Investigate the impact of digital branding on university education in the case of Unicaf University To assess the impact of digital marketing on student recruitment. To determine impact of social media marketing on university brand image. To determine impacts of website advertising on university brand image. To determine impacts of banner advertising on university brand image. To determine impacts of pop-up on university brand image

1. INTRODUCTION AND BACKGROUND
 The act of advancing labor and products through the utilization of digital innovation, primarily the Internet,
yet in addition mobile telephones, show advertising, and different forms of digital media, is known as
digital marketing.
 Digital marketing communication, according to the literature, is a significant vehicle for organizations to
get a larger market share (Mintel Academic, 2014). Increased brand recognition and enhanced brand image
are two advantages of a strong digital marketing communication strategy (Headington, 2008). According to
literature, a favorable and well-known brand is a valuable asset to a firm since it is a potent instrument in
influencing a consumer's buying choice. Digital marketing approaches may now be used as a marketing
strategy that has a significant impact on customer purchasing behavior.
 While digital marketing is most firmly connected with the Internet, different stations incorporate remote
text messaging, mobile texting, mobile applications, webcasts, electronic announcements, and digital TV
and radio broadcasts (Schulze et al., 2015).
 Digital marketing has changed how brands and businesses use innovation for marketing during the 1990s
and 2000s. Digital marketing efforts are developing more far and wide and effective as digital platforms
become more coordinated into marketing plans and day to day existence, and as clients progressively use
digital gadgets rather than actual storefronts (Wymbs, 2011). Search engine improvement (SEO), search
engine marketing (SEM), content marketing, force to be reckoned with marketing, content computerization,
crusade marketing, data-driven marketing, web based business marketing, social media marketing, social
media streamlining, email direct marketing, show advertising, and optical circles and games are on the
whole instances of digital marketing strategies that are turning out to be more typical in our high level
society. Without a doubt, digital marketing has developed past the Internet to incorporate non-Internet
procedures that give digital media, like mobile telephones (SMS and MMS), callback, and cell ringtones on
pause (Schulze et al., 2015).

2. PROBLEM STATEMENT
 The demands on higher education have risen as a result of globalization, as has rivalry among universities. As a result,
colleges and universities have begun to evaluate their internet presence as a possible competitive advantage (Hruska,
2020).
 A digital presence for an institution helps it to nurture potential customers from brand recognition to consideration,
where they learn more about your products and services (Naidoo, 2020). Digital platforms have enabled many
institutions to create this easy presence using internet.
 The use of digital marketing on search engines like Google, Yahoo, and Baidu may help an institution's brand image by
increasing its visibility and making it more easily recognized (Hruska, 2020). By registering a website in search engines
with specific keywords and effective search engine marketing strategies, a website may be found more frequently by
the public than other companies, enhancing the company's brand image.
 Unicaf University is one of the well-known universities that uses digital marketing to enhance its brand image, such as
registering its website on Google, Yahoo, and Baidu with keywords such as “university” and “scholarship.” Every Google
search including keywords will take consumers to the Unicaf University website. More so, Unicaf has been using digital
platforms since its entrance in Zambia. The university’s online presence is undeniably good as this can, for example be
witnessed by the constant Facebook sponsored ads, google pop up ads etc. which by design redirects the client to their
(University) official website. However, till date Unicaf is still not a common word among prospective students, it’s still
not a common choice of university to most average Zambians, especially those pursing undergraduate programs. This
raises a lot of questions such as: Do prospective students visit the Unicaf website at all? Are the digital platforms Unicaf
use effective enough to sell its services? What is the relationship between these digital platforms and the university
brand image?

3. RESEARCH OBJECTIVES
 GENERAL OBJECTIVES
 Investigate the impact of digital branding on university education in the case of
Unicaf University
 SPECIFIC OBJECTIVES
 To assess the impact of digital marketing on student recruitment.
 To determine impact of social media marketing on university brand image.
 To determine impacts of website advertising on university brand image.
 To determine impacts of banner advertising on university brand image.
 To determine impacts of pop-up on university brand image

4. SIGNIFICANCY AND SCOPE OF THE STUDY
 The focus was on Only students from Unicaf University in Lusaka
 The results to this study are expected to:
1. Be utilized by Unicaf Zambia as an assessment and study material in order
to sustain the performance of their digital marketing program in their
efforts to recruit new students while improving brand image.
2. It can also be used by other Universities and colleges for the same reason.
It can also provide inspiration and references for other relevant studies in
Branding and digital marketing field.

5. Literature Review
Author and year Title Results
Ghali (2019) Impact of digital
platforms on the brand
awareness of Ghanaian
private institutions
All of the social media indicators were found to be
significant predictors of brand awareness, the findings
highlight digital platforms as an important tool in
boosting brand awareness among students.
Trusov et al. (2009) Investigate the impact
of digital marketing on
Brand image in Russia
the significant impact of Digital Marketing in terms of
building public opinions, creating awareness, and
building of brands. He further stated that Digital
Marketing enhances the customer experiences
Susilo (2020) Uderstanding how
digital platfoms ie.
social media, website
and search engine
marketing affect the
university brand image
there was a positive correlation which suggested that
university should increase their attention on improving
their social media, website and engine marketing in
order to build up better brand image among their
students or customers

6. THEORETICAL FRAMEWORK
 The PCDL
 PCDL model created by Ghodeswar in 2008, presents a design that can be
adjusted when building brands in a cutthroat market and comprises of four
stages; positioning, imparting the message, conveying execution, and utilizing
brand value. When planning to comprehend the college point of view, the
PCDL model is one of a handful of the structures taking the organization
viewpoint and is therefore applicable to this review.
 AIDA Model
 The AIDA, otherwise called order of effects models or progressive models,
contends that purchasers go through a progression of cycles or stages while
settling on buy decisions (Amble, 1999). These are successive, direct models
that accept customers go through a progression of mental (thinking) and
emotional (feeling) stages before acting (buying or having a go at something).

7. CONCEPTUAL FRAMEWORK
WEBSITE
(X2)
WEBBANNER
(X3)
POP-UPS/POP-UNDERS
(X4)

8. RESEARCH METHODOLOGY
 Research Approach
 The research relied on both quantitative and qualitative
 Research Design
 Triangulation design
 Model Specification
 Y = β0 + β1𝑋1 + β2𝑋2 + β3𝑋3 + β4𝑋4 + 𝜀
 Where: Y = Brand Images
X1 = Social-media
X2 = Website
X3 = Web-Banner
X3 = Pop Ups
𝛽0 = Y-intercept of the line
𝛽1 = (i = 1,2,3,4) are estimates of the coefficient
ε = An error term measurin
g variation unaccounted for the independent variables.

9. RESEARCH METHODOLOGY’ CONT’D
 Estimation Procedure
 The following experiments were carried out:
1. Multi-collinearity The Breusch Pagan Godfrey Test was used to administer the test.
2. Autocorrelation Analysis. Based on the premise that the error term's covariance across
time should be zero. As a result, the mistakes are unrelated to one another. To test for
correlation, the Durbin Watson Test was performed.
 Data Analysis
 On the basis of the questionnaire results, data analysis was carried out. The data was
summed up utilizing the Microsoft Excel and the Statistical Package for Social Sciences
(SPSS v.26). Descriptive statistics were utilized to portray the information, including
frequencies, rates, and means. This was utilized to examine quantitative information
utilizing inferential measurements. The subjective information from the meetings were
combined into emergent topics after which thematic analysis was used

10. RESULTS AND ANALYSIS
 Digital platforms Access
 1.70 mean corresponded to 70% of students who claim they use Facebook mostly for
digital platforms. The second highest average of 1.50 corresponds to 50% of respondents
who said they use Instagram as a digital platform. The next highest average was 1.30,
indicating that 66% of respondents use Twitter as a digital channel
N Mean Standarddeviation
Valid Missing
MostoftenaccessFacebook 186 0 1.70 .470
MostoftenaccessInstagram 186 0 1.50 .513
MostoftenaccessTwitter 186 0 1.25 .444

11.  Frequency of visits to the website https://unicafuniversity.ac.zm in one week
 53.8% (100) visited the website more than 6 times a week, followed by 29% (54) respondents
indicating that they visited the website 4 to 6 times a week while only 11.8% (22) indicated
that they visited the website to 3 times a week
RESULTS AND ANALYSIS CNT’D
PerWeek Frequency Percent
Valid
1-3times 22 11.8
4-6times 54 29
>6times 100 53.8
Total 186 100

12.  Web Banners
53.8% (100) who view the website pay attention to the web banners each time they
visit the website. 29% (54) respondents indicated even when they visit the website its
only sometimes that they view the web banner. 11.8% (22) indicated that they were not
sure
RESULTS AND ANALYSIS CNT’D
Frequency Percent
Valid
Notsure 22 11.8
Sometimes 54 29
EachtimeIvisitthewebsite 100 53.8
Total 186 100

13.  Pop-Ups
55.4% always click on pop-ups when they appear, 30% (56) only click on pop-ups
sometimes, and just 14.5 percent (227) always click on Unicaf pop-ups when they
appear.
RESULTS AND ANALYSIS CNT’D

14. DISCUSSION OF FINDINGS
 Multicollinearity Test
 Multicollinearity does not occur because VIF <10
Coefficients
Model
UnstandardizedCoefficients Standardized
Coefficients
t Sig.
CollinearityStatistics
B Std.Error Beta Tolerance VIF
1
(Constant1) 1.104 .283 3.895 .000
SOCIAL .419 .077 .395 5.437 .000 .764 1.308
WEBSITE .077 .077 .076 .89 .100 .409 1.556
WEB-B .0.07 .004 .138 1.355 .177 .389 2.569
POP .087 .099 .088 .880 .380 .407 2.459
a.DependentVariable:BRAND

15. DISCUSSION OF FINDINGS
 Autocorrelation Test
The Durbin-Watson statistical value is 1.363 based on the SPSS test results. According to
Sudarmanto (2005), as the Durbin-Watson statistical value approaches 2, the observational
data does not contain autocorrelation. As a result of this observation, it is possible to
deduce that there is no autocorrelation since the Durbin-Watson statistical value is 1.363.
ModelSummary
Model R RSquare AdjustedRSquare Std. Error of the
Estimate
Durbin-Watson
1 .531a .282 .270 .57740 1.363
a.Predictors:(Constant),POP,WEB-B,WEBSITE,SOCIAL
b.DependentVariable:BRAND

16. DISCUSSION OF FINDINGS
 Multiple Linear Regression Analysis
 After a regression analysis, the multiple linear regression of this research becomes:
 Y = 1.104 + .419𝑋1 + .07𝑋2 + .0.07𝑋3 + .087𝑋4
Where: Y = Brand Images, X1 = Social-media, X2 = Website, X3 = Web-Banner, X3 = Pop Ups
Coefficients
Model
UnstandardizedCoefficients Standardized
Coefficients t Sig.
CollinearityStatistics
B Std.Error Beta Tolerance VIF
1
(Constant1) 1.104 .283 3.895 .000
SOCIAL .419 .077 .395 5.437 .000 .764 1.308
WEBSITE .077 .077 .076 .89 .100 .409 1.556
WEB-B .0.07 .004 .138 1.355 .177 .389 2.569
POP .087 .099 .088 .880 .380 .407 2.459
a.DependentVariable:BRAND

17. DISCUSSION OF FINDINGS
 t-Test
 Digital platforms and brand image
 H1 is accepted for the variable Digital platforms, t = 3.895. Sig = 0.007 and significance <0.05; thus,
H1 is accepted. Ho was rejected therefore, there was a significant effect of the implementation of
Digital platforms on the brand image of Unicaf University of Zambia if the website, web banners
and pop ups were controlled with a 95% confidence level.

Coefficients
Model
UnstandardizedCoefficients Standardized
Coefficients
t Sig.
B Std.Error Beta
1
(Constant1) 1.104 .283 3.895 .000
SOCIAL .419 .077 .395 2.233 .007
WEBSITE .077 .077 .076 3.890 .000
WEB-B .0.07 .004 .138 2.107 .000
POP .087 .099 .088 1.860 .000

18. DISCUSSION OF FINDINGS
 t-Test
 Website and brand image
 H2 is accepted for variable website, t = 3.890 Sig = 0,000 and significance <0.05; thus, H2 is received. Ho
was rejected therefore, there was a significant effect of website implementation on Unicaf University of
Zambia if the Digital platforms, web banners and pop ups were were controlled with a 95% confidence
level.
Coefficients
Model
UnstandardizedCoefficients Standardized
Coefficients
t Sig.
B Std.Error Beta
1
(Constant1) 1.104 .283 3.895 .000
SOCIAL .419 .077 .395 2.233 .007
WEBSITE .077 .077 .076 3.890 .000
WEB-B .0.07 .004 .138 2.107 .000
POP .087 .099 .088 1.860 .000

19. DISCUSSION OF FINDINGS
 t-Test
 Web banner and brand image
 H3 is accepted for variable search engine marketing, t = 2.107. Sig = 0,000 and significance <0.05; thus, H3
is accepted. Ho was rejected therefore, there was a significant effect of the implementation of Web
banners on the brand image of Unicaf University of Zambia if the Digital platforms, website and pop ups
were were controlled with a 95% confidence level.
Coefficients
Model
UnstandardizedCoefficients Standardized
Coefficients
t Sig.
B Std.Error Beta
1
(Constant1) 1.104 .283 3.895 .000
SOCIAL .419 .077 .395 2.233 .007
WEBSITE .077 .077 .076 3.890 .000
WEB-B .0.07 .004 .138 2.107 .000
POP .087 .099 .088 1.860 .000

20. DISCUSSION OF FINDINGS
 t-Test
 Pop ups and brand image
 H3 is accepted for variable search engine marketing, t = 1.860. Sig = 0,000 and significance <0.05; thus, H3
is accepted. Ho was rejected therefore, there was a significant effect of the implementation of Pop ups on
the brand image of Unicaf University of Zambia if the Digital platforms, website and web banners were
controlled with a 95% confidence level.
Coefficients
Model
UnstandardizedCoefficients Standardized
Coefficients
t Sig.
B Std.Error Beta
1
(Constant1) 1.104 .283 3.895 .000
SOCIAL .419 .077 .395 2.233 .007
WEBSITE .077 .077 .076 3.890 .000
WEB-B .0.07 .004 .138 2.107 .000
POP .087 .099 .088 1.860 .000

21. DISCUSSION OF FINDINGS
 F Test
 Ho is rejected and H5 is accepted, because the significance is smaller than α (alpha), namely: Significance
value of 0.000 compared to α (alpha) of 0.05 There is a significant and positive effect from Digital
platforms, Website, Web banners and pop-up ads Marketing simultaneously to Brand Images of Unicaf
University of Zambia with a 95% confidence level.
ANOVAb
Model
SumofSquares
df MeanSquare
F Sig.
1
Regression 23.364 3 7.788 23.360 .000
Residue 59.343 178 .333
Total 82.706 182
a.Predictors:(Constant),SOCIAL,POP,WEB-B,WEBSITE
b.DependentVariable:Brand

22. DISCUSSION OF FINDINGS
 R2 Test
 The study model performs well with an adjusted R square value of 77 percent, which explains that Digital
platforms, website, web banners, and pop-up/under marketing impact brand image by 77 percent and the
remaining 23 percent is explained by additional factors outside of the suggested mode
Model R RSquare
AdjustedRSquare Std.ErroroftheEstimate
1 .831a .782 .770 .57740
a.Predictors:(Constant),SOCIAL,POP,WEB-B,WEBSITE

23. CONCLUSIONS AND RECOMMENDATION
 It was established that Digital platforms marketing, Website, Web banner advertising and
Pop-up advertising significantly and positively affects brand image of Unicaf University
Zambia. It was therefore concluded that Digital platforms marketing significantly and
positively affects brand image of Unicaf University Zambia. These results concur with those of
Susilo & Sawlani (2020) who found that digital platforms ie. social media, website and search
engine marketing simultaneously and partially affect the university brand image. The authors
found that there was a positive correlation which suggested that university should increase
their attention on improving their social media, website and engine marketing in order to
build up better brand image among their students or customers. The results also second
those of Trusov et al. (2009) who carried out research to investigate the impact of digital
marketing on Brand image in Russia and a significant impact of Digital Marketing in terms of
building public opinions, creating awareness, and building of company brand

24. CONCLUSIONS AND RECOMMENDATION CNT’D
 Because digital marketing has a positive impact on branding, Universities should integrate digital
marketing in their marketing plan. They should ensure they work on making their websites and
Digital platforms pages very attractive, informative and responsive to improve site banners and
improve prospective student interaction with the site. This together with constant but varying pop
up about the university will generate traffic to the website and therefore boost student
enrolments. Shirisha (2018) highlighted some benefits of Digital platforms, such as cost-
effectiveness, enhanced exposure, time efficiency, higher engagement of students, and brand
building enhancement.
 Recommendations to Unicaf
 The study has made the following suggestion based on the findings of this research:
 Unicaf and other Universities must pay attention to their Digital platforms, website, web-banner,
and pop-up/under when attempting to build a positive brand image. When universities improve
their digital marketing, they may create a more favourable brand image. Many Zambian
universities continue to ignore digital marketing, and as a result, their Digital platforms accounts
and websites are so outdated that it is frequently discovered that universities do not provide
updated materials on their websites. In order to create their brand image, they must use digital
marketing tactics.


25. CONCLUSIONS AND RECOMMENDATION CNT’D
 Recommendations to Unicaf
 study endeavoured to explore marketing of university education using digital platforms in
Zambia. And it explored Digital platforms, website, web banners and pop-ups/under as the
strategies of digital marketing. Therefore, future research should divert from education and
investigate the impact of online marketing on customer retention for retail companies in
Lusaka.

26. LIMITATIONS OF THE STUDY
 COVID-19 – In accordance with the Covid-19 health guidelines Due to their
fear of contracting the illness, interacting with the CBD responders will be
difficult. However, throughout data collecting, the researcher will follow all
health guidelines, such as 1 meter separation and masked up (including
providing masks to farmers respondents who do not have masks).
 • Time limitation – In the sense that the time allotted for performing this study
was it is possible that all important data will not be gathered.
 • Cost implications – Because this study is unfunded and the researcher has the
means to self-finance logistics, stationery, and secretarial services, there may
be delays in completing tasks due to the cost of printing data collecting
instruments such as questionnaires. To overcome this obstacle, the researcher
used mostly paperless mobile data collection approaches (when really
necessary)