Emilio Javier Flores Villacrés[1]
https://orcid.org/ 0000-0003-1402-1462
Wendy Paola
Quimi Franco1
https://orcid.org/ 0000-0002-5566-2357
Dennise Ivonne Quimi Franco1
https://orcid.org/0000-0002-5527-6245
Alberto Rodríguez
Garate 1
https://orcid.org/0009-0001-3680-0283
Fechas de
recepción: 03/04/2026
Fecha aceptación: 26/05/2026
This study
evaluated the impact of investment in social networks on the return on
investment (ROI) in Ecuadorian companies, considering the economic sector and
the size of the company. Through a quantitative methodology, 384 companies were
analyzed using descriptive statistics, ANOVA tests and linear regression. The
results showed that the relationship between investment and ROI is weak and not
significant (R˛ = 0.00734), suggesting that other factors, such as the quality
of advertising strategies, could be more determinant. Medium and small
companies in the commerce sector presented the highest ROIs, while micro-enterprises
stood out in the service sector, reflecting their ability to optimize
resources. However, the high variability in investments and returns indicates a
lack of standardization in digital strategies. Assumption checks confirmed the
validity of the statistical analysis, although the included variables explained
only a small fraction of the ROI variation. It is concluded that companies must
improve their digital strategies through training, adoption of advanced
technologies and differentiation by sector. This is consistent with the
literature reviewed, which highlights the importance of campaign segmentation
and optimization to maximize ROI. This study offers recommendations for
Ecuadorian companies to strengthen their competitiveness in the digital
environment.
Keywords: Digital marketing; Information technology; Small and medium-sized enterprises; Social Media
In the Ecuadorian economic panorama, small and medium-sized
enterprises (SMEs) represent an essential pillar of productive activity. These
companies make up more than 99% of the country's business fabric, according to
data from the National Institute of Statistics and Censuses (INEC,
2021), and play a crucial role in job creation and gross domestic
product (GDP). However, they face significant challenges related to
technological modernization and the integration of digital tools into their
operations. As global markets go digital (Sahabuddin
et al., 2024), Ecuadorian SMEs face increasing pressure to adapt to new
technologies that allow them to compete effectively, both locally and
internationally. Among these tools, social networks emerge as one of the most
accessible and effective platforms for business promotion and income generation
(Dunford
et al., 2024). However, the use of these tools is still underutilized,
with less than 40% of SMEs implementing digital strategies as an integral part
of their business model (Interactive
Symbol Marketing Agency, 2024).
International literature supports the importance of
investing in digital strategies to improve business profitability (Cheratian
et al., 2024). Almestarihi
et al. (2024) They point out that measuring the return on investment (ROI)
in paid advertising campaigns is crucial to evaluate the financial impact of
digital strategies. According to their analysis, companies that allocate
resources to well-planned digital advertising achieve significant increases in
revenue and brand awareness. This finding resonates with the need for
Ecuadorian SMEs to optimize their limited financial resources and justify each
expenditure, especially in an economic context characterized by budgetary
constraints.
Another relevant study is that of Andrei
and Veltri (2024), which explores how
detailed product descriptions on digital platforms influence purchasing
decisions. Although its main focus is the market for illicit goods, the
underlying principle of persuasion through visual and textual content is highly
applicable to Ecuadorian SMEs. This concept reinforces the importance of
creating engaging content tailored to the characteristics of local audiences,
which can be a critical differentiator in a saturated market.
For its part, Aridor
and Che (2024) They analyze the impact of
privacy regulations on the effectiveness of targeted advertising. In the
Ecuadorian context, where there is still no robust legislation around data
protection, SMEs can take advantage of this lack of restrictions to implement personalized
and targeted campaigns, albeit with an ethical approach. This approach can be
crucial to maximizing the impact of digital advertising investments.
Bajaj
et al. (2024) They present the concept of neuromarketing applied to
programmatic advertising, highlighting how artificial intelligence tools can
enhance the effectiveness of digital strategies. This study underscores the
importance of understanding consumer behavior in order to design campaigns that
not only capture attention, but also drive action. For Ecuadorian SMEs,
adopting emerging technologies such as neuromarketing could represent a
significant competitive advantage, especially against larger and more well-resourced
competitors.
In terms of business sustainability, Birim
et al. (2024) They look at how companies can balance their financial goals
with social and environmental impact through optimized advertising strategies.
In Ecuador, where sustainability is gaining importance in the business
environment, these strategies could be implemented by SMEs to not only increase
their revenues, but also align with the demands of more conscious consumers.
Hariyanto
(2024) offers a practical example of the impact of social media on
SMB profitability by analyzing how the use of TikTok helped an Indonesian small
business increase its sales. This case illustrates how digital platforms can
level the playing field for small businesses, providing access to massive
audiences with relatively low investment. This approach is particularly
relevant for Ecuadorian SMEs, many of which operate with limited advertising
budgets.
Lambrecht
et al. (2024) they highlight the phenomenon of inter-temporal substitution
between television advertising and online sales, a finding that has direct
implications for SMEs seeking to diversify their promotion channels. In the
Ecuadorian context, where television remains a dominant advertising medium,
this study highlights the need to integrate digital strategies to complement
and, in some cases, replace traditional marketing methods.
The work of Zhou
et al. (2024) in the use of machine learning algorithms to optimize
advertising strategies reinforces the idea that advanced technologies can be
key tools for SMEs. Although access to these technologies may be limited in
Ecuador, their implementation could be facilitated through collaborations with
digital marketing agencies or government training programs.
Finally, Wang
et al. (2024) They examine the impact of European legislation on digital
advertising, underlining the importance of considering the regulatory context
when designing advertising strategies. Although Ecuadorian SMEs operate in a
less regulated environment, anticipating future regulations can allow them to
stay ahead of the curve and develop more sustainable and effective strategies.
These international studies coincide in highlighting
that digital strategies, when implemented correctly, have a positive and direct
impact on business profitability. This article seeks to adapt and apply these
lessons to the Ecuadorian context, exploring how local SMEs can overcome
barriers such as lack of knowledge, limited resources, and cultural resistances
to make the most of digital opportunities.
Despite these opportunities, the central problem lies
in the underutilization of digital tools by Ecuadorian SMEs. This is due to a
variety of causes, including a lack of technological training, the perception
of high upfront costs, and a cultural resistance to change (Henriksen
et al., 2024; Sulistyaningsih et al., 2024; Yang et al., 2024).
These limitations not only restrict the scope of marketing strategies, but also
directly affect revenue generation. As a result, many SMEs lose competitiveness
to companies that have already adopted more advanced digital approaches.
The causes of this problem include, firstly, the lack
of digital training among SME owners and managers, who often lack the necessary
skills to implement social media strategies (Aridor
& Che, 2024). Second, financial
constraints limit their ability to invest in digital advertising, while the
absence of a clear regulatory framework contributes to the perception of
uncertainty about the return on these investments (Cheratian
et al., 2024). Finally, cultural factors, such as the preference for
traditional methods of promotion, hinder the adoption of emerging technologies (Hariyanto,
2024).
The effects of these limitations are significant. The
competitiveness of SMEs is affected, resulting in lower market share and
reduced revenues (Zouaoui
& Hamdi, 2024). In addition, the lack of
digital presence prevents these companies from establishing strong and lasting
relationships with their customers, thus limiting their ability to build
customer loyalty (Yenipazarli,
2024). In the long term, these shortcomings can lead to the
stagnation or even closure of many SMEs, affecting both their employees and the
local economy as a whole (Fang
et al., 2024).
The theoretical rationale for this study is based on
evidence that well-designed digital strategies can transform companies'
financial performance. Studies such as Dunford et al. (2024) demonstrate how
digital platforms can be regulated to optimize advertising and protect
consumers, while Zhou et al. (2024) highlight the role of advanced technologies
in personalizing and improving advertising strategies. These principles are
applicable to the Ecuadorian context, where SMEs can benefit significantly from
increased adoption of information technologies.
Methodologically, this study will be based on a mixed
approach that combines quantitative and qualitative methods. The quantitative
analysis will focus on measuring the ROI of digital campaigns, while the
qualitative analysis will explore SMB managers' perceptions of the barriers and
opportunities associated with digital marketing. This approach, inspired by
studies such as that of Wang et al. (2024), will allow a more comprehensive
understanding of the problem.
In practical terms, this paper seeks to provide
specific recommendations for Ecuadorian SMEs to adopt digital strategies
effectively. This includes designing training programs, promoting public
policies to support digitalization, and developing clear indicators to assess
the impact of social media investments.
The overall objective of this study is to evaluate the
impact of investment in social networks on the profitability of SMEs in
Ecuador. To this end, three specific objectives are proposed. The first is to
conduct a thorough review of the relevant scientific literature to identify
best practices and trends in digital marketing. The second focuses on
developing a robust quantitative methodology to measure the ROI and other key
indicators of digital ad campaigns. The third seeks to interpret the results obtained
to generate practical and theoretical recommendations that benefit Ecuadorian
SMEs.
Overall, this research seeks to contribute to the
understanding of how SMEs in Ecuador can overcome current barriers and take
advantage of the potential of social networks to increase their profitability,
thus strengthening their competitiveness in a globalized and constantly
evolving market.
The present study adopts a quantitative
approach, characterized by the use of numerical data
and statistical analysis to explore the relationship between investment in
social networks and the profitability of small and medium-sized enterprises
(SMEs) in Ecuador. This approach is appropriate due to the nature of the
problem, which requires an objective measurement of the financial impact of
digital strategies in a specific business context. The design, population,
sample, study variables, and statistical procedure used are described in detail
below.
The
research design is non-experimental, since the study variables will not be
manipulated. Instead, existing data will be observed and analyzed to identify
relationships between variables. The design is cross-sectional, which implies
that the data were collected at a single point in time, specifically during the
year 2023. This approach provides a snapshot of the current situation of SMEs
in Ecuador with respect to the use of social networks and their profitability.
The
study is also descriptive and correlational. On the one hand, the main
characteristics of the participating companies are described, including their
investment patterns in social networks and their profitability indicators. On
the other hand, it seeks to establish relationships between these variables,
particularly if the use of digital strategies is associated with a higher
return on investment (ROI).
The
target population of this study comprises the 1,246,162 companies registered in
Ecuador in 2023, according to the Directory of Companies and Establishments
(DIEE) published by the National Institute of Statistics and Census (INEC).
These companies cover various economic sectors, sizes and levels of
digitalization, although the analysis focuses on small and medium-sized
enterprises (SMEs), due to their predominance and relevance in the Ecuadorian
economic context.
Given
the size of the population, it would be unfeasible to conduct a census study
due to time and resource constraints. Therefore, a representative sample was
determined using a statistical calculation based on a confidence level of 95%
and a margin of error of 5%. According to this calculation, 384 companies were
selected to be part of the sample. This size is sufficient to guarantee
statistically significant and generalizable results within the context of SMEs
in Ecuador.
The
selection of the companies was carried out through stratified random sampling,
dividing the population into strata according to their economic sector
(commerce, manufacturing, services, etc.) and size (micro, small or
medium-sized enterprises). This approach ensures that the sample reflects the
diversity of the population and allows for comparative analyses between
different types of companies.
In
this study, the following variables were analyzed:
Independent
variable: The amount of investment in social networks, expressed in US dollars,
obtained from the breakdown of marketing and advertising expenditures in the
financial statements of the companies.
Dependent
variable: The return on investment (ROI), calculated using the formula: This
indicator measures the return generated for each dollar invested in social
networks, providing a direct metric of the financial impact of these
strategies.
Control
variables: Factors such as firm size (micro, small, or medium-sized) and
economic sector (trade, manufacturing, services) were included to control for
their possible influence on the relationship between the primary and dependent
variables.
In
this study, data collection was carried out from official secondary sources,
specifically the financial statements of the companies registered until 2023
with the Superintendence of Companies of Ecuador. This approach allowed access
to reliable and uniform quantitative data on the variables of interest: the
amount of investment in social networks as an independent variable and the
return on investment (ROI) as a dependent variable.
The
use of official financial data guarantees the objectivity and accuracy of the
information, eliminating possible biases introduced by self-reported answers or
subjective perceptions of the participants. In addition, this collection method
offers a wider and more uniform coverage of the
selected sample, facilitating statistical analysis and interpretation of the
results.
The
financial statements analyzed included details on
marketing and advertising expenses, breaking down investments specifically
aimed at social networks. Likewise, gross income and operating profit were
taken to calculate ROI as an indicator of profitability. This data was
collected in digital format through the Superintendence of Companies platform,
complying with the procedures and regulations established for access to
business information.
384
companies were selected from the database of the Superintendence of Companies,
using a stratified random sampling. The strata were defined according to the
size of the company (micro, small and medium) and the economic sector
(commerce, manufacturing, services, etc.), ensuring an adequate representation
of the total population of companies registered in Ecuador.
The
financial statements of the selected companies were downloaded directly from
the database of the Superintendence of Companies. To ensure the integrity of
the data, it was verified that the companies had complete and updated financial
information corresponding to the fiscal year 2023.
Once
extracted, the financial data was coded in a structured database, organizing
the information according to the variables of interest: amount invested in
social networks, revenue generated, total costs and ROI. Control variables,
such as economic sector and firm size, were also included.
The
statistical analysis was designed to address the descriptive and correlational
objectives of the study. The techniques employed include:
Descriptive
analysis: Descriptive statistics (mean, median, mode, standard deviation and
ranges) were calculated to describe the main characteristics of the
participating companies. This included the average amounts invested in social
networks, the average ROI and the variations according to the size and economic
sector of the companies.
Correlation
tests: Pearson's correlation coefficient was used to explore the relationship
between social media investment and ROI. This analysis allowed
to determine the strength and direction of the association between the
variables.
Multiple
linear regression: A multiple linear regression model was applied to analyze
the impact of the amount of investment in social networks on ROI, controlling for the size of the company and the economic
sector. This model made it possible to identify the specific contribution of
investment in social networks to business profitability.
Cross-group
comparisons: Analyses of variance (ANOVA) were performed to compare the average
ROI between companies from different economic sectors and sizes. This allowed
us to identify possible significant differences in the impact of social
networks according to the business context.
Visualization
of results: The results were presented using graphs and tables, including
scatter plots to show the relationship between social media investment and ROI,
bar charts for cross-industry comparisons, and summary tables with descriptive
statistics.
The
study was conducted in strict ethical standards. The financial data used is
public and available through the Superintendence of Companies, which guarantees
that its use is legally and ethically appropriate. No identifiable information of the participating companies was disclosed, protecting
their confidentiality. In addition, the results were presented in an aggregated
form to avoid the identification of individual companies.
While
the official financial data-based approach offers significant advantages, it
also has certain limitations. First, financial statements may not reflect all
the details of digital marketing strategies, especially in companies with less
detailed financial records. Second, the cross-sectional
design limits the ability to establish definitive causal relationships between
social media investment and ROI. Finally, the exclusion of companies with
incomplete data could introduce a bias into the sample.
The
revised methodology provides a robust approach to measure the impact of social
media investment on the profitability of SMEs in Ecuador. By using official
financial data and advanced statistical techniques, this study guarantees the
objectivity and accuracy of its findings, contributing to academic knowledge
and offering practical insights for the development of effective digital
strategies in the Ecuadorian business context.
According
to Table 1, investment in social networks varies significantly between economic
sectors and company sizes. In the trade sector, medium-sized companies have an
average investment of $2597.17, which is slightly higher than
micro-enterprises, which invest $2461.11 on average. Small businesses, on the
other hand, have a similar average of $2587.76. However, microenterprises show
a lower dispersion in the amounts invested, as evidenced by their standard
deviation of $1096.65, while small and medium-sized companies present greater
variability in their investments. This suggests that, in the commerce sector,
microenterprises tend to maintain more homogeneous strategies in their budgets
for social networks.
In the
manufacturing sector, medium-sized companies lead in average investment with
$3103.97, a significantly higher figure than microenterprises, which reach an
average of $2710.09, and small companies, which invest an average of $2556.85.
This pattern indicates that mid-sized manufacturing companies tend to allocate
greater resources to digital strategies, possibly reflecting a more strategic
approach or greater financial capacity to take advantage of social media
marketing opportunities. However, small businesses in this sector show the
highest standard deviation ($1462.35), evidencing greater heterogeneity in
their investment levels.
In the
services sector, small businesses have the highest average investment in social
networks, with $2775.83, surpassing both microenterprises ($2426.09) and
medium-sized enterprises ($2668.54). The highest median also corresponds to
small businesses, reaching $2940.79, suggesting that a considerable number of
companies in this group allocate high amounts to social networks. In contrast,
microenterprises in this sector, although they have a lower average, show less
dispersion in their amounts, which indicates greater consistency in their
investment strategies.
Table 1. Descriptive statistics
|
|
Economic Sector |
Company Size |
N |
Media |
Medium |
Desv.Est. |
Minimum |
Maximum |
|
Social Media Investment (USD) |
Trade |
Medium |
43 |
2597.174 |
2464.430 |
1440.5686 |
529.520 |
4958.260 |
|
Micro |
44 |
2461.111 |
2406.790 |
1096.6475 |
586.810 |
4694.840 |
||
|
Small |
36 |
2587.762 |
2077.690 |
1139.7081 |
893.870 |
4985.600 |
||
|
Manufacturing |
Medium |
43 |
3103.973 |
3364.400 |
1353.9761 |
669.450 |
4984.390 |
|
|
Micro |
40 |
2710.090 |
2445.645 |
1278.4201 |
630.100 |
4927.150 |
||
|
Small |
51 |
2556.852 |
2213.500 |
1462.3467 |
501.630 |
4977.110 |
||
|
Services |
Medium |
41 |
2668.542 |
2490.470 |
1379.9244 |
791.220 |
4985.460 |
|
|
Micro |
46 |
2426.091 |
2244.420 |
1264.2974 |
571.030 |
4940.370 |
||
|
Small |
40 |
2775.827 |
2940.785 |
1339.3140 |
523.700 |
4926.780 |
||
|
ROI (%) |
Trade |
Medium |
43 |
0.438 |
0.444 |
0.0916 |
0.195 |
0.625 |
|
Micro |
44 |
0.418 |
0.409 |
0.0974 |
0.246 |
0.642 |
||
|
Small |
36 |
0.431 |
0.431 |
0.0869 |
0.262 |
0.607 |
||
|
Manufacturing |
Medium |
43 |
0.428 |
0.416 |
0.0969 |
0.139 |
0.621 |
|
|
Micro |
40 |
0.421 |
0.401 |
0.0716 |
0.304 |
0.572 |
||
|
Small |
51 |
0.417 |
0.417 |
0.0937 |
0.238 |
0.593 |
||
|
Services |
Medium |
41 |
0.397 |
0.395 |
0.0831 |
0.233 |
0.611 |
|
|
Micro |
46 |
0.434 |
0.420 |
0.0804 |
0.269 |
0.639 |
||
|
Small |
40 |
0.406 |
0.409 |
0.0928 |
0.192 |
0.630 |
Source: Own elaboration
In terms of
return on investment (ROI), the commerce sector stands out with a higher
average ROI among medium-sized companies, reaching 43.8%. This group also has
the highest median at 44.4%, indicating more consistent and effective returns
relative to their investment. Small businesses in this sector follow closely
with an average ROI of 43.1%, while microbusinesses have a slightly lower
return of 41.8%. The highest standard deviation in this group corresponds to
microenterprises (0.0974), reflecting greater variability in their financial
results.
In the
manufacturing sector, the average ROI is relatively similar between
medium-sized companies (42.8%) and micro-enterprises (42.1%), while small
companies have a slightly lower average ROI of 41.7%. However, microenterprises
have a lower standard deviation (0.0716), suggesting more consistent returns
compared to the other categories. This could reflect a more uniform
optimization of digital strategies in this segment.
The
services sector, on the other hand, shows a different behavior.
Microenterprises achieve the highest average ROI at 43.4%, outpacing both
medium-sized (39.7%) and small businesses (40.6%). This finding is remarkable,
as it indicates that microenterprises, although they invest less on average,
manage to optimize their resources to obtain better returns. On the other hand,
small companies present the greatest variability in results, as reflected by
their standard deviation of 0.0928.
Figure
1. Mustache Box
Source: Own elaboration
Figure 1,
both representations suggest that size and economic sector
have a significant impact on social media investment and associated returns.
While medium-sized companies tend to lead in investment, micro-enterprises in
the service sector are better able to optimize the available resources to
obtain a higher ROI. This analysis reinforces the need to customize digital
strategies according to the size and sector of each company, focusing on
maximizing the efficiency of the investments made.
In general
terms, medium-sized companies in the commerce sector stand out as the ones that
most efficiently convert their investment in social networks into financial
returns. However, microenterprises in the service sector are also notable for
their ability to optimize resources, achieving high ROIs with more modest
investments. On the other hand, the high variability in the results of small
companies suggests a lack of standardization in the strategies used, which
could represent an opportunity for improvement for this group.
These
results highlight the importance of adapting digital marketing strategies not
only to the size of the company, but also to the specific characteristics of
each sector. In addition, they underscore the need to optimize social media
investment to maximize ROI, especially in sectors such as manufacturing and
services, where returns tend to be less consistent. This analysis provides a
basis for Ecuadorian companies to adjust their strategies and more effectively
take advantage of the opportunities offered by social media in today's
competitive environment.
The
correlation matrix in Table 2 shows a very weak relationship between return on
investment (ROI) and investment in social networks, represented by a
correlation coefficient of 0.044. This value indicates that there is
practically no significant linear association between these two variables. In
other words, the data doesn't show that an increase in social media spend is
directly related to a positive or negative change in ROI.
This result
suggests that, although companies are allocating resources to social networks,
other factors may be influencing the returns obtained. For example, the
effectiveness of the strategies used, the quality of the content, or even
external factors such as market characteristics or the target audience, could
have a greater impact on ROI. It is necessary to deepen the analysis by
including additional variables or different methodologies to better understand
the relationship between investment and return in this business context.
Table 2. Correlation Matrix
|
|
ROI (%) |
Social Media Investment (USD) |
|
ROI (%) |
— |
|
|
Social Media Investment (USD) |
0.044 |
— |
Source: Own elaboration
Table 3 of
ANOVA presented analyzes whether there are significant differences in return on investment (ROI, %) depending on the economic
sector, the size of the company and the interaction between both variables. The
results suggest that none of these factors have a statistically significant
impact on ROI, indicating that the variations observed in the data cannot be
directly explained by these variables.
As for the
effect of the economic sector, the sum of squares is 0.01777, with 2 degrees of
freedom (gl), which results in a square mean of
0.00889. The calculated F-value is 1.126, and the associated p-value is 0.325.
This p-value, being greater than 0.05, indicates that there are no significant
differences in ROI between economic sectors (trade, manufacturing, and
services). This means that no matter what industry a company belongs to, the
return generated by social media investments doesn't vary significantly.
In relation
to the size of the firm, the sum of squares is 0.00279, with 2 degrees of
freedom, resulting in a square mean of 0.00140. The value of F is 0.177, and the p-value is 0.838. This result suggests that there are no
statistically significant differences in ROI according to the size of the
company (micro, small or medium). This means that, in this analysis, companies
of different sizes achieve similar returns from their investments in social
networks.
Likewise,
the interaction between the economic sector and the size of the company does
not show a significant effect on ROI. The sum of squares associated with this
interaction is 0.04171, with 4 degrees of freedom, and a mean square of
0.01043. The value of F is 1.321, and the p-value is
0.262. This p-value indicates that there is no significant interaction between
these two variables, i.e., the impact of the economic sector on ROI does not
depend on the size of the company, nor vice versa.
Finally,
the residuals show a sum of squares of 2.96003, with 375 degrees of freedom,
resulting in a square mean of 0.00789. This component reflects the variation in
ROI that cannot be explained by the economic sector, the size of the company or
the interaction between both variables, the results of the ANOVA analysis
indicate that neither the economic sector, nor the size of the company, nor the
interaction between these variables have a significant effect on the ROI
obtained by investments in social networks. This suggests that other factors,
possibly related to the quality and effectiveness of advertising strategies,
could play a more important role in determining return. Therefore, it is
recommended to explore additional variables and dig deeper into the design of
marketing campaigns to identify the key elements that influence the return on
digital investments.
Table 3. ANOVA - ROI (%)
|
|
Sum of Squares |
Good luck |
Mean Square |
F |
p |
|
Economic Sector |
0.01777 |
2 |
0.00889 |
1.126 |
0.325 |
|
Company Size |
0.00279 |
2 |
0.00140 |
0.177 |
0.838 |
|
Economic Sector ✻ Company Size |
0.04171 |
4 |
0.01043 |
1.321 |
0.262 |
|
Waste |
2.96003 |
375 |
0.00789 |
|
|
The results of this study reveal that although
Ecuadorian companies are investing in social media marketing strategies, this
investment does not show a statistically significant impact on ROI. This can be
due to several factors. First, many companies may not be optimizing their use
of social media, either due to a lack of knowledge or poorly executed
strategies. Previous studies such as that of Almestarihi
et al. (2024) have highlighted that ROI depends not only on investment, but
also on the quality of the strategies employed, such as appropriate audience
segmentation and the creation of relevant content.
In sectoral
terms, although the trade sector showed a higher average ROI, the differences
were not significant. This could be explained by the inherent characteristics
of the sectors. Companies in the retail sector may benefit more directly from
social media due to the transactional nature of their business, while sectors
such as manufacturing and services could experience a more indirect or
long-term impact.
The lack of
a meaningful relationship between investment and ROI may be a
reflection of a resource allocation strategy that does not maximize the
potential of social media. According to Bajaj, et al. (2024), neuromarketing
and data analytics can be crucial tools to optimize the return on investment in
digital campaigns, strategies that may be absent in many Ecuadorian SMEs.
Another possible explanation for these results is that smaller companies, while
achieving a slightly higher ROI, may be limited by their resources and not
reach the level of investment needed to deliver more significant results. This
finding is consistent with Birim et al. (2024), who
suggest that small businesses face greater barriers to implementing effective
advertising strategies.
The fact
that the ANOVA analysis did not detect significant differences between company
sizes or sectors could be related to the homogeneity in social media marketing
practices between these categories. This suggests an opportunity for companies
to develop more differentiated strategies tailored to their specific contexts.
Finally, the results of linear regression raise the need to explore additional
variables that can moderate or mediate the relationship between social media
investment and ROI, such as the quality of the content, the type of platform
used, or the level of interaction of the target audience with the campaigns.
These factors could be the subject of future research to better understand the
dynamics of digital profitability in Ecuadorian SMEs.
Although
social media represents a valuable tool for SMEs, the results of this study
underscore the importance of designing and executing more effective strategies
that can translate investments into tangible economic benefits. This finding
has practical implications for companies and policymakers, who should focus
their efforts on training companies in the strategic use of these platforms and
in constantly monitoring the impact of their digital campaigns.
This study has evaluated the impact of
investment in social networks on the return on investment (ROI) in Ecuadorian
companies, considering the size of the company and the economic sector. The
conclusions are then presented organized in relation to the objectives set and
contrasted with the findings of the relevant references.
The literature
highlights the importance of measuring and optimizing digital strategies to
maximize ROI. For example, Almestarihi et al. (2024)
emphasize that the success of digital campaigns depends to a
large extent on adequate measurement of return and well-executed
strategies. This study confirmed that, although Ecuadorian companies invest in
social networks, the average ROI was relatively low and did not show a
significant relationship with the amounts invested. This is in line with the
findings of Bajaj et al. (2024), who stress that the use of advanced
technologies, such as neuromarketing, is crucial to optimizing results, an
approach that seems to be absent in most of the companies analyzed.
Additionally,
Aridor and Che (2024) argue that regulations and
ethical data management can also influence the effectiveness of digital
strategies. Although this aspect was not directly evaluated in the present
study, it could be relevant in the Ecuadorian context, where companies still
face challenges in the implementation of ethical and transparent strategies.
The
methodology used made it possible to analyze the differences in investment and
ROI according to the size of the company and the economic sector. However, the
ANOVA results and regression analysis showed that neither industry nor firm
size has a significant effect on ROI. These results differ from the findings of
Hariyanto (2024), who highlighted that
microenterprises can significantly benefit from well-designed digital
strategies, especially on platforms such as TikTok. While micro-enterprises in
the service sector showed a slightly higher ROI in this study, the relationship
was not statistically significant, suggesting that these companies are not
maximizing the potential of their social media investments.
The
descriptive analysis also reflected a high variability in investments within
sectors and company sizes, which coincides with the findings of Birim et al. (2024), who highlight that companies face
challenges in the efficient allocation of resources for digital advertising.
This reinforces the need for more personalized and optimized strategies.
The results
showed that the relationship between social media investment and ROI is very
weak (R˛ = 0.00734). This suggests that factors other than those considered in
this study, such as the quality of advertising strategies or the level of
interaction with consumers, could be influencing the results more. This finding
is consistent with Lambrecht et al. (2024), who argue that the effectiveness of
digital investments is influenced by multiple factors, including consumption
patterns and the characteristics of advertising content.
In
addition, the commerce sector showed the best results in ROI, especially in
medium and small companies, which could be related to the transactional nature
of this sector. However, this behavior was not significant enough in the
statistical analysis, indicating that the strategies used are not yet
optimized. This is in line with the findings of Zhou et al. (2024), who argue
that optimizing advertising strategies requires the use of advanced tools, such
as machine learning, to improve targeting and maximize returns.
Training in
digital strategies: Companies, especially micro and small ones, should invest
in training on segmentation, content design and use of advanced platforms, as
suggested by Bajaj et al. (2024). This could improve the effectiveness of your
campaigns and increase ROI.
Adoption of
advanced technologies: Incorporating tools such as machine learning and data
analytics for campaign optimization is essential, as proposed by Zhou et al.
(2024).
Differentiation
of strategies by sector: The results suggest that strategies should be adjusted
to the context of the economic sector. For example, the services sector could
benefit from strategies focused on loyalty and branding, while the commerce
sector should prioritize transactional strategies.
Constant
ROI evaluation: Following the recommendations of Almestarihi
et al. (2024), companies should implement clear metrics and regular reviews to
measure and adjust their social media investments.
Although investments in social networks are a
key tool for Ecuadorian companies, the results of this study suggest that these
investments do not directly translate into a significant ROI. This reinforces
the need to design more effective and optimized strategies, taking advantage of
emerging technologies and adapting campaigns to the specific characteristics of
each sector and company size. These findings provide a solid basis for future
research and for the development of policies and programs that strengthen the
ability of Ecuadorian companies to compete in a globalized digital marketplace.
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