Determiners of Family Life Cycle as a Demographic Segmentation Practices on Consumer Choice of Ready to Wear Clothes in Kenya

Determiners of Family Life Cycle as a Demographic Segmentation Practices on Consumer Choice of Ready to Wear Clothes in Kenya

Dr. Ann Kwamboka Orangi, Dr. Ombui

Kirinyaga University, Nakuru, Rift Valley, Kenya

DOI: https://dx.doi.org/10.47772/IJRISS.2024.807035

Received: 29 April 2024; Revised: 14 May 2024; Accepted: 18 May 2024; Published: 30 July 2024

ABSTRACT

The family life cycle can be defined as the series of stages typically through which most of the family progresses, with different characteristics of the stages. These characteristics are related to their demographics, Clothing companies in the fashion industry are becoming more tuned in segmenting to what their customers want and giving them what they want. In order to overcome the stiff competition ready to wear business ought to understand their market characteristics. However, there was a deficiency in information on Family life cycle as demographic segmentation practices on consumer choice. This study therefore seeks to fill the existing knowledge gap by focusing Family life cycle as demographic segmentation on consumer choice of readymade clothes. The study adopted descriptive survey design to obtain in-depth information from the respondents on the influence of family life cycle as demographic segmentation on consumer choice of the ready to wear clothes in Kenya. The study adopted Nassiuma’s (2008) formula to get the overall sample size of 83 respondents from ready to wear entrepreneurs’ businesses from the three cities in Kenya. Both closed-ended and open Questionnaire was used to collect data desirable for the study. The findings showed that the relationship between family life cycle on consumer choice of ready to wear clothes was established to be positive, and statistically significant (r = 0.563; p < 0.05). This implied that family life cycle had a significant influence on consumer choice of ready to wear clothes.

Keywords: Family cycle, Ready to wear garment

INTRODUCTION

Demographic segmentation is a precise form of audience identification based on data points like age, gender, marital status, family size, income, education, race, occupation, nationality, and/or religion. It’s among the four main types of marketing segmentation, and perhaps the most commonly used method. Instead of reaching an entire market or broad customer base, a brand uses this method to speak directly to a defined subset of the market. Market Penetration Strategy (2015) concur that today’s Kenyan economy is the ability to customize products and services that often call for the most micro of segments practices that stand out. McDermott, (2010), The Textile Loft has worked with many local designers including Kenya fashion show, (2017) recommended that fashion segment should improve and work towards its goal.

Ready to Wear Clothes

The concept of the family life cycle has gained popularity in the last few decades due to its relevance in the consumer decision-making process. For example, as a person grows older, his buying choices depend less on his own needs and more on his family’s collectively marketers by understanding the stage of a person in the family life cycle can anticipate their needs and can shape marketing strategies according to them. Furthermore, as stated by William (2018), the family life cycle model helps to profile consumers’ using the businesses to determine which set of audiences they should appeal to.

It was in the 1960s when Wells and Gruber (1966) came up with the concept of family segmentation which they named a family life cycle. The family life cycle is used for targeting and positioning consumers since it is mainly concerned with the different phases and generations that a typical family passes through. King, (1996), defines the term “jua kali” as informal work or services that people perform to earn a living. The term “jua kali” derives from Kiswahili. The word “jua” means the “sun,” and “kali” means “hot” or “fierce,” hence the “hot or fierce sun.  “Jua kali” seems to have been coined during the colonial period, referring to small-scale dealings those school dropouts, the unemployed, the under-employed, and the poor, who had failed to get absorbed in the formal colonial economic sector.  Kenyans’ Clothes entrepreneurs are jua kali sector that accounts for 75% of the total employment in Kenya while contributing 18.4 percent of the country’s Gross Domestic product (reference). This sector has caught the attention of Government and other private sector in a bid to move the country to a middle level economy as envisaged in the development blue print of vision 2030. The Kenyan Government is strategizing on how to create an enabling environment for this sector with the realization that the sector is the key pillar of the country to realize its vision 2030 (Statistics from the economic survey, 2009).

Problem Statement       

Demographic segmentation is used for targeting specific audiences. You cannot effectively communicate with an audience when you know nothing about the segment. And a personalized, targeted approach is essential to effectively manage the advertising expenses. But despite the increase in the use of ready to wears clothes the sale of second-hand clothing, called “mitumba,” has also been on the rise.  In order to overcome the stiff competition ready to wear business ought to understand their market characteristics. A study done by Lawan and Ramat, (2012) on Evaluation of Socio-Cultural Factors Influencing Consumer buying behaviour of Clothes, found out that demographic variable such as gender, income, culture and social class have a great degree of influences on clothes buying decisions. This study therefore required to fill the existing knowledge gap by focusing on family life as a demographic segmentation on consumer choice of readymade clothes. The study seeks to answer the research question: what are the influences of family life cycle as demographic segmentation practices on consumer choices of ready to wear clothes in Kenya.

Objectives of the Study

The study was guided by both general and specific objectives

General Objective

The general objective of this study was to examine the influence of family life cycle as a demographic segmentation practices on consumer choice of ready to wear clothes in Kenya

Specific Objectives

1. To determine the influence of family life cycle on consumer choice of ready to wear clothes in Kenya.

 Research Hypotheses

The study was guided by the following research hypotheses

H2: Family life cycle has no significant influence on consumer choice of ready to wear clothes in Kenya.

Conceptual Framework

The Conceptual framework is a hypothetical model that identifies the concept under study and their relationships. It presents in a diagrammatic form of the way the researcher has conceptualized the relationship between variables. The independent variable is arranged on the left while the dependent variable is on the right side of the diagram. The independent variables in this study consist of  family life which was important aspects of demographic segmentation. The moderating variable was environmental. The dependent variable was consumer choice.

Figure 1: Conceptual framework

LITERATURE REVIEW

Family life cycle on consumer choice of ready to wear clothes

Family lifecycle is one of the important demographic variables which can have an influence on consumer. People of different family lifecycle can respond differently to the various attributes of brands. They may want to purchase them from different places or look for different dimensions of luxury value. The perception of different luxury values like functional value, financial value, individual value and social value can be different for people of different family lifecycle. Different kind of meaningful life events such as marriage, divorce and childbirth and illness may affect consumer behaviour. Widing et al (2003) however critics the fact that it does not take into account the changing factors which might occur e.g divorces yet it has seemed to be useful for marketers.

Ching-Yaw Chen et al. (2012) in their study explain and explore the differences in Taiwanese women’s purchasing decisions towards two different categories: luxury goods and general products. They considered the hypothesis that women of different family lifecycle, have significant differences in their purchase decisions (purchase motives, sources of information, product categories and other alternatives) for luxury goods. After testing this hypothesis, it was rejected and it was concluded that family status has a significant influence on purchase of luxury brands. Sathyanarayan et al. (2015) study the role of family life cycle variables in the polarization of luxury value of branded products in Chennai city. The study reveals that, statistically there is a highly significant difference in family lifecycle with respect to factors of luxury brand among the shoppers in the sample. Based on the mean value, it was noted that, the high level of functional, individual, social and luxury value is perceived by the married shoppers when compared to unmarried in the sample.

The need for personal gratification and aspirations has led to greater emphasis on having things which make life better and easy. It means that consumers want to improve their life. Cornell (2002) says that luxury can be characterized by a strong element of human involvement, scarcity or limited supply and value recognition by others. Majority of customer believe that luxury goods have to have some part of it handmade and that the brand has to be able to answer to customer’s wishes and needs with special and customized offers (Kapferer and Bastien 2008).

The study by Seringhaus, (2005) considers the impact of culture on purchase of luxury brands but other demographic variables like family lifecycle have not been considered. Also, the study by Nelson et al. (2005) shows the purchase behaviour of Indians towards local and international brands but other variables like family lifecycle, occupation, age, gender and education have not been considered. Seringhaus (2005) has studied the presence of luxury brands online but the impact of demographic variables like family lifecycle on purchase of luxury brands has not been studied.

The study by Mandel et al. (2006) considers the psychographic profile of consumer’s demographic profile has not been considered. Wiedmann et al. (2007) have considered financial value, functional value, personal value, social value and luxury value for luxury brands but impact of demographic and psychographic variables on purchase of luxury brands has not been considered. The study by Heilman et al. (2007) is an interesting study on consumer behaviour not undertaken by other authors but other variables like family lifecycle, occupation, age and income also need to be considered. Fionda and Moore (2009) have emphasised on a clear brand identity, premium pricing, heritage and exclusivity as a characteristic of luxury brands but other things like quality and product integrity have not been considered. Berthon et al. (2009), in their article present a philosophical analysis of luxury brands, focusing on their aesthetics and degree of ephemerality. The gap in the study is that purchase of luxury brands with respect to demographic variables like family lifecycle, occupation, age, gender, education etc. has not been considered.

Environment as an influence of market segmentation practices

To address the environmental impacts of fast fashion at its source, and to find a niche in this increasingly competitive market, some manufacturers are aiming to develop “eco-fashions.” The Kenya bureau statistics has defined eco-fashions as “identifying the general environmental performance of a product within a product group based on its whole life-cycle in order to contribute to improvements in key environmental measures and to support sustainable consumption patterns.” The KBS is developing standards for a labeling system to identify garments that meet criteria as environmentally friendly. However, even without such specific standards for what constitutes an environmentally friendly garment, industry is taking a broadening diversity of approaches. Suzanne and Kristyne, (2012).

Issues of environmental health and safety do not apply only to the production of synthetic fabrics. Cotton, one of the most popular and versatile fibers used in clothing manufacture, also has a significant environmental footprint. This crop accounts for a quarter of all the pesticides used in the United States, the largest exporter of cotton in the world, according to the USDA. The U.S. cotton crop benefits from subsidies that keep prices low but production high. The high production of cotton at subsidized low prices is one of the first spokes in the wheel that drives the globalization of fashion. Cooper, (2012).

RESEARCH METHODOLOGY

Introduction

Research methodology is a systematic, theoretical analysis of the methods applied to a field of study. It’s a process of collection of information for the purposes of making decisions in research.

Research Design

Research design is a structure within which research is conducted. It gives the arrangement for the collections and analysis of the data in a manner that aims to combine relevance to the research purpose (Kothari et al, 2003). The study adopted descriptive survey design to obtain in-depth information from the respondents on the influence of demographic segmentation on consumer choice of the ready to wear clothes in Kenya. Zikmund, Babin, Carr and Griffin (2010) say that descriptive research describes the characteristics of objects, people, group, organization, or environment.

Target Population

The target population refers to the group of people or the study subjects who are similar in one or more ways and which forms the subject of the study. The target population of the study forms the basis of this research study. The unit of analysis was entrepreneurial businesses which sell ready to wear clothes and are registered and licensed with the permit by Governments. The study also targeted customers visiting the ready to wear entrepreneurs.

Sample and Sampling Techniques

According to Mugenda, and Mugenda, (2010) sampling techniques is the process of collecting a portion of the target population to represent the entire population. The study purposively selected ready to wear entrepreneurs’ business from the three cities in Kenya namely: Nairobi, Mombasa and Kisumu, the reason for choosing these three cities was because they are the regional location of headquarters for various international companies and organizations. They are the central business district, houses of Kenya’s fashion businesses, they are centre of government, and local government and their organizations business are transacted. Table 3.1 below indicates the number of merchandising shops from the three cities.

Table 3. 1: Number of ready to wear business from the three cities

Source: Shopify staff, Nov. 2023

The study adopted Nassiuma’s (2008) formula to get the overall sample size from the ready to wear entrepreneurs’ business from the three cities in Kenya. According to Kothari (2004) a study population that exceeds 100 should be sampled. Relative to this assertion Nassiuma’s (2008) formula was employed to determine the size of the sample as follows.

Where

n = Represents sample size,

N = Represents study population

C = Represents coefficient of variation (21% ≤ C ≤ 30%), and

e = Represents error margin (2% ≤ e ≤ 5%).

Calculating the sample size,

n          =    ____ 348 (0.21)²_____

                   0.21²+ (348-1)0.02²

n          =          83.28

n          =          83 respondents

Stratified random sampling design was adopted as the sampling design where the total target population was divided into stratus. Entrepreneur’s shops from each city was considered as stratum. And some of the population from each stratum was randomly selected for inclusion in the overall sample to get a sample size of 83. The method guarantees ready to wear entrepreneurs’ business from each city an equal chance of being selected.

Table 3. 2: Sample Size

Fisher et al. (1995) formula was used to calculate the customer sample size as indicated:

n   =     Z²pq

                d²

Where:

n = The desired sample size (if the target population is greater than 10,000).

Z = Standard score at 95% level of significance (1.96)

p = Proportion of the target population with characteristics under study which is 0.50 where the figure in not known.

q = 1-p (which is 0.50)

d = Level of statistical significance at 95% confidence level (which is 0.05)

Therefore:

n =       (1.96)² * (0.50) * (0.50)          =          0.9604             =384.160

                        (0.05)²                                                              0.0025

n =    (3.8416) (0.2491).1           = 0.9604          = 384.16          = 384

                (0.0025)                           0.0025

FINDINGS AND DISCUSSIONS

Response Rate

Response rate was defined as the number of questionnaires that are filled completely and returned or collected against the questionnaires that are issued to the respondents. To this effect, 83 questionnaires were issued out of which 71 were fully completed representing 85.54 per cent response rate which was way above the accepted questionnaire return rate of 70 per cent.

Background Information

The study examined the background information of respondents in respect to their academic qualifications and working experience.

Gender Category

Respondents were classified according to their gender category. Table 4.2 shows the distribution of respondents based on their gender.

Table 4. 1: Gender Category

The study found out that majority of managers/owners of ready to wear business in Kenya were female as supported 52% of the sampled respondents. It was also established that 48% of managers/owners of fashion merchandising shops were male. The findings implied that majority of entrepreneur in the ready to wear industry are female.

Age Category

Respondents were classified according to their age category. Table 4.1 shows the distribution of respondents based on their age.

Table 4. 2: Distribution of Respondents by Age Category

The study found that majority of managers/owners of ready to wear shops in Kenya were 31-40 years as supported by 39% of the sampled respondents. It was also established that 37 % of the respondents were aged 41-50 years while 9 respondents representing 13% were aged above 50 years. A total of 8 managers/owners of ready to wear shops under study were aged 18-30 years. The findings implied that majority of entrepreneur in the ready to wear industry were middle aged. Age is a major factor in the fashion industry. The fashion industry is highly dominated by middle-aged which explains the high.

Academic Qualifications

The respondents’ academic qualification was of particular interest to the study. Table 4.3 illustrates respondents spread in terms of highest academic qualifications.

Table 4. 3: Distribution of Respondents by Academic Qualifications

It was established that 7% of the respondents had “O” Level education as their highest academic qualifications, 48% had attained diploma education, 24% of the respondents had attained first degree as their highest academic qualifications, 18% of the respondents had attained master’s degree as their highest academic qualifications while, 3% of the respondents had a PhD education. This implies that majority of the respondents had attained diploma as their highest academic qualifications.

Period of business existence

The period which the business had been in existence was sought and ascertained.  The results emanating from the analysis are presented in Table 4.4.

Table 4. 4: Duration of business existence

A total of 18 respondents representing 25% of the sampled population stated that the business had in existence for less than 5 years, 32% of the sampled population stated that the business had been in existence for 6-10 years, 25% of the sampled population stated that the business had been in existence for 11-20 years while 18% of the sampled population stated that the business had been in existence for more than 20 years. This implies that majority of ready to wear clothes shops had been in existence for 32.

Descriptive Findings and Discussions

This part illustrates descriptive findings and discussions relative to study objectives. The findings are presented in measures of central tendencies (means) and measures of variation or dispersion (standard deviations). The analysis of the collected data was in line with the following five-point Likert scale.

Influence of family life cycle on consumer choice of ready to wear clothes

In addition, the researcher sought to determine influence of family life cycle on consumer choice of ready to wear clothes. The findings resulting from the analysis are presented in Table 4.5.

Table 4. 5: Influence of family life cycle on consumer choice of ready to wear clothes

The study as shown in Table 4.4 revealed that families with low income are reluctant in purchasing ready to wear clothes due to the high cost associated to them (mean ≈ 5.00; std dev < 1.000). The respondents also agreed that married customers tend to spend less on ready to wear clothes compared to their single counterparts (mean ≈ 4.00; std dev < 1.00). Moreover, majority of the respondents agreed (mean ≈ 4.00; std dev < 1.000) that poor communication along the various departments hinders information flow resulting to lack of transparency. –

The respondents also agreed that (mean ≈ 4.00; std dev > 1.000) the consumption pattern of consumers is mostly influenced by their marital status. Moreover, majority of the respondents agreed (mean ≈ 4.00; std dev < 1.000) that single people tend to spend more on ready to wear clothes compared to older couples. The findings agree with Gunter and Furnham, (2012) findings. They concluded that single people have a tendency of purchasing new fashionable items due to the fact that they have no other economic obligations. This is opposed to married people, who have a large economic obligation and thereby they prioritize their economy different.

In addition, majority of the respondents agreed (mean ≈ 4.00; std dev < 1.000) that young couples are more concerned about the fashion of ready to wear clothes compared to their old counterparts. Finally, majority of the respondents agreed (mean ≈ 4.00; std dev < 1.000) that retired customers tend to spend more on ready to wear clothes since they have more disposable income. The standard deviation ranged between 0.61229 and 1.14000 indicating that the dispersion of the respondents from the mean was minimal. Ching-Yaw Chen et al. (2012) in their study concluded that family status has a significant influence on purchase of luxury brands

Influence of environment on consumer choice of ready to wear clothes

The study further sought to determine the influence of environment on consumer choice of ready to wear clothes. The respondents’ opinions are indicated in Table 4.9

Table 4. 6: Descriptive Statistics on environment on consumer choice of ready to wear clothes

From the findings the respondents admitted that involvement of external auditors ensures accountability in the procurement process (mean = 4.7451; std dev = 0.4401). The respondents further agreed that separation of key internal functions contributes to accountability in procurement system (mean = 3.157; std dev = 1.362). In addition, the respondents agreed that adherence to the set laws and regulations ensures fairness in the procurement system. (mean ≈ 4.00; std dev<1.000). The respondents agreed that an efficient bids evaluation criteria stops cases of corruption. (mean ≈ 5.00; std dev < 1.000). The respondents further agreed that involving all the stakeholders in the procurement process to ensure transparency. (mean = 3.9412; std dev = 1.10294). Finally, the respondents agreed that operate within legal trading practices (Price fixing, undercutting over pricing, Cartel pricing and merchandising and hording). (mean = 4.516; std dev = 0.504).  positioning must take into account the competitive and legal environment in which the ready to wear businesses operates. Businesses must consider the implications of its positioning decisions of competitors

Inferential Statistics

Family life cycle on consumer choice of ready to wear clothes.

In addition, the study sought to establish the influence of family life cycle on consumer choice of ready to wear clothes. The findings of the study are as shown in Table 4.7.

Table 4.7: Family life cycle on consumer choice of ready to wear clothes.

**. Correlation is significant at the 0.01 level (2-tailed).

The relationship between family life cycle on consumer choice of ready to wear clothes was established to be positive, and statistically significant (r = 0.563; p < 0.05). This implied that family life cycle had a significant influence on consumer choice of ready to wear clothes.

Demographic segmentation practices on consumer choice of ready to wear clothes.

The study ascertained the influence of family life cycle and environmental conditions on consumer choice of ready to wear clothes in Kenya. The results in relation to the foregoing are illustrated in Tables 4.8

Table 4.8: Model Summary

As illustrated in Table 4.9, the relationship between independent variables and dependent variables was established to be positive moderately strong. The R-Squared was the variation of the dependent variable in respect to the changes in the independent variables. The R-squared in this study was 0.447, which shows that the independent variable (Family Life Cycle) can explain 44.7% of the dependent variable.

The analysis of variance in this study was used to determine whether the model is a good fit for the data. From the findings, the p-value was 0.000 which is less than 0.05 and hence the model is good in predicting how the four independent variables (Family Life Cycle) influence consumer choice of ready to wear clothes in Kenya. Further, the F-calculated was (9.295) which shows that the model was fit in predicting the influence of the independent variables on the dependent variable.

CONCLUSIONS

Summary

The major study findings are summarized in this section. It outlines the summary of the findings in line with the objective of the study.

 Influence of family life cycle on consumer choice of ready to wear clothes

Respondents admitted that families with low income are reluctant in purchasing ready to wear clothes due to the high cost associated to them. It was also clear that consumption pattern of consumers is mostly influenced by their marital status and positively affects the choice of ready to wear clothes. The findings also revealed that retired customers tend to spend more on ready to wear clothes since they have more disposable income. The study also found out that family life cycle has a significant influence on consumer choice of ready to wear clothes.

Influence of environmental conditions on consumer choice of ready to wear clothes.

The sampled respondents admitted that their products meet specifications of Kenya bureau of standards. It was however not clear whether their product meet international standards. In addition, the respondents agreed that they operate within the trading rules and regulations. The study established that a positive, moderately strong and statistically significant relationship existed between tendering process on successful implementation of ethical procurement practices.

Conclusions

The study drew conclusions in respect of age, family life cycle and environmental conditions on consumer choice of ready to wear clothes.

Influence of family life cycle on consumer choice of ready to wear clothes

Respondents admitted that families with low income are reluctant in purchasing ready to wear clothes due to the high cost associated to them. They also agreed that consumption pattern of consumers is mostly influenced by their marital status. It was also clear that single people tend to spend more on ready to wear clothes compared to old couples. The findings agree with Gunter and Furnham, (2012) findings. They concluded that single people have a tendency of purchasing new fashionable items due to the fact that they have no other economic obligations. This is opposed to married people, who have a large economic obligation and thereby they prioritize their economy different. The study also further found that young couples are more concerned about the fashion of ready to wear clothes compared to their old counterparts. Retired customers tend to spend more on ready to wear clothes since they have more disposable income.

Influence of environmental conditions on consumer choice of ready to wear clothes.

The sampled respondents admitted that they take into account the competitive and legal environment in which the ready to wear businesses operates. It was however not clear whether their products meet international standards. In addition, the respondents were committal on the views that they operate within legal trading practices (Selling of the finished products). The study further established that their products meet specifications of Kenya bureau of standards

RECOMMENDATION

Businesses of ready to wear clothes should inform consumers about the high quality materials and handcrafting of ready to wear clothes and emphasize on unique, quality product. Managers of ready to wear clothes should emphasize   the positive, functional, aesthetic and emotional experience of owning and buying the ready to wear products. Knowledge and understanding of these differences and similarities can help in designing suitable marketing campaigns.

From a market positioning perspective, monitoring the evaluative criteria of consumers can help ready to wear business to recognize and focus on the specific luxury dimensions, with special reference to marital status. Business owners should strive to understand how people of different marital status respond to the different luxury value dimensions and how the ready to wear clothes can cater to the requirements of each group in the family life cycle.

AREAS OF FURTHER STUDY

The researcher suggested that further study should be conducted on the influence of personal and situational factors influencing consumer choice of imported readymade clothes in Kenya.

Determinants of consumer behavior towards   Mitumba clothes Entrepreneurs traders in Kenya

REFERENCES

1. Bartton, A., Sumantra, G. &Julian, B. (2006). Transnational Management: Text,Cases, and Readings in Cross-Border Management, (4^th Ed) Irwin-McGraw-Hill:Boston (MA).
2. Berger, J. (2016). Invisible Influence: The Hidden Forces that Shape Behavior, University of Pennsylvania’s Wharton School, Simon & Schuster UK.
3. Berger, J. (2013). Contagious: Why Things Catch marketing, University of Pennsylvania’s Wharton School, Simon & Schuster UK.
4. Ching yawchel et al. (2012) (2016). “The Price of Transparency”. The Business of Fashion https://www.businessoffashion.com/articles.
5. Cooper, R. (2012), “Design leadership in a vortex of change”, Design Management Journal, 7 (4) 3-5.
6. Creswel, J. (2012). Educational research: Planning, conducting and quantitative and qualitative research. Upper SaddleRver, NJ: Prentice Hall.
7. Etgar, M. and Dalia R. (2007). “Determinant Factors of Failures in Foreign Markets.”International Review of Retail, Distribution and Consumer Research 17 (1) 79–100.
8. Fashion Cities Africa (2017.) Brighton Museum & Art Gallery the Fashion Cities Africa publisher, Intellect.
9. Fishier, h. (1996). Research into teacher effectiveness: A model of teacher Effectiveness: Report by Hay McBer to the Department for Education and Employment of the United Kingdom
10. Frafimow and fishbein. (2011). A Typology of Consumer Needs In Research in Marketing 4(3) 83-104.
11. Gruber, (1996). “10 things you need to know about water impacts of the fashion Industry”
12. Johnson, B., & Christensen, L. (2012). Educational research: Quantitative, qualitative, and mixed approaches. Los Angeles, CA: SAGE publication.
13. King, K. (1996) Jua Kali Kenya: Change and Development in an Informal Economy, 1970-1995. London: James Currey; Nairobi: East African Educational Publishers; Athens: Ohio University Press.
14. Kothari, C. (2005) Quantitative Techniques, 2nd ed., New Delhi: Vikas Publishing House Pvt. Ltd.
15. Krishnakumar, M. (2014). “The Role of Visual Merchandising in Apparel Purchase Desision”.  IUP Journal of Management Research. 13 (1): 37–54.
16. Lawan A. L., and Ramat, Z. (2012), Evaluation of Socio-Cultural Factors Influencing Consumer Buying Behaviour of Clothes. Department of Marketing, Ramat Polytechnic, P.M.B 1070, Maiduguri in Borno State, Nigeria.
17. Mccallum, Bennett. T (1977) price- level stickiness and the Feasiblity of monetary stabization policy with rational Expecyations, Journal of political Economy, 85, pp. 627-34.
18. McCarthy, E. J. (1964). Basic Marketing. Richard D. Irwin. Homewood, IL.
19. McDermott, K.  (2010). Style for all: why fashion, invented by kings, now belongs to all of us  (An illustrated history), ISBN 978-0-557-51917-0 — Many hand-drawn color illustrations, extensive annotated bibliography and reading guide.
20. Mclnnes, William (1964). “A Conceptual Approach to Marketing.” in Theory in Marketing.
21. Mohd, S. (2011). The Moderating Effect of Location on Small Firm Performance. College of   Business, Universiti Utara Malaysia, Malaysia.
22. Moriya, N. (2008). Noise Multivariate Optimal Joint analysis in longitudinal Stochastic processes in Progress in applied mathematics Modeling”. In Fengshan yang. Progress in applied mathematics moedlling. Nova Science Publishaers, Inc pp.223-260.
23. Mugenda, A. & Mugenda, O. (2003). Research methos: quantintative and qualitative approaches. Africa centre for Technology (ACTS), Nairobi Kenya.
24. Mugenda, A. (2008). Social Science Research. Nairobi: Acts Press.
25. Nathalie, R., Eveline, S, and Steven S (2016) Style that’s sustainable: A new fast-fashion formula. Article October. Southern California.
26. Nelson, R. (2005). Strategies for Developing Sustainable Competitive Advantage at Siginon Freight Ltd, Kenta (Doctoral dissertation), School of Business, University of Nairobi.  News. Retrieved 13 May 2012.
27. Shopify Staff, (2023). Essential Fashion Merchandising Guide. market research to product selection to price setting, Nov 3. United States of America.
28. Webster, F, E. (2011). “The Changing Role of Marketing in the Corporation,” Journal of Marketing. 56(3) 1-17.
29. Wiley and Sons, Inc.
30. William T, and James P. (2006). The Research Methods Knowledge Base (2006)
31. Yoganandan. G, Saravanan. R, & Senthil Kumar (2013). Problems Faced by Small Knitwear Exporters in Tirupur, Tamil Nadu. Life Science Journal, 10 (7), 1145-1153.
32. Zikmund, G., Babin, J., Carr, C., & Griffin, M. (2010). Business research methods (8th ed.). Mason, HO: Cengage Learning.

AUTHOR

Author: Dr. Ann Kwamboka Orangi

Core Author: Dr: Ombui

Kirinyaga University, Nakuru, Rift Valley, Kenya