Social 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 of social 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 hierarchical linear 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.
INTRODUCTION
Throughout its long history, the world has witnessed many successive developments. The most important 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 and interaction 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 the creation 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 find a constant increase in people who create information content online (Kent & Taylor, 2016), 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 advertising products or services. It typically refers to online advertising over the internet, which is interconnected 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 be interactive with current and prospective customers. Singh and Singh (2018) highlights that young 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 online social proficiency. Our study will provide insights into effective ways of ensuring digital marketing campaigns on social media resonate with university students. It will explore better strategies and pinpoint areas that need improvement while determining the actual contribution of 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 alongside comprehensive research methodology outlined evidently within the third part; fourth exploring hidden 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 the connection 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 space for 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 marketing on 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 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 affect consumer buying behavior in the food and beverage industry. The research consisted of a literature 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 highlight associations 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 METHODS
This 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 to generalize the results related to the study sample on the r: consumers, consumers who make purchases 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:
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 the questionnaire and received 845 valid responses suitable for analysis. This sample represents approximately 10% to 20% of the population, which is considered highly appropriate for this type of study, according to Casteel and Bridier, (2021). Moreover, the base rule confirmed the sample size's adequacy (Thompson, 2012). It is important to note that the study population is homogeneous, comprising undergraduate or diploma students.
Study Hypotheses: The model has led to updates in our hypotheses, which we outline below:
We adopted the quantitative approach in our data analysis, interpretation of correlational relationships, and examination of the impact between various study variables (Lorenz-Spreen et al., 2023). Furthermore, we employed induction to analyze and generalize the results to the study 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:
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 reliability of the research instrument. The values of these measures were obtained and are presented in Table 1.
Table 1. Statistics of Item-Total
Scale Mean |
Scale Variance |
Correlation |
Alpha Cronbach |
|
Q |
94,4125 |
383,0068 |
0,56425 |
0,951 |
W |
94,215 |
383,1422 |
0,584333 |
0,951167 |
B |
94,95804 |
372,2326 |
0,673222 |
0,950333 |
Social media |
94,4044 |
382,369 |
0,887 |
0,949 |
E-Marketing |
94,215 |
382,705 |
0,791 |
0,95 |
buying decision |
94,958 |
371,535 |
0,9 |
0,948 |
ALL |
94,5716 |
378,036 |
1 |
0,948 |
Sources: Output SPSS
The obtained results from the Table indicate that each item in the questionnaire exhibits a reliability coefficient higher than 0.90. Since this value surpasses the acceptable threshold for reliability (0.70), it is considered suitable for scientific research purposes. Additionally, the overall scale achieved a reliability coefficient of 0.95, indicating high stability in the questionnaire.
Using the Central Limit Theorem, a sample can conform to a normal distribution in terms of the test 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 to support 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 |
Min |
Max |
Mean |
Std. Dev |
|
Q |
845 |
1,125 |
5 |
2,8762 |
0,9633 |
W |
1 |
5 |
4,06565 |
1,020498 |
|
B |
1 |
5 |
3,3226 |
1,303608 |
|
Social media |
1,88 |
5 |
3,8762 |
0,71687 |
|
E-Marketing |
1,17 |
5 |
4,0657 |
0,78962 |
|
buying decision |
1 |
5 |
3,4659 |
0,9325 |
|
ALL |
1,35 |
5 |
4,021 |
0,6986 |
|
Observations |
845 |
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 of importance as perceived by the researchers. The standard deviation was less than 2, indicating a limited dispersion in the sample responses (Todisco et al., 2021). These findings align with the works 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 a standard 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 in electronic 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 of influence, while the standard deviation was more significant than one but less than 2. These findings are consistent with the study conducted by McGrath et al. (2020), which involved a controlled 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 is within 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 these findings.
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:
|
(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 (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 |
Sum of Squares |
df |
Mean Square |
F |
Sig. |
|
1 |
Regression |
222,566 |
1 |
222,566 |
702,213 |
0,000 b |
Residual |
229,471 |
724 |
0,317 |
-- |
-- |
|
Total |
452,037 |
725 |
-- |
-- |
-- |
|
2 |
Regression |
367,556 |
2 |
183,778 |
351,981 |
0,000 b |
Residual |
377,496 |
723 |
0,522 |
- |
- |
|
Total |
745,052 |
725 |
- |
- |
- |
|
3 |
Regression |
669,338 |
1 |
669,338 |
6400,41 |
0,000b |
Residual |
75,714 |
724 |
0,105 |
- |
- |
|
Total |
745,052 |
725 |
- |
- |
- |
a. DV: E-Marketing
b. Constant: Social media
Sources: Output SPSS
Table 4. The Summary Models
Model |
R |
R Sq |
R Sq Adj |
Std. Err |
1 |
0,815 a |
0,673 |
0,673 |
0,6345 |
2 |
0,818 a |
0,584 |
0,591 |
0,8337 |
3 |
0,948a |
0,898 |
0,898 |
0,32338 |
a. Constant: Social media
Sources: Output SPSS
Table 5. The Summary of Coefficients a
Model |
Unstandard Coefts |
Standard Coefs |
t |
Sig. |
Collinearity Statistics |
|||
B |
Std. Err |
Beta |
Tolerance |
VIF |
||||
1 |
Constant |
1,07 |
0,115 |
- |
9,305 |
0 |
- |
- |
Social media |
0,773 |
0,029 |
0,702 |
26,499 |
0 |
1 |
1 |
|
2 |
Constant |
-0,569 |
0,202 |
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 |
|
3 |
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
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 F-value 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 the equation:
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, the adjusted 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 acceptance of 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 when the electronic marketing variable was introduced. This suggests that including electronic marketing as a mediating variable has strengthened the relationship between social media and the 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 relationship can be expressed as follows:
|
(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).
The association between social media and electronic marketing has improved the relationship with purchase decisions, as seen by the more excellent correlation when compared to both the original 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 the modified R-square value of 0.9. This suggests that when social media and direct marketing interact 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 the significance level of 0.000. The results of the research by Priansa and Suryawardani (2020) and Al-Azzam and Al-Mizeed (2021), which highlighted the influence of digital marketing on purchasing decisions, are consistent with our findings (Table 5).
The interaction between electronic marketing and social media on purchase decisions has a regression coefficient of 2.5560-. According to Preacher et al. (2006) and Aiken and West (1991), regarding interactions involving the mediating variable, the interaction regression on purchasing 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:
|
(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 research variables related to gender among the sample students.
Table 6. Statistics of Group
Gender |
N |
Mean |
Std. Deviation |
Std. Err. Mean |
|
Social media |
F |
530 |
4,8482 |
0,56257 |
0,03256 |
M |
313 |
4,1001 |
0,78585 |
0,04567 |
|
E-Marketing |
F |
532 |
4,1081 |
0,83076 |
0,03602 |
M |
313 |
3,9493 |
0,65172 |
0,04679 |
|
buying decision |
F |
532 |
3,3954 |
1,02782 |
0,04456 |
M |
313 |
3,1231 |
0,94831 |
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 suggests that gender does not affect university students' levels of electronic marketing and purchase decisions. Regarding communication techniques, prior research supports the findings of (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:
The sample responses regarding communication methods according to statistical significance. However, there are statistically significant differences concerning electronic marketing and purchase 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 that in an empirical study examining e-marketing targeting the University of Algiers students. By analyzing how social media influences buying behavior and identifying successful business tactics that shape it, they hoped to offer valuable insights into consumer trends and preferences among this population group. The study’s data was gathered through a quantitative survey of students, and the findings promise to offer businesses important takeaways as they navigate this market.
We may draw the following conclusions from the research's findings:
ACKNOWLEDGMENTS: None
CONFLICT OF INTEREST: None
FINANCIAL SUPPORT: None
ETHICS STATEMENT: None
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