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Analysis of the social capital investment for the viability of small, micro and medium enterprises (SMMEs) in the peri-urban poor communities of George municipality in Western Cape Province, RSA.

UJ CSBD, 2012 VENUE: UJ SOWETO CAMPUS Victor Mmbengwa1* ,Jan Groenewald1 , Mazuru Gundidza2 and Amidou Samie3

TABLE OF CONTENTS

• INTRODUCTION
• AIM & OBJECTIVES
• MATERIALS AND METHODS
• RESULTS AND DISCUSSION
• CONCLUSION

INTRODUCTION

• The returns on business investment –social

capital

• Businesses in township have challenges in

harnessing social capital

• Katungi et al., (2007) found that household

homogeneity has influence on social capital development.

• What is a social capital?

Mmbengwa V, 2012,

define Social capital as an intangible exchange of business experience and skills

Definition of social capital varies according to context and cultural

• rientation.

Durlauf & Fafchamps (2004) found that: Networks and organizations generate personalized trust and enhance information exchange

The aim and objective of the study was to:

Investigates the social capital investment by farming SMMEs in poor communities of George Municipality.

• The research used both qualitative

and quantitative methods.

MATERIALS AND METHODS

RESULTS AND DISCUSSION

• DESCRIPTIVE ANALYSIS
• FACTORIAL ANALYSIS
• INFERENTIAL ANALYSIS

RESULTS AND DISCUSSION

• DESCRIPTIVE ANALYSIS

GENDER MALE FEMALE AGE DESCRIPTIVE VARIABLES Mean 49.45 43.49 Median 50.00 45.00 N 67.00 59.00 Minimum 28.00 26.00 Maximum 70.00 62.00 Standard Deviation 9.23 10.32 Variance 85.22 106.56 Lower conf. interval (95%) 47.20 40.80 Upper conf. Interval (95%) 40.80 46.18 Range 42.00 56.00 Skeweness

• 0.098
• 0.68

Kurtosis

• 0.57

1.53 Lower quartile (Q25) 43.00 37.00 Upper quartile (Q75) 57.00 52.00 P-Values (95%) 1.00ns 0.05*

*=Significant , ns= non significant

RESULTS AND DISCUSSION

• FACTORIAL ANALYSIS

Factor loadings (Unrotated). Extraction: Principal axis factors Variable Factor 1 Rank on loading Study group 0.6305 6 Organised training 0.6465 5 Farm magazine readership 0.6726 3 Networking with experts 0.6793 2 Involvement of relatives in farming 0.6664 4 Children who passed tertiary agric education 0.5071 7 Membership of association 0.7687 1

• Expl. Var

3.8015 Prp.Totl 0.4224

Factorial analysis of the dependent variables

RESULTS AND DISCUSSION

• INFERENTIAL ANALYSIS

Kruskal-Wallis one-way ANOVA for assessing the attendance of farming study groups

Multiple Comparisons p values (2-tailed); Catergorise Kruskal-Wallis test: H ( 2, N= 126) =2.955971 p =.228 Depend.: Catergorised Age 1 R:71.160 2 R:61.444 3 R:69.500 35 yrs and less 36 to 60yrs 61 and more 0.703752 1.000000 0.703752 1.000000 1.000000 1.000000

Keys: 1=attend, 2=attend irregularly and do not attend

Kruskal-Wallis one-way ANOVA for assessing the attendance of farming of training organized by government department

Multiple Comparisons p values (2-tailed); Catergor Kruskal-Wallis test: H ( 2, N= 126) =.5905830 p =.7 Depend.: Catergorised Age 1 R:69.500 2 R:62.983 3 R:69.500 Age 35 yrs and less Age 36 to 60 yrs Age 61 yrs and more 1.000000 1.0000 1.000000 1.0000 1.000000 1.000000

Keys: 1=attend, 2=attend irregularly and do not attend

Kruskal-Wallis one-way ANOVA for assessing the farmers who reads farmers magazines

Multiple Com parisons p values (2-tailed); Catergorised Ag Kruskal-Wallis test: H ( 2, N= 126) =.4644585 p =.7928 Depend.: Catergorised Age 1 R:69.500 2 R:63.093 3 R:69.500 Age 35yrs and less Age 36 to 60yrs Age 61 yrs and more 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000

Kruskal-Wallis one-way ANOVA for assessing the networking with professional experts

Multiple Comparisons p values (2-tailed); Catergorised Ag Kruskal-Wallis test: H ( 2, N= 126) =.4644585 p =.7928 Depend.: Catergorised Age 1 R:69.500 2 R:63.093 3 R:69.500 Age 35yrs and less Age 36 to 60yrs Age 61 yrs and more 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000

Kruskal-Wallis one-way ANOVA for assessing the involvement of relatives in farming

Multiple Comparisons p values (2-tailed); Catergorised Ag Kruskal-Wallis test: H ( 2, N= 126) =5.233401 p =.0730 Depend.: Catergorised Age 1 R:84.786 2 R:62.124 3 R:69.500 Age 35 yrs and less Age 36 to 60 yrs Age 61 yrs and more 0.332216 1.000000 0.332216 1.000000 1.000000 1.000000

Kruskal-Wallis one-way ANOVA for assessing the children who have studied agriculture at tertiary level

Multiple Comparisons p values (2-tailed); Catergorised Age Kruskal-Wallis test: H ( 2, N= 126) =2.279499 p =.3199 Depend.: Catergorised Age 1 R:71.941 2 R:62.047 3 R:69.500 Age 35 yrs and less Age 36 to 60 yrs Age 61 yrs and more 0.898133 1.000000 0.898133 1.000000 1.000000 1.000000

Kruskal-Wallis one-way ANOVA for assessing the membership to associations

Multiple Comparisons p values (2-tailed); Catergorised Age Kruskal-Wallis test: H ( 2, N= 126) =4.970000 p =.0833 Depend.: Catergorised Age 1 R:73.540 2 R:60.843 3 R:69.500 Age 35yrs and less Age 36 to 60yrs Age 61 yrs and more 0.361026 1.000000 0.361026 1.000000 1.000000 1.000000

CONCLUSSION

• Lack of social capital investment from youth evident
• Awareness of the importance of social capital investment

to youth is important.

• Experience appears to correlate positively to social

capital investment.

• Therefore, youth development aimed at social capital

development will be inevitable for the viability and sustenance of the farming SMMEs in George

THANK YOU!!!