Ibero-American Journal of Exercise and Sports Psychology

Research Article - (2024) Volume 19, Issue 2


Dhafer Obaid Faraj Al-Amrani1*, Dr. Mortada movaghar2, Dr. Abolhasan Husseini3 and Dr. Mahmoud yahyazadeh4
*Correspondence: Dhafer Obaid Faraj Al-Amrani, PH.D Associate Professor, Department of Business Administration, Faculty of Economics and Administrative Sciences, University of Mazandaran, Babolsar, Iran, Email:
1PH.D Associate Professor, Department of Business Administration, Faculty of Economics and Administrative Sciences, University of Mazandaran, Babolsar, Iran
2PH.D Associate Professor, Department of Business Administration, Faculty of Economics and Administrative Sciences, University of Mazandaran, Babolsar, Iran
3PH.D Associate Professor, Department of Business Administration, Faculty of Economics and Administrative Sciences, University of Mazandaran, Babolsar, Iran
4PH.D Associate Professor, Department of Business Administration, Faculty of Economics and Administrative Sciences, University of Mazandaran, Babolsar, Iran

Received: 10-Mar-2024 Published: 24-Mar-2024


The current research aims to improve the performance of SMEs using online marketing, demonstrate the importance of using the Internet in marketing, and explain its role in raising the performance of SMEs in marketing and entering markets.

Customer experience and response are among the necessary strategies in this field. The sample in this research is marketing managers, Company managers, and Professor specialized in this field. Data was collected by distributing questionnaires to the current research sample. Our current research specializes in marketing via social media and data analysis through the use of the statistical analysis system (PLS) as well as the use of the statistical analysis system (IPA) to obtain accurate data. The study showed that there is a strong relationship between the performance of companies and the methods of using the Internet in marketing, and it showed Also, the integrated use of online communication tools in the sales process (B2B) and (B2C) helps companies develop the scope of their work in the field of marketing.


Online marketing. B2C. Performance of institutions. SMEs


The latest continuous development of electronic communication tools in the way of marketing for enterprises, The use of electronic means of communication through search engines and web analytics has led to a redefinition of selling between businesses (B2B) and between businesses and customers (B2C), Direct access via the Internet to large amounts of data and accurate knowledge of customer details It makes it easier for sales representatives and those responsible for online marketing programs in marketing institutions, Especially small and medium-sized, they can target the most profitable business operations outlets, and manage customer relationships more easily, Whereas a recent survey showed that institutions that adopt electronic media have reduced their costs by 40% to 60%, And that the growth of its revenues was higher than its counterparts that do not use online marketing, as well as improving the level of performance of its business. (McKinsey & Company, 2017).

The Internet has accelerated the growth of Enterprise business, especially through the export via the Internet, in recent decades, Internet technology has developed at an unprecedented rate, Where the Company mentioned (McKinsey and Company 2016) that about 900 million people are internationally connected on social media, And that 360 million people were active in electronic commerce, And that approximately 12% of the trade in merchandise in the world It is conducted through electronic commerce. (Ding, Guan, Chan, & Liu, 2020).

In this research, we will clarify the role of online marketing in improving the performance of small and medium enterprises. The research consists of three sections: the first section is internet marketing, the second section is marketing performance, and the third section is the field aspect of the research.

The first topic: Online Marketing

In light of the global changes and new challenges through which the world has become one village and the developments in the field of information and communication technology, Recent years have witnessed a new shift in the performance of marketing activities, It is mainly based on the use of information technologies (especially the Internet) to carry out marketing activities. This method is called online marketing ( Maria-Cristina Stoiana, , Pavlos Dimitratosb, Emmanuella Plakoyiannakic, 2018).

1. The concept of Online marketing

Online marketing is one of the most prominent activities that take place via the Internet, as it appeared with the advent of the Web, The marketing function is one of the functions of the organization that seeks to facilitate the Exchange and flow of products from the Producer to the customer.

1. Online marketing several definitions:

It defines the various marketing functions associated with the use of internet technology, It not only expresses advertising through various websites, but also extends to various jobs via the line Such as: e-mail and various social networking sites. (Watson, Weaven. Perkins Sardana, Robert W. Palmatier, 2018).

Online marketing is also known as: “a new philosophy concerned with the study of modern marketing business, This is based on the marketing of goods, services, information and ideas through the Internet and other electronic means. (Setkute & Dibb, 2022).

2. The importance of Online marketing:

The importance of Online marketing is clear in that it is a method an effective way to conclude deals between dealers through electronic communication, away from paper documents In addition, Online marketing is a distinct method, To reach world markets at the lowest costs, and in a very record time. (Stallkamp & Schotter, 2021)

There are several points that show the importance of Online marketing. (Li, J., Chen, L., Yi, J., Mao, J., & Liao, J., 2019)

2-1 Online marketing is a specialized business, and an effective way to expand the scope of local markets, So that they are connected to each other in the world, And then contribute to the marketing of goods and services.

2-2-Helps to quickly respond to customer requests when compared to traditional paper transactions that take longer.

2-3- Online marketing helps to provide information systems, Supports administrative decision-making through an accurate information exchange system, Scientific methods achieve the ability to control and control the accounting.

2-4 Save time and effort, as the consumer does not need to leave the house, The purchase only needs to enter the site and click on the product that he wants, Enter the electronic payment card information.

The researcher added other points. (Domurath., A., Coviello, N., Patzelt, H., & Ganal, B, 2020):

2-5- Providing more information about commodities and giving the opportunity to choose, And by visiting many sites electronic, Through the site, it is possible to obtain all information about the goods targeted for purchase and their specifications.

3. Characteristics of Online marketing

There are important characteristics of Online marketing, which are as follows. (Sheng, 2019):

3.1 Wide service: Online marketing provides a wide service and customers dealing with the site can The catalog deal with it at any time and without the company owner of the site knowing who read her digital message.

3.2 Global Online marketing:The media used in Online marketing know no geographical boundaries, So that it is possible to shop from wherever the customer is, through his personal account on the company’s website.

3.3 The speed of changing concepts

The electronic market is concerned with the rapid change of concepts, the activities it covers, and the rules it governs, because electronic commerce is linked to the means and techniques of digital communication information technologies that change and develop rapidly. Therefore, the legal arrangement to which they are subject are subject to rapid change in a manner compatible with developments in technologies, communication and information.

4. Online marketing rules

So that SMEs can achieve goals and objectives to carry out marketing operations via the internet in dealing with its customers B2C through the internet, it must observe a set of general rules, including the following. (Matarazzo, M., Penco, L., Profumo, G., & Quaglia, R., 2021):

4.1 Knowing and identifying customers who use the internet, whose number has reached about 42 million users in the arab world, With a growth rate of the number of internet users in the middle east reaching 11.76%.

4.2 Determine the groups that should be targeted with the organization's products or services, specifying the market or markets that must be dealt with through the network, as well as, the target age groups.

The researcher added other rules of online marketing. (Bill., F., Feurer, S., & Klarmann, M.., 2020):

4.3 The organization determines the desires and needs of its customers targeted B2C, what goods or services can be provided to them to meet their needs.

4.4 Studying competitors and determining the services they provide via the internet to customers.

4.5 Determine the appropriate way to display the organization's products and services to the referee visitors on the site.

5. Online marketing skills

Organizations adopting online marketing must have specific skills and qualifications that can be mentione. (Adamides & Karacapilidis, 2020):

5.1 Familiarity with the skills of dealing with technological tools on the Internet.

5.2 Familiarity and knowledge of how to design websites.

5.3 The need to master the English language as a common cost to use it on the network.

5.4The ability to follow up and make a quick reaction, which enables workers in marketing via the internet, Follow-up of the successive developments in the websites.

The researcher added other skills, namely.

5.5 Enjoy the skill of rapid development.

The second topic: Performance Improvements for SMEs:


The concept of organizational performance, marketing performance, as well as internet marketing are among the most important approaches to achieving excellence and developing competitive capabilities that enable organizations to maintain their market share. The trend towards applying the concepts and techniques of measuring organizational performance and marketing performance have become a vital requirements in organizations, especially SMEs.

6. The concept of performance

Performance is the common denominator of the efforts of workers within an organization, the traditional trends in management look at outstanding performance through an orientation and insistence on Administrative and operational practices that aim to improve the overall performance of the institution, as for the development in the concept of marketing performance, it came in several stages, whether in the industrial framework as large organizations or SMEs, It is noticeable that this development focused on financial indicators only, according to considerations that were affected by social and humanitarian conditions . (Pradhan & Jena, 2017)

It defines organizational performance as “the organizations ability to use its resources efficiently, and produce outputs consistent with its objectives and appropriate for its users, It was also defined as "the integrated system for the production of the organization's work in light of its interaction with the elements of its internal and external environment.". (Chawla, V., Lyngdoh, T., Guda, S., & Purani, K., 2020)

The organization's marketing performance was defined as "the extent of the organization's ability to satisfy and satisfy its customers by providing them with services that fit their needs and match what they were waiting for, Within the limits of continuous monitoring of competitors and taking into account their reactions while achieving the desired marketing results, of profits, sales and market share".This definition focuses on the needs and requirements of customers, which can be identified through the use of a diverse communication mix that allows reaching the customer and knowing what is waiting for him. This on the one hand, and on the other hand on competition because of its strong impact on the marketing performance of SMEs .(Cacciolatti & Lee, 2016)

7. Marketing performance metrics:

The marketing performance process is a process with multiple measures, and the theoretical literature related to marketing focused on three measures of marketing performance, which are: the first measure is (marketing effectiveness) means the extent of success through which the objectives of the organization are achieved, The second measure is (sales growth), which is the percentage of sales compared to competitors, while the third one is (market share), it is compared to the market share of the Company that has the largest market share in the same sector.(Mu J., Bao, Y., Sekhon, T., Qi, J., & Love, E., 2018)

7.1 Marketing Effectiveness:

It is defined as “the ability to achieve goals in the form of an increase in the volume of sales, Market share, customer satisfaction, development of human resources working in the field of marketing, and achieving the desired growth of the institution".

It is also known as “the performance of the organization Corresponding to the organizational goals previously defined in the planning stage.” The focus of effectiveness is more on the objectives than on the input aspect, and thus it is related to the concept of efficiency.(de Perea J. G. Á., Ramírez-García, C., & Del Cubo-Molina, A., 2019).

7.2 Market share:

The market share is one of the important and effective indicators that enhance the competitive position of the organization, Organizations, in general, seek to maintain or increase their market share by providing products and services that are suitable, of good quality, and at an appropriate Price, And develop its activities and events in line with market needs and customers’ expectations. Market share is an effective indicator to measure the organization’s success and its ability to achieve singularity and profit in the market.(Groza M. D., Locander, D. A., & Howlett, C. H. 2016).

7.3 Sales growth:

Sales growth is the most acceptable goal for all businesses, The reality of perceptions of the success or failure of many companies depends on evaluations of annual growth metrics and criteria, We note over time in the base on which the growth in sales is measured, These changes can include an increase in the number of stores, markets, or sales representatives, This question is addressed using normal market standards, and sales staff.(Rouziès D., Onyemah, V., & Iacobucci, D., 2017).

8. Indicators for measuring marketing performance in organizations:

Contemporary management has focused on the concept of marketing performance as an entry point for achieving growth and continuity, however, the topic of marketing performance measurement is still new, And based on what we have previously done by researchers in the same field, We decided to divide the marketing performance indicators according to the following demands.

8.1 The first requirements: measurement indicators of human performance:

Marketing management depends entirely on the human element to do its job, This requires them to monitor and improve the performance levels of their human resources. (Lin W. L., Yip, N., Ho, J. A., & Sambasivan, M, 2020)

8.1.1 Indicators for measuring the performance of the marketing manager:

The marketing manager is the governing element that undertakes the decision-making process that is directly related to the marketing results achieved in terms of sales volume, turno ver and market share. Therefore, the performance of the latter is measured by its ability to direct resources and marketing actors towards achieving the general objectives of the institution. (Caseiro & Coelho, 2019)

9. The relationship of Online marketing to improving organizational and marketing performance:

The emergence of new approaches in administrative and organizational thought led to an increase in organizations' focus on marketing leaders in improving the level of marketing performance of these organizations, Contemporary organizations, in their essence, and in their needs and desires of those working in them and those dealing with them, are renewable and changing, which necessitates the existence of an administration that possesses distinctive characteristics, So that they are able to effectively employ resources and lead the organization in a competitive atmosphere. (Glavas C., Mathews, S., & Bianchi, C.., 2017).

It turns out that marketing via the internet is one of the important factors that help improve the organizational and marketing performance of the economic institution. There must be a self-contained interest based on this subject. Considering that online marketing is one of the tasks that require academic qualifications, in particular, the need to pay attention to the human resource as the main source of creative ideas in this field, Activating the information and communication system, and paying attention to research and development activities.(Saura J. R., Palacios-Marqués, D., & Iturricha-Fernández, A.., 2021).

10. SMEs in Iraq:

The SMEs sector in Iraq is a fertile sector it can work to support the country and the economy and rid it of many problems that plague it, such as unemployment, poverty and recession. In order for this sector to play its role, the necessary environment must be provided for it, which helps its growth and sustainability. Through the development of special legislative frameworks that take into account the circumstances of these institutions and their small size. (Abdul Sattar Abdul Jabbar Musa, 2012).

Develop policies that enhance the role of this sector and encourage entry into it. Providing the necessary funding sources and necessary facilities for this sector. Finally, interest in promoting the products of these institutions, not only inside the country, but also abroad. Therefore, defining a specific definition of SMEs in Iraq needs to define several criteria, and the most important of these criteria are: the number of employees, assets, sales, and the level of investments. (Ahmed Kamel Hussein, 2008).

SMEs in Iraq are defined as" institutions with a limited budget and scope of work". In Iraq, a small institutions is also defined as “an institutions that has (1-9) workers, while a medium enterprise is one that employs (10-29) workers.”(Omar Khalaf panic, 2013)

Research data analysis


The correct classification and analysis of data and the correct use of statistical techniques will eventually lead to reliable results. After the researcher has collected, extracted and classified the data, a new stage of the research process, which is known as data analysis, should begin. In the analysis stage, the important point is that the researcher must analyze the information and data in the direction of the goal, answering the research question or questions and also evaluating his hypotheses (Hafez-Niya, 2017). In order to validate the hypothesis in any type of research, data analysis is of particular importance, and today, in most researches that rely on the information collected from the research subject, data analysis is the main It is considered the most important part of research. Raw data are analyzed using statistical software and after processing, they are provided to users in the form of information. In this chapter, the research results are presented using descriptive and inferential statistics. This chapter consists of two parts. In the first part, the descriptive information of the demographic variables is presented in the form of tables and graphs of frequency and percentage distribution. In the second part, the results related to the questions are presented using inferential statistics.

11. Presentation of descriptive research results

In order to provide a proper perspective on the characteristics of the studied people, in this section, the distribution of the frequency of sample people according to demographic variables is presented. Also, in order to better compare the distribution of the studied people, in addition to stating the number of people in terms of demographic variables, these figures are also stated as a percentage. Finally, the graphs related to the distribution of people are also given below the related table so that a better comparison can be drawn.

Gender: The results of the distribution of sample people based on gender showed that 44 (88.0%) of the respondents are male and 6 (12.0%) are female. Table and graph 1-4 show the results of this distribution.

According to education: the results of the distribution of sample people based on education showed that the highest frequency is related to bachelor's education in the number of 35 people (70.0 percent). Table and graph 4-2 show the results of this distribution.

According to age: the results of the distribution of sample people based on age showed that the highest frequency is related to the age group of 31 to 40 years, with the number of 193 people (0.44%). Table and graph 3-4 show the results of this distribution.

Work experience: The results of the distribution of sample people based on work experience showed that the highest frequency is related to the group of 6 to 10 years of experience in the number of 26 people (52.0%). Table and graph 4-4 show the results of this distribution.

Occupation: The results of the distribution of sample people based on occupation showed that the highest frequency is related to the group of marketing managers with the number of 19 people (38.0%). Table and graph 4-5 show the results of this distribution (Table 1).

Table 1:Central indicators, dispersion and distribution of factors.

  Causal conditions Central category Cross conditions Strategic conditions requirements Basic conditions
Mean 45.2200 39.8600 27.2400 17.9600 26.7200 27.3600
Std. Deviation 1.54246 2.33002 1.79068 1.27711 1.30993 1.02539
Variance 2.379 5.429 3.207 1.631 1.716 1.051
Skewness 1.214 -.288 .090 .322 -.419 .986
Kurtosis 2.760 -.059 -.666 -1.005 .650 .919

In Table 1, some concepts of descriptive statistics of variables including mean, standard deviation, skewness and kurtosis are presented. Meanwhile, the central parameters are a group of descriptive parameters of a statistical distribution that express the characteristics of the data relative to the center of the distribution. The mean, as the balance point and the center of gravity of a statistical distribution, is one of the suitable central indicators to show the centrality of the data. For example, the average of Causal conditions is equal to 45.22, which shows that most of the data related to this variable are concentrated around this point.

Another group of community describing parameters are dispersion parameters. Dispersion parameters are a measure to determine the degree of dispersion of data from each other or their degree of dispersion compared to the average. One of the most important dispersion parameters is the standard deviation. The higher the standard deviation of a statistical distribution, it indicates that these data have more dispersion. Among the variables of this research, Central category has the highest dispersion with a value of 2.33. Skewness indicates the Asymmetric of the abundance curve. If the coefficient of skewness is zero, the society is completely symmetrical, and if the coefficient is positive, there is a skew to the right, and if it is negative, there is a skew to the left. In general, if the skewness and kurtosis are not in the range (2, -2), the data is far from the normal distribution. (Of course, some statisticians may consider this interval to be smaller or larger). The value of observed skewness for the studied variables is in the range (2, -2). It means that in terms of the skewness of the research variables, it is normal and its distribution is symmetrical. The stretching value of the variables is also in the range (2, -2). This shows that the distribution of the variables has a normal distribution.

12. Inferential analysis of research data

12.1 Validation of measurement models (confirmatory factor analysis)

The most important goal of confirmatory factor analysis is to determine the power of the predefined factor model with a set of observed data. In other words, confirmatory factor analysis is trying to determine whether the number of factors and variable loadings measured on these factors are consistent with what was expected based on the theory and theoretical model. In other words, this type of factor analysis tests the degree of conformity between the theoretical construct and the empirical construct of the research. In this method, the relevant variables and indicators are first selected based on the primary theory, and then factor analysis is used to see if these variables and indicators are loaded on the predicted factors as expected. Or is their composition changed and loaded on other factors?

In this type of factor analysis, the researcher's basic assumption is that each factor is related to a specific subset of indicators. The minimum necessary condition for confirmatory factor analysis is that the researcher assumes the number of factors in the model before anything, but at the same time, usually this researcher involves his expectations about which factors will load. For example, the researcher tries to determine whether the variables used to construct and display a latent variable really belong to each other or not. Other applications for confirmatory factor analysis can be drawn, which include:

1. Determining the validity of a factor model

2. Comparing the power of two different models that are responsible for the same set of data.

3. The significance test of a specific factor loading

4. Testing the relationship between two or more factor loads

5. The test of whether the set of factors are correlated with each other or not?

6. Evaluating the level of convergent validity of a set of variables (the level of internal consistency between them)

7. Measuring the validity of a scale or index by showing that compatible items are loaded on one factor. Therefore, with the help of this method, it is possible to remove the inconsistent scale items that have very high or low loads on several factors from the scale. Because these variables cannot be assigned to a specific factor.

Confirmatory factor analysis is one of the oldest statistical methods used to investigate the relationship between hidden variables (main variables) and observed variables (questionnaire items) and represent the measurements model (Burn, 1994). Factor analysis is based on two types of exploratory factor analysis and confirmatory factor analysis. In exploratory factor analysis, the researcher tries to discover the underlying structure of a relatively large set of variables, and the initial assumption is that each variable may be related to each factor. In other words, the researcher does not have any initial theory in this method. In confirmatory factor analysis, the basic assumption is that each factor is related to a specific subset of variables. The minimum necessary condition for confirmatory factor analysis is that the researcher has a certain presupposition about the number of factors in the model before conducting the analysis, but at the same time, the researcher can include his expectations based on the relationships between variables and factors in the analysis. To evaluate the validity of the measurement models, we calculated the following values and if the conditions listed in Table 6-4 are met, we can claim that the measurements model has appropriate and favorable conditions (Table 2), (Figure 1).

Table 2:Conditions for establishing reliability and convergent validity.

Limit Source
  • Composite reliability and Cronbach's alpha should be above 0.7.
(Joseph et al. 2016)
Convergent validity
  • Factor loadings should be significant (t>1.96). Standard factor loadings should be greater than 0.4.
  • CR>AVE
  • AVE>0/5
  • Rho_A>0/7
Divergent validity
Model fit indices
  • GOF>0/36
  • SRMR<0/1
*AVE: Average variance Extracted, CR: Construct Reliability, MSV: Maximum Shared Squared variance, GOF; Goodness of fit

Figure 1. Values of operating loads in standard mode.

As the results show, the factor load values of all the items are greater than 0.4 and therefore the measurement model is a homogeneous model and the factor load values are aceptable values (Figure 2).


Figure 2. t statistic values of factor loadings.

The results of the significance of the t-statistic values in the table showed that the t-statistic values for all items were reported to be greater than 2.58. This means that the relationship between the items and the related variable is Accepted at the confidence level of 99% (Table 3).

Table 3:The results of factor loading values of observable variables.

first time second time
objects factor loading T Statistics P Values factor loading T Statistics P Values
causal conditions Q1 0.644 11.21 0.000 0.889 56.059 0.000
Q2 0.778 19.542 0.000
Q3 0.833 29.206 0.000
Q4 0.846 24.84 0.000
Q5 0.846 26.375 0.000
Q6 0.638 8.5 0.000
Q7 0.839 29.621 0.000
Q8 0.799 15.497 0.000
Q9 0.762 18.437 0.000
Q10 0.799 22.455 0.000
central category Q11 0.753 23.127 0.000 0.899 51.648 0.000
Q12 0.762 20.168 0.000
Q13 0.822 32.373 0.000
Q14 0.623 9.821 0.000
Q15 0.743 14.857 0.000
Q16 0.777 19.617 0.000
Q17 0.673 10.504 0.000
Q18 0.67 12.611 0.000
Q19 0.677 12.363 0.000
cross conditions Q20 0.79 23.746 0.000 0.834 34.283 0.000
Q21 0.734 17.166 0.000
Q22 0.731 16.587 0.000
Q23 0.735 9.278 0.000
Q24 0.73 15.472 0.000
Q25 0.72 16.125 0.000
strategic conditions Q26 0.821 22.09 0.000 0.805 30.386 0.000
Q27 0.872 41.367 0.000
Q28 0.855 46.067 0.000
Q29 0.715 16.749 0.000
requirements Q30 0.728 17.908 0.000 0.869 47.104 0.000
Q31 0.739 21.216 0.000
Q32 0.776 18.763 0.000
Q33 0.761 17.658 0.000
Q34 0.805 23.057 0.000
Q35 0.777 19.738 0.000
basic conditions Q36 0.72 11.095 0.000 0.841 28.905 0.000
Q37 0.798 17.47 0.000
Q38 0.793 16.961 0.000
Q39 0.862 36.935 0.000
Q40 0.745 16.903 0.000
Q41 0.733 17.099 0.000

3) Cronbach's alpha and composite reliability: Cronbach's alpha method is used to calculate the internal consistency of measurement tools, including questionnaires or tests that measure different characteristics. In such a tool, the answer to each question can have different numerical values. To calculate Cronbach's alpha, first, the variance of the scores of each subset of the questionnaire questions and the total variance should be calculated.

Cronbach's alpha index assumes that the observable variables of each measurement model have the same Weights and in fact equates their relative importance. In order to solve this problem, the index proposed by Verts et al. (1974) with the title of composite reliability is used. In this index, because the factor loadings of the items are used when calculating, it shows the composite reliability values more and better than Cronbach's alpha (Table 4).

Table 4:Cronbach's alpha and composite reliability.

    Dimensions Total
  Cronbach's Alpha rho_A Composite Reliability Cronbach's Alpha Rho A Composite Reliability
online marketing basic conditions 0.867 0.870 0.901 0.967 0.968 0.969
causal conditions 0.928 0.931 0.940
central category 0.886 0.893 0.908
cross conditions 0.835 0.836 0.879
requirements 0.858 0.858 0.894
strategic conditions 0.833 0.839 0.889

The results of examining Cronbach's alpha coefficient and composite reliability in Table 9-4 showed that the values of these indices for all hidden variables are greater than 0.7, and therefore the reliability of the measurement tools was confirmed using these two indices (Hensler et al., 2011 and Heyer, 2017).

B: Validity of measurement tools

1) Convergent validity: The meaning of the convergent validity index is to measure the degree of Explanations of the hidden variable by its observable variables (Barkley et al., 1995). For the extracted average variance index, the minimum value of 0.5 is an aceptable value, which indicates that the observable variables explain at least 50% of the variance of the hidden variable (Table 5).

Table 5:Average Variance Extracted.

    Average Variance Extracted (AVE)
  Dimensions Total
online marketing basic conditions 0.867 0.635
causal conditions 0.928
central category 0.886
cross conditions 0.835
requirements 0.858
strategic conditions 0.833

As can be seen in Table 10-4, the results of the analysis of the extracted variance values of the hidden variables of the research showed that all the variables had values greater than 0.5. Based on this, it can be said that the convergent validity of the measurement tools was confirmed using the extracted average variance index.

2) Diagnostic or divergent validity: Diagnostic or divergent validity measures the ability of a reflective measurement model to differentiate the hidden variable observables of that model from other observables in the model. Diagnostic validity is actually a complement to convergent validity, which indicates the differentiation of indicators of a latent variable from other indicators in the same structural model.

A) Fornell-Larker test: According to this criterion, a hidden variable should have more dispersion among its observables compared to other hidden variables, so that it can be said that the hidden variable in question has high diagnostic validity. Accordingly, the root mean extractad of each latent variable must be greater than the maximum correlation of that latent variable with other latent variables (Fornell and Larcker, 1981) (Table 6).

Table 6:Fornell-Larcker Criterion.

  basic conditions causal conditions central category cross conditions requirements strategic conditions
basic conditions 0.777          
causal conditions 0.681 0.782        
central category 0.678 0.764 0.725      
cross conditions 0.620 0.646 0.711 0.740    
requirements 0.716 0.670 0.712 0.703 0.765  
strategic conditions 0.603 0.646 0.676 0.693 0.679 0.818

Based on the results obtained from Table 5, the root mean extracted of each hidden variable is more than the maximum correlation of that hidden variable with other hidden variables. Based on this, the divergent validity of the measurement model was confirmed using the Fornell-Larker test.

b) Cross-sectional load test: based on this test, it has been suggested that the factor load of each observable variable on its related hidden variable should be higher than the factor load of the same observable variable on other hidden variables (Hair et al., 2017) (Table 7).

Table 7:Cross Loadings

basic conditions causal conditions central category cross conditions requirements strategic conditions
Q1 0.461 0.644 0.56 0.437 0.506 0.424
Q2 0.487 0.778 0.55 0.503 0.525 0.498
Q3 0.523 0.833 0.561 0.52 0.517 0.54
Q4 0.565 0.846 0.59 0.522 0.537 0.467
Q5 0.549 0.846 0.63 0.499 0.503 0.52
Q6 0.436 0.638 0.495 0.46 0.412 0.409
Q7 0.553 0.839 0.611 0.449 0.512 0.519
Q8 0.562 0.799 0.618 0.567 0.561 0.53
Q9 0.567 0.762 0.666 0.522 0.571 0.571
Q10 0.597 0.799 0.672 0.56 0.577 0.549
Q11 0.583 0.666 0.753 0.551 0.536 0.541
Q12 0.549 0.616 0.762 0.523 0.569 0.505
Q13 0.568 0.691 0.822 0.579 0.564 0.584
Q14 0.368 0.368 0.623 0.466 0.427 0.372
Q15 0.499 0.595 0.743 0.457 0.49 0.499
Q16 0.515 0.607 0.777 0.636 0.576 0.577
Q17 0.424 0.43 0.673 0.475 0.472 0.392
Q18 0.45 0.473 0.67 0.527 0.479 0.425
Q19 0.422 0.456 0.677 0.542 0.513 0.466
Q20 0.447 0.502 0.603 0.79 0.53 0.472
Q21 0.496 0.511 0.569 0.734 0.471 0.533
Q22 0.406 0.387 0.551 0.731 0.519 0.425
Q23 0.431 0.467 0.488 0.735 0.484 0.477
Q24 0.473 0.512 0.497 0.73 0.51 0.485
Q25 0.494 0.482 0.534 0.72 0.603 0.671
Q26 0.51 0.51 0.564 0.583 0.571 0.821
Q27 0.438 0.573 0.583 0.57 0.543 0.872
Q28 0.545 0.609 0.552 0.597 0.551 0.855
Q29 0.478 0.407 0.511 0.514 0.563 0.715
Q30 0.545 0.486 0.553 0.594 0.728 0.665
Q31 0.557 0.497 0.554 0.578 0.739 0.597
Q32 0.583 0.536 0.533 0.552 0.776 0.456
Q33 0.632 0.524 0.522 0.497 0.761 0.497
Q34 0.625 0.531 0.552 0.509 0.805 0.501
Q35 0.666 0.5 0.551 0.495 0.777 0.396
Q36 0.72 0.594 0.584 0.474 0.573 0.518
Q37 0.798 0.623 0.559 0.499 0.607 0.51
Q38 0.793 0.515 0.48 0.444 0.579 0.416
Q39 0.862 0.541 0.566 0.5 0.672 0.5
Q40 0.745 0.435 0.465 0.491 0.616 0.444
Q41 0.733 0.445 0.489 0.478 0.615 0.406

The degree of differentiation of the items of hidden variables in the model was done, it showed that the factor load of each item on its related hidden variable was higher than the factor load of the same observable variable on other hidden variables. Based on this, the differential validity of the measurement tools was confirmed using the index of mutual factor loadings.

Model predictive power or shared redundancy is another criterion to check the structural model. The purpose of this index is to check the ability of the structural model to predict in an eye-opening way. The most famous and well-known criterion for measuring this ability is the Q2 index, based on this criterion, the model must predict the indicators of the reflective endogenous current variable. The values obtained from this test are positive, which indicates the appropriate quality of the structural model (Henseler et al., 2009). Regarding the predictive power of the model regarding endogenous latent variables, three values of 0.02, 0.15, and 0.35 have been introduced as weak, medium, and strong values for this index, respectively (Hensler et al., 2009) (Table 8).

Table 8:Construct Cross validated Redundancy.

basic conditions 2,400.000 1,446.096 0.397
causal conditions 4,000.000 2,203.038 0.449
central category 3,600.000 2,184.301 0.393
cross conditions 2,400.000 1,544.850 0.356
requirements 2,400.000 1,409.519 0.413
strategic conditions 1,600.000 946.204 0.409

The results of Table 7 showed that the predictive power of the model was reported to be strong.

Importance-Performance (IPA) and Gap Analysis (GA) techniques

The increasing importance of the importance/performance analysis model in online marketing and identifying the strengths and weaknesses of the system and its efficiency in identifying priorities and adopting improvement strategies has caused the aforementioned model to be used in various research and operational fields (Khalifa and Rozavi , 2013: 39). Conceptually, the IPA model is a multi-indicator model. In order to use this model, the indicators that are to be analyzed should be specified. In fact, the effectiveness of the IPA model is strongly dependent on its indicators or analytical components. Therefore, in any field, the first step in using the IPA model is to identify the quality components in that field. It is obvious that the extraction of analytical components based on the methodology and theoretical concepts with a certain theoretical approach can guarantee the strengthening of the theoretical support of the components, regarding how to identify them, and provide the necessary strategies (Fathi Vajargah et al., 2010: 58). As stated, in the importance/performance analysis model, each component is evaluated from the two dimensions of "importance" and "performance". The data related to the level of importance and the performance of indicators are displayed on a two-dimensional grid where the Y-axis indicates the importance dimension and the X-axis indicates the performance dimension. This two-dimensional network is called the importance-performance matrix. The importance-performance matrix, which actually has four parts (or quadrants) and in each quadrant, there is a specific strategy, is an aid to the decision-making process. This matrix is used to know the degree of priority of indicators for improvement (Khalifa and Razavi, 2013: 39). In the IPA model, indicators can be measured on a scale of 5, 7 or 9 degrees, the data related to the importance and level of performance of each of them is collected using a questionnaire. For this purpose, customers are asked two questions about each index, the importance of the desired index and the level of performance in that index. Since the separate analysis of performance dimension and importance dimension data, especially when both sets of data are studied at the same time, may not be meaningful, therefore, the data related to the level of importance and performance of indicators, on a grid of two Next, where the Y-axis indicates the importance dimension and the X-axis indicates the performance dimension, they are displayed (Fathi et al., 2010:59). The IPA Echenique provides the possibility of simultaneous comparison of the importance of different features of a product and the performance related to those features, and based on this comparison, the strengths and weaknesses of that product are identified. In this way, the factors that are more important to the customer are identified. IPA is based on the assumption that the level of customer satisfaction with the characteristics of a product is basically derived from their expectations and their judgment about the performance of that product (Ziaei and Abbaspour, 2010: 83). As Figure 9-4 shows, the IPA table consists of four parts, which are:

Focus of attention (main weaknesses): Features that are of high importance from the point of view of customers, but the products performance in this field is weak and has caused customer dissatisfaction.

Retention (main strengths): In this part, the characteristics considered are of high importance from the point of view of customers and the performance of the product has been such that it has caused customer satisfaction. These features are an opportunity to gain a competitive advantage.

Low priority (sub-weaknesses): In this section, the performance of the product is not satisfactory, but these features are not very important for customers.

Costing (sub-strengths): In this section, the product performance is satisfactory, but these features are not very important for customers, therefore, the resources of this section can be directed to other sections (Ziaei and Abbaspour, 2010: 84) (Diagarm 3), (Table 9).


Diagarm 3. IPA matrix.

Table 9:Importance-Performance Map [online marketing].

Importance Performance Standardized value of performance Gap P-I Rank by gap
basic conditions 0.17 74.931 0.17 0 6
causal conditions 0.305 73.302 0.166 -0.139 1
central category 0.238 70.114 0.159 -0.079 2
cross conditions 0.153 77.167 0.175 0.022 4
requirements 0.17 72.838 0.165 -0.005 5
strategic conditions 0.12 73.33 0.166 0.046 3

According to the results obtained from the gap analysis, it was found that causal conditions, central category and strategic conditions have the largest negative gap, respectively, and it shows the distance between the current situation and the desired situation.

Strategic conditions and cross conditions have the largest positive gap and it shows the favorable situation of these factors (Figure 4).


Figure 4. IPA matrix for variables.

Based on the results obtained from the importance and performance map values for the variables, it was found that creating causal conditions with a value of 0.305 is the most important. In terms of performance, standard payments in the amount of 79,784 are the first priority.


1. It was noted that there are few studies in Iraq that look at improving the performance of SMEs in online marketing in Iraq.

2. Using marketing performance improvement as a new topic in line with online marketing.

3. There is a strong relationship between the performance of companies and the methods of using the Internet in marketing

4. The integrated use of online communication tools in the sales process helps (B2B) and (B2C) companies develop their business in terms of geographical distances and the type of relationship with the customer.

5. In the quantitative section, the statistical analysis program (PLS) and the statistical system (IPA) were used to know and analyze the answers obtained through the questionnaire distributed to the research sample.


1. Using modern technology as a strategy for the infrastructure of SMEs that operate the online marketing system.

2. Use online methods and tools, the obvious things like search engine optimisation and SEM.

3. Given that the culture of society has tended towards using the Internet to meet their needs, this requires changing the culture of managers and aligning them with the culture of society.

4. Forming a work team within each institution whose responsibility is to supervise the progress of business and follow up and evaluate the work steps of the marketing team within the institution.

5. Delegating powers to the agencies implementing online marketing operations within institutions to implement marketing work in accordance with requirements, changes and developments in the field of business to prevent delays as well as deviation from routine.


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