SMEs in Saudi Arabia are discussed as guaranteeing employment for the large proportion of employees in the country’s private sector (Mohammad 2015, p. 31). In addition, the development of SMEs contributes to attracting more Saudis to work in the private sector of the country and facilitates its economic progress (Adeyemi et al. 2015; Mohammad 2015).
Thus, SMEs are viewed today as a force leading to the growth of economies through the active involvement of the principles of entrepreneurship to support their progress. As a result, the main modern lenses through which the economic strategies of SMEs are discussed are entrepreneurial marketing (Renton et al. 2015). The reason is in the fact that entrepreneurial marketing provides SMEs with a variety of opportunities to apply innovation and flexibility as the main tools among others (Hussain & Jalal 2015; Renton et al. 2015).
The sustainability interests of SMEs develop along with the focus of the modern business world on the ideas of social responsibility and balance (Khurshid et al. 2015). This approach allows addressing both company and community’s needs (Shields & Shelleman, 2015).
The incorporation of sustainable practices and strategies in the work and operations of SMEs is the characteristic feature of these companies’ modern development because of the intentions to improve the company’s development in terms of addressing the social, economic, and environmental aspects (Diehl et al. 2015; Shields & Shelleman, 2015). Aiming at achieving sustainable development, Saudi Arabian SMEs choose different tools and methods, including the reference to the corporate social responsibility that is closely related to the idea of establishing sustainability in SMEs (Khurshid et al. 2015).
In spite of the fact that SMEs in Saudi Arabia seem to be rather slow while adopting the principles of sustainable development, it is possible to notice the active interest of businessmen in these practices (Khan & Quaddus 2015; Khurshid et al. 2015; Shields & Shelleman, 2015). However, the tempos of adopting sustainable practices are still slow, and the main reasons include the lack of understanding of the role of balancing and addressing the social and environmental needs of SMEs developing in different contexts (Johnson 2015).
Entrepreneurial marketing is a choice for those companies that plan to use the available resources most efficiently as it works with a limited number of materials, and the focus on innovation to achieve higher results is important. Such an approach is actively used in SMEs because of their orientation to quick and sustainable growth (Karimi et al. 2015).
Entrepreneurship and the development of entrepreneurial marketing are viewed in Saudi Arabia as effective steps toward enhancing the growth of SMEs associated with the economic progress of the country (Alzalabani 2015). It is possible to state that the focus on the ideas of entrepreneurship and entrepreneurial marketing allows the owners of SMEs to adapt to the constantly changing environments of the business and markets (Alzalabani 2015).
In this study, EM is defined as the proactive use of available opportunities to attract more customers while using the most innovative techniques and methods to leverage resources and create additional value. This definition is adopted with references to the explanation of the term provided by Morris et al. (2002) in their study.
Past studies on the development of SMEs in different countries used various samples sizes, depending on the type and purpose of the research. Table X illustrates sample sizes and types of the research used in some studies that were conducted during the period of 2008-2014.
Table X. Sample Sizes Used in Past Studies.
Table X demonstrates that researchers used both small and large sample sizes to examine SMEs in Saudi Arabia. The smallest sample sizes were typical of the exploratory researches conducted in Saudi Arabia by Alsaleh (2012) and Danish and Smith (2012). The mixed methods and quantitative studies involved a larger number of samples. In 2009, Sohail and Alashban studied SMEs referring to a sample of 214 respondents. The largest sample size of 560 respondents was used by Hacioglu et al. (2012) to study the situation regarding SMEs in Turkey. According to Table X, the previous quantitative studies used sample sizes that were even smaller than the recommended size of 40 respondents. The sample size selected for this study can be discussed as appropriate to contribute to the success of the quantitative research on SMEs in Saudi Arabia.
G Power Test was also conducted to identify the appropriate sample size for the study. The power of the sample size is important to be determined to avoid the presentation of non-significant results during the analysis stage. The determination of the power of the sample size allows selecting the most appropriate number of respondents for the study to expect statistically significant results. G Power Test also allows avoiding the situation when the number of respondents is very small and not appropriate to guarantee the provision of the statistically significant results (Faul et al. 2007). Figure X represents the results of the G Power Test for the appropriate sample.
According to Figure X, the most appropriate sample size for the current study is 138 respondents. While selecting 200 respondents for the final sample size, it is possible to guarantee the presentation of statistically significant results. From this point, selecting the sample larger than it is proposed according to G Power Test, it is possible to be safe and avoid errors.
A statistical issue that is associated with method errors is known as common method bias. This bias can lead to measurement error in the study, and it can have a significant impact on the empirical results. These techniques are procedural remedies that are used along with statistical remedies
In terms of protecting the respondent’s privacy and reducing the evaluation apprehension in relation to the used questionnaire, it is stated that the answers from the survey are confidential. Moreover, it is noted that there are no right or wrong answers, and respondents should answer the questions as honestly as possible. With references to improving the scale items, the researcher took careful steps in the construction of the questionnaire and selection of items. Thus, the unfamiliar terms were avoided to be included in the questionnaire, and it was ensured that the questions were simple and rather specific.
Self-administered survey questionnaires were distributed to all SMEs’ owners and managers participating in the study. Questionnaires were personally provided to the study participants along with cover letters that explained the aspects of the study, as well as ethical considerations. The letters also presented guidelines for completing the questionnaire. The sampling frame includes SMEs associated with the Chamber of Commerce in the Eastern, Central, and Western regions of Saudi Arabia. While focusing on this frame, 123 SMEs were selected for the study. When SMEs participating in the study were determined, self-administered survey questionnaires were distributed to their owners and managers according to the purpose of the research.
To collect the completed questionnaires, the drop-off and pick-up method was chosen. According to this data collection method, the questionnaires are distributed or delivered to respondents personally, and when the questionnaires are completed, the researcher travels to respondents and gathers the data (Allred & Ross-Davis 2011). The obvious advantages of this data collection method are in possibilities to increase the response rate because of the personal interaction with respondents; to motivate respondents to complete the questionnaire fully; to provide respondents with an opportunity to ask questions associated with the work with questionnaires (Allred & Ross-Davis 2011). The personal contact between a researcher and a respondent increases the participant’s responsibility and decreases the possibility of errors while filling in questionnaires (Allred & Ross-Davis 2011). These aspects are reasons for selecting the drop-off and pick-up data collection method for this study.
For kurtosis, the researchers choose the range depending on the type of the distribution, but a different range can be selected with the focus on the type of the analysis, and researchers can refer to ranges between -1 and +1, -2 and +2, -3 and +3 (Park 2008). Many researchers refer to kurtosis between -1 and +1. The acceptable values for the asymmetry and kurtosis can be between -2 and +2. However, Park (2008) notes that traditionally, kurtosis for univariate normal distribution is regarded as equal to 3 without dependence on the sample size. As a result, kurtosis should be discussed with references to the range between -3 and +3 while speaking about the univariate analysis. Therefore, referring to Table 5.6 below, it can be stated that the data distribution typical of this study is normal.
Specifically, correlation analysis is concerned with determining the significance or the strength of a relationship between the constructs used in the study. The correlational coefficient is measured with the focus on the range between 1 and -1, and this aspect indicates that the value of one construct can be accurately determined by knowing the value of the other constructs. On the other hand, any correlation value that is 0 is an indication of the absence of any relationship between the discussed constructs. Table X presents the results of the correlation analysis for business sustainability, resource leverage, customer intensity, innovativeness, and value creation to demonstrate how the determined constructs are related to each other and whether the strength of one construct can be associated with the strength of the other construct.
According to Table X, the significant correlation is observed for Customer Intensity and Resource Leveraging (.781), Innovativeness and Proactiveness (.749), Value Creation and Customer Intensity (.738), Value Creation and Resource Leveraging (.726), Proactiveness and Resource Leveraging (.714), and Proactiveness and Customer Intensity (.712). The correlation between other combinations of constructs can be viewed as less significant as it is lower than 0.7.
In spite of the fact that the response rate for this study was high, it is important to address the problem of the non-response bias to guarantee that study results are statistically significant. Thus, the non-response bias should be measured in those situations when any response can influence the data if the sample size is comparably small. All constructs were measured with the help of the independent sample t-test, including SMEs business sustainability, proactiveness, innovation, resource leveraging, customer intensity, and value creation.
The value of the mean for SME owners’ and managers’ responses did not indicate any significant difference in variables. According to Pallant (2001), the significant value that is more than 0.05 (p > 0.05) while following Levene’s test should be considered as an indicator of equal variances among responses given by SMEs’ owners and managers. The 2-tailed test (p > 0.05) was used additionally to test the non-response bias. The results indicate that differences in SME managers’ and owners’ responses are not significant, and it is possible to avoid the non-response bias in this study.
It is possible to apply different statistical remedies to detect the common method variance (CMV) in the study. The reference to Harman’s one-factor analysis is typical of researchers who usually use the test to check how variances in the data are related to an examined single factor (Podsakoff et al. 2003). To conduct Harman’s one-factor analysis with the help of the SPSS software, the data file was uploaded, and the factor analysis was performed. According to Podsakoff et al. (2003), it is possible to observe the problem associated with the common method bias when a single latent factor can influence the results for the larger variance. For this study, the single factor conducted while analyzing the data with the help of the SPSS software does not represent the problem for the data interpretation, and the common method bias is avoided.
The table presents the minimum and maximum values for constructs, as well as the mean and standard deviation.
Among the constructs, Proactiveness had the lowest mean value of 2.0720 with a maximum of 5.14 and a standard deviation of.76988, while Economic Sustainability has the highest mean value of 2.5457 with a maximum of 5.50 and a standard deviation of.82974. The mean of Innovativeness is 2.4543, the maximum is 6.00, and the standard deviation is 1.07081. The mean for Customer Intensity is 2.2326, the maximum is 5.22, and the standard deviation is.84807. For Resource Leverage and Value Creation, the values are rather similar having the mean of 2.3531 and 2.2615 accordingly. The mean for Social Sustainability is lower than for Economic Sustainability (2.4990). The data for Environmental Sustainability was not analysed. It is possible to state that the values of standard deviations of all constructs appeared in a range between.76988 and 1.07081, which indicates the existence of reasonable and acceptable variability within the whole dataset.
The other constructs have an effect on business sustainability that can be discussed as medium or large. Thus, Proactiveness has a large effect on business sustainability (0.368), and Resource Leveraging has a medium effect (0.340). In their turn, Customer Intensity has large effects on Innovativeness, Proactiveness, and Resource Leveraging (0.464, 0.475, and 0.549 accordingly) when Value Creation has medium effects on Innovativeness, Proactiveness, and Resource Leveraging (0.270, 0.325, and 0.321 accordingly).
According to Hair et al. (2013), the predictive value is measured more appropriately with references to the cross-validated redundancy (Q² > 0). The estimations related to the cross-validated redundancy in this particular study are presented in Figure 5.2. Table 5.15 summarizes the predictive relevance of Q² values. Thus, based on the result generated after the data analysis, it has been shown that all constructs can be regarded as having a predictive effect. From this point, the predictive relevance is observed because the determined scores are higher than zero for the constructs measured in this study, and the predictive relevance is typical of the discussed endogenous factors.
According to Table X, the factor of customer intensity demonstrates the highest score (0.516). The lowest score is presented for business sustainability (0.202) about which the predictive effects are measured. Innovativeness, Proactiveness, Resource Leveraging, and Value Creation also exhibit the predictive effect in spite of the fact that their scores are lower (0.367, 0.317, 0.361, and 0.482 accordingly). These data allow speaking about the high level of prediction that is typical of this study.
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