The aim of the study was to identify effective communication strategies in digital advertising for SME’s in Saudi Arabia. The study analyzed the relationship between these strategies and competitive advantage using the content analysis method. The study population was represented by all digital advertisements implemented by SMEs in the Kingdom through social media platforms such as Snapchat, Instagram, and X during 2022. A total of 600 digital advertisements were analyzed. It was found that 7 communication strategies were applied in the digital advertising message at a moderate level, including motivation, brand personality, prosecution, innovative promise, as well as simulation, initiative, and priority. The study also found that only 6 strategies affect competitive advantage, except for the motivation strategy. There was a positive correlation between communication strategies, cost and differentiation elements only. The study showed a moderate impact of communication strategies on competitive advantage. Finally, the study provided some important recommendations to help SMEs activate communication strategies based on digital advertising in the kingdom.


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Digital advertising is a powerful tool that can help businesses achieve their objectives, improve their competitive position, and increase sales. To achieve these goals, various strategies are employed, each with a different focus on communication, persuasive content, creative messaging, medium performance, and target audience (Kjerstin & Cotter, 2022). Despite their differences, all these strategies have been shown to have a positive impact on business outcomes (Al-Mubarak, 2017). By leveraging the right digital advertising strategy, businesses can effectively reach their target audience and achieve their desired results (Huang & Liu, 2022).

Attention all business owners in Saudi Arabia! Are you struggling to compete in the digital market? Fear not, as advertising agencies in the Kingdom have established special departments for digital advertising and strategy design. They have hired expert writers to help you compete in the digital market. Additionally, many Saudi entrepreneurs have established digital advertising agencies to sell advertising spaces on social media platforms such as YouTube, Facebook, Snapchat, and Instagram. They have also added digital advertising to search engine optimization and global websites. These steps have made it easier for your products to access regional and international markets, providing high marketing value for your business. In other words, these procedures have created an increasing need for SMEs to compete with larger companies in different markets (Karim & Batoo, 2017). Don’t miss out on this opportunity to take your business to the next level (Huhet al., 2020).

Small and medium-sized enterprises (SMEs) are the backbone of most economies worldwide. They are the most effective development model, capable of fighting poverty and unemployment, improving export capacity, and generating state financial surpluses. However, despite their immense potential, SMEs face numerous obstacles that hinder their growth and competitiveness (Al-Sbini, 2017). These challenges weaken their ability to capitalize on opportunities and address marketing problems, which are among the most pressing issues they face. Therefore, it is crucial to support SMEs and remove the barriers that limit their potential. By doing so, we can unlock their full capacity to drive economic growth, create jobs, and improve the lives of people around the world (Al-Nsour, 2019).

Small and medium enterprises (SMEs) are a top priority for the Kingdom of Saudi Arabia. The government has taken significant steps to ensure their success and has incorporated them into the national economy plans, including the Kingdom’s Development Vision 2030. As a result, SMEs receive many preferential advantages that make them large and highly competitive projects. In fact, SMEs contribute 20%–35% of GDP to the national economy, according to Vision 2030. The General Authority for Small and Medium Enterprises (GSME) has been established as an incubator for the development of these enterprises, and the government has worked tirelessly to market and export their products to foreign markets. By supporting SMEs, the Kingdom is investing in the future of its economy and ensuring long-term growth and prosperity for all its citizens.

In today’s world, small and medium-sized enterprises (SMEs) must embrace the digital environment to effectively grow their businesses. With the rapid advancements in technology, the local business environment has become more competitive than ever before. To stay ahead of the game, SMEs must utilize digital advertising strategies to achieve their marketing and communication objectives. This includes improving their competitive position, making an impact on the consumer, and promoting openness to global markets. By doing so, SMEs can take advantage of the enormous potential of the digital world and ensure their long-term success (Al-Attin, 2017).

Research Objectives

  • Recognize the communication message strategies for digital advertising for Saudi SMEs, using content analysis for a sample of digital advertising.
  • Analysis of the relationship between message strategies in digital advertising and the competitive advantage of SMEs in the Kingdom.

Research Significance

  • Enriching the field of knowledge on the subject of digital advertising and its effects, bridging the scientific gap and the lack of literature that has researched digital advertising strategies; and its relationship to SMEs’ competitive advantage.
  • Focus on how SMEs exploit the advertising message strategies in digital advertising for the purposes of excellence and uniqueness in the market.
  • Linking the results of the applied study to the Kingdom of Saudi Arabia Vision 2030, which calls for attention to the development of SMEs.

Literature Review

Concept and Importance of Advertising Message

Traditional advertising has been a cornerstone of marketing for decades. It involves non-personal communication that businesses and non-profit organizations provide through various means (Žaneta, 2015). This communication is directed towards a random selection of individuals, with the aim of informing or persuading them to buy certain products (Ali, 2020). It may also provide marketing and activation incentives or simply promote the organization’s brand. However, with the rise of digital advertising, businesses can now reach a wider audience through online communication tools provided through computers or smart devices. Digital advertising can take various forms, such as visual ads through videos, search engine content, or social networking displays (Al-Nsouret al., 2023a). By leveraging the power of digital advertising, businesses can connect with their target audience more effectively and efficiently than ever before.

Digital advertising has become an integral part of our lives, but it’s not without its problems. The rapid development of internet-based technology has led to various issues associated with digital advertising (Mselle & Belkheri, 2019). According to experts, digital advertising includes contextual ads on search engines, pages, blogs, rich media ads, and email ads, which are offered by the network’s server collection (Alnsour, 2022). Despite the means used, the aim of digital advertising remains the same-to persuade consumers of the merits of the commodity or service being marketed. Advertising posted on the internet is not different in content from traditional advertising, but the Internet provides additional tools and means to facilitate access and interaction with the user. Many researchers agree that digital advertising is non-personal communication by a well-known sponsor or source (Musa, 2015). Advertising presents information, products, services, or ideas. These announcements seek to influence and persuade the public through the mass media. In this case, internet-related digital advertising means include websites, social networks, smartphone applications, and computer applications (Porter, 1990). It’s important to acknowledge the impact of digital advertising on our psyche and take necessary measures to ensure that it’s fair, helpful, and safe (Alnsour, 2022). By doing so, we can enjoy the benefits of digital advertising without compromising our privacy and security (Alnsouret al., 2021).

Relationship between Communication Strategies and Competitive Advantage

In today’s competitive business world, having a unique advantage over your competitors is crucial. This is where competitive advantage comes into play. It is a system that enables a company to deliver customer value in an efficient and sustainable manner, while outperforming its competitors (Al-Hourani, 2018). By meeting the customer’s needs and desires and encouraging them to acquire the product on an ongoing basis, a company can achieve a competitive advantage (Al Abdulkareem & Al-Nsour, 2021). This can be attained by carrying out activities at a lower cost or with better effectiveness than competitors, using available resources to provide greater value to the customer (Wiktora & Sanak-Kosmowsk, 2021). A company with a competitive advantage has resources and enablers that give it internal strength and make it stronger from the beneficiaries’ point of view. This is reflected in its goods and services of different value to the target customers. The competitive advantage is that the company has a unique position over its competitors, enabling it to deliver a distinguished product in a more successful and profitable manner (Al-Sbini & Al-Khawlani, 2017). Therefore, it is essential for companies to strive for a competitive advantage to stand out in the market and succeed (Mselle & Belkheri, 2019).

Nowadays, a company’s success depends on its ability to gain a competitive advantage. One way to achieve this is through a communication strategy that is based on digital advertising messages (Alnsour, 2022). By doing so, the company can meet the customer’s needs and provide better value for its products. This success is linked to a communication strategy that focuses on doing something better than competitors. The company’s competitive market excellence is mainly based on providing products that are distinct from competitors. According to Porter (1993), competitive advantage arises when new and more effective methods are discovered compared to competitors in the introduction and supply of these products (Al-Sbini, 2017). Therefore, it is essential for enterprises that seek to excel in their working environment to possess and maintain a competitive advantage, which can be seen as a way to outperform others (Liu, 2018).

Marketing communication strategies are essential for creating a unique identity for an organization that sets it apart from its competitors (Al-Sbini, 2017). This helps the organization maintain its competitive advantage over a long period of time. The Foundation can achieve this by effectively utilizing its technical, material, financial, and organizational resources, as well as its capabilities, knowledge, and values (Alnsour, 2022).

In the business world, competitiveness is the key to success. However, when it comes to digital advertising, linking competitiveness to message strategies can be a complex and ambiguous task. This is because an organization’s competitiveness depends on various interrelated factors, such as customer and shareholder values and interaction in a competitive environment. To overcome this challenge, a comprehensive approach to competitive advantage is proposed (Zhaoet al., 2011). This approach involves mapping several dimensions that measure the organization’s competitiveness, highlighting its strengths and weaknesses, opportunities, and threats to loyalty (Kjerstin & Cotter, 2022). By adopting this approach, businesses can formulate a winning strategy that sets them apart from their competitors. So, if you want to stay ahead of the game, it’s time to take a comprehensive approach to competitive advantage (Perloff, 2020).

In highly competitive domestic and global markets, modern institutions need to possess or develop a competitive advantage to succeed (Bahos, 2020). This is where mission strategies come into play. By implementing these strategies, organizations can provide value to their customers, meet their needs, and ensure their loyalty (Kjerstin & Cotter, 2022). This, in turn, helps build a positive reputation and image of the organization in the minds of customers (Hijabet al., 2022). Mission strategies also enable organizations to achieve excellence in the goods and services they provide, as well as in the resources, competencies, and strategies used in the competitive environment. By doing so, organizations can explore new competitive areas, such as entering new markets, attracting new customers, providing new products, and identifying opportunities that can be obtained (Al-Nsour et al., 2023). In short, mission strategies are crucial for organizations to achieve their strategic goals and gain a competitive advantage (Alnsouret al., 2021). They help organizations build a future vision of the goals they seek to achieve and pave the way for success in today’s dynamic business environment (Al-Sbini, 2017).

Research Methodology

Discovering the most effective digital advertising strategies for small and medium-sized enterprises (SMEs) is crucial in today’s competitive market. Our study is descriptive research that analyzes the analytical curriculum of digital advertising used by SMEs on social media platforms such as Snapchat, Instagram, and X during the year 2022. We surveyed 150 SMEs registered with the SME Authority and the National Entrepreneurship Centre and found that 30 in the retail sector were using digital advertising on social media. We analyzed 20 electronic advertisements for each of the 30 SMEs, resulting in a total of 600 digital advertisements analyzed. Our findings will help SMEs to optimize their digital advertising strategies and achieve their business goals.


This research is a game-changer for small and medium-sized enterprises (SMEs) looking to improve their digital advertising. The study used the content analysis technique to analyze the structural relationships between measurement variables and underlying metrics. To achieve this, the research employed Structural Equation Modeling and PLS Smart Version 3.5.1, which are the most advanced and reliable methods for analyzing data and testing hypotheses. The results of the study’s data test are presented in Tables I and II, which demonstrate the effectiveness of the research methodology. SMEs can use this research to improve their digital advertising and stay ahead of the competition.

Communication strategies AVE CR Cross loading
Information + Cost 0.666 0.800 0.808
Prosecution + Cost 0.793 0.846 0.822
Innovative promise + cost 0.852 0.945 0.922
Lifestyle + Cost 0.773 0.872 0.878
Brand personality + Cost 0.678 0.807 0.820
Prosecution + Differentiation 0.714 0.833 0.838
Motivation + Differentiation 0.740 0.850 0.856
Innovative promise + Differentiation 0.712 0.835 0.907
Lifestyle + Differentiation 0.775 0.873 0.880
Brand personality + Differentiation 0.776 0.874 0.881
Information + Focus 0.726 0.841 0.852
Innovative promise + Focus 0.845 0.942 0.918
Lifestyle + Focus 0.666 0.799 0.949
Create Style + Focus 0.760 0.861 0.880
Table I. Outcomes of Construct Validity for Communication Strategies
Strategies VIF
Create style (CS) 1.171
Information (INFO) 1.348
Lifestyle (L) 1.715
Propulsion (M) 1.675
Public prosecution (P) 2.069
Brand personality (BT) 1.514
Innovative promise (Z) 2.816
Table II. VIF
  • The distribution of load cross-loading is a crucial aspect of the measurement model. It refers to the distribution of items to other underlying variables, and according to statistical rules, the acceptable cross-loading value should exceed 0.7 (Fornell & Lacker, 1981). The value of the distribution of cross-loading to all items in Tables IIII meets the requirements of the statistical rule. This means that all items in latent variables are statistically significant, reliable, and can be relied upon to test the study hypotheses. Therefore, we can confidently conclude that the study results are valid and trustworthy.
  • Alpha Cronbach’s is a powerful tool that helps to evaluate the internal consistency of a test. It is a statistical measure that determines whether the test accurately measures what it is intended to measure. Cronbach’s alpha is the most commonly used measure for evaluating internal consistency, and it is widely accepted that the acceptable value of the test must be greater than 0.7 and less than 0.95. The results of the table indicate that the value falls within the acceptable limits of 0.7–0.95, which means that there is an acceptable degree of internal consistency between the scale phrases and the measurement of what is intended to be measured. Therefore, it is crucial to use Alpha Cronbach’s to ensure that the test is reliable and valid. By doing so, we can be confident that the test accurately measures what it is intended to measure, and we can make informed decisions based on the results.
  • The Variance Inflation Factor (VIF) test is a numerical measure used to determine the extent of the overlapping linear relationship between independent variables in a structural model. VIF is calculated for each independent variable, and a high value indicates that it overlaps largely with other independent variables in the model. The PLS-SEM analysis program calculates the value of VIF, which starts from 1 and has no ceiling. According to statistical rules, VIF value less than 5 means that the problem of an overlapping linear relationship is not serious, while a value greater than 5 indicates high collinearity. VIF value above 10 means that the linear overlapping relationship is very serious, and regression estimates are completely inaccurate. Based on the results in Table II, all VIF test values are less than 5, indicating that the level of linear interrelationship collinearity between independent variables is not serious. Therefore, the statistical calendar requirements for this test have been met.
Relationship Std. beta Std. error T-value P-value Statistical decision
Communication strategies ⟶ Cost 0.055 0.065 0.641 0.522 No relationship
Communication strategies ⟶ Differentiation 0.120 0.005 2.558 0.002 Positive relationship
Communication strategies ⟶ Focus 0.163 0.007 2.836 0.005 Positive relationship
Communication strategies ⟶ Competitive advantage 0.195 0.143 3.367 0.001 Positive relationship
Table III. Direct Path Coefficients for Communication Strategies

Research Findings

The results of the content analysis are in, and they reveal that Saudi SMEs can benefit from utilizing 14 different communication strategies for digital advertising. While 7 of these strategies are currently being used at an average level, the remaining 7 are being underutilized. The arithmetic mean values range from 1.01 to 2.16, indicating that there is significant room for improvement. By focusing on medium-grade communication sub-strategies such as motivation, brand personality, prosecution, innovative promise, and simulation, initiative, and priority, SMEs can take their digital advertising efforts to the next level and achieve greater success.

The path analysis method was used to test the study’s hypotheses and evaluate the path factors of communication strategies. This method dismantles the links between latent variables and explains their respective impact, providing a comprehensive understanding of the causal relationships between variables. Path analysis, closely related to multiple regression, allows for several theoretical suggestions about cause and effect without manipulating variables. However, it is important to note that causal correlations between variables are insufficient to establish the validity of causal assumptions. Tables III and IV reflect the supposed causal relationships between variables. Exogenous variables are independent, and endogenous variables are dependent. By using this method, we can gain a deeper understanding of the complex relationships between variables and make informed decisions based on the results.

Relationship Std. beta Std. error T-value P-value Statistical decision
Information ⟶ Competitive advantage 0.172 0.061 1.415 0.158 Positive relationship
Prosecution ⟶ Competitive advantage 0.077 0.096 0.809 0.419 Positive relationship
Innovative promise ⟶ Competitive advantage 0.097 0.103 0.938 0.348 Positive relationship
Creating style ⟶ Competitive advantage 0.087 0.061 1.415 0.158 Positive relationship
Motivation ⟶ Competitive advantage 0.048 0.06 0.619 0.536 No relationship
Lifestyle ⟶ Competitive advantage 0.140 0.065 2.163 0.031 Positive relationship
Brand personal ⟶ Competitive advantage 0.177 0.071 2.504 0.013 Positive relationship
Table IV. Direct Path Coefficients for Sub-Communication Strategies

Path analysis is a powerful statistical technique that extends the regression model. It helps to obtain the correlation matrix and identify the causal relationship between two variables. By examining the model’s suitability using statistical indicators such as Standardized Beta and t-Statistics, we can gain valuable insights into the relationship between the variables. According to Coffman and MacCallum (2005), a P-Value below 0.05 (Probability of Errors) is statistically acceptable, which means that there is a clear directional relationship between the two variables. The Standardized Beta is an indicator of the relationship direction, and the reference (−) indicates a negative relationship between the two variables. By using path analysis, we can gain a deeper understanding of the relationship between variables and make informed decisions based on the results.

The current study examines the direct impact of communication strategies in digital advertising on elements of competitive advantage. The path coefficient is used to provide indicators of the mutual relationships between the two variables. The directional relationship (path direction) is accepted or rejected based on the results of Tables III and IV. Most presumed correlation relationships have P-Values less than 0.05. All six hypotheses in the table have been empirically demonstrated, except for the “motivation” strategy. Independent variables have been found to have an impact on competitive advantage. The study found a positive relationship between communication strategies and differentiation (Beta = 0.120, P-Value = 0.002), communication strategies and focus (Beta = 0.163, P-Value = 0.005), and communication strategies and overall competitive advantage (Beta = 0.195, P-Value = 0.001). At the sub-strategy level, all communication strategies were found to be statistically significant and positively linked to competitive advantage (except for the motivation strategy). The study confirmed the positive relationship between the totality of communication strategies and the elements of differentiation and focus, along with all six sub-communication strategies (except motivation) at a significant P < 0.01 level.

f2 is a powerful statistical tool that can help you determine the impact of regression models. Introduced by Cohenet al. (2008), this measure is specifically designed for models with mixed effects and hierarchical linear models. By using the Cohen model, you can accurately calculate the size of the effect under the current decline model (Cohen, 1988). According to the statistical rule, an f2 value above 0.35 indicates a significant impact, while an f2 value between 0.15 and 0.35 indicates an intermediate effect. If the f2 value is less than 0.15, it is considered to have a low impact, and if it is less than 0.02, it is deemed to be ineffective. By using f2, we can make informed decisions about the effectiveness of regression models. So why wait? Start using f2 today and take statistical analysis to the next level!

The current study delves into the impact of digital advertising strategies on competitive advantage. The results of the f2 impact volume test, as shown in Table V, reveal that communication strategies have a medium-sized impact on competitive advantage. Interestingly, advertising strategies did not affect the cost (f2 = 0.003) but had a medium volume effect on differentiation (f2 = 0.15) and concentration (f2 = 0.27). The study concludes that communication strategies contribute significantly to the impact on competitive advantage, with impact sizes ranging from weak to medium and values of less than 0.15 and not more than 0.27. When examining the impact of all communication strategies on competitive advantage, it was evident to an average degree (f2 = 0.302). These findings highlight the importance of communication strategies in achieving a competitive advantage in the digital advertising landscape.

Construct Q2 Statistical decision
Cost −0.004 No predictive relevance
Differentiation 0.02 Predictive relevance
Focus 0.10 Predictive relevance
Table V. Outcomes of Q2

The R2 coefficient of determination is a powerful statistical tool that helps researchers analyze trends and measure the ratio of variability in the dependent variable (DV) according to one or more independent variables (IDV). It is used to assess the strength of the linear relationship between two variables, making it an essential tool for any researcher. According to the R2 decision-making rule, values below 0.02 are considered weak, values between 0.02 and 0.26 are considered average, and values above 0.26 are considered strong. In the first hypothesis, the difference in communication strategies for digital advertising explains the ratio of disparity in competitive advantage. The results show that the discrepancy between strategies and competitive advantage is moderate (R2 = 0.23). Based on this statistical decision-making base, communication strategies explain the moderate disparity in competitive advantage. Therefore, it is crucial for researchers to rely on the R2 coefficient of determination to analyze trends and measure the ratio of variability in the dependent variable. With this tool, researchers can gain valuable insights into the relationship between variables and make informed decisions based on their findings.

The Q2 model’s predictability can be measured more effectively by using both R2 and Q2, according to studies (Chin, 1988). The Blindfolding technique can achieve the predictive relationship, Predictive Relevance, for a wide range of complex data. The PLS methodology involves deleting a certain set of indicators and then predicting the deleted part based on the calculated part transactions of the parameters. Q2 technology can restructure data collected with the help of a structural model and performance capabilities (Frornell & Cha, 1981). The statistical decision-making rule indicates that Q2 greater than 0.00 provides predictive capacity (Hair, 2014). The results of Table V below show that the analysis of competitive advantage through cost (Q2 = −0.004), differentiation (Q2 = −0.02), and concentration (Q2 = −0.10) provides a satisfactory level of prediction. As we have previously learned, f2 ranges from 0.003 to 0.27, which means that the predictive importance of the competitive advantage variable is medium. Therefore, using both R2 and Q2, along with the Blindfolding technique and PLS methodology, can help achieve a satisfactory level of prediction for a wide range of complex data.

To ensure that a large complex model is accurately predicting outcomes, it is essential to evaluate its overall performance. The GOF model is a powerful tool that can help you achieve this. By using Smart-PLS to calculate the statistical quality level, it can determine the geometric mean medium of the AVE and R2 index for all endogenous constructs (Wetzelset al., 2009). This will give you a clear understanding of the model’s predictive strength and its ability to deliver accurate results (Chinet al., 2010). The GOF model is particularly useful for evaluating reflective parameters, but it can also be applied to formative parameters (Vinziet al., 2010). By emphasizing the contribution of these parameters to the predictability of the Inner Model, it can increase the overall performance of the model. To ensure that your model has a good fit, you should follow the statistical decision-making rule. If the value of the statistical quality level is greater than 0.36, your model is considered to have a good fit. By using the GOF model, you can be confident that your model is delivering accurate results and making a positive impact on business1 1If appropriate validity values are less than 0.1 that means they are inappropriate.-If the validity values are appropriate between 0.1 and 0.25, it means that it is less appropriate.-If the validity values are appropriate between 0.25 and 0.36, this means that they are of medium suitability.-If the validity values are more than 0.36 it means they are of high relevance. . The GOF test results in Table VI are in, and they’re impressive. Based on the statistical decision-making rule we used earlier, the general model (measurement and construction) has a high predictive capability, as we saw in the previous section. The test value of the entire model used in this study (GOF = 0.623) is greater than the authorized value of 0.36, which means that the model’s overall performance is exceptional. It’s safe to say that the model is highly appropriate for the current study and the variables being studied.

Construct f2 value Decision
Cost 0.003 Not affected
Differentiation 0.15 Medium size effect
Focus 0.27 Medium size effect
Communication strategies 0.302 Medium size effect
Construction R2 value Result
Competitive advantage 0.23 Medium power of explanation
Measurement GOF value Result
GOF 0.623 High GOF
Table VI. Statistical Tests of Communication Strategies

Discussion and Recommendations

The study’s findings revealed that while some brand personality strategies, innovative promise, information, public allegation, and the creation of consumer habit and lifestyle were used moderately, and other strategies such as simulation, unique sale, initiative, priority, direction, symbolic association, status, and advertising orders were not activated within the advertising communication message. This indicates that there is a significant opportunity for SMEs in Saudi Arabia to improve their digital advertising messages by incorporating these strategies. By doing so, they can create a more effective and persuasive advertising message that resonates with their target audience. The study’s outcome reinforces previous research in the area of digital advertising and advertising strategies used in SMEs in general. The study conducted by Rábováa (2014) confirmed that these enterprises focused on specific strategies to achieve their communication message. Therefore, it is essential for SMEs to consider all available advertising strategies to create a comprehensive and effective advertising message that highlights their brand’s personality, product, and facility.

Our study has shown that providing information about products and services is a crucial aspect of communication strategies. In fact, the use of information strategy ranked fifth among the communication strategies in general. This finding is consistent with previous studies that have confirmed that customers are more likely to be persuaded to buy products through digital sites when they are provided with information such as product prices, payment details, and instructions on how to use the product. Moreover, our study has revealed that small and medium-sized enterprises (SMEs) may face challenges in showcasing their details to advertisers. Therefore, to achieve an impact on viewers, SMEs may need to use more motivation, information, and brand personality strategies compared to other strategies. This is supported by the study by Finket al. (2020), which showed that effective marketing of projects on Facebook is based on showcasing the brand’s mental image and distinguishing it for the public to increase its influence. In conclusion, our study highlights the importance of providing information to customers and using effective communication strategies to persuade them to buy products and services. SMEs, in particular, may need to focus on using more motivation, information, and brand personality strategies to achieve their marketing goals (Al-Nsour, 2019).

Did you know that sharing information through digital social platforms can help attract consumers to your brand? A study conducted by Ahmad (2016) and Musa and Harun confirmed this fact. Social media content marketing plays a crucial role in conveying effective information to consumers and maintaining interaction with brands. Therefore, it’s essential to focus on brand personality strategies and information strategies to make your marketing efforts more effective. By doing so, you can create a strong brand image and attract more customers to your business. The success of small and medium-sized enterprises (SMEs) is crucial to the growth of our national economy. As such, it is imperative that we pay attention to the role of advertising in these businesses. Advertising is the primary marketing tool for SMEs, and it is essential that we develop effective strategies to enhance their competitive advantage. To achieve this, we must work together to develop the role of advertising and adapt it to serve the institutions in the best way possible. We must also expand the teaching of advertising as a specialization that requires a lot of skills and effective application of its strategies. Modern courses should be developed and taught at universities and higher institutes to give students the knowledge and skills needed to contribute to the achievement of enterprises’ objectives. We must focus on communication and persuasive strategies and adopt more influential methods, such as creative strategies, to enhance the competitive advantage of SMEs. It is also recommended to conduct studies across other sectors, such as insurance companies, telecommunications, and banks, to obtain more accurate results and evaluate the forms and strategies of competing advertising in offline environments. Let us work together to ensure that SMEs have the tools they need to succeed. By paying attention to advertising research and developing effective strategies, we can help these businesses thrive and contribute to the growth of our national economy.

Implications and Conclusion

The media richness theory, developed by researchers Robert Lengel and Richard Daft, highlights the importance of choosing the right communication channel to reduce ambiguity and facilitate easy decoding of the message. This is particularly crucial for startups that often face ambiguity in their advertising messages. The theory emphasizes the significance of interactive two-way communication between the sender and the audience, which is reflected in digital advertising and the interactive features of the internet. Channels that provide more information are considered richer and less ambiguous, leading to more effective communication. The richness of information reduces ambiguity and provides a common understanding of the message. The theory also suggests that technological means possess a certain amount of information and diversity of content, which can overcome uncertainty and suspicion experienced by individuals. Therefore, it is no surprise that many small and medium-sized enterprises (SMEs) focus on digital advertising due to its interactive advantages and low cost, which help in clear communication of their products and lead to greater impact. By choosing the right communication channel, SMEs can ensure that their message is conveyed effectively, leading to increased brand awareness and sales.


  1. Ahmad, N. S., Musa, R., & Harun, M. H. M. (2016). The impact of social media content marketing (SMCM) towards brand health. Procedia Economics and Finance, 37(1), 331–336. doi: 10.1016/S2212-5671(16)30133-2.
     Google Scholar
  2. Al Abdulkareem, M., & Al-Nsour, I. (2021). Impact of the financial control of marketing communication activities on competitive advantage of Saudi service organizations. EPRA International Journal of Economic and Business Review, 9(5). May. India. 2021.
     Google Scholar
  3. Al-Attin, M. (2017). Impediments to the success of small and medium- sized enterprises in Al-Mafraq governorate in Jordan [Master thesis]. Faculty of Finance and Business Management, Al-Bayt University, Jordan.
     Google Scholar
  4. Al-Hourani, AA. (2018). The impact of competitive strategies on achieving a competitive advantage in Jordan Pharmaceutical Manufacturers [Master Thesis]. Faculty of Business, Amman Arab University.
     Google Scholar
  5. Al-Mubarak, H. B. A. (2017). Modern strategies for advertising cam- paign planning: An analytical study of the scientific literature. Journal of Public Relations and Advertising, 4, 82–132.
     Google Scholar
  6. Al-Nsour, I. (2019). The economic effects of capital financing on SME’s in Jordan. A comparative study between Islamic banking financing & microfinancing institution [PhD Dissertation]. Al Madinah Interna- tional University, College of Business in Malaysia.
     Google Scholar
  7. Al-Nsour, I. A., & Yusak, N. A. B. M. (2023a). Effect of perceived trust on buying decision of fashion products via Facebook: New evidence from Jordan. Res Militaris, 13(3), 674–686.
     Google Scholar
  8. Al-Nsour, I. A., & Yusak, N. A. B. M. (2023b). Impact of social media marketing on the buying intention of fashion products. Res Mili- taris, 13(3), 617–632.
     Google Scholar
  9. Al-Sbini, S (2017). The effect of social entrepreneurship on the business performance an applied study on Islamic banks working in Jordan [Master Thesis]. Faculty of Finance & Admin. Science. Al-Madinah International University. Malaysia.
     Google Scholar
  10. Al-Sbini, S. F., & Al-Khawlani, M. A. (2017). Impact of social entrepreneurship on competitive advantage of Islamic banks work- ing in Jordan. European Journal of Business & Management, 9(32), 124–137.
     Google Scholar
  11. Ali, N. H. S. (2020). Strategy for the Design of Contemporary Advertising using Modern Digital Media. Journal of Architecture, Arts and Humanities, 19, 664–689.
     Google Scholar
  12. Alnsour, I. (2022). The impact of the use of electronic social platforms on the pre- behavior of the Saudi buyer during the coronavirus pandemic. Journal of Administrative and Economic Sciences, Qassim University, 15(1), 102–132.
     Google Scholar
  13. AL-Nsour, I. A., Somili, H. M., & Allahhham, M. I. (2021). Impact of Social Networks Safety on Marketing Information Quality in the COVID-19 Pandemic in Saudi Arabia. The Journal of Asian Finance, Economics and Business, 8(12), 223–231.
     Google Scholar
  14. Bahos, N. (2020). Impact of the Internet on activating the institution’s marketing communications and improving its performance [PhD Dessertation]. Faculty of economic and commercial sciences, Uni- versity of Djilali, Algeria.
     Google Scholar
  15. Chin, W. W. (1998). The partial least squares approach for structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Lawrence Erlbaum Associates Publishers.
     Google Scholar
  16. Chin, W. W. (2010). How to write up and report PLS analyses. In: Vinzi, V. E., Chin, W. W., Henseler, J., Wang, H. (Eds.), Handbook of partial least squares: Concepts, methods and applications (pp. 655– 690). Springer.
     Google Scholar
  17. Coffman, D. L., & MacCallum, R. C. (2005). Using parcels to convert path analysis models into latent variable models. Multivariate Behavioral Research, 40(2), 235–259. doi: 10.1207/s15327906mbr4002_4.
     Google Scholar
  18. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates, Publishers.
     Google Scholar
  19. Cohen, L., Manion, L., & Morrison, K. (2008). The methodology of educational research. Athens: Metaichmio.
     Google Scholar
  20. Cotter, K., & Thorson, K. (2022). Judging value in a time of information cacophony: Young adults, social media, and the messiness of do-it-yourself expertise. The International Journal of Press/Politics, 27(3), 629–647.
     Google Scholar
  21. Fink, M., Koller, M., Gartner, J., Floh, A., & Harms, R. (2020). Effec- tive entrepreneurial marketing on Facebook-A longitudinal study. Journal of Business Research, 113, 149–157.
     Google Scholar
  22. Fornell, C., & Cha, J. (1994). Partial least squares. Advanced Methods of Marketing Research, 407, 52–78.
     Google Scholar
  23. Fornell, C., & Lacker, D. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382–388. Sage Publications, Inc.
     Google Scholar
  24. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis (7th ed.). Pearson Education.
     Google Scholar
  25. Hijab, A. S. T., Abdelbari, A., Al-Samadisi, A., & Rashid, H. (2022). [The role of innovative marketing in maximizing brand value]. Journal of Financial and Commercial Research, 23(2), 176–209. Huang, M., & Liu, T. (2022). Subjective or objective: How the style of text in computational advertising influences consumer behaviors? Fundamental Research, 2(1), 144–153.
     Google Scholar
  26. Huh, J., & Malthouse, E. C. (2020). Advancing computational advertising: Conceptualization of the field and future directions. Journal of Advertising, 49(4), 367–376.
     Google Scholar
  27. Karim, Y., & Batool, F. (2017). Impact of emotional ads, online ads and repetition ads on customer buying behavior. Journal of Marketing and Consumer Research, 2(4).
     Google Scholar
  28. Kingdom of Saudi Arabia (2024). Saudi Vision 2030 (Report). http://www.vision2030.gov.sa/ar.
     Google Scholar
  29. Liu, C.-H., Horng, J.-S., Chou, S.-F., Zhang, S.-N., & Lin, J.-Y. (2023). Creating competitive advantage through entrepreneurial factors, collaboration and learning. Management Decision, 61(7), 1888– 1911. doi: 10.1108/MD-07-2022-0914.
     Google Scholar
  30. Mselle, Y., & Belkheri, W. (2019). The Role of Marketing Communica- tion in Increasing the Sales of the Algerian Economic Corporation, Field Study of Workers of the Sonlagaza Umm Al-Qaqi Foundation. Algeria: Umm Al-Baqi University.
     Google Scholar
  31. Musa, A. (2015). Assessment of the degree of effectiveness of electronic advertising and its impact on electronic marketing performance in business organizations: Field study applying to the hotel sector in Makkah governorate. Egypt. Journal of Financial and Commercial Research, 1, 195–231.
     Google Scholar
  32. Perloff, R. M. (2020). The Dynamics of Persuasion: Communication and Attitudes in the Twenty-First Century. London: Routledge.
     Google Scholar
  33. Porter, M. E. (1990). The Competitive Advantage of Nations. New York: Free Press.
     Google Scholar
  34. Porter, M. (1993). The competitive advantage of nations. Journal of Development Economics, 40(2), 399–404. doi: 10.1016/0304-3878(93)90095-5.
     Google Scholar
  35. Rábováa, T. K. (2014). International Conference on Strategic Innovative Marketing, IC-SIM. Madrid: Spain Marketing communication of HGLKANJs.
     Google Scholar
  36. Vinzi, V. E., Trinchera, L., & Amato, S. (2010). PLS path modeling: From foundations to recent developments and open issues for model assessment and improvement. In: Vinzi, V. E., Chin, W. W., Henseler, J., Wang, H. (Eds.), Handbook of partial least squares: Concepts, methods and applications (pp. 47–52). Springer.
     Google Scholar
  37. Wetzels, M., Odekerken-Schröder, G., & Van Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Quarterly, 33(1), 177–195.
     Google Scholar
  38. Wiktora, J. W., & Sanak-Kosmowsk, K. (2021). The competitive function of online advertising: An empirical evaluation of companies’ com- munication strategies in a digital world. Procedia Computer Science, 192(2), 4158–4168. doi: 10.1016/j.procs.2021.09.191.
     Google Scholar
  39. Zhao, Y., Li, Y., Lee, S. H., & Bo Chen, L. (2011). Entrepreneurial orientation, organizational learning, and performance: Evidence from China. Entrepreneurship Theory and Practice, 35(2), 293–317. doi: 10.1111/j.1540-6520.2009.00359.x.
     Google Scholar
  40. Zˇ aneta, R. (2015). Measurement of business performance in relation to competitors. Economics and Management Journal, 7(2), 13–19.
     Google Scholar