Örgütsel Davranış Araştırmaları Dergisi
Journal Of Organizational Behavior Research
Cilt / Vol.: 9, Sayı / Is.: 2, Yıl/Year: 2024, Sayfa/Pages: 73-86
https://doi.org/10.51847/LSsHBTTTHg

2528-9705

THE EFFECT OF SOCIAL MEDIA ON PURCHASING BEHAVIOUR THROUGH EMARKETING: EMPIRICAL STUDY FOR ALGERIAN UNIVERSITY STUDENTS
Nadia HIMA1, Djamel BENAROUS1, Bilal LOUAIL2*, Walid HAMADI3
1 Department of Commerce, Faculty of Economic, Commercial and Management Sciences, University of Batna 01,

Algeria.
2 Department of Economics, Faculty of Economic, Commercial and Management Sciences, University of M'Hamed
BOUGARA Boumerdes, Algeria.
3 Management Laboratory for Sustainable Economic Institutions, El-Oued University, Algeria.
*Corresponding Author
E-mail: b.louail@univ-boumerdes.dz

ABSTRACTSocial media platforms are widely utilized by individuals of varying demographics and are recognized as a significant
source for promotion and digital marketing, influencing consumer purchasing choices. The prevalence of these platforms
has further amplified during the COVID-19 pandemic. This research aimed to examine the association and influence ofsocial media on purchasing decisions via digital marketing while considering disparities in gender and age. The study was
conducted among a sample of students from the University of Algiers, with 845 electronic questionnaires distributed
through various communication channels. The collected data was analyzed employing SPSS version 24 and hierarchicallinear regression. The study's findings established a correlation and impact between social media usage and purchasing
decisions facilitated by digital marketing. This study will provide valuable insights for the decision-makers in Algerian
universities and academic scholars. This topic is seen as a valuable contribution to the existing body of literature, which
will be utilized by future recipients.
Keywords: Social media, E-marketing, Purchasing behaviour, Algiers students, Empirical study.

INTRODUCTION
Throughout its long history, the world has witnessed many successive developments. The mostimportant of which is widespread technology and media, which have imposed new challenges
in the context of virtual reality. This has changed individual lifestyles through the different
platforms of social media that have attracted millions of individuals of different genders,
cultures, and ages. This affiliation to platforms has generated new types of communication andinteraction affecting daily life operations such as shopping, advertising, and various purchasing
processes.
The widespread of social networks led to a side sharing of data by all its users. This led to thecreation of an environment characterized by an abundance of information, where we find that
more than half of social network platform users have access to their accounts several times a day
and use it also to evaluate the information they get in their actual daily lives. Moreover, we finda constant increase in people who create information content online (Kent & Taylor, 2016),
Geliş tarihi/Recieved: 04.04.2024 – Kabul tarihi/Accepted: 02.09.2024 – Yayın tarihi/Published: 15.09.2024© 2024 Journal of Organizational Behavior Research. Open Access - This article is under the CC BY NC SA license(https://creativecommons.org/licenses/by-nc-sa/4.0/)

74 Örgütsel Davranış Araştırmaları Dergisi

Journal of Organizational Behavior Research
Cilt / Vol.: 9, Sayı / Is.: 2, Yıl/Year: 2024, Sayfa/Pages: 73-86
according to Veil et al. (2011) findings. Social media plays a central role in sharing knowledge
among individuals. Social media sites like Facebook and micro-blogging applications like Twitter
provide convenient forums for discussing various user issues and concerns.
Digital marketing involves electronic communications technologies utilized in advertisingproducts or services. It typically refers to online advertising over the internet, which isinterconnected physically through numerous computer networks that publish diverse
information types and stand as an accessible source (Priansa & Suryawardani, 2020). Social
media platforms effectively offer accessibility features that improve users' knowledge of products
or service offerings as consumers' preferences change towards digital buying channels.
Social media sites have revolutionized online networks. They are enabling businesses to beinteractive with current and prospective customers. Singh and Singh (2018) highlights thatyoung people use social media as a marketing channel for companies. This trend pushes
persuasive communication models to adapt to these demographics' needs since they prefer social
media channels to other advertising models.
This investigation aims to understand university students' attitudes towards social media
platforms when making purchasing decisions across different genders and age groups, given
marketers' increasing interest in promoting their products through these online mediums. As
digital natives, college students have positively impacted consumer behavior due to their onlinesocial proficiency. Our study will provide insights into effective ways of ensuring digitalmarketing campaigns on social media resonate with university students. It will explore better
strategies and pinpoint areas that need improvement while determining the actual contributionof these platforms concerning buying patterns among unique demographic audiences. The
outcomes from this research will inform marketers of the status quo regarding effective
promotion channels via digital access preferences and customer behavior patterns.
The structure of this study divides it into six explicit sections, each serving its purpose masterfully
for an understanding of the nature of our investigation underway. Part one talks about
introducing one's self to what comes next- essential background information towards significant
literature explored comprehensively featured inside part two-culminating alongsidecomprehensive research methodology outlined evidently within the third part; fourth exploringhidden discoveries revealed belonging undercurrents pointing beyond paper's pages both
discussed robustly within fifth segment preceding concluding sixth section.

Literature Review
In numerous studies, such as Miah et al. (2022), we have researched various variables to assess
the impact of social media on online shopping behavior. Specifically, Miah et al. (2022) analyzed
how social media influences apparel purchases in Jaipur City. Their research explored theconnection between online shopping behavior in Jaipur and social media through data analysis
and reviewing past studies. Their findings indicated that social media platforms provide a spacefor marketers to connect with consumers personally. These platforms including Twitter,
LinkedIn, blogs, and Facebook, have made significant strides in terms of promoting interaction
between buyers and sellers. Another study by Zulqurnain et al. (2016) evaluated how marketingon social media affects consumer perception of brands and their purchasing decisions. Surveys
conducted among university students with 145 participants reported a 97% acceptance rate for

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the analysis results. The study confirmed that social media marketing positively influences
consumers' purchasing behaviors.
Lastly, Kumar et al. (2020) examined how Malaysian restaurants use social media to affectconsumer buying behavior in the food and beverage industry. The research consisted of aliterature review followed by data analysis that aimed to assess the effects of social media on
target audiences' buying behaviors in Malaysia’s food industry. Overall, these studies highlightassociations between different contexts, such as shopping for apparel products in Jaipur City or
making purchasing decisions through advertisements on various platforms like LinkedIn or
blogs.
MATERIALS AND METHODSThis section will outline the methodology adopted in this study and construct the model. We will
also present the hypotheses that can help address the research problem.

Study Methodology
The study relied on quantitative methodology in analyzing data and an inductive approach togeneralize the results related to the study sample on the r: consumers, consumers who makepurchases through e-marketing via social media platforms. The sample size used was 845
individual students from the University of Algiers.

Study Variables
In this research, we utilized the following variables: Social Media (Independent Variable): Social media encompasses programs that facilitate thegathering, communication, participation, and, in some instances, collaboration orentertainment for individuals and communities. These programs are typically accessed
through web browsers or mobile applications (apps) (Von Muhlen & Ohno-Machado,2012). In our literature review, we encountered instances where social media (SM) was
equated with other terms, such as social networking sites (Hellemans et al., 2020). It is also
defined as media generated by consumers and content created by users, including Web 2.0(Santos, 2022). SM represents a collection of internet-based applications built on thefoundation of the Web 2.0 system, enabling global internet users to interact, communicate,and share ideas, content, experiences, perspectives, information, and relationships (Salim etal., 2022).
 Electronic Marketing (Mediating Variable): It should be noted that social media marketing(SMM) is defined as a « social and managerial process through which individuals andgroups obtain what they need and want » (Shahzad et al., 2020).
 Purchase Decision (Dependent Variable): The process of making a purchase involves various
fundamental buying activities, including searching for information, applying analyticaltechniques, focusing on future considerations, and relying on control mechanisms.
Additionally, adopting a particular purchasing approach is influenced by four vital
situational factors: the significance of the purchase, the complexity of the task, the level of
uncertainty involved, and the number of choices available. These factors and the buyer's
perceptions play a significant role in shaping the purchase decision (Lăzăroiu et al., 2020).

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Journal of Organizational Behavior Research
Cilt / Vol.: 9, Sayı / Is.: 2, Yıl/Year: 2024, Sayfa/Pages: 73-86
The Procedures and Methodology of the Field Study
The study model and its hypotheses are as follows in Figure 1:

Figure 1. The Model Specification of Study.
Source: By researchers

Sample and Population of the Study: The research population consists of all students at Algeria
University, including its three branches, totaling an estimated 9,750 students. For the study
sample, the researcher employed various electronic means of communication to distribute thequestionnaire and received 845 valid responses suitable for analysis. This sample represents
approximately 10% to 20% of the population, which is considered highly appropriate for thistype of study, according to Casteel and Bridier, (2021). Moreover, the base rule confirmed thesample size's adequacy (Thompson, 2012). It is important to note that the study population ishomogeneous, comprising undergraduate or diploma students.

Study Hypotheses: The model has led to updates in our hypotheses, which we outline below:
 Hypothesis 1: Our survey takers hold affirmative opinions about the study's given variables.
 Hypothesis 2:- Null Hypothesis (H0): Among the students analyzed during this research project. Social
media did not show a statistically significant impact on purchase decisions prompted bye-marketing channels at or beyond a confidence level equal to 0.05.
- Alternative Hypothesis (H1): Social media significantly influenced purchase decisions
carried out through electronic advertising among students sampled for this research
project about levels of confidence presented near or at 0.05 significance levels.
Hypothesis 3:- Null Hypothesis (H0): Amongst students researched, gender is not an attribute leading torecognizable differences relating to study-based parameters as measured using anothersimilarly relevant statistical parameter within a confidence interval equivalent to or
exceeding .05.
- Alternative Hypothesis (H1): Across our sample population, there exists discernible
variance between how members answer questions based on gender-specific distinctions

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against tested measures that have rivaling statistical counterparts with a matched degreeof confidence values expressed near or equaling a .05 threshold.
Hypothesis 4:- Null Hypothesis (H0): Variables studied across age lines among student participants
prove indistinguishable from one another as studied when gauged with comparable andsubstantive degrees of certainty levels reachable around values maintained above
equivalence with a .05 confidence interval.
- Alternative Hypothesis (H1): Significantly meaningful disparities exist based on variables
under review across segmented age ranges between students comprising our study groupwhen interpreted against confidence levels established at or above a .05 interval class.

We adopted the quantitative approach in our data analysis, interpretation of correlational
relationships, and examination of the impact between various study variables (Lorenz-Spreen etal., 2023). Furthermore, we employed induction to analyze and generalize the results to thestudy population.
We relied on a questionnaire as the primary data collection tool to collect data from the study
sample. This questionnaire was distributed to students of Algerian universities through various
communication channels, resulting in the collection of (845) responses.
The questionnaire consisted of 23 items divided into three parts:
1. Personal Information - This section aimed to gather personal information from the students,
including gender, age, educational qualification, and the communication method used.
2. Independent Variable - This part focused on the Social Media Variable and comprised Eightquestions.
3. Medial Variable - This section addressed the mediating variable, electronic marketing, andencompassed (6) questions.
4. Dependent Variable - This part investigated the dependent variable, purchase decision, andincluded (9) questions.
5. Likert scale was utilized in the questionnaire to assess participants' responses.

Validity of the Questionnaire and Statistical Analysis Method
The questionnaire underwent a thorough review by specialized professors to ensure its validity.
Certain items were modified, added, or rephrased during this process, and new items were
included as necessary. After these revisions, the questionnaire reached its final form.
The alpha coefficient and split-half reliability measures were employed to assess the reliabilityof the research instrument. The values of these measures were obtained and are presented inTable 1.
Table 1. Statistics of Item-Total
Q
W
B
Social media

Scale Mean
94,4125
94,215
94,95804
94,4044

Scale Variance
383,0068
383,1422
372,2326
382,369

Correlation
0,56425
0,584333
0,673222
0,887

Alpha Cronbach
0,951
0,951167
0,950333
0,949

78 Örgütsel Davranış Araştırmaları Dergisi

Journal of Organizational Behavior Research
Cilt / Vol.: 9, Sayı / Is.: 2, Yıl/Year: 2024, Sayfa/Pages: 73-86
E-Marketingbuying decisionALL

94,215
94,95894,5716

382,705
371,535
378,036

0,791
0,9
1

0,95
0,948
0,948

Sources: Output SPSSThe obtained results from the Table indicate that each item in the questionnaire exhibits areliability coefficient higher than 0.90. Since this value surpasses the acceptable threshold forreliability (0.70), it is considered suitable for scientific research purposes. Additionally, theoverall scale achieved a reliability coefficient of 0.95, indicating high stability in thequestionnaire.
Using the Central Limit Theorem, a sample can conform to a normal distribution in terms of thetest for normal distribution if the sample size is large enough (Knief & Forstmeier, 2021).
SPSS (24) was used as the statistical program to perform the required statistical analyses. The
subsequent statistical instruments were employed:
We computed the mean and standard deviation, and the correlation coefficient was employed
for the objectives of identification and interpretation. The f and t values were calculated tosupport or contradict the theories. The model utilized was the hierarchical linear regression
model (Lai et al., 2022). We examined significant differences between the research variables.

Descriptive Statistics for Study Variables
Table 2. Statistics of Descriptive
Observations
Q
W
B
Social media
E-Marketingbuying decision
ALL
Observations

845

Min
1,125
1
1
1,88
1,17
1
1,35

Max
5
5
5
5
5
5
5
845

Mean
2,8762
4,06565
3,3226
3,8762
4,0657
3,46594,021

Std. Dev
0,9633
1,020498
1,303608
0,71687
0,78962
0,9325
0,6986

Sources: The Output of SPSS

Table 2 displays the means and standard deviations for each variable, which can be interpreted
as follows:
If we apply the range calculation rule, which is represented by (upper and lower limit) / level,
where 5 - 1 / 3 = 1.33, we can classify the variables as follows:
Weak: between 1 and 1.33
Average: between 1.34 and 3.46
Strong: between 3.47 and 5
Regarding the variable "Social media," the mean score was 3.88, indicating a high level ofimportance as perceived by the researchers. The standard deviation was less than 2, indicating

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a limited dispersion in the sample responses (Todisco et al., 2021). These findings align with theworks of (Jacobs et al., 2016). However, they differ from the study conducted by Van Aelst et al.
(2021), which suggested that television holds greater importance than social media for news
consumption in the studied countries.
Concerning the variable "E-Marketing," the mean score at the firm level was 4.0657, with astandard deviation of less than 1. This supports the findings of Trainor et al. (2011), who
described electronic marketing as a combination of technology, business, human, and resource
factors that positively impact company performance. Additionally, the survey results from 522
Belgian companies emphasized the significance of market orientation and technology inelectronic marketing, leading to enhanced customer retention and satisfaction, as indicated by
Zaoui et al. (2021).
For the variable "Buying decision," the mean score was 3.4659, reflecting a moderate level ofinfluence, while the standard deviation was more significant than one but less than 2. Thesefindings are consistent with the study conducted by McGrath et al. (2020), which involved acontrolled simulation with122 university students and demonstrated that trust in a website
significantly influences online purchasing decisions based on website features. This finding is
also supported by Sharma and Klein (2020).
With a variation of less than one, the aggregate mean score for all replies was 3.7090, which iswithin the firm level. This suggests that the first hypothesis. According to which respondents had
favorable views towards the research variables is correct; the following parts will detail thesefindings.
RESULTS AND DISCUSSION
Our focus in this section is to test the hypotheses stated in our research while providing insight
into observed results. Our Second Hypothesis illustrates a meaningful impact between social
media usage and purchase decision-making through digital marketing amongst students in our
sample - contrary to what was hypothesized under the Null Hypothesis.
Understanding how these effects play into dependent variables requires us to analyze Lai et al.
(2022) equation models specifically designed for that purpose.
A – Initial Model:
𝑌𝑖 = 𝑎𝑖 + 𝑏1 ∗ 𝑋𝑖 + 𝜀𝑖

(1)

B – Reduced Model:
𝑌𝑖 = 𝑎𝑖 + 𝑏1 ∗ 𝑋𝑖 + 𝑏2 ∗ 𝑍𝑖 + 𝜀𝑖

(2)

C – Final Model:
𝑌𝑖 = −1.18𝐸 − 14 + 0.889 ∗ 𝑋𝑖 + 0.123 ∗ 𝑍𝑖 + 2.556 ∗ 𝑋𝑖 ∗ 𝑍𝑖 + 𝜀𝑖

(3)

Our hypothesis was tested by analyzing the determination coefficient and F and T values
calculations for all three applicable models. These models feature variables such as X

80 Örgütsel Davranış Araştırmaları Dergisi

Journal of Organizational Behavior Research
Cilt / Vol.: 9, Sayı / Is.: 2, Yıl/Year: 2024, Sayfa/Pages: 73-86(independent), Y(dependent), and Z(mediating) along with XZ (interaction between
independent variable and mediator).
A – Initial Model: 𝑌𝑖 = 𝑎𝑖 + 𝑏1 ∗ 𝑋𝑖 + 𝜀𝑖
Table 3. ANOVAa Test
Model
1

2

3

Sum of Squares

df

Mean Square

F

Sig.

Regression

222,5661

222,566

702,2130,000 bResidual

229,471

724

0,317

--

--

Total

452,037

725

--

--

--

Regression

367,5562

183,778

351,9810,000 bResidual

377,496

723

0,522

-

-

Total

745,052

725

-

-

-

Regression

669,3381

669,338

6400,410,000bResidual

75,714

724

0,105

-

-

Total

745,052

725

-

-

-

a. DV: E-Marketingb. Constant: Social media
Sources: Output SPSS

Table 4. The Summary Models
Model
1
2
3

R
0,815 a0,818 a0,948a

R Sq
0,673
0,584
0,898

R Sq Adj
0,673
0,591
0,898

Std. Err
0,6345
0,8337
0,32338

a. Constant: Social media
Sources: Output SPSS

Table 5. The Summary of Coefficients a
Model

Unstandard Coefts

1

2

3

Standard Coefs

Collinearity Statistics
t

Sig.

B

Std. Err

Beta

Tolerance

VIF

Constant

1,07

0,115

-

9,305

0

-

-

Social media

0,773

0,029

0,702

26,4990

1

1Constant-0,5690,202

0.0001

-3,17

0.002

0

0

Social media

0,791

0,112

0,597

17,212

0.002

0,475

1,97

E-Marketing

0,161

0,048

0,125

3,376

0,001

0,508

1,97

Constant-1,18E-14

0

-

.

.

-

-

Social media

0,889

0

0,629

16,465.

1

1

E-Marketing

0,667

0

0,519

80,003

.

1

1

ALL

2,556

0

1,89

174.332.

1

1

a. Dependent Variable: E-Marketing

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HIMA et al.
Sources: Output SPSS

The correlation coefficient achieved a solid negative value of 0.70, indicating a robust
relationship. Moreover, the adjusted R-squared value (R-2) reached 0.49, indicating that social
media accounts for 50% of the variability observed in electronic marketing. Additionally, the Fvalue of 702,213, with a significance level of 0.000, suggests the acceptance of the initial model.
There is no multicollinearity among the variable components, as indicated by the VIF value of
1.000. The regression coefficient for social media about electronic marketing is 0.773. With a
T-value of 26,499, significant at 0.000 (less than 0.01) (Tables 3-5). the expressed model in theequation:
Y_i = 1.07 + 0.773*X_i + ε_i(4)

It is deemed valid. This result signifies that social media has a significant impact on electronic
marketing. These findings are consistent with the research of Priansa and Suryawardani (2020)and Suryani and Margery (2020), highlighting the significant influence of social media
advertising, electronic marketing, and product quality on consumer decisions.

B- Reduced Model: 𝑌𝑖 = 𝑎𝑖 + 𝑏1 ∗ 𝑋𝑖 + 𝑏2 ∗ 𝑍𝑖 + 𝜀𝑖
The correlation coefficient between social media, electronic marketing, and purchase decisions
reached a solid negative value of 0.818, suggesting a robust relationship. Furthermore, theadjusted R-squared value (R-2) of 0.584 indicates that combined social media and electronic
marketing explain 49.3% of the variability observed in the purchase decision (Table 4).
Interestingly, when both variables are considered together, the explanatory power has no
significant improvement compared to the initial model. This implies that introducing electronic
marketing as a second variable does not enhance the relationship with the purchase decision.
Additionally, the F-value of 351,981, with a significance level of 0.000, supports the acceptanceof the reduced model (Table 3). These findings are consistent with the conclusions of Suryani
and Margery (2020) and Iblasi et al. (2016).
The regression coefficient for social media about the purchase decision increased to 0.861 whenthe electronic marketing variable was introduced. This suggests that including electronic
marketing as a mediating variable has strengthened the relationship between social media andthe purchase decision, aligning with the concept of mediation proposed by Baron and Kenny(1986). The T-values of 17,212 and 3,376, respectively, with significance levels of 0.000 and
0.001 (both less than 0.05), indicate the model's validity (Table 5). Therefore, the relationshipcan be expressed as follows:
𝑌𝑖 = −0.668 + 0.861 ∗ 𝑋𝑖 + 0.161 ∗ 𝑍𝑖 + 𝜀𝑖

(5)C- The final model: 𝑌𝑖 = 𝑎𝑖 + 𝑏1 ∗ 𝑋𝑖 + 𝑏2 ∗ 𝑍𝑖 + 𝑏3 ∗ 𝑋𝑖 ∗ 𝑍𝑖 + 𝜀𝑖
The correlation coefficient between social media, electronic marketing, and purchasing
decisions reached a solid positive value of 0.95, indicating a robust relationship. There were 19
strong and positive correlation relationships without multicollinearity, as confirmed by the VIF
value of 1.000. This suggests that there is no interference from linear multicollinearity among
the components of the variables (Yıldız, 2021) (Table 4).

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The association between social media and electronic marketing has improved the relationshipwith purchase decisions, as seen by the more excellent correlation when compared to both theoriginal and decreased models. Ninety percent of the variance in purchase decisions may be
explained by the interaction between social media and electronic marketing, according to themodified R-square value of 0.9. This suggests that when social media and direct marketinginteract as a mediating variable, the interpretative coefficient significantly improves, positively
affecting purchase choices (Table 3).
Moreover, the adoption of the final model is supported by the F-value of 6400.410 at thesignificance level of 0.000. The results of the research by Priansa and Suryawardani (2020) andAl-Azzam and Al-Mizeed (2021), which highlighted the influence of digital marketing onpurchasing decisions, are consistent with our findings (Table 5).
The interaction between electronic marketing and social media on purchase decisions has aregression coefficient of 2.5560-. According to Preacher et al. (2006) and Aiken and West(1991), regarding interactions involving the mediating variable, the interaction regression onpurchasing decisions validates the electronic marketing mediation model between social media
and purchasing decisions. The model's validity is confirmed by the significance level of 0.000,
which may be stated as follows:
𝑌𝑖 = −1.18𝐸 − 14 + 0.889 ∗ 𝑋𝑖 + 0.123 ∗ 𝑍𝑖 + 2.556 ∗ 𝑋𝑖 ∗ 𝑍𝑖 + 𝜀𝑖

(6)Where:
Based on the information, social media plays a significant role in influencing purchasing
decisions through electronic marketing.
Hypothesis 3 states:
H0: Among the pupils in the sample. Gender-related differences in the research variables are
not statistically significant, with a 0.05 confidence level.
H1: At a confidence level of 0.05, there is a statistically significant influence of the researchvariables related to gender among the sample students.
Table 6. Statistics of Group
Social media
E-Marketingbuying decision

Gender
F
M
F
M
F
M

N
530
313
532
313
532
313

Mean
4,8482
4,1001
4,1081
3,9493
3,3954
3,1231

Std. Deviation
0,56257
0,78585
0,83076
0,65172
1,02782
0,94831

Std. Err. Mean
0,03256
0,04567
0,03602
0,04679
0,04456
0,06808

Sources: Output SPSS

We find no statistically significant differences in the sample responses regarding gender-based
electronic marketing and purchase decisions from the two tables above (Table 6); this suggeststhat gender does not affect university students' levels of electronic marketing and purchasedecisions. Regarding communication techniques, prior research supports the findings of

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HIMA et al.

(Kanwal et al., 2022), who discovered no connection between gender and the online buying
process. Thus, at a confidence level of 0.05, there are no statistically significant differences
between social media and responsible behavior among the students in the research sample that
may be linked to the gender variable.
H4:- H0: At a confidence level of 0.05, there are no statistically significant variations in the study
variables among the students in the research sample that can be ascribed to the age variable.
- H1: At a confidence level of 0.05, there is a statistically significant impact among the study
variables among the research sample students attributable to the age variable.
The sample responses regarding communication methods according to statistical significance.
However, there are statistically significant differences concerning electronic marketing andpurchase decisions, which is highly logical considering the impact of maturity on electronic
marketing and behavior, as indicated by the study (Kanwal et al., 2022). In addition, the study
found a significant difference between age and online trading characteristics.
CONCLUSION
How do social media channels affect our purchasing habits? Researchers set out to explore thatin an empirical study examining e-marketing targeting the University of Algiers students. By
analyzing how social media influences buying behavior and identifying successful businesstactics that shape it, they hoped to offer valuable insights into consumer trends and preferencesamong this population group. The study’s data was gathered through a quantitative survey ofstudents, and the findings promise to offer businesses important takeaways as they navigate thismarket.
We may draw the following conclusions from the research's findings:
 The research factors are seen favorably by the study sample respondents. Demonstrate, social
media usage and its degree of practice have become perceptible realities in a number ofprocesses and operations carried out by persons functioning as high-impact media sources.
 Analytical reading reveals that using social media platforms affects purchase decisions, andan increased focus on social media content enhances electronic marketing; this was also
highlighted in the study. the study discovered that E-commerce platforms are influenced byelectronic and social media marketing in shaping shopping decisions. The study used a sample
size of 125 and applied path analysis as an analytical technique. The results showed thatsocial media marketing influences consumer decisions by 45.35%, affecting consumer
decisions in the purchasing process by 35.73%. Additionally, social media marketing
influences purchase decisions by 81.08%.
 The findings showed no statistically significant variations linked to gender in terms ofutilizing social media or making purchases through electronic marketing, suggesting thatgender has no impact on the answers provided by male and female students.
 The presence of disparities in the sample answers attributable to the age variable is anoteworthy finding, and this can be connected to the idea that their age influences students'

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cognitive maturity—the degree to which they are aware of social media platforms while
making judgments about purchases.
 The research shed light on a contemporary phenomenon: using social media platforms to
make purchase decisions through electronic marketing; this can positively impact improving
media messages across various social media platforms.
ACKNOWLEDGMENTS: None
CONFLICT OF INTEREST: None
FINANCIAL SUPPORT: None
ETHICS STATEMENT: None
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