarding higher education Ajzen (1991). According to studies by Teo et al. (2019), students are more likely to trust digital marketing when their peers or relatives use it. Dwivedi et al. (2020) identified that alumni recommendations and social media recommendations enhance credibility. Chatterjee and Kar (2020) identified that parents' influence is very prominent where family has an integral part to play in matters concerning education-based decisions.

 

2.5.  Digital Literacy and Perceived Behavioral Control

Digital platforms' utilization confidence among the students is labeled as perceived behavioral control (PBC) by Ajzen (2002). Students' proficiency in utilizing digital marketing material for interaction depends upon digital literacy, accessibility, and past experiences Al-Qeisi et al. (2015). Islam et al. (2021) discovered that students who are skilled in digital tools are more likely to search for universities online. Mariani and Predvodnik (2021) emphasized that mobile-friendly websites and easy-to-use interfaces enhance engagement, while obstacles like poor internet connectivity diminish participation Boateng and Narteh (2016).

 

2.6. Trust in Digital Information

Trust plays a crucial role in students’ reliance on digital marketing. Gefen et al. (2003) emphasized that transparency and credibility enhance trust in digital content. Zhao et al. (2020) found that students prefer official university websites over third-party ads due to concerns about misinformation. Shareef et al. (2019) cautioned that outdated or misleading digital marketing content damages trust and discourages engagement.

 

 

 

 

2.7.  Intention to Use Digital Marketing for Higher Education

Students' willingness to use digital marketing is influenced by attitude, subjective norms, and perceived behavioral control Venkatesh et al. (2016). Lin and Kim (2022) discovered that students with positive past experiences are more inclined to use digital platforms when making academic choices. Dwivedi et al. (2021) proposed that universities employing AI chatbots and virtual counseling enhance student confidence in digital marketing. Rutter et al. (2017) reported that customized program suggestions increase student engagement.

 

3.  Research Methodology

3.1. Research Design

The research uses a quantitative research design with a survey-based method to explore the determinants of students' intention to use online marketing channels for seeking higher education. A structured questionnaire was prepared to gather primary data from students in Chennai, India. The research adopts a descriptive research design, collecting data at one point in time to examine students' attitudes and behaviors. Quantitative approaches are popular in educational marketing research because they can offer statistical information about consumer behavior Hair et al. (2019).

 

3.2. Sample and Location

The population to be targeted by this research is students in post-secondary higher schools, undergraduate studies, and postgraduate studies in Chennai. There is a wide range of education in the city, with a combination of government and private educational institutions, providing a suitable site for studying how students interact with digital marketing. The sample comes from various institutions to provide an adequate representative data set.

 

3.3. Sample Size and Sampling Technique

The sample size was calculated using Krejcie and Morgan's (1970) formula for sample size calculation for a representative sample from a finite population. Owing to the large number of students in Chennai, 208 students were sampled to make it statistically significant as well as for generalizability of the data.

A stratified random sampling method was used to choose participants. The method provides equal representation from various educational levels (higher secondary, undergraduate, and postgraduate), thereby minimizing bias Saunders et al. (2019). Stratified sampling is specifically effective in education research, as it provides an accurate examination of differences in students' digital marketing involvement across various educational levels Etikan and Bala (2017).

 

3.4. Data Instrumentation and Collection

Data was gathered through a structured self-completed questionnaire administered in both online and offline channels. The questionnaire was developed from validated scales of prior research on digital marketing adoption Venkatesh and Bala (2008), Ajzen (1991).

The survey tool includes 30 statements, grouped under the following variables: Attitude Toward Digital Marketing (6 items) – Modified from Alalwan (2018),Subjective Norms (6 items) – Based on Teo et al. (2019), Perceived Behavioral Control (6 items) – Taken from Mariani and Predvodnik (2021),Trust in Digital Information (6 items) – Altered from Gefen et al. (2003),Intention to Use Digital Marketing for Higher Education (6 items) – Modified from Venkatesh et al. (2016) and Responses were elicited using a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree), a widely accepted approach in behavior research to quantify participants' perceptions and attitudes Likert (1932).

 

4.  Data Analysis and Result

4.1. Reliability Analysis Results

Table 1

Table 1
Construct	Number of Items	Cronbach’s Alpha	Reliability Level
Attitude Toward Digital Marketing	5	0.720	Acceptable
Subjective Norm	5	0.728	Acceptable
Perceived Behavioral Control	5	0.733	Acceptable
Trust in Digital Information	5	0.830	Good
Intention to Use Digital Marketing Channels	5	0.690	Marginal

 

The reliability test ascertains internal consistency among measurement items based on Cronbach's Alpha. Reliable values fall within 0.7–0.8 for acceptable reliability and above 0.8 as strong reliability. The construct that has the strongest alpha is Trust in Digital Information at 0.83 and is very consistent. Attitude, Subjective Norm, and Perceived Behavioural Control rank between the 0.72–0.733 threshold values, and these items are capable of measuring intended construct reliably. Yet, the Intention to Use Digital Marketing Channels construct has a slightly lower alpha (0.69), indicating the possibility of potential refinement, for example, revising or deleting weaker items.

Table 2

Table 2 Independent Samples t-Test for SITU by Gender
Gender	N	Mean	Std. Deviation	Std. Error Mean
Male	132	3.2909	0.77178	0.06717
Female	74	3.2838	0.69598	0.08091

 

Independent samples t-test revealed no significant gender difference in SiTU scores between males (M = 3.2909, SD = 0.77178) and females (M = 3.2838, SD = 0.69598). The minor mean difference of 0.0071 implies that gender does not have any influence on SiTU scores, which means that there are equivalent perceptions within both groups.

Table 3

Table 3
Test	F	Sig.	T	Sig. (2-tailed)	Std. Error Difference	95% CI (Lower)	95% CI (Upper)
Equal variances assumed	1.368	0.244	0.066	0.948	0.10827	-0.20634	0.22059
Equal variances not assumed	-	-	0.068	0.946	0.10516	-0.20051	0.21476

 

An independent samples t-test was done to test males and females on SiTU scores. No difference was significant, t(204)=0.066,p=0.948t(204) = 0.066, p = 0.948t(204)=0.066,p=0.948. Equal variances were reported by Levene's test (F=1.368,p=0.244F = 1.368, p = 0.244F=1.368,p=0.244). Males' mean scores (M = 3.2909, SD = 0.77178) and females' mean scores (M = 3.2838, SD = 0.69598) were very similar to each other, with a difference of 0.00713. The 95% confidence interval was -0.20634 to 0.22059. Therefore, gender does not have a significant influence on SiTU scores.

Descriptive Statistics

Table 4

Table 4
Variable	Sample Size (N)	Min	Max	Mean	Std. Deviation
Attitude toward Digital Marketing	208	1.00	5.00	3.5269	0.63341
Subjective Norm	208	1.00	5.00	3.1433	0.77475
Perceived Behavioural Control	208	1.00	5.00	3.4019	0.74859
Trust in Digital Information	208	1.00	5.00	3.2577	0.82083
Intention to Use Digital Marketing	208	1.00	5.00	3.2883	0.74373

 

The descriptive statistics reflect participants' attitudes towards digital marketing. Attitude toward Digital Marketing recorded the highest mean (M = 3.5269, SD = 0.63341), reflecting a positive attitude overall. Subjective Norm recorded the lowest mean (M = 3.1433, SD = 0.77475), reflecting moderate social influence. Perceived Behavioral Control and Trust in Digital Information (ST) recorded equivalent means (M = 3.4019, M = 3.2577), reflecting a neutral attitude. Intention to Adopt Digital Marketing (M = 3.2883, SD = 0.74373) indicates a moderate level of adoption likelihood. The findings indicate that trust and social influence need to be improved to enhance digital marketing adoption.

Correlation Analysis

Table 5

Table 5 Correlation Matrix
Variable	AT	SN	PB	T	ITU
Attitude toward Digital Marketing (AT)	1	.282**	.462**	.307**	.390**
Subjective Norm (SN)	.282**	1	.511**	.480**	.139*
Perceived Behavioural Control (PB)	.462**	.511**	1	.573**	.092
Trust in Digital Information (T)	.307**	.480**	.573**	1	.133
Intention to Use Digital Marketing (ITU)	.390**	.139*	.092	.133	1

 

The findings reveal substantial positive relations among variables. There is a moderate correlation between Attitude towards Digital Marketing (SAT) and SiTU (r =.390, p <.01), meaning that attitude is positive with the intention to use digital marketing channels. Trust (ST) and Perceived Behavioral Control (SPB) are very correlated (r =.573, p <.01), which implies that trust reinforces perceived control. Subjective Norm (SSN) weakly correlates with SiTU (r =.139, p <.05), which shows a slight social influence. There is no significant direct effect of SPB and ST on ITU. These findings indicate that enhancing attitudes and social influence would be able to propel digital marketing adoption.

Table 6

Table 6  Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of Estimate
1	.415	.172	.156	.68335

 

Interpretation

The regression model explains 17.2% of the variance (R² = 0.172) in Intention to Use Digital Marketing (SiTU). The adjusted R² value (0.156) suggests that after adjusting for predictors, the model remains a weak but significant fit. The standard error (0.68335) indicates the average deviation of predicted values from actual values. Although the model explains some variation in SiTU, additional factors may influence digital marketing adoption.

Table 7

Table 7 ANOVA (Model Fit Test)
Model	Sum of Squares	df	Mean Square	F	Sig.
Regression	19.532	8	4.883	10.457	.000
Residual	93.86	201	0.467
Total	113.392	208

                                        

The ANOVA test validates that the regression model is significant statistically (F = 10.457, p < 0.001). This implies that at least one of the predictors (SAT, SSN, SPB, ST) has a significant effect on SiTU (Intention to Use Digital Marketing Channels). The model, however, accounts for just 17.2% of the variance, implying that other variables not considered might affect digital marketing adoption.

Table 8

Table 8
Predictor	B (Unstandardized)	Std. Error	Beta (Standardized)	t	Sig.
Constant	1.689	0.305	-	5.536	.000
Attitude	0.508	0.085	0.434	5.977	.000**
Subjective Norm	0.079	0.074	0.082	1.061	.29
Perceived Control	-0.187	0.087	-0.189	-2.152	.033*
Trust	0.061	0.073	0.067	0.828	.409

 

The regression analysis shows that Attitude toward Digital Marketing  is the most significant predictor (B = 0.508, p < 0.001), i.e., a positive attitude has a significant positive effect on the intention to use digital marketing. Perceived Behavioral Control (SPB) has a negative impact on  (B = -0.187, p = 0.033), i.e., those who perceive that they have less control over digital marketing are less likely to adopt it. Subjective Norm and Trust  have no effect on intention to use electronic marketing channel (p > 0.05). This underlines the significance of improving positive attitudes in overcoming perceived challenges in the use of digital marketing.

 

5.  Discussion and suggestion

The research concluded that Attitude toward Digital Marketing has a great impact on the Intention to Use Digital Marketing Channels (SiTU) (B = 0.508, p < 0.001). This is consistent with Ajzen (1991) Theory of Planned Behavior (TPB), which postulates that attitude will be a powerful predictor of intention to behave. Perceived Behavioral Control (SPB) has a negative effect on SiTU (B = -0.187, p = 0.033), which shows that those who perceive they are not in control of digital marketing are less inclined to adopt it. Subjective Norm (SSN) and Trust (ST) did not significantly influence it, unlike in previous research, which highlighted their contribution to the adoption of digital Venkatesh et al. (2003).

The results concur with earlier literature highlighting attitude as a significant motivator for digital marketing usage Davis et al. (1989), Venkatesh et al. (2003). Subjective Norm, however, had minimal effects (r = .139, p < .05), contrasting research findings in which peer pressure contributed significantly Taylor & Todd, (1995).

 

5.1.  Conclusion and suggestions

 The study supports the Theory of Planned Behavior Ajzen (1991) by showing that Attitude toward Digital Marketing is the most important predictor of Intention to Use Digital Marketing Channels (SiTU) (B = 0.508, p < 0.001).  SiTU is negatively impacted by perceived behavioral control (SPB) (B = -0.187, p = 0.033), suggesting that people are less willing to use digital marketing tools if they feel less in control of them.  Subjective Norm (SSN) and Trust (ST), however, had no discernible effects on SiTU.  According to these results, adopting digital marketing can be facilitated by enhancing attitude and lowering perceived challenges Davis et al. (1989), Venkatesh et al. (2003).

 

5.2. Practical implication

 Through instructional initiatives, engaging content, and streamlined user interfaces, organizations should concentrate on fostering positive attitudes Venkatesh and Bala (2008).  User engagement can be increased by offering practical training and resolving perceived issues.  Furthermore, by guaranteeing data protection, openness, and credibility in online marketing platforms, strategies should improve digital trust Gefen et al. (2003).

 

5.3.  Limitations

The cross-sectional design of the study limits the capacity to draw conclusions about causality Podsakoff et al. (2003). Generalizability is impacted by the sample's demographic and geographic limitations. Furthermore, external technological and economic aspects were not taken into account. For more thorough insights, future research should employ longitudinal techniques and larger sample sizes (Hair et al. (2010).

 

5.4. Future studies

Future studies should use longitudinal methods to monitor the adoption trends of digital marketing throughout time Venkatesh et al. (2012).  The generalizability of the sample will be enhanced by extending it to diverse cultural and industrial situations.  Additionally, moderating elements including digital infrastructure, psychological resistance to change, and technological literacy should be investigated by researchers Rogers (2003).  Qualitative research may offer more profound understandings of the ways in which perceived behavioral control hinders adoption.  Additionally, it is important to investigate new technologies like blockchain-based trust mechanisms, AI-driven marketing, and customization algorithms to learn how they affect user adoption patterns in digital marketing Dwivedi et al. (2021).

 

CONFLICT OF INTERESTS

None. 

 

ACKNOWLEDGMENTS

None.

 

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