In this section the results of the binary logistic regression model predicting the small retailers’ survival in terms of growth is discussed. Before the results are discussed, the following should be noted (as discussed in detail in section 4.3.7.2) regarding the validity and reliability of the logistic regression model predicting the small retailers’ survival in terms of growth:
The Hosmer and Lemeshow test indicated goodness of fit of the logistic regression model with a p-value of 0.494. Therefore the model is assumed to be adequate.
The overall correct prediction classification improved from 74.2% (model 0 – business income contracted or stayed the same) to 76.4% (model 1 - business income grew over the past year) as seen in appendix C. Therefore, the results of the binary logistic regression model with the dependent variable income growth over the past year are discussed.
An odds ratio larger than 1 indicates that the independent variable increases the small retailers’ odds of growing their income, whereas an odds ratio smaller than 1 indicates that the independent variable decreases their odds of growing their income.
Table 6.3 provides the results of the 12 independent variables that were included in the binary logistic regression model. Three independent variables were identified as statistically significant predictors of the model (p-values ≤ 0.05) (highlighted in table 6.3). The odds ratios of these three independent variables were considered in order to determine whether the independent variables had a positive or negative influence on the dependent variable.
Table 6.3 Independent variables in the equation for the binary logistic regression
Source: Compiled by the researcher from survey results
The results indicated that the following independent variables are statistically significant predictors, at the 5% level of significance, of the growth (in terms of income change patterns) of the business: maintaining a low inventory level; maintaining a high inventory level; and inventory collection. Having allowed for the other variables in the equation, the odds ratios of the 12 independent variables further indicated that:
Each decrease in the frequency of maintaining a low inventory level, increases the odds of the business’ survival (growing their income) by 159.07% ( 0.3861 ).
Each increase in the frequency of maintaining a high inventory level, increases the odds of the business’ survival (growing their income) by 232.9% (3.329 more likely).
Each increase in the frequency of inventory collection, increases the odds of the business’ survival (growing their income) by 75% (1.750 more likely).
A decrease in the frequency of the independent variable:
maintaining a low inventory level EF30
will decrease the likelihood of the retailer’s survival (growing their income).
An increase in the frequency of the independent variables:
maintaining a high inventory level EF31
inventory collectionEF32
will increase the survival of the retailers (growing their income).
The results of the second binary logistic regression model indicated that the independent variables: maintaining a low inventory level; maintaining a high inventory level and inventory collection, are statistically significant predictors of the growth (in terms of income change patterns) of the formal independent small retail businesses.
6.5 Conclusion
The purpose of this chapter was to determine whether the survival of the formal independent small retail businesses could be predicted by using two binary logistic regression models. The potential survival of the retailers was tested in terms of the age (binary logistic regression model 1) and the income change patterns (binary logistic regression model 2) of the businesses.
The set of independent variables consisted of all items related to the three logistical supply chain drivers. Principal component analyses were conducted using principal component extraction and varimax rotation to determine the factor structure of the items, in order to reduce the number of independent variables for use in the binary logistic regression models.
Twelve independent variables were identified, consisting of 8 factors and 4 items.
The first binary logistic regression model that was developed included the 12 independent variables with the dependent variable age. The results indicated that the following independent variables: maintaining a low inventory level; having excess storage;
transportation dependency; financial considerations; and inventory collection, are statistically significant predictors of the age of a business. The second binary logistic regression model that was conducted included the 12 independent variables with the dependent variable growth. The results indicated that the independent variables:
maintaining a low inventory level; maintaining a high inventory level; and inventory collection, are statistically significant predictors of the growth (in terms of income change patterns) of a business.
The literature findings in chapters 2 and 3, together with the results of the statistical analyses (reported on in chapters 5 and 6), now allows for recommendations to be made on how small retailers can improve their odds of survival by the management of the logistical supply chain drivers (chapter 7). The two literature and eight empirical findings in chapter 6 are presented in the table below.
Table 6.4 Literature and empirical findings in chapter 6
Literature findings in this chapter
LF23: A small business that has been in business for 5 year or longer is deemed as surviving.
LF24: A small business that shows an increase in their income is deemed as surviving.
Empirical findings in this chapter
EF25: Each decrease in the frequency of maintaining a low inventory level, increases the odds of the business’ survival.
EF26: Each decrease in the frequency of having excess storage, increases the odds of the business’ survival.
EF27: Each decrease in the frequency of transportation dependency, increases the odds of the business’ survival to survive beyond 5 years.
EF28: Each increase in the frequency of financial considerations, increases the odds of the business’ survival to survive beyond 5 years.
EF29: Each increase in the frequency of inventory collection, increases the odds of the business’ survival to survive beyond 5 years.
EF30: Each decrease in the frequency of maintaining a low inventory level, increases the odds of the business’ survival in terms of growing their income.
EF31: Each increase in the frequency of maintaining a high inventory level, increases the odds of the business’ survival in terms of growing their income.
EF32: Each increase in the frequency of inventory collection, increases the odds of the business’ survival in terms of growing their income.
Source: Compiled by the researcher
Chapter 7
Conclusions and recommendations
7.1 Introduction
The purpose of the final chapter is to draw conclusions on how the small retailers operating within Soweto manage their three logistical supply chain drivers in terms of responsiveness and cost-efficiency and to propose recommendations on the management of the three logistical supply chain drivers by the small retailers in order to increase their odds of survival. These conclusions and recommendations are based on the two literature chapters (chapters 2 and 3), and the empirical research reported on in chapters 5 and 6. This chapter is structured to first state the primary research problem and secondary research objectives that were addressed in this study and then to focus on each chapter by identifying the secondary research objective(s) addressed within the specific chapter and the steps taken to address the secondary research objective(s) (see section 7.3). Conclusions and recommendations for each secondary research objective are presented in section 7.4.
Thereafter, an overview of the entire study is provided in table 7.1. This will link the secondary research objectives on the one hand with the questionnaire, the literature and empirical findings, and the conclusions and recommendations on the other. The chapter is concluded by listing the limitations that were faced in the study and identifying future research opportunities.