Research Article: 2019 Vol: 18 Issue: 6
Ahmad Al Adwan, Al-Ahliyya Amman University
Khaled Mahmoud Aladwan, American University of Madaba
Ahmad Samed Al-Adwan, Al-Ahliyya Amman University
Jordan, E-Marketing Strategic, Small Business Enterprises, E-promotion, Marketing Effectiveness.
The rapid growth of the internet over the last two decades is an unprecedented phenomenon in the history of ICT and has fundamentally altered how many organizations perform their operations. As the rest of the world has adopted e-commerce, many B2B and B2C organizations have chosen to capitalize on the trend due to the numerous advantages offered by the modern business model (Shaltoni, 2016; Alhadid & Qaddomi, 2016; Fink, 1998). In this context, Jordan considered one of the more advanced economies in the region regards to internet penetration and growth of e-markets. Several service providers have begun specializing in e-commerce facilities although it is still in its infancy for the business community’s needs. Consequently, the Jordanian government has begun a major e-procurement initiative, which aimed at stimulating the development of a full-featured government e-procurement platform (Bazazo et al., 2017). The Jordanian government has also begun developing e-commerce legislation, although the regulations and tax laws covering e-commerce transactions are still unclear and do not cover all use cases (Ahmad et al., 2015; Seyal et al., 2004; Qureshi et al., 2010).
E markets offer numerous advantages to both established and small scale businesses if implemented in the right manner. This research intends to explore the possible merits of E-marketing if implemented in small enterprises. For instance, there’s continuous selling since the advertisement is online (Khraim & Alkarableiah, 2015). Also, audience of e-marketing is increased as it is not limited to physical encounters with a potential customer. Additionally, the use of social media in e-marketing provides positive reviews for a company hence increase popularity while negative reviews give an opportunity for rectification of corporate image. E-marketing avoids additional costs of building physical stores hence cost of products and services is subsidized. Moreover, e-marketing provides an opportunity for clients to order goods of their specification hence avoid impulse buying. However, the lack of computer literacy and resources means that only large and established businesses take advantage of the rising trend with smaller commerce having to rely on the traditional brick and mortar model (El-Gohary, 2012; Abu Bakar & Ahmed, 2015; Hair Jr & Lukas, 2014). Large international organizations were the early adopters of e-commerce and IT in their operations due to extensive resources, their size also limited their flexibility in adapting to changing market trends (Eid & El-Gohary, 2013). Empirical evidence indicates that for a firm to successfully, adapt the use of e-market, they have to integrate its internal process into the market platform. Therefore, established companies have a challenge of adjusting already developed systems top fit the e-market platform. The businesses need to have full knowledge of these adjustments as well as incorporate the use of Web technologies that involve user-generated content (Mazzarol, 2015; Chen & Lien, 2013). Burns (2016) explains that small businesses can harness more from the advantage of flexibility in that, unlike well-established companies, for new businesses, their process is easier, more adaptable and their burden is lighter. The absence of legacy systems also means that they can begin with the latest technologies allowing real-time synchronization with e-market platforms. However, these small enterprises being new in the market lack strong relationships and business partners. Therefore, they have to collaborate with many partners to provide more opportunities for mutual success. Finally, it is very important to identify which type of e-market gives the small business enterprises the best benefits and less troublesome concerns to increase their sales (Rahayu & Day, 2015; Al-Azzam, 2017; Baker & Sinkula, 2015; Qteishat et al., 2015; Mzee et al., 2015).
Research Model and Hypothesis
Overview of previous findings of e-marketing in Jordan
According to Al-Weshah (2018) most of e-marketing usage has not been exploited in Jordan, with the main hindrance being privacy of information. Most industries still rely on traditional forms of marketing. While Jordan's population is currently on the rise, one knowledge form of online advertising is through social media which heavily relies on loyal testimonials of commerce. Most payments in Jordan is through cash. E-marketing could offer numerous advantages that could foresee its future successful implementation.
The research uses a model outlined in Figure 1 below to guide the hypothesis formulation, data collection, and analysis. Figure 2 provides a representation of how Jordanian businesses can capitalize on the growing trends of e-commerce to increase sales through loyal testimonials, e-brand positioning, e-promotion, and the optimal selection of buyer intent keywords, whose implementation would lead to increased sales.
Considering the multitude of advantages and efficiencies offered by e-markets both locally and internationally, Jordanian small enterprises can improve their local sales by integrating IT into their operations. Based on the above arguments the hypotheses developed for this research discussed below:
Loyal Testimonials
Loyal testimonials play significant role in e-marketing since e-marketing to a large extent relies on social media and online reviews. One way to earn positive reviews on the internet is to sell user published content instead of manufacturer’s content. Client produced content implies any type of substance, for example, web journals, dialog gatherings, web-based life posts, digital broadcasts, illustrations, video or sound (basically any type of mixed media) made by clients (Grandon & Pearson, 2004; Sheikh et al., 2017). To achieve this, e-marketers shall rely on what is trending on social media platforms so that they can sell them, and by doing so positive reviews by customers will have positive contributions to their sales. Loyal testimonials are directly related to customer satisfaction which is enhanced by sell of client produced content (Adwan & Aladwan, 2019).
H1 Loyal testimonials have a significant impact on the performance of online promotions and sales performance through the generation of new customers.
E-brand Positioning
In marketing, positioning is a strategy designed to differentiate a brand from competing products. It provides guidelines on what the business should do to market its product or service to customers in a specific niche including promotions, pricing, distribution, packaging, and differences from the competition. Positioning aims to establish a unique impression in the customer’s mind, which helps the customers, associate a brand with specific attributes distinct from competitors (Chong et al., 2018). In e-marketing for small businesses, sellers can utilize brand positioning by quick delivery of products ordered, which may be uncommon for already established businesses. By doing so, unique advantage over other competitors is achieved. For a successful brand positioning sellers should concentrate on the relevance of the product, uniqueness, and attainability (Al Adwan, 2019).
H2 E-brand positioning influences consumer perceptions of a brand and purchase intent.
E-Promotion
E-marketing can enhance product visibility through social media platforms available. Product advertising through sites such as Facebook, Twitter, and Instagram favor small businesses due to their affordable cost of advertising. E-promotion attracts more audience and is more convenient for use than the old traditional methods of press adverts and TVs. While using online as a marketing tool, it is important to build trust since many spammers have found way to deliver untrue information. Online platforms allow businesses to engage with their consumers in addition to collecting real-time feedback about brand initiatives thereby helping businesses to manage brand reputation. These are all important aspects associated with increased sales performance as brand awareness and reputation increase (Grandon & Pearson, 2004).
H3 E-promotion has a significant positive impact on sales performance.
Attractive Keyword
Product and service sales through sponsored keyword promotion on search engines depend on an effective selection of keywords that describe a business’ offerings accurately. A study by Chong et al. 2016 concluded that online advertising promotes direct sales of the promoted products while the use of general keywords improves indirect sales of other products. Moreover, the impact of keywords on indirect sales depends on product type and market demand. For primary products, the use of specific keywords results in a higher marginal contribution to indirect sales compared to general keywords. However, for accessory products, the selection of general keywords generates a higher marginal contribution to indirect sales compared to specific keywords. Therefore, the keyword selection process should base on the particular type of product (main or accessory) that a firm sells. Keyword analysis can also help in evaluating consumer interest in a product prior to launching which would allow a business to adapt its offerings to current consumer needs. Chong et al. 2016 used pre-launch search volumes of keywords to gauge consumer interest in a product before launching. The study concluded that blog content has permanent effects on pre-launch consumer interest in a product while advertising only produces temporary effects. In this context, the fourth hypothesis is:
H4 Attractive Keyword has a significant impact on the performance of online promotions and sales performance.
The respondents in the present research were mainly general managers, working in the marketing department of small firms in Jordan. However, this study focused mostly on the manufacturing sector firms due to their contribution to the GDP of Jordan. By use of the cluster proportionate sampling technique, the firms used in this study been selected according to their particular locations in the Jordan state (Haugh & Robson, 2005).
Questionnaires administered in various companies located in Jordan. These questionnaires distributed to 110 companies in Jordan. The data that been gathered was to be used for analysis. In answering the research questions, the researcher chose a quantitative methodology and research design, which allowed for the collection of empirical data. The main advantage of quantitative research is the objectivity and replicability of data collected which enhances its validity. Furthermore, quantitative data allows for the performance of parametric and non-parametric statistical tests to derive in-depth insights. The Table 1 shows the profile of the respondents who participated in the research.
Table 1: Profile Of The Respondents | |
Characteristics | Number |
---|---|
Gender | |
Male Female |
63 47 |
Age | |
24 and under 25-34 35-44 45-54 55-64 65 and older |
12 35 30 17 14 2 |
Education level | |
Diploma Degree Masters PHD |
28 36 28 18 |
Duration of work experience | |
Less than 5 6-10 11-15 16-20 21-25 26 and above |
32 13 16 18 8 23 |
Table 1 shows the characteristics of the managers who participated in the research. For instance, 63 of the respondents were male while 47 were female. The Table 1 also shows the age gap of the respondents and their education level together with their work experience.
Measurements
There are four types of measurement scales. There are nominal, ordinal, interval and ratio.
Nominal
Nominal is used for labeling and does not have a quantitative value. For instance, the labeling of interviewees as either male or female. Therefore, nominals do not possess any numerical significance.
Ordinal
Ordinal offers quantitative value of variables by placing them according to their respective orders. For instance, four can be indicated as a level of happiness and can be said to be greater than a level of three. Ordinal level of measurement, however, does not describe the actual difference between the orders. Four can be said to be great than three but not to a precise extent in that instance. The educational level of interviewees is ordinal since diploma can be described to be lower than the PhD. Similarly, ordinals do not have numerical significance.
Interval
It provides both order and the exact difference between them. However, intervals do not have a true zero and they cannot be multiplied or divided to provide useful information. For instance, the temperature has zero as a value but in statistics zero is not useful. In this research, no interval level of measurement was used.
Ratio
Ratios possess true zero hence provide a wide possibility in terms of statistical analysis. For instance, multiplications, divisions, and measures of central tendency can be obtained from ratios. Age and duration of work experience provide ratios as levels of measurements thus provided good statistical analysis.
The data analysis for this study consisted of three main steps. These steps include:
1. Exploratory study
2. Confirmatory study
3. Testing the structural model by using spss software
Exploratory Study
Exploratory Data Analysis (EDA) is the initial phase of the information examination process. Specialists and information experts use EDA to comprehend and condense the substance of a dataset, typically with a particular inquiry or to get ready for progressively complex measurable displaying later on phases of information examination. EDA depends on information perceptions that enable the researcher to recognize and distinguish examples and highlights in a dataset that they generally would not have had the capacity to scan for EDA enables performs the following functions:
? Recognize the mistakes made amid the information gathering and the zones where the information might miss.
? Record the fundamental information structure.
? Distinguish the most powerful factors in the informational collection.
? Rundown and feature oddities and exceptions. Check of recently proposed speculations.
Reliability test conducted using SPSS and the data represented in the Table 2 obtained. The crobanch’s alpha obtained is 0.896. This means that the satisfaction of this research was really good based on the answers given. The respondents could either agree or disagree with the argument. The questions been drawn from the hypothesis.
Table 2: Citc Analysis Of The Performed Of Each Construct | |||||
Item-Total Statistics | |||||
---|---|---|---|---|---|
Scale Mean if Item Deleted | Scale Variance if Item Deleted | Corrected Item-Total Correlation | Squared Multiple Correlation | Cronbach's Alpha if Item Deleted | |
LT | 4.16 | 1.514 | 0.846 | 0.811 | 0.839 |
EB | 4.06 | 1.436 | 0.847 | 0.745 | 0.836 |
EP | 4.23 | 1.682 | 0.745 | 0.717 | 0.877 |
AK | 3.91 | 1.588 | 0.662 | 0.535 | 0.909 |
Note: **Loyal Testimonial (LT), E-Brand (EB), E-Promotion (EP), Attractive Keywords (AK)
Confirmatory Study (Measurement Model)
The confirmatory analysis is where you assess your proof utilizing conventional measurable instruments, for example, criticalness, induction, and dependability. Right now, you are truly testing your suppositions. A significant part of the corroborative information examination is a quantitative appraisal of such things as the probability that any deviation from the model you manufactured could occur by possibility and when you have to begin scrutinizing your model.
The confirmatory analysis incorporates such things as a testing hypothesis, getting gauges with a given dimension of precision, relapse investigation, and investigation of deviations. Along these lines, your corroborative information examination is where you present your discoveries and contentions to the court.
This Table 3 shows factor analysis of four questions that are trying to measure the effects of loyal testimonials, E-brand, E-promotion, and Attractive Keywords to the increase in sales in the local E-market of Jordan. The component matrix Table 3 shows that the value is decreasing from the first question to the fourth question. The determinant is 0.048. From the above Table 3, we see that loyal testimonials have the greatest effect on the loyal e-market while attractive branding has a lower impact or effect.
Table 3: Exploratory Factor Analysis | ||||||
Correlation Matrix | ||||||
---|---|---|---|---|---|---|
Q1 | Q2 | Q3 | Q4 | |||
Correlation | LT | 1.000 | 0.800 | 0.847 | 0.585 | |
EB | 0.800 | 1.000 | 0.677 | 0.731 | ||
EP | 0.847 | 0.677 | 1.000 | 0.495 | ||
AK | 0.585 | 0.731 | 0.495 | 1.000 | ||
Sig. (1-tailed) | LT | - | 0.000 | 0.000 | 0.000 | |
EB | 0.000 | - | 0.000 | 0.000 | ||
EP | 0.000 | 0.000 | - | 0.000 | ||
AK | 0.000 | 0.000 | 0.000 | - | ||
a. Determinant = 0.048 | ||||||
Component Matrixa | ||||||
Component 1 | ||||||
LT | 0.928 | |||||
EB | 0.918 | |||||
EP | 0.866 | |||||
AK | 0.790 | |||||
Extraction Method: Principal Component Analysis. a. 1 component extracted. |
The Table 4 shows the significance value, which is 0.009. There is also the likelihood value, which is 0.003. The level of significance is greater than the likelihood value. This is an analysis of 110 cases.
Table 4: Fit indices explanations. Fit indices were tested using chi-square | |||
Value | df | Asymptotic Significance (2-sided) | |
---|---|---|---|
Pearson Chi-Square | 142.174a | 105 | 0.009 |
Likelihood Ratio | 149.058 | 105 | 0.003 |
N of Valid Cases | 110 | ||
a. 144 cells (100%) have expected count < 5. The minimum expected count is 0.16. |
Table 5 shows that the variance is not equal for the four components. These components are the four hypotheses. The aspect of loyal testimonials as an approach to increase E-marketing has the highest value. Hence, it plays the greatest role in ensuring that online sales are high.
Table 5: Confirmatory Factor Analysis Explanations | ||||||
Total Variance Explained | ||||||
---|---|---|---|---|---|---|
Component | Initial Eigenvalues | Extraction Sums of Squared Loadings | ||||
Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | |
LT | 3.078 | 76.941 | 76.941 | 3.078 | 76.941 | 76.941 |
EB | 0.580 | 14.506 | 91.447 | |||
EP | 0.223 | 5.565 | 97.012 | |||
AK | 0.120 | 2.988 | 100.000 | |||
Extraction Method: Principal Component Analysis. |
1. 46.4% of original grouped cases correctly classified.
2. Cross-validation is done only for those cases in the analysis. In cross-validation, each case classified by the functions derived from all cases other than that case.
3. 11.8% of cross-validated grouped cases correctly classified.
Table 6 shows that loyal testimonies would be a great approach to increasing sales through e-marketing. This was, greatly supported by most of the respondents.
Table 6: Discriminant Analysis Explanations | |||||
Classification Resultsa,c | |||||
---|---|---|---|---|---|
H1 | Predicted Group Membership | Total | |||
1 | 2 | ||||
Original | Count | 1 | 33 | 45 | 78 |
2 | 14 | 18 | 32 | ||
% | 1 | 42.3 | 57.7 | 100.0 | |
2 | 43.8 | 56.3 | 100.0 | ||
Cross-validatedb | Count | 1 | 9 | 69 | 78 |
2 | 28 | 4 | 32 | ||
% | 1 | 11.5 | 88.5 | 100.0 | |
2 | 87.5 | 12.5 | 100.0 |
Note: 1 represents accept, 2 represents reject
1. 60.9% of original grouped cases correctly classified.
2. Cross-validation is done only for those cases in the analysis. In cross-validation, each case classified by the functions derived from all cases other than that case.
3. 60.9% of cross-validated grouped cases correctly classified. The above Table 7 shows that the e-branding hypothesis would be a great approach to increasing sales through e-marketing.
Table 7 : H2: Classification Results,C | |||||
H2 | Predicted Group Membership | Total | |||
---|---|---|---|---|---|
1 | 2 | ||||
Original | Count | 1 | 45 | 22 | 67 |
2 | 21 | 22 | 43 | ||
% | 1 | 67.2 | 32.8 | 100.0 | |
2 | 48.8 | 51.2 | 100.0 | ||
Cross-validatedb | Count | 1 | 45 | 22 | 67 |
2 | 21 | 22 | 43 | ||
% | 1 | 67.2 | 32.8 | 100.0 | |
2 | 48.8 | 51.2 | 100.0 |
Note: 1 represents accept, 2 represents reject.
This number of respondents who supported this and those who rejected differed with just a small margin.
1. 47.3% of original grouped cases correctly classified.
2. Cross-validation is done only for those cases in the analysis. In cross validation, each case classified by the functions derived from all cases other than that case.
3. 47.3% of cross-validated grouped cases correctly classified.
The above Table 8 shows that e-promotion would be a great approach to increasing sales through e-marketing. The number of respondents who supported this was greater than that of those who rejected. This shows that this would be a great approach.
Table 8: H3:Classification Resultsa,C | |||||
H3 | Predicted Group Membership | Total | |||
---|---|---|---|---|---|
1 | 2 | ||||
Original | Count | 1 | 37 | 48 | 85 |
2 | 10 | 15 | 25 | ||
% | 1 | 43.5 | 56.5 | 100.0 | |
2 | 40.0 | 60.0 | 100.0 | ||
Cross-validatedb | Count | 1 | 37 | 48 | 85 |
2 | 10 | 15 | 25 | ||
% | 1 | 43.5 | 56.5 | 100.0 | |
2 | 40.0 | 60.0 | 100.0 |
1. 50.0% of original grouped cases correctly classified.
2. Cross-validation is done only for those cases in the analysis. In cross-validation, each case classified by the functions derived from all cases other than that case.
3. 24.5% of cross-validated grouped cases correctly classified.
The above Table 9 shows that attractive keywords would be a great approach to increasing sales through e-marketing. This number of respondents who supported this was high by a great margin. This shows that the approach could be a great method of increase sales.
Structural Model
Table 9: H4:Classification Resultsa,C | |||||
H4 | Predicted Group Membership | Total | |||
---|---|---|---|---|---|
1 | 2 | ||||
Original | Count | 1 | 29 | 21 | 50 |
2 | 34 | 26 | 60 | ||
% | 1 | 58.0 | 42.0 | 100.0 | |
2 | 56.7 | 43.3 | 100.0 | ||
Cross-validatedb | Count | 1 | 15 | 35 | 50 |
2 | 48 | 12 | 60 | ||
% | 1 | 30.0 | 70.0 | 100.0 | |
2 | 80.0 | 20.0 | 100.0 |
The modeling of auxiliary equations is a technique for multivariate factual investigation that breaks down basic connections. This strategy is a mix of factor examination and various relapse investigations and utilized to dissect the basic connections between estimated factors and concealed structures. The analyst inclines toward this technique since he assesses different and between related conditions in a solitary examination (Alrousan & Jones, 2016; Black & Babin, 2019). Structural modeling is a confirmatory technique. It involves regression, t-test and R2.
Table 10: Regression Analysis: Anovaa | ||||||
Model | Sum of Squares | Df | Mean Square | F | Sig. | |
---|---|---|---|---|---|---|
1 | Regression | 712.152 | 4 | 178.038 | 1.793 | .136b |
Residual | 10428.903 | 105 | 99.323 | |||
Total | 11141.055 | 109 | ||||
a. Dependent Variable: age b. Predictors: (Constant), H4, H3, H2, H1 |
Table 10 shows the regression analysis of the four hypotheses. From the analysis, the F value is 1.793. The R square value is 0.064 (Table 11). The variance proportions for the hypotheses keep on increasing gradually (Table 12).
Table 11: Model Summary | |||||||||
Model | R | R Square | Adjusted R Square |
Std. Error of the Estimate |
Change Statistics | ||||
---|---|---|---|---|---|---|---|---|---|
R Square Change | F Change | df1 | df2 | Sig. F Change | |||||
1 | 0.253a | 0.064 | 0.028 | 9.966 | 0.064 | 1.793 | 4 | 105 | 0.136 |
a. Predictors: (Constant), H4, H3, H2, H1 |
Table 12: Collinearity Diagnosticsa | ||||||||
Model | Dimension | Eigenvalue | Condition Index | Variance Proportions | ||||
---|---|---|---|---|---|---|---|---|
(Constant) | H1 | H3 | H2 | H4 | ||||
1 | 1 | 4.837 | 1.000 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
2 | 0.073 | 8.135 | 0.62 | 0.05 | 0.05 | 0.02 | 0.03 | |
3 | 0.055 | 9.406 | 0.23 | 0.01 | 0.12 | 0.08 | 0.35 | |
4 | 0.022 | 14.663 | 0.15 | 0.02 | 0.28 | 0.54 | 0.57 | |
5 | 0.013 | 19.323 | 0.01 | 0.92 | 0.55 | 0.37 | 0.04 | |
a. Dependent Variable: age |
From the data analysis above, EDA used to perform the initial analysis. It helped to Recognize the mistakes made amid the information gathering and the zones where the information might miss, Record the fundamental information structure, Distinguish the most powerful factors in the informational collection, Rundown and feature oddities and exceptions, and Check of recently proposed speculations. Reliability test conducted using SPSS and the data represented in the table below obtained. The Cronbach’s alpha obtained is 0.896. This means that the satisfaction for this research was good. This is because the data represented in the variables is consistent. Exploratory factor analysis performed and the results show that there were no missing values. For the factor analysis, the results show that loyal testimonials have the greatest effect on the loyal e market while attractive branding has a lower impact or effect. During the construction of SEM, the regression value is 1.793. This value shows that the duration of work experience and age of the respondent do not assist in predicting the choice that the respondent will make. That is whether they strongly agree or strongly disagree. The R2 is 0.64. This value represents the amount of variance in the strength value that accounted for and explained for by the hypothesis and the age of the respondents. The results show that the four components which include loyal testimonies, e-branding, e-promotion and attractive key words are approaches that could assist in increasing e-marketing in the Jordanian small scale business. This supported by the number of managers who supported the approaches.
Based on the study there are several limitations, which are determined, by various economic, social and political forces. These forces need to identify. In addition, the limitations to the study pose the risk for error and thus, more suitable measures, should use. A one-dimensional variable used in the research. Nevertheless, it is imperative to note that the variables in the Jordan Market are multi-dimensional and should considered and deduced as such when making inferences in relation to various aspects of the Jordan Market. This involves considering the dynamics involved in the market. There are therefore, multiple avenues to explore in order to solve the underlying market forces in the Jordan market by using a multi-dimensional approach.