AN ANALYSIS OF MOTIVATIONAL FACTORS INFLUENCING CREDIT DEMAND
AMONG SMALL-SCALE FARMERS A CASE STUDY OF UASIN GISHU COUNTY
Name
DDBA 8110 - Business Operations
Walden
2022
PRESENTATION, DISCUSSION AND INTERPRETATION OF FINDINGS
This chapter will discuss the analysis of the data collected and presented in forms of graphs and
tables. The information presented will be interpreted and discussed to evaluate the effects of
advertising on credit demand, the effects of customer relations on credit demand, the effects of
interest rates on credit demand and the impact of loan tenure on credit demand.
Background Information
The study sought to establish the background of the respondents participating in the study with
an aim of ensuring that the population of the respondents was well represented in the study.
Gender of the Respondents
The researcher sought to establish the gender of the respondents with an aim of ensuring the
respondents were well represented for whole period of the research. The research findings were
recorded and tabulated in the table below for interpretation purposes.
Table 4.1 Gender of the Respondents
Frequency Percentage
Male 28 56
Female 22 44
Total 50 100
The study revealed that 56% of the respondents were male while 44% were female.
56%
44%
Male
Female
Figure 4.1 genders of the respondents
The researches revealed that majority of the respondents were male having 56% of the entire
population as compared to 44% of the female respondents. Both genders were well represented
and that the study was not biased on any specific gender. The opinions of both gendered was
considered.
Age of the Respondents
The researcher sought to establish the age of the respondents participating in the study with an
Aim of ensuring the respondents were well distributed with relation to their age. The research
findings were tabulated as indicated in the table below for interpretation purposes.
frequency percentage
20-30 years 11 22
30-40 years 19 38
40-50 years 13 26
Above 50 years 8 16
Total 50 100
Table 4.2 Age of the Respondent
The research findings indicated that majority of the respondents were aged between 30-40 years
as indicated by 38%,the study further revealed that 26% were aged between 40-50 years,22%
aged between 20-30 years and 16% were aged above 50 years.
percentage
0
5
10
15
20
25
30
35
40
20-30 years
30-40 years
40-50 years
Above 50 years
Figure 4.2 Age of the respondents
The research findings were interpreted to mean that the respondents were well distributed in
relation to their ages. The opinions of diverse age brackets were considered as very important to
the study to allow for comparison of both age groups.
Education Level of the Respondents
The researcher sought to establish the education levels of the respondents participating in the
study with an aim of ensuring the respondents were well distributed with relation to their
education levels. The research findings were tabulated as indicated in the table below for
interpretation purposes.
Table Education Level of Respondents
Frequency Percentage
Secondary 17 34
Post secondary certificate 21 42
Degree 9 18
masters 3 9
Total 50 100
The research established that 18% of the respondents had a university degree, 42% had post
secondary certificate, and 34% had secondary education while 9% had masters level of
education.
Percentage
0
5
10
15
20
25
30
35
40
45 34
42
18
9
Secondary
Post secondary certificate
Degree
masters
Figure 4.3 Education Levels of the Respondents
The findings indicated that majority of the respondents had a post secondary certificate as shown
42% of the same. However, the education level of the respondents was ideal and advantageous to
the study because the respondents could read, interpret and answer the questionnaire
appropriately.
Specific information
The researcher sought to establish the effects of interest rates on credit demand. The results were
recorded and tabulated in the table below for easy interpretation.
Interest rates on credit demand
The researcher sought to establish the effects of interest rates on credit demand. The results were
recorded and tabulated below for interpretation purposes.
Table 4.4 Interest Rate on Credit Demand.
4Descriptive SA A
U
D D SD
TOTA
L
Low interest rates lead to high credit
demand
4
FREQUENCY 39 26 12 3 1 481
PERCENTAGE 48 31 15 4 2 4100
Fluctuation interest rates affects credit
demand
4
FREQUENCY 35 24 15 5 2 481
PERCENTAGE 43 30 18 6 3 4100
Lack of clear interest rate policy affects
credit demand
4
FREQUENCY 31 21 18 5 6 481
PERCENTAGE 38 26 22 6 8 4100
Interest charged on quantity of money
borrowed affects credit demand
4
FREQUENCY 28 23 24 5 1 481
PERCENTAGE 35 28 29 6 2 4100
From the analysis above, 48% of the respondents strongly agreed, 31% agreed, 15% were
undecided, 4% disagreed while 2% strongly disagreed with the idea that Low interest rates lead to
high credit demand. However, 43% strongly agreed, 30% agreed, 18% were undecided, 6% strongly
disagreed while 3% strongly disagreed with the idea that Fluctuation interest rates affects credit demand.
Nevertheless, 38% strongly agreed, 26% agreed, 22% were undecided, 6% strongly disagreed while 8%
strongly agreed with the idea that Lack of clear interest rate policy affects credit demand. Finally,35%
strongly agreed, 28% disagreed, 29% were undecided, 6% disagreed while 2% strongly disagreed with
the idea that Interest charged on quantity of money borrowed affects credit demand
From the above study, it is apparent that low interest rate leads to high credit demand as indicated by 79%
of the respondents who were for this idea. This might be attributed to the idea that farmers will generally
go for credit facilities with low interest rates. This because, at all cost any business man will try to
minimize on the expenses that would accrue due to high interest as charged by some financial institution
as to maximize on the profits.
However, majority too agreed that fluctuation interest rates affects demand of credit as indicated by 73%
of the respondents who were for this idea. This might be attributed to the idea that there is euphoria of
fear of even rise of interest rates and hence this discourages farmers from acquiring credit facilities.
Farmers or clients expect a fixed rate of interest rates that would enable them plan for their finances and
hence be able to have a clear indication of what might happen in the future.
Lack of clear interest rates policy affects credit demand as indicated by 64% of the respondents who were
for this idea. This might be attributed to the idea that a poorly defined interest rates policy leaves client
without a clear picture of what they are supposed to do for instance they might feel like the financial
institutions are out to con and fraud them using the poorly defined policies thus leading to mass exodus of
clients. On the contrary, well defined interest policies not only give clients a clear picture of what is
required but also ensure retention by impacting confidence in clients.
Interest charged on quantity of money borrowed affects credit demand as indicated by 63% of
respondents who were for this idea. This might be attributed to the idea that clients go for the quantity of
money that has little interest of money. For instance most of the financial institutions usually charges
interest basing on quantity of money taken by clients. High amount of money borrowed usually has low
interest and vice versa. This definitely will make clients go for highest amount with an aim of reducing
the rate of inertest to be payed back.
REFERENCES
Ajzen, I., (2003). The theory of planned behavior: Organization behavior and human decision
processes. 50 (2)
Akoten, J. E., Sawada, Y., and Otsuka, K. (2006).“The determinants of credit access and its
impact on micro and small enterprises: The case of garment
producers in Kenya”. Economic development and cultural change,.
Alex Winter-Nelson and Anna A. Temu (2005). “Liquidity constraints, access to credit and pro-
poor growth in rural Tanzania”. Wiley interscienceDOI: Beck
Bekele, S., Obare, G. and Muricho, G. (2008).“Rural market imperfections and the role of
institutions in collective action to improve markets for the
poor”.National resources forum 32.
Dorward, A. Kydd, J. and Poulton, C. (2005). “Beyond liberalization: Development coordination
policies for African small-holder agriculture”. IDS bulletin 36 (2) 80-
Douglas Jose, H. Crumly, J. (1993). “Psychological types of farm/ranch operators’ relationship
to financial measures”. Review of Agricultural Economics 15(1)
Gabre-Madhin, E. Z., (2001). “Market institutions, Transaction costs and social capital in the
Ethiopian grain market”. IFPRI Research report, 124. International food
policy research institute Washington, DC.
Grabher(2003) “The embedded firm, on the socio- economics of industrial networks”
Routledge London .
Humphrey and Schmitz (1996) “The C approach to industrial policy”. World Development
“Technological Capability of Micro-enterprises in Kenya’s informal
sector” A Report on Technovation vol.19 (2)
Keen, C, Wetzels, M., Ruyter, K.,& Feinberg, R. (2004). E-Tailers versus retailers which factors
determine consumers preferences. Journal of Business Research, 57(7), 685-
Kliebenstein, J. B., Heffernan, W.D., Barret,D. A., Kirtley, C. L., (1981). “Economic and
sociological factors in farming”.Journal of American Society.Farm
managers Rural Appraisers
Liao, T. F (1999).“Interpreting probability models: logit, probit and other generalized linear
models: Quantitative applications in the social sciences”. Series 101, Sage
university paper.
Malecki and Tootle,(1996). “The Role of Networks in Small firm
competitiveness”.InternationalJournal of Technology Development, issue
Morduch J.,(1999). “The micro-finance promise.”Journal of Economics 37(4).
Mosley, P. and A. Verschoor, (2005). “Risk attitudes and vicious cycle of Poverty”. The
European journal of development Research17(1).
Muradian, R. and Pelupessy W.( 2005). “Governing the coffee chain: The role of voluntary
regulatory systems”. World Development Report 33 (12) 2029-44.
Nyikal, R. A. (2000). “Financing AgriculturalProduction in Kenya: an Economic Analysis of
the Credit Market.” PhD Thesis.University of Nairobi.
Owuor, G., (2008) “Evaluation of the Effects of Group-Credit Linkages on Smallholder
Farmers’Productivity and Poverty Reduction in Nakuru and Kakamega
Districts, Kenya”. PhD Thesis, University of Egerton.
Obare, G., Joanne W. K., Mario H., Michael W., (2006). “Agriculture, Income Risks and Rural
Poverty Dynamics: Strategies of Smallholder Producers in
Kenya”.International Association of Agricultural Economists Conference,
Gold Coast, Australia, August 12-18, 2006
Poulton, C., Kydd, J., and Doward, A., (2006).“Overcoming market constraints on pro-poor
agricultural growth in Sub-Saharan Africa”. Development Policy Review,
Rosenzweig M., (1999). “Risk implicit contracts and the Family in Rural areas of low-income
countries”.The Economic journal98 (393).
Rougoor, C. W., Trip, G., Huirne, R. B. M., Renkema, J. A.,(1998). “How to define and study
farmers’ management capacity: Theory and use in agricultural
Sanders, J. H., Southgate, D. D., and Lee, J. G. (1995).The economics of soil degradation:
technological change and policy alternatives.SMSS Technical monograph,
Washington: World Soil Resources.
Shepherd, A. (2007).“Approaches to linking producers to markets”.FAORural infrastructure and
Agro-industries Division Rome.
Stephen, R. Boucher, Catherine Guirkinger, Carolina Trivelli (2008). “Direct elicitation of credit
constraints: conceptual and practical issues with an application to Peruvian
agriculture”.Journal of development studies 4 (2)
