Factors Affecting Consumer Buying Attitude towards “Made in Ethiopia” Shoes brands: The Case of Wolaita Sodo University Academic Staffs, Higher Education institution, Ethiopia
Shebiru Tilahun Gossaye
PhD. Fellow,
Department of Marketing Management,
Wolaita Sodo University
email. shebiru2018@gmail.com
Legese Lemma Chuma
Department of Marketing Management,
Wolaita Sodo University
email. legeselemma3@gmail.com
This research has been conducted to assess factors affecting consumers buying attitude towards domestic shoe. The primary goal of this study is to examine the variables that influence consumers' attitudes toward domestic shoe purchases in the context of Wolaita Sodo University staff. The research design adopted was descriptive research and explanatory. The study employed mixed type of research approaches. Data were collected from academic staff of Wolaita Sodo University through survey questionnaires and interview. Samples of 320 respondents were selected using Probability sampling technique method. Participants of the study from different colleges and schools of Wolaita Sodo University by using Stratified Proportionate Sampling technique and systematic sampling technique were used in order to distribute Questionnaires. Primary types of data were used additionally some previously conducted literature and books were used for empirical and theoretical review. The data were collected through self-administered questionnaire and interviews. After the data was checked through testes like; Reliability by Cronbach’s alpha coefficient the data analysis was done using descriptive analysis and inferential statistics to test the hypothesis. To establish the association and examine the impact of the independent variables, Spearman's rho and ordinary logistic regression were performed using SPSS 22 Version. The result from ordinary logistic regression analysis concludes that all independent variables (price, quality, design, social status, promotion and brand) have statistically significant effect on consumer buying attitude. Finally the researcher has recommended the company to consider the influence of price, quality, design, social status, promotion and brand on consumers buying attitude when they offer domestic shoe in the market. The companies should understand the influences of each factor and develop strategies for each of them to positively influence consumers buying attitude, because price, quality, design, social status, promotion and brand has highly influence consumers buying attitude.
Keywords: buying attitude, consumer, price, quality, design, social status, domestic shoe and ordinal logistic regression
Introduction
Background of the Study
Globalization has changed the picture of World Economy, by increasing the cross-border trade, exchanges of currency, free flow of Capital, movement of people and flow of information. Globalization has introduced the concept of border-less and integrated world economy. Globalization has given a new thought to the businesses worldwide. A lot of Strategic changes have been occurred in the businesses. Now target market for businesses is not only their home land, but the overall world (Intriligator, 2010).
Globalization comprises unlimited transport of goods, services, ideas and people. It reveals interaction and subsequent integration of the people and nations into a common system. International trade is an agent that promotes commoditization of social and environmental values, resources and services. In view of this, the rich and dynamic African culture has been diluted. Many aspects of people’s every-day life are in the process of homogenized with those of people living far away (World Trade Organization, 2018). Because of this globalization company’s try to use the opportunities by join in to foreign markets and offer goods and services to satisfy foreign customers’ needs and wants.
When we see the footwear industry in Africa, the production of footwear is important for development of Africa, but it confronted stiff competitive challenges. The footwear industry has good resource base, is labor intensive technology and employs many people According to UNIDO (2002), as cited in Endale (2011), the African footwear sub-sector seems isolated from the fast pace of technological innovation taking place globally. Poor design capabilities, poor supervisory and managerial skills, poor knowhow of appropriate inputs and marketing technique contribute to less quality products and less competitiveness.
Industrial sector in Ethiopia include shoes and leather industries are at infant stage are significant contributor to the overall economy of the country with the other development strategy including the technological innovation, improving production and creating employment opportunities position by the government. The globalization of market has brought considerable foreign goods to Ethiopian consumers (Saha, & Bhattacharyya, S. 2010)
This provides consumers with many opportunities to access different alternative products or services. Because of this, consumers of different nations exhibit different behaviors regarding the purchase of domestic and foreign products. This study, therefore, focuses on investigation of factors affecting attitude of the consumers towards domestic shoe products. Factors affecting consumers’ attitude toward domestic shoe product according to quality, country of origin, price, social status, family and friends influence consumers buying decision of footwear products and their attitude towards local footwear products. (Z. Ismail, S. Masood. Z, Tawab 2012).Thus, it is interesting to study the factors affect consumers buying attitude towards domestic shoes since understanding the factors influencing consumers buying attitude the backbone for the company’s failure and success in today’s business environment.
Globalization and increased international business activities have caused the emergence of the global market, new foreign competitors to the forefront, a wider range of foreign products for customers and broadened their choices (Hsieh, 2007). For businesses to succeed in the long run and increase their consumer base, an aggressive business climate and its rapid development across national borders are unquestionably now necessary (Aboulnasr, 2007).
In addition, access to information, higher levels of education and technological progress have also made it possible for consumers to become more aware of the products and services available throughout the world. Consequently, companies consider product differentiation the key priority in pursuing to attain a constant competitive advantage in this challenging global environment (Baker and Ballington, 2012).
Ethiopia ranks first in Africa in livestock population and even 10th in the world, but these resources were not exploited yet despite the abundant raw material and cheap manpower. The country is working hard to exploit this huge potential so that the sector contributes its share to the growth of GDP and the wellbeing of the key role players in this sector is enhanced (Institute Of Leather Industry Development 2012). As mentioned on the previous study domestic market covered by the foreign footwear products. Such as Chinese shoes which had flooded the market in around 2000, it has been growing vigorously (Sonobe, T. 2014).
On this study consumers shoe choice to a large degree influenced by the quality of the product. Domestic shoes face strong competition from imported leather shoes on the local shoes market (Endalew Adamu, 2018). The footwear industry face stiff competition from cheap imported shoes from abroad and this import pressure has its own effect on domestic footwear producers, as the domestic footwear producers are at their infant and adolescence stage, studying this effect is paramount important.
In previous study which mentioned above there is a gap. Mainly the researcher’s focus on limited geographical areas for example in Addis Ketema Sub-City, in Gondar Town, and in Addis Ababa, they are use only stractured questionaries but not use interviwesand there is no objective measurements were used to measure the variables like Econometrics model. Additionally, no study conducted by the researchers on factors affecting consumers buying attitude in Wolaita Sodo University.Therefore researchers has conducted the study to bridge these gaps by probing factor affecting consumers buying attitude in Wolaita Sodo University towards domestic shoe by adopting price, quality, design, social status, promotion and brand as study independent variable.
This study's main goal was to determine the elements that influence consumers' buying attitudes regarding domestic shoe products at Wolaita Sodo University in Ethiopia.
H1: price has statistically significant impact on consumers buying attitude.
H2: quality has statistically significant effect on consumers buying attitude.
H3: product design has statistically significant effects on consumers buying attitude.
H4: Product social status has statistically significant effect on consumers buying attitude.
H5: promotion has statistically significant effect on consumers buying attitude
H6: Brand has statistically significant effect on consumers buying attitude
Conceptual framework of the study
The following figure demonstrates the factors affecting consumers ‘attitude towards domestic shoes products. Many researchers agree that the following variables affect buying attitude of consumers towards domestic and global brand products. The factors are included in the framework after review different literatures. Accordingly, many researchers agree that the following variables commonly affect the attitude of consumers towards domestic shoes products.
Source:Modified from Zemenu Aynadis, (2014) and Endalew Adamu, (2018)
This study was conducted to investigate the factors affect consumers buying attitude towards domestic shoe the case of Wolaita Sodo University academic staff. This study were adapt descriptive and explanatory research design as it tries to assess what factors and at what level it affect consumer’s attitude towards domestic shoes products. Both qualitative and quantitative methods were used in this research, and the pertinent data were collected through formal interviews and structured questionnaire surveys. The target population of this study was academic staff from seven colleges & three schools of Wolaita Sodo University main campus they are 1599 in number. The sample of respondents was selected from the sampling frame as shown in the table below. In order to get adequate representation from the total population the researcher will determine the samples size of the study by using stratified sampling formula presented by Yamane (1967:886) to calculate sample size. Based on this formula, the sample size was calculated as follows:
Where e is the degree of precision, N is the size of the entire population, and n is the sample size. At a 95% level of confidence and a 5% level of error, the following sample size was calculated:-
Therefore out of 1599 Academic staffs of Wolaita Sodo University 320 were selected and participate in the study. To distribute this sample size to each stratum, the researcher was used proportionality formula such as:-
Where N= the total population
ni = sample of strata,
Ni = population of strata,
n = total sample size.
Table 3.1. List of schools and colleges.
|
S. No |
S Strata |
Popn of Strata |
Sample size of stratum |
|
|
1. |
Business and Economics |
25 117 |
|
23 |
|
2. |
College of Medicine and Health Science |
432 |
|
86 |
|
3. |
College of Natural and Computational Sciences |
214 |
|
43 |
|
4. |
College of Agriculture |
168 |
|
34 |
|
5. |
College of Education and Behavioral Studies |
45 |
|
9 |
|
6. |
College of Engineering |
314 |
|
63 |
|
7. |
College of social Science &Humanities |
167 |
|
33 |
|
8. |
School of Informatics |
84 |
|
17 |
|
9. |
School of Veterinary Medicine |
36 |
|
7 |
|
10. |
School of Law |
22 |
|
5 |
|
Total |
N = 1599 |
|
n = 320 |
|
Population Strata
Source: HRM data of Wolaita Sodo University (2022)
This study basically depends on primary data in which the researchers prepared the questionnaires that were distributed to Academic staff of Wolaita Sodo University main campus. The secondary data was used only for supporting the finding obtained from analysis of primary data. To obtain necessary data for this study the researchers were used questionnaire and interviews. The researchers were used structured questionnaire and interviews to collect primary data from the representatives of the targeted population of the study to investigate the consequence of independent variables on outcome variable.
Statistical Package for Social Science (SPSS) Version 22 was utilized to assist in the analysis of all research topics. Data were reviewed and adjusted to ensure completeness, accuracy, and consistency after being collected through questionnaires and interviews, and then entered into a computer for analysis. In order to analyze the data, the two sets of Statistics: descriptive and inferential analysis. In addition to using percentages, frequency distributions, tables, and means for descriptive analysis, inferential analysis also uses correlation and an ordinal logistic regression model to examine the data. The model built around two sets of variables, specifically dependent variable (Consumers Buying Attitudes) and independent variables (price, quality, design, social status, promotion and brand name).
Model specification
The ordinal logistic model is specified as follows: According to Long & Freese, (2006) the outcome variable in an ordinal logistic regression model contains more than two levels. It calculates the likelihood of an outcome being at or below a specified level given a set of explanatory factors. According to Liu (2009) and Long & Freese (2006), the ordinal logistic regression model can be written in the logit form as follows:
Logit [(x)] = ln (Y) j′
= "j" + "("1X1" "2X2" "... "pXp")"
= αj + − (β1X1 −β2X2 −…− βpXp)
The probability of falling into category j, given a collection of predictors, is represented by the formula j(x) = (Y j | x1, x2... xp), where j = 1, 2... The cut points are J1, j, and the logit coefficients are 1, 2... p. The Proportional Odd model calculates the J-1 cut points when there are j categories. The PO model presupposes that every predictor's logit coefficient is independent.
Therefore, across J-1 response categories, this model forecasts cumulative logit. The estimated cumulative odds and the cumulative probabilities falling into the jth group can then be determined using the cumulative logit. The ordinal logistic regression model can be presented in several ways, and different software programs may estimate parameters differently (Liu, 2009).
The Proportional Odds model is extended in the generalized ordinal logistic regression scenario by relaxing the PO assumption. If a particular predictor in this model violates the assumption, its impact can be freely assessed across many categories of the dependent variable.
The model is expressed as:
The above formula can also be expressed as proposed by Fu (1998) and Williams (2006):
This model estimates the odds of being beyond a certain category relative to being at or below that category, where, in both equations, j are the intercepts or cut points, and β1j, β2j... βpj are logit coefficients. Generally, a positive logit coefficient indicates that an individual is more likely to be in a higher category as opposed to a lower category of the outcome variable.
By using the ANOVA statistical model, which is an analysis of variance, and ordinal logistic regression analysis to determine the relationship between the independent and dependent variables, the relevance of the analytical model is evaluated. Prior to implementing the final phase, a pilot research was carried out to fine-tune the technique and test apparatus, such as a questionnaire. As advised by (John, A., Robert, & David, 2007), questionnaires were evaluated on probable responders to make them objective, pertinent, appropriate for the situation, and dependable.
Internal consistency between and/or among different items of the same construct is measured by reliability. The said scales with an alpha between 0.80 and 0.96 are considered to have very excellent quality, the stated scales with an alpha between 0.70 and 0.80 are considered to have good reliability, and the stated scales with an alpha between 0.60 and 0.70 are considered to have medium reliability, (William, 2010).
Also the results of correlation analysis show that all the independent variables i.e. price, quality, design, social status, promotion and brand name had positively and significantly correlated with the dependent variable i.e. Design and social status (0.833 and 0.739 respectively) are a strong determinant of consumer buying attitude, which is reflected in the table by strong positive correlation with consumer buying attitude. This correlation clearly shows that, as design and social status increase, consumer buying attitude will also moves to the same direction. Besides, the variable brand name (0.283) shows a weak positive correlation between consumer buying attitudes. The variables price, quality and promotion show a moderate positive correlation with consumer buying attitude (0.502, 0.388 and 0.425 respectively).
|
Variable |
Group |
Frequency |
Percent |
|
Gender
|
Male |
237 |
77% |
|
Female |
72 |
23% |
|
|
Total |
309 |
100% |
|
|
Group |
Frequency |
Percent |
|
|
Age
|
24-30 |
114 |
37% |
|
31-37 |
126 |
41% |
|
|
38-44 |
36 |
12% |
|
|
45 and above |
33 |
10% |
|
|
Total |
309 |
100% |
|
|
Group |
Frequency |
Percent |
|
|
Education |
1st degree |
45 |
15% |
|
2nd degree |
213 |
69% |
|
|
3rd degree (PhD) |
51 |
16% |
|
|
Total |
309 |
100% |
|
|
Group |
Frequency |
Percent |
|
|
Marital status
|
Single |
117 |
38% |
|
Married |
174 |
56% |
|
|
Divorced |
12 |
4% |
|
|
Widowed |
6 |
2% |
|
|
Total |
309 |
100% |
Source: - (Survey Result, 2022).
Reliability Test Result
Cronbach’s (1951) developed a reliability measure designed after his name, Cronbach’s alpha. Cronbach’s alpha is a coefficient of reliability. It is usually used as a measure of the internal reliability of a psychometric test score for a sample of examinees. According to William, (2010), stated scales with alpha between 0.80 and 0.96 are measured to have very good quality, scales with alpha between 0.70 and 0.80 are taken to have good reliability, and a coefficient between 0.60 and 0.70 shows fair reliability. Hence, in the course of data analysis a commonly used Cronbach’s alpha reliability test was managed to maintain the quality of the study.
Table 4.2 Reliability test of dependent and independent variables
|
Item-Total Statistics |
||||
|
|
Scale Mean if Item Deleted |
Scale Variance if Item Deleted |
Corrected Item-Total Correlation |
Cronbach's Alpha if Item Deleted |
|
Price |
22.9288 |
19.086 |
.504 |
.869 |
|
Quality |
22.6764 |
19.408 |
.531 |
.864 |
|
Design |
22.7767 |
17.612 |
.836 |
.824 |
|
Social status |
23.4531 |
16.622 |
.812 |
.824 |
|
Promotion |
23.0259 |
18.230 |
.672 |
.845 |
|
Brand name |
22.6246 |
21.034 |
.489 |
.868 |
|
Cba |
23.9709 |
17.353 |
.685 |
.843 |
|
Descriptive Statistics |
|||||
|
|
N |
Minimum |
Maximum |
Mean |
Std. Deviation |
|
Price |
309 |
1.00 |
5.00 |
3.9806 |
1.01911 |
|
Quality |
309 |
1.00 |
5.00 |
4.2330 |
.92796 |
|
Design |
309 |
1.00 |
5.00 |
4.1327 |
.88560 |
|
Social status |
309 |
1.00 |
5.00 |
3.4563 |
1.04238 |
|
Promotion |
309 |
1.00 |
5.00 |
3.8835 |
.95313 |
|
Brand name |
309 |
1.00 |
5.00 |
4.2848 |
.69070 |
|
Cba |
309 |
1.50 |
5.00 |
2.9385 |
1.07031 |
|
Valid N (list wise) |
309 |
|
|
|
|
Source: (Survey result, 2022)
Source: Survey Result (2022)
This study had calculated correlation of dependent variable with the independent variables. From the table price, quality, design, social status, promotion and brand name had appositive correlation with consumer buying attitude. This connection offers a preliminary image of the type of link that exists between the explanatory factors and the buying attitude of consumers.
As the correlation matrix indicates: design and social status (0.833 and 0.739 respectively) are a strong determinant of consumer buying attitude, which is reflected in the table by strong positive correlation with consumer buying attitude. This correlation clearly shows that, as design and social status increase, consumer buying attitude will also moves to the same direction. Besides, the variable brand name (0.283) shows a weak positive correlation between consumer buying attitudes. The variables price, quality and promotion show a moderate positive correlation with consumer buying attitude (0.502, 0.388 and 0.425 respectively).
According to Field (2005) there is no Multicolinearity problem in the variables when their variance inflation factors (VIF) is between 0.1 up to 10and the tolerance value was 0.673. As indicted below in the table VIF all the variables are between the above ranges, therefore there is no Multicolinearity problem in this research.
Table 4.5.1. Multicolinearity test
Source: SPSS 22 output (2022)
The results of ordinal logistic regression versus a reduced model (intercept) with a complementary log-log link function are presented in the Model Fitting section. If there is a correlation between the dependent variable and the set of independent variables, the final model's statistical significance must be taken into account. In contrast to the model with intercept and independent variables, which has a -2LL of 437.393, the model with only an intercept has a -2LL of 812.547. The final model significantly outperforms the baseline intercept alone model, as shown by the statistically significant chi-square statistic (p =.000). The difference (Chi square statistics) is 812.547-437.393 = 375.154.
Table 4.5.2 Model Fitting Information
In full maximum likelihood situations, the likelihood-ratio test of deviation is employed. The fitted model and observed data are in agreement, according to the null hypothesis. The fit is deemed to be excellent if the P-value is higher than 0.05. The next table illustrates the data clearly, and the P-values of Pearson are 0.05 and the deviance value is > 0.05. Based on each finding, it is evident that the model fits the data extremely well. The null hypothesis is accepted, and since the p value was significant, it may be concluded that the observed data were consistent with the estimated values in the fitted model, indicating that the model was well-fitted.
Table 4.5.3. Goodness of fit
Source: (SPSS result 2022)
Despite several attempts, there is no R-squared for logistic regression that can be compared to the R-squared for OLS regression. Based on previous researcher Yeabsira, (2019) and Geda, (2013) interpretation way the researcher try to summarize the result that constitutes a “good” R2 value depends upon the nature of the outcome and the explanatory variables Here, the pseudo R2 values (e.g. Nagelkerke = 74%) indicates that there is relatively large proportion of the variation in score between consumer buying attitude. This indicates that 74% of change in consumer buying attitude is a result of those factors.
Source: (SPSS result 2022)
This test compares the ordinal model with a single set of coefficients for all thresholds (labeled Null Hypothesis) to a model with a separate set of coefficients for each threshold (labeled General). If the general model fits the data significantly better than the ordinal (proportional odds) model (i.e. if p<.05), reject the proportional odds assumption. Given the substantial value (p=.000) as indicated below, this is not a result for the acceptance of the assumption of the proportional odd ratio.
Table 4.5.5. Test of Parallel Lines
|
|
||||
|
Model |
-2 Log Likelihood |
Chi-Square |
Df |
Sig. |
|
Null Hypothesis |
265.493 |
|
|
|
|
General |
.000b |
265.493 |
36 |
.000 |
|
The null hypothesis states that the location parameters (slope coefficients) are the same across response categories. |
||||
|
a. Link function: Logit. |
||||
|
b. The log-likelihood value is practically zero. There may be a complete separation in the data. The maximum likelihood estimates do not exist. |
||||
Tests of model effects are quantitatively significant when they show results for price, quality, design, social status, brand name, and promotion display since the p-value is less than 0.05.
Table 4.5.6. Tests of Model Effects
|
Source |
Type III |
||
|
Likelihood Ratio Chi-Square |
Df |
Sig. |
|
|
Price |
23.635 |
1 |
.000 |
|
Quality |
10.554 |
1 |
.001 |
|
Design |
45.062 |
1 |
.000 |
|
Social status |
52.045 |
1 |
.000 |
|
Brand name |
15.829 |
1 |
.000 |
|
Promotion |
51.132 |
1 |
.000 |
|
Dependent Variable: consumer buying attitude Model: (Threshold), price, quality, design, social status, brand name, promotion |
|||
In the Parameter Estimates table shows the coefficients, their standard errors, the Wald test and associated p-values (Sig.), the 95% confidence interval of the coefficients and odds ratios. If p-values less than alpha level, they are statistically significant; otherwise not. The thresholds are shown at the top of the parameter estimates output, and the threshold coefficients are representing the intercepts, specifically the point (in terms of a logit). All independent variables were statistically significant. The findings are displayed as proportional odds ratios (with the coefficient exponentiated) in column exp b. There is also calculated the lower and upper 95% confidence interval.
Table 4.5.7. Parameter Estimates
Source: (SPSS result 2022)
Table 4.6.Summary of Hypothesized and actual impact
|
Independent variables |
Measurements |
Expected r/ship with CBA |
Actual result |
Statistical sign. test |
Hypothesis status |
|
Price |
Questionnaires with five Likert scale |
Positive |
Positive |
Significant at 5% |
Accepted |
|
Quality |
Questionnaires with five Likert scale |
Positive |
Positive |
Significant at 5% |
Accepted |
|
Design |
Questionnaires with five Likert scale |
Positive |
Positive |
Significant at 5% |
Accepted |
|
Social status |
Questionnaires with five Likert scale |
Positive |
Positive |
Significant at 5% |
Accepted |
|
Promotion |
Questionnaires with five Likert scale |
Positive |
Positive |
Significant at 5% |
Accepted |
|
Brand |
Questionnaires with five Likert scale |
Positive |
Positive |
Significant at 5% |
Accepted |
Source: survey questionnaire (2022)
The effects of Price
As shown from the table the estimated logit regression coefficient, β = 0.642, p < 0.05 (p= .004), indicated that the predictor variable, price had a significant effect on consumer buying attitude. Odd ratio =e^ (-0.642) = 0.52624, indicated that the odds of being at or below a particular consumer buying attitude level relative to beyond that level is decreased by a factor of 0.52624 with one-unit increase in the product price. In other words, a higher level of price was related to the likelihood of being in a consumer buying attitude level. As shown from the table the estimated logit regression coefficient, β = 0.467, p < 0.05 (p= .040), indicated that the predictor variable, quality had a significant effect on consumer buying attitude. Odd ratio =e^ (-0.467) = 0.63128, indicated that the odds of being at or below a particular consumer buying attitude level relative to beyond that level is decreased by a factor of 0.63128 with one-unit increase in the product quality. In other words, a higher level of quality was related to the likelihood of being in a consumer buying attitude level.
As shown from the table the estimated logit regression coefficient, β = 2.611, p < 0.05 (p= .000), indicated that the predictor variable, quality had a significant effect on consumer buying attitude. Odd ratio =e^ (-2.611) = 0.07346, indicated that the odds of being at or below a particular consumer buying attitude level relative to beyond that level is decreased by a factor of 0.07346with one-unit increase in the product design. In other words, a higher level of design was related to the likelihood of being in a consumer buying attitude level. To estimate the probability of being beyond a category of consumer buying attitude, which is the complement of the probability of being at or below a category, it is only necessary to exponentiated 2.611; this results in odd ratio = 13.607, indicating that the odds of being beyond a consumer buying behavior level was 13.607 time greater with one-unit increase in design other variables being held constant.
As shown from the table the estimated logit regression coefficient, β = 1.606, p < 0.05 (p= .000), indicated that the predictor variable, social status had a significant effect on consumer buying attitude.Odd ratio =e^ (-1.606) = 0.20068, indicated that the odds of being at or below a particular consumer buying attitude level relative to beyond that level is decreased by a factor of 0.20068with one-unit increase in the social status. In other words, a higher level of social status was related to the likelihood of being in a consumer buying attitude level.
To estimate the probability of being beyond a category of consumer buying attitude, which is the complement of the probability of being at or below a category, it is only necessary to exponentiated 1.606; this results in odd ratio = 4.983, indicating that the odds of being beyond a consumer buying behavior level was 4.983 time greater with one-unit increase in social status other variables being held constant.
As shown from the table the estimated logit regression coefficient, β = 0.790, p < 0.05 (p= .005), indicated that the predictor variable, promotion had a significant effect on consumer buying attitude.Odd ratio =e^ (-0.790) = 0.45384, indicated that the odds of being at or below a particular consumer buying attitude level relative to beyond that level is decreased by a factor of 0.45384with one-unit increase in the promotion. The results in odd ratio = 2.204, indicating that the odds of being beyond a consumer buying behavior level was 2.204 time greater with one-unit increase in promotion other variables being held constant.
As shown from the table the estimated logit regression coefficient, β = 0.658, p < 0.05 (p= .009), indicated that the predictor variable, brand name had a significant effect on consumer buying attitude.Odd ratio =e^ (-0.658) = 0.51788, indicated that the odds of being at or below a particular consumer buying attitude level relative to beyond that level is decreased by a factor of 0.51788 with one-unit increase in the brand name. In other words, a higher level of brand name was related to the likelihood of being in a consumer buying attitude level.
Conclusion
As a result of analysis conducted in this study the following important conclusions are drawn below. As such, the result of this research proves that price, quality, design, social status, promotion and brand name have a relationship with consumer buying attitude. This research has proved that consumers are affected by price, quality, design, social status, promotion and brand name of domestic shoe during they buy it. In this regard, it can be concluded about how each independent variables influence consumers buying attitude as below: Depend up on the hypothesis of the research state that price has an important impact on consumer’s buying attitude; the hypothesis result revealed that quality has moderate positive correlation with consumer buying attitude. The third hypothesis result revealed that design has dominant determinant of consumer buying attitude, which is reflected in the table by strong positive correlation with consumer buying attitude.
The other independent variable is social status, the hypothesis result revealed that social status has dominant determinant of consumer buying attitude, which is reflected in the table by strong positive correlation with consumer buying attitude. The finding of this study states that promotion has moderate positive correlation with consumer buying attitude. The result shows that there is moderate positive correlation between promotion and consumer’s buying attitude. Based on the finding this study states that brand name has weak positive correlation with consumer buying attitude. The result shows that there is weak positive correlation between brand name and consumer’s buying attitude.
Based on the response of interview here the researcher concludes the responses of respondents in short. As the response of interview the dependent variable was highly affected by independent variables i.e. price, quality, design, social status, promotion and brand name affect the consumers buying attitude towards domestic shoe. The researchers conclude the results of dependent variable i.e., consumer buying attitude try to measure the buying attitudes of respondents towards domestic shoe the researcher measured by using five Likert type questions. Generally the findings were shows that consumers have no good buying attitude for domestic shoe. Here the implication was consumers buying attitudes are highly affected by independent variable I.e. (price, quality, design, social status, promotion and brand name).
In this section some viable recommendations are forwarded on the bases of the research findings
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