THE
EFFECTIVENESS OF INTEGRATED MARKETING COMMUNICATIONS ON PURCHASE INTENTIONS OF
GENERATION Z IN YANGON, MYANMAR
*Myo Aung, **Asst Prof Dr. Siriwan
Kitcharoen, ***Dr. Bhumiphat Gilitwala
*MBA Student, **Assistant Vice President for
Educational Innovation and Graduate Studies, ***Program Director MBA,
Assumption University
This research aims to examine
the effectiveness of Integrated Marketing Communications (IMC) on the purchase
intentions of Generation Z consumers in Yangon, Myanmar. The framework includes
digital advertising, influencer marketing, content marketing, public relations,
social media marketing, and purchase intention as predictors of perceived
influence. A quantitative research design was applied, using structured
questionnaires distributed to 385 respondents aged 18–27 years in Yangon
through purposive sampling. Content validity was tested with Item Objective
Consistency (IOC), while Cronbach’s Alpha confirmed construct reliability. Data
were analyzed with descriptive statistics and regression techniques using
Jamovi software to evaluate the hypotheses and establish causal relationships.
The findings indicate that influencer marketing and social media marketing have
the strongest impact on Generation Z’s purchase intentions. Content marketing
and public relations also play significant roles, while digital advertising
shows only a marginal effect. These results highlight that Gen Z consumers are
influenced more by effective information flow and interactive engagement on
social media platforms than by mere exposure to digital advertising. The study contributes to IMC literature in
emerging markets and provides practical insights for marketers in designing consumer-centric
strategies through credible influencers, engaging content, and active social
media presence.
Keywords: Integrated Marketing
Communications, Generation Z, Purchase Intention, Digital Advertising,
Influencer Marketing, Content Marketing, Public Relation, Social Media
Marketing
In
the fast-changing business landscape of today, integrated marketing
communications (IMC) has become a vital strategic tool. It requires the
coordination and integration of several marketing tools and channels such as
advertising, sales promotion, public relations, direct marketing, and digital
media to provide a consistent and powerful message to target audiences (Belch
and Belch, 2021). Particularly in Myanmar, the fast expansion of digital
platforms and rising smartphone penetration have substantially changed customer
behavior, especially among younger generations.
Generation
Z are those who were born between 1997 and 2012, is becoming a major consumer
segment in Myanmar. Digital nativity, social media fluency, and a desire for
customized and genuine brand engagements define this cohort (Williams et al.,
2012; Seemiller and Grace, 2016). Understanding how IMC techniques affect Gen
Z's purchasing intents gets increasingly important as companies move toward
customer-centric and data-driven strategies.
Though
Myanmar faces political and financial difficulties, digital transformation
keeps advancing and provides marketers chances to use integrated campaigns
successfully. There is little academic study available on how these techniques
affect Gen Z's purchase intentions in a local setting. This study tries to
close this gap by assessing how well integrated marketing campaigns affect Gen
Z's purchase intentions in Myanmar.
The primary objective of this study
is to evaluate the effectiveness of integrated marketing communications on the
purchase intentions of Gen Z consumers in Yangon, Myanmar. The specific
objectives are:
Purchase intention is defined as consumers’ reaction against
marketing messages, which can include interest in purchase, evaluation, and
finally a decision to buy. Based on Ajzen's (1991) work on the Theory of
Planned Behavior, behavioral intention is the strongest predictor of actual
behavior because it forms the bridge between attitudes and behaviors. Several
studies in consumer behavior reveal a strong association between favorable
purchase responses and purchase likelihood (Belch & Belch, 2021).
Digital advertising is the identification, access, and use of
digital tools and technologies by consumers in daily practice. It includes the
knowledge of platforms, advertisements and the new media content which shape
the perception and behaviours of consumers (Rothman, 2019).
Influencer marketing is the practice of leveraging
individuals with a prominent online identity and credibility to advertise
products or services to their followers. Influencers are opinion leaders, whose
endorsement can influence consumer perception and purchase intention (Freberg
et al., 2011). Unlike traditional celebrities, digital influencers build
credibility with their followers by being perceived as authentic, relatable,
and frequently engaging with their audiences (Casaló et al., 2018). Influencer
content is often integrated into social media platforms including Instagram,
TikTok, and YouTube, which has made promotional influencer marketing one of the
fastest growing methods of Integrated Marketing Communications.
Content marketing means the creation, composition, design,
and distribution of relevant and immersive marketing content that meets the
interests and emotional needs of particular consumers (Ashley & Tuten,
2015). Meaningful content either informs, entertains, or inspires consumers and
elevates them closer to the brand; contributing to the overall brand
engagement. Scholars have shown that personalized, creative, and authentic
content enhances the consumer-brand relationship significantly (Hollebeek &
Macky, 2019).
As defined by Grunig and Hunt (1984), public relations (PR)
is a strategic communication process that develops mutual-benefit relationships
between organizations and their various publics. When applied to marketing, PR
creates a corporate reputation, credibility, and trust among consumers, through
a variety of ways such as media relations, publicity campaigns, and various
forms of corporate social responsibility. The research supports this argument,
as PR activities have a positive and favourable impact on brand perception and consumer
attitudes. PR functions as a mechanism of IMC (Wilcox et al., 2015).
2.6 Social Media Marketing
Social media marketing is defined as a process of reaching
consumers and promoting brand offerings through social media platforms like
Facebook, Instagram, TikTok, and Twitter (Mangold & Faulds, 2009). Social
media differs from typical media channels because it allows for various forms
of two-way communication, allowing consumers to comment on, share, and even
co-create content with brands. Several researchers find social media engagement
leads to more awareness of the brand, increased trust of the brand, and
positive purchase intention outcomes (Duffett, 2017; Kaplan & Haenlein,
2010).
2.7 Relationship
between Digital Advertising and Purchase Intention
Digital advertising is emerging as an
essential part of Integrated Marketing Communications (IMC) with sophisticated
segmentation, customization, and interactivity. Internet advertising includes
banner ad, search engine ad, video ad, and programmatic ad on various digital
media. For digitally empowered Generation Z consumers who are always accessible
via smartphones and digital devices, internet advertising is a highly effective
channel to build awareness, generate engagement, and drive purchasing intent
(Duffett, 2017).
2.8 Relationship between Influencer
marketing and Purchase Intention
Influencer marketing is a powerful
driver of Internet-era Integrated Marketing Communications (IMC), best suited
in an era when consumer trust in conventional advertisement forms is low. It is
a definition of collaboration between individuals and businesses who possess
power over target publics through media like Instagram, YouTube, and TikTok.
For Gen Z consumers, who are extremely peer-referral, authenticity, and
relatability-sensitive, influencer marketing is a powerful driver of purchase
intentions (Schouten et al., 2020).
2.9 Relationship between Content
Marketing and Purchase Intention
Content marketing is the process of
developing and sharing valuable, consistent, relevant, and informative content
with the aim of attracting and retaining a clearly defined audience. Content
marketing is not selling the product directly but aims to offer content that is
either educational or entertaining in character so that it allows it to build
trust and build an emotional relationship with the brand (Pulizzi, 2012). In
the world of Gen Z—cynical toward traditional advertising and super-duper
smitten with authenticity—content marketing assumes a chief role in influencing
buying intention. The value addition of content marketing is that it can create
brand-consumer relationship, which is a major precursor of buying.
2.10 Relationship between Public
Relation and Purchase Intention
Public relations (PR) is an activity of business
communications employed to establish and maintain a positive reputation in
front of target groups. As the central component of Integrated Marketing
Communications (IMC), PR helps build credibility, instills confidence, and
communicates a brand's values to the masses through media, press releases,
sponsorships, events, and corporate social responsibility (CSR) activities. Its
greatest strength lies in its power to influence perception and reputation, the
two most powerful drivers of purchase intention (Grunig & Grunig, 2008). Positive
PR generates two-way communication between the public and an organisation.
Excellence Theory of PR advocates a symmetrical approach in which brands not
only speak but also listen and converse with customers (Grunig & Grunig,
2008).
2.11 Relationship between Social Media Marketing and Purchase
Intention
Social media marketing (SMM) is one
of the most dynamic and powerful elements of Integrated Marketing
Communications (IMC) in the age of the internet. SMM defines the use of social
networking platforms such as Facebook, Instagram, TikTok, YouTube, and Twitter
to interact with customers, disseminate brand messages, and stimulate buying
behavior. In Generation Z consumers, who are perpetually online and spend
considerable hours on social media websites each day, SMM is halfway through
influencing brand attitudes, trust, and ultimately purchase intention (Appel et
al., 2020).
This study's conceptual framework is based on and extends the
literature on Integrated Marketing Communications (IMC) and the consumer's
purchase intention. The work of Mangold and Faulds (2009) on social media
implications regarding consumer purchase intention, Duffett (2017) on the
influence of digital marketing on young consumers' brand attitude, and
Djafarova and Bowes (2021) on influencer credibility in relation to Gen Z
consumers and their online purchasing behavior have added to the IMC
literature. This study is based on a research area that has been a point of
mention in the academic literature on IMC. This current study will measure five
IMC dimensions (digital advertising, influencer marketing, content marketing,
public relations and social media marketing), and the relationship they have
with the purchase response and purchase intention. The conceptual framework for
this study is illustrated in Figure 1.
Figure 1: Conceptual Framework
(Constructed by Author)
·
H1:
Digital advertising has a significant effect on purchase intention of Gen Z
consumers in Yangon.
·
H2:
Influencer marketing has a significant effect on purchase intention of Gen Z
consumers in Yangon.
·
H3:
Content marketing has a significant effect on purchase intention of Gen Z
consumers in Yangon.
·
H4:
Public relations has a significant effect on purchase intention of Gen Z
consumers in Yangon.
·
H5:
Social media marketing has a significant effect on purchase intention of Gen Z
consumers in Yangon.
Using a quantitative research approach, this study looked at Generation Z consumers’ purchase intentions in Myanmar and what effects different Integrated Marketing Communication (IMC) preferences had on their decisions to purchase. A structured questionnaire was developed using measures validated in earlier research, to ensure the research instrument measured the right constructs. The survey instrument was divided into three sections. Section one asked screening questions to verify the respondents were members of Generation Z and were living in Yangon. Section two gathered demographic information such as gender, age, education level, and occupation. Section three used a five-point Likert scale (1 = strongly disagree, 5 = strongly agree) on measuring six variables: digital advertising, influencer marketing, content marketing, public relations, social media marketing, and purchase intention.
Prior to fully administering the questionnaire, 30 respondents pilot tested the questionnaire for clarity and improved any unclear items. Reliability was examined with Cronbach’s Alpha, with coefficients over 0.7 suggested the acceptable threshold indicating internal consistency (George & Mallery, 2003). Data collection was done online via Facebook, Instagram, and university-related social media, utilizing Generation Z’s digital engagement. This method had the researchers seek a more diverse and representable sample with the least cost and convenience in mind.
The final dataset consisted of 385, valid responses, which is a sufficient sample size for conducting Structural Equation Modeling (SEM). Saunders et al. (2016) noted that a clear definition of target population improves quality and comparability of the research. Descriptive statistics (means, standard deviations, frequencies, and percentages) were used to analyze the demographic characteristics and provide a summary of responses. Inferential statistics tested the study’s hypotheses. Pearl correlation analyzed the strength and direction of relationships between IMC variables and purchase intention, whereas multiple regression analysis tested for the variables of IMC that were significant to Generation Z consumers’ purchase intention.
The
study based on the data collected from the survey. The analysis was conducted
using Jamovi and is presented in four main parts. First, reliability testing
was performed using Cronbach’s alpha to determine whether the questionnaire
items were consistent and reliable. Second, descriptive analysis was applied,
using frequencies and percentages to summarize the demographic information of
the respondents. Third, the mean and standard deviation were calculated to show
the average scores and the variability of responses for each variable. Finally,
multiple linear regression was used to test the research hypotheses and examine
the relationships between the variables.
Reliability
tests were carried out to assess the internal consistency of the constructs
used in this research. Values for Cronbach's alpha must be greater than or
equal 0.70, and considered acceptable to indicate that researchers can have
confidence in the reliability of the measurement scale (Hair et al., 2019). The
results indicated all of the constructs for this study were above the minimum.
Table 1 : Reliability Test
Result
|
Variables Measurement of Items |
Number of Items |
Cronbach’s Alpha |
Strengths of association |
|
Purchase Intention |
5 |
0.824 |
Good |
|
Digital Advertising |
5 |
0.823 |
Good |
|
Influencer Marketing |
5 |
0.746 |
Acceptable |
|
Content Marketing |
5 |
0.702 |
Acceptable |
|
Public Relation |
4 |
0.835 |
Excellent |
|
Social Media Marketing |
5 |
0.760 |
Acceptable |
Construct
by Author
The sample (N = 385) had a nearly equal gender distribution:
47.5% male, 49.6% female, and 2.9% prefer not to say. In terms of age, most
respondents were in the 21–24 years age group (54.8%) followed by 25–28 years
(30.4%) and 18–20 years (14.8%). Based on educational attainment, 42.3%
reported being undergrads, 39.7% were bachelor’s degrees, and 13.8% were
master’s degrees. In terms of monthly income, most respondents' income was
400,001–700,000 Kyats (36.6%) followed by 200,001–400,000 Kyats (23.1%). In
terms of professional status, the respondents were composed of private
employees (25.2%), students (21.0%), and self-employed (19.0%) respondents
while the rest is composed of smaller responses.
Table 2: Frequencies
of Gender
|
Gender |
Frequency |
Percentage |
|
Male |
183 |
48% |
|
Female |
191 |
50% |
|
Prefer not to say |
11 |
3% |
|
Total |
385 |
100% |
|
Table 3: Frequencies
of Age |
||
|
Age |
Frequency |
Percentage |
|
21-24 years old |
211 |
55% |
|
25-28 years old |
117 |
30% |
|
18-20 years old |
57 |
15% |
|
Total |
385 |
100% |
|
Table 4: Frequencies
of Education |
|
|
|
Level of Education |
Frequency |
Percentage |
|
Bachelor Degree |
153 |
40% |
|
Master Degree |
53 |
14% |
|
Under Bachelor Degree |
16 |
42% |
|
other |
163 |
4% |
|
Total |
385 |
100% |
|
Table 5: Frequencies of Income |
||
|
Monthly Income |
Frequency |
Percentage |
|
200,000-400,000 Kyats |
89 |
23% |
|
500,000-700,000 Kyats |
141 |
37% |
|
800,000 Kyats and Above |
75 |
20% |
|
Less than and equal to 150,000 Kyats |
80 |
21% |
|
Total |
385 |
100% |
|
Table 6: Frequencies
of Professional Status |
||
|
Professional Status |
Frequency |
Percentage |
|
Private employee |
97 |
25% |
|
Self-employed |
73 |
19% |
|
State enterprise employee |
12 |
3% |
|
Searching for job |
43 |
11% |
|
Students |
81 |
21% |
|
Others |
79 |
21% |
|
Total |
385 |
100% |
The results reveal that respondents
expressed generally positive views across most constructs and these findings
indicate that while respondents demonstrated strong digital advertising and
engagement with social media, actual purchase-related behaviors were more
moderate.
Table 7: Regression
Results
|
Predictors |
Mean Range |
Interpretation |
|
Purchase Intention (PI) |
3.61 – 4.24 |
Moderate to High |
|
Digital Advertising (DA) |
Above 4.0 |
High |
|
Influencer Marketing (IM) |
3.49 – 4.45 |
Mixed/Moderate |
|
Content Marketing (CM) |
4.14 – 4.46 |
High |
|
Public Relation (PR) |
3.36 – 3.81 |
Moderate |
|
Social Media Marketing (SMM) |
Up to 4.47 |
High |
Construct
by Author
Multiple regression analysis was
conducted to examine the impact of Digital Advertising (DA), Content Marketing
(CM), Influencer Marketing (IM), Public Relation (PR), and Social Media
Marketing (SMM) on Purchase Intention (PI).
The model was statistically
significant, with R² = 0.513, indicating that approximately 51.3% of the
variance in Purchase Intention was explained by the predictors. This reflects a
moderately strong explanatory power (Cohen, 2013).
Table 8: Model
Fit Measures
|
Model |
R |
R² |
Adjusted R² |
|
1 |
0.716 |
0.513 |
0.507 |
|
Note. Models
estimated using sample size of N=385 |
|||
Table 9 : Hypothesis Testing Results
|
Predictor |
β (Standardized) |
t-value |
p-value |
Result |
|
Digital Advertising (H1) |
0.0728 |
1.97 |
0.050 |
Supported |
|
Influencer Marketing (H2) |
0.3249 |
6.67 |
< .001 |
Supported |
|
Content Marketing (H3) |
0.1662 |
3.17 |
0.002 |
Supported |
|
Public Relation(H4) |
0.1481 |
3.39 |
< .001 |
Supported |
|
Social Media Marketing (H5) |
0.2163 |
4.17 |
< .001 |
Supported |
Influencer
Marketing (IM) provided the strongest positive effect (β = 0.325, p <
0.001), suggesting that being able to manage information effectively is
critically linked to perceived influence.
• Social Media
Marketing (SMM) also yielded strong and significant effects (β = 0.216, p
< 0.001), reflecting the discussion around social media engagement
strategies.
• Content
Marketing (CM) was also relevant but contributed at a more moderate level
(β = 0.166, p = 0.002). This aligns with the general idea that by
delivering relevant quality content, you build influence.
• Public
Relation (PR) has a link (β = 0.148, p < 0.001) which probably means
human purchase behaviors have a positive connection to perceived influence.
• Digital
Advertising (DA) only has a marginal relatedness (β = 0.073, p = 0.050).
This implies that awareness does not have a strong influence, alone, without
relations, strategies, and planning.
The results indicated
high digital awareness and strong social media engagement, though
purchase-related responses were moderate. Regression results revealed that
Influencer Marketing and Social Media Marketing had the strongest effects,
while Content Marketing and Public Relations were also significant predictors.
Digital Advertising was weak but marginally significant. These findings
highlight the importance of actionable digital engagement over mere awareness.
The results support prior findings (Kaplan & Haenlein, 2010; Mangold &
Faulds, 2009).
The results of this study also provide support for
understanding how Integrated Marketing Communications (IMC) factors
significantly impact Generation Z consumers' purchase intentions in Myanmar.
First, research indicates that digital advertising is significantly central to
behavior transformation as broadly defined as closing the gap between the
informed consumer and brand content in an engaging manner (Mansell &
Faulds, 2009). When consumers are more knowledgeable and connected, they will
more likely positively associate with brands or organizations, leading to
positive engagement.
Second, while all the IMC factors were important in impacting
purchase intention; influencer marketing was the most influential in shaping
the attitudes of consumers. In fact, Gen Z consumers were very responsive to
social media influencers, viewing them as relatable, credible and authentic in
their follower's understanding of their product. To this end, Djafarova and
Bowes (2021) present an argument that influencer credibility and relatability
can be very effective in online purchasing decisions.
Third, content marketing is important to ensure that Gen Z
audiences pay attention to brand messages, but also more broadly to content
that is high quality, can grab their attention and even consume. As the
aforementioned authors note (Kotler and Keller, 2016), content across platforms
is important to maintain consistency and recalls as this can help convert
in-purchase intentions.
Public relations and brand image appeared to also give a positive
strategic impact to purchase intention; achieving trust of younger, and
emerging market consumers will require transparency in communications, and
pro-active relationship management. In like manner, social media marketing has
a greater perceived impact because Generation Z engages with interactive,
participatory, digital channels through product assessment or purchase.
Lastly, the final purchase response conceptualized through
consumer attitudes, evaluations, and prior involvement- had a positive significant
effect on purchase intention. This finding confirmed that consumer emotional
and behavioral reactions play an important role in purchase decision-making
leading toward actual consumption. In summary, our overall findings serve to
demonstrate the importance of communicating through an IMC approach to generate
effective engagement with Generation Z consumers in Myanmar.
The findings yield several practical implications for
marketers and organizations in Myanmar who want to connect with Generation Z
consumers through an integrated marketing communication (IMC) strategy. First,
organizations should broaden their digital awareness campaigns supplemented
with clear easy-to-follow information as a way for Generation Z consumers to better
assess their purchase decision- effortlessly across varying online platforms.
Second, when collaborating with influencers, organizations
should align their influencer strategies to ensure brand propositions integrate
brand values and the influencer's credentials. Micro-influencers and brand
ambassadors can be effective influencers, almost as a customer from generation
Z, as they typically have greater engagement with niche communities.
Third, organizations should ensure they create quality
content because Generation Z favors more appealing and engaging layouts.
Organizations should create greater content variety to sustain Generation Z
attention span and obtain preferred media formats like short video clips, live
streams, storytelling campaigns, etc.
Fourth, public relations should simultaneously regard
transparency, responsiveness, and authenticity in its communicative stance.
Open communication to create strong relationships to build brand trust and
credibility with consumers.
Fifth, organizations can conduct note leveraging social media
marketing to improve purchase competence in the long run, especially as
Generation Z consumers engage with these platforms for long periods of time
every day; whether through Facebook, Instagram, or TikTok. There are examples
of connecting content and brand through polls, social media challenges,
user-generated content, etc.
Finally, organizations will need to continually track and
evaluate consumer purchase responses regarding integrated marketing
communications. Organizations are typically able to utilize consumer feedback,
shopping engagement, purchase satisfaction, etc. to adjust marketers IMC
strategies.
This current research, although informative, has certain
limitations that should be addressed. First, the data were collected from
Generation Z consumers in Yangon, which could limit the generalization of the
data to other consumers in Myanmar. Future research could take a more regional
or cultural view and expand the findings to a greater proportion of the Myanmar
population.
Second, the data were self-reported through a survey which
has its biases related to self-reported data. Future research could employ a
combination of methods and collect mixed-methods data using surveys and in-depth
interviews or observational studies to improve the validity.
Third, only five dimensions of integrated marketing
communication were included in this current study, but even more could be added
such as price, peer influence, cultural values, etc. Future research could
expand the International Consumer Price Knowledge Model to add additional
factors.
Finally, in this current study we captured consumer behavior
at one point in time. Given the rapid digital evolution, a longitudinal study
on tracking Generation Z consumer responses to IMC marketing applications from
the traditional to the ubiquitous online might be useful.
In summary, by acknowledging the limitations of this
research, future research might facilitate a deeper and richer perspective on
the interplay between integrated marketing communication strategies and
Generation Z consumer behavior; providing both academic engagement and
practical wisdom.
1.
Ajzen,
I. (1991). The theory of planned behavior. Organizational Behavior and Human
Decision Processes, 50(2), 179–211.
2.
Alalwan,
A. A., Dwivedi, Y. K., & Rana, N. P. (2016). Factors influencing adoption
of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust.
International Journal of Information Management, 37(3), 99–110. https://doi.org/10.1016/j.ijinfomgt.2017.01.002
3.
Appel,
G., Grewal, L., Hadi, R., & Stephen, A. T. (2020). The future of social
media in marketing. Journal of the Academy of Marketing Science, 48(1), 79–95.
4.
Appel,
G., Grewal, L., Hadi, R., & Stephen, A. T. (2020). The future of social
media in marketing. Journal of the Academy of Marketing Science, 48(1), 79–95.
https://doi.org/10.1007/s11747-019-00695-1
5.
Ashley,
C., & Tuten, T. (2015). Creative strategies in social media marketing: An
exploratory study of branded social content and consumer engagement. Psychology
& Marketing, 32(1), 15–27. https://doi.org/10.1002/mar.20761
6.
Belch,
G. E., & Belch, M. A. (2021). Advertising and promotion: An integrated
marketing communications perspective (12th ed.). McGraw-Hill.
7.
Belch,
G. E., & Belch, M. A. (2021). Advertising and promotion: An integrated
marketing communications perspective (12th ed.). McGraw-Hill Education.
8.
Boateng,
H., & Okoe, A. F. (2015). Consumers’ attitude towards social media
advertising and their behavioural response: The moderating role of corporate
reputation. Journal of Research in Interactive Marketing, 9(4), 299–312.
https://doi.org/10.1108/JRIM-01-2015-0012
9.
Casaló,
L. V., Flavián, C., & Guinalíu, M. (2010). Determinants of the intention to
participate in firm-hosted online travel communities and effects on consumer
behavioral intentions. Tourism Management, 31(6), 898–911.
https://doi.org/10.1016/j.tourman.2010.04.007
10. Chou, S. Y., Chen, C. W., &
Lin, J. Y. (2018). The influence of mobile service quality and switching costs
on customer loyalty: The mediating role of trust. International Journal of
Mobile Communications, 16(1), 1–26. https://doi.org/10.1504/IJMC.2018.088807
11. Cohen, J. (2013). Statistical power
analysis for the behavioral sciences (2nd ed.). Routledge.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019).
Multivariate data analysis (8th ed.). Cengage Learning.
12. Creswell, J. W., & Creswell, J.
D. (2018). Research design: Qualitative, quantitative, and mixed methods
approaches (5th ed.). Sage.
13. De Veirman, M., Cauberghe, V.,
& Hudders, L. (2017). Marketing through Instagram influencers: The impact
of number of followers and product divergence on brand attitude. International
Journal of Advertising, 36(5), 798–828. https://doi.org/10.1080/02650487.2017.1348035
14. Digital 2023. Myanmar internet user
statistics and digital consumers. We Are Social & Hootsuite.
15. Djafarova, E., & Bowes, T.
(2021). ‘Instagram made me buy it’: Generation Z impulse purchases in fashion.
Journal of Retailing and Consumer Services, 59, 102345.
https://doi.org/10.1016/j.jretconser.2020.102345
16. Djafarova, E., & Rushworth, C.
(2017). Exploring the credibility of online celebrities' Instagram profiles in
influencing the purchase decisions of young female users. Computers in Human
Behavior, 68, 1–7. https://doi.org/10.1016/j.chb.2016.11.009
17. Duffett, R. G. (2017). Influence of
social media marketing communications on young consumers’ attitudes. Young
Consumers, 18(1), 19–39.
18. Duffett, R. G. (2017). Influence of
social media marketing communications on young consumers’ attitudes. Young
Consumers, 18(1), 19–39. https://doi.org/10.1108/YC-07-2016-00622
19. Duffett, R. G. (2017). Influence of
social media marketing communications on young consumers’ attitudes. Young
Consumers, 18(1), 19–39.
20. Duffett, R. G. (2017). Influence of
social media marketing communications on young consumers’ attitudes. Young
Consumers, 18(1), 19–39. https://doi.org/10.1108/YC-07-2016-00622
21. Etikan, I., Musa, S. A., &
Alkassim, R. S. (2016). Comparison of convenience sampling and purposive
sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4.
22. Etikan, I., Musa, S. A., &
Alkassim, R. S. (2016). Comparison of convenience sampling and purposive
sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4. https://doi.org/10.11648/j.ajtas.20160501.11
23. Fox, J., & Weisberg, S. (2023).
car: Companion to Applied Regression. [R package]. Retrieved from https://cran.r-project.org/package=car.
24. George, D., & Mallery, P.
(2003). SPSS for Windows step by step: A simple guide and reference (4th ed.).
Allyn & Bacon.
25. Godey, B., Manthiou, A., Pederzoli,
D., Rokka, J., Aiello, G., Donvito, R., & Singh, R. (2016). Social media
marketing efforts of luxury brands: Influence on brand equity and consumer
behavior. Journal of Business Research, 69(12), 5833–5841. https://doi.org/10.1016/j.jbusres.2016.04.181
26. Grunig, J. E., & Grunig, L. A.
(2008). Excellence theory in public relations: Past, present, and future. In E.
L. Toth (Ed.), The future of excellence in public relations and communication
management (pp. 327–347). Routledge.
27. Hair, J. F., Black, W. C., Babin,
B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.).
Pearson.
28. Hollebeek, L. D., & Macky, K.
(2019). Digital content marketing’s role in fostering consumer engagement,
trust, and value. Journal of Interactive Marketing, 45, 27–41.
29. Hollebeek, L. D., & Macky, K.
(2019). Digital content marketing’s role in fostering consumer engagement,
trust, and value: Framework, fundamental propositions, and implications.
Journal of Interactive Marketing, 45, 27–41.
https://doi.org/10.1016/j.intmar.2018.07.003
30. Holleeek, L. D., & Macky, K.
(2019). Digital content marketing’s role in fostering consumer engagement,
trust, and value. Journal of Interactive Marketing, 45, 27–41.
31. Hsu, C. L., & Lu, H. P. (2004).
Why do people play on-line games? An extended TAM with social influences and
flow experience. Information & Management, 41(7), 853–868.
https://doi.org/10.1016/j.im.2003.08.014
32. Kaplan, A. M., & Haenlein, M.
(2010). Users of the world, unite! The challenges and opportunities of social
media. Business Horizons, 53(1), 59–68.
33. Kaplan, A. M., & Haenlein, M.
(2010). Users of the world, unite! The challenges and opportunities of social
media. Business Horizons, 53(1), 59–68.
34. Katz, E., Blumler, J. G., &
Gurevitch, M. (1973). Uses and gratifications research. Public Opinion
Quarterly, 37(4), 509–523.
35. Kim, S., & Ferguson, M. A.
(2019). Dimensions of effective corporate social responsibility programs in
enhancing corporate reputation. Public Relations Review, 45(1), 103–113.
https://doi.org/10.1016/j.pubrev.2018.12.001
36. Kim, S., & Ferguson, M. A.
(2019). Dimensions of effective CSR programs in public relations. Public
Relations Review, 45(1), 103–113.
37. Kitchen, P. J., & Burgmann, I.
(2015). Integrated marketing communication: Making it work at a strategic
level. Journal of Business Strategy, 36(4), 34–39.
https://doi.org/10.1108/JBS-12-2014-0145
38. Kotler, P., & Keller, K. L.
(2016). Marketing management (15th ed.). Pearson Education.
39. Krejcie, R. V., & Morgan, D. W.
(1970). Determining sample size for research activities. Educational and
Psychological Measurement, 30(3), 607‑610.
40. Lavidge, R. J., & Steiner, G.
A. (1961). A model for predictive measurements of advertising effectiveness.
Journal of Marketing, 25(6), 59–62.
41. Lee, M. K. O. (2006). The role of
subjective norm and perceived usefulness in the acceptance of Internet banking.
International Journal of Electronic Commerce, 10(3), 59–87.
https://doi.org/10.2753/JEC1086-4415100303
42. Lou, C., & Yuan, S. (2019).
Influencer marketing: How message value and credibility affect consumer trust.
Journal of Interactive Advertising, 19(1), 58–73.
43. Lou, C., & Yuan, S. (2019).
Influencer marketing: How message value and credibility affect consumer trust
of branded content on social media. Journal of Interactive Advertising, 19(1),
58–73. https://doi.org/10.1080/15252019.2018.1533501
44. Mangold, W. G., & Faulds, D. J.
(2009). Social media: The new hybrid element of the promotion mix. Business
Horizons, 52(4), 357–365.
45. Mangold, W. G., & Faulds, D. J.
(2009). Social media: The new hybrid element of the promotion mix. Business
Horizons, 52(4), 357–365. https://doi.org/10.1016/j.bushor.2009.03.002
46. Mangold, W. G., & Faulds, D. J.
(2009). Social media: The new hybrid element of the promotion mix. Business Horizons,
52(4), 357–365.
47. Nunnally, J. C., & Bernstein,
I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
48. Ohanian, R. (1990). Construction
and validation of a scale to measure celebrity endorsers' perceived expertise,
trustworthiness, and attractiveness. Journal of Advertising, 19(3), 39–52.
49. Pulizzi, J. (2012). The rise of
storytelling as the new marketing. Publishing Research Quarterly, 28(2),
116–123. https://doi.org/10.1007/s12109-012-9264-5
50. R Core Team (2024). R: A Language
and environment for statistical computing. (Version 4.4) [Computer software].
Retrieved from https://cran.r-project.org. (R packages retrieved from CRAN
snapshot 2024-08-07).
51. Revelle, W. (2023). psych:
Procedures for Psychological, Psychometric, and Personality Research. [R
package]. Retrieved from https://cran.r-project.org/package=psych.
52. Samsudeen, S. N., & Mohamed, R.
(2019). University students’ intention to use e‐learning systems: A study
of higher educational institutions in Sri Lanka. Interactive Technology and
Smart Education, 16(3), 219–238. https://doi.org/10.1108/ITSE-11-2018-0092
53. Schouten, A. P., Janssen, L., &
Verspaget, M. (2020). Celebrity vs. influencer endorsements in advertising: The
role of identification, credibility, and Product-Endorser fit. International
Journal of Advertising, 39(2), 258–281.
https://doi.org/10.1080/02650487.2019.1634898
54. Seemiller, C., & Grace, M.
(2016). Generation Z goes to college. Jossey-Bass.
55. Sekaran, U., & Bougie, R.
(2019). Research methods for business (7th ed.). Wiley.
56. Tabachnick, B. G., & Fidell, L.
S. (2019). Using multivariate statistics (7th ed.). Pearson.
57. The jamovi project (2024). jamovi.
(Version 2.6) [Computer Software]. Retrieved from https://www.jamovi.org.
58. Venkatesh, V., Morris, M. G.,
Davis, G. B., & Davis, F. D. (2003). User acceptance of information
technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
https://doi.org/10.2307/30036540
59. Williams, K. C., Page, R. A.,
Petrosky, A. R., & Hernandez, E. H. (2012). Multi-generational marketing:
Descriptions, characteristics, lifestyles, and attitudes. Journal of Applied
Business and Economics, 11(2), 21-36.
60. Yau, H. K., & Ho, K. K. W.
(2015). The influence of subjective norm on intention to use e-learning: An
empirical study in Hong Kong higher education. International Journal of
Educational Technology in Higher Education, 12(1), 1–13. https://doi.org/10.1186/s41239-015-0012-9
61. Zhou, T., Lu, Y., & Wang, B.
(2010). Integrating TTF and UTAUT to explain mobile banking user adoption.
Computers in Human Behavior, 26(4), 760–767.
https://doi.org/10.1016/j.chb.2010.01.013