Francis Omillo-Okumu
University of Eldoret, Kenya
E-mail: omillofrancis@gmail.com
Submission: 25/06/2018
Revision: 11/07/2018
Accept: 26/07/2018
ABSTRACT
The purpose of this study is to predict
the effect of buyers’ bargaining power (customers’ price sensitivity, knowledge
level, union, ability to integrate backward, switching costs and resale buying)
on incomes of small food manufacturers. A survey of perceptions of 132 sampled
small food processors in Nairobi and Busia Counties was done. From the
gradation of the perceptions on seven-point likert scale, inferences were made
on buyers bargaining power influence on the larger population of small food
manufacturers in Kenya. On one hand, the study revealed that every unit of
buyer’s sensitivity to prices, not unionized, integrated backwards and bought
for goods for resale accounted for a positive change small
food processors’ income by 0.011, 0.013, 0.005
and 0.010, respectively. On the other hand, the study showed a negative change
of 0.006 and 0.008 in incomes of small agro-food processors with every unit
change in the level of buyer’s knowledge and shifted to alternative
product, respectively. Using Ordinary Least
Square (OLS) linear regression statistical inference, there was no single
standalone buyers’-bargaining-power-factor that significant influenced incomes
of small food manufacturers in Kenya. However, the amalgam of the buyers
bargaining power cues actually did influence the incomes (t=8.294, p= 0.00, sig
<0.05, 2 tailed).
Given the findings, the study recommends that marketers of food products should
treat buyers bargaining powers factors as a whole and not as individual
components. Further studies should
consider structural equation modeling to determine a model with critical
buyers-bargaining-powers factors.
Keywords: Buyers’ bargaining power; Small
agro-food processors; Income
1. INTRODUCTION
Buyers bargaining power means the pressure and advantage
customers have to lower price, improve quality, increase competition and better
terms of purchase of food products. The term was first coined by Michael Porter
in 1979 as one of the five forces model to analyze any industry’s
competitiveness. Since then it has been a key research element that would help
enterprises satisfy potential customers by developing products that are
competitive and advantageous in the market
(YANG; TREWN, 2003).
Buyers have the potential for future profits and growth
of small food manufacturing enterprises. The extent of their bargaining power
would either reduce or increase the incomes of an enterprise, especially in a
hyper-competitive market landscape. The buyers bargaining power is a result of
multiple factors. They include: customers’ price sensitivity, knowledge level,
union, ability to integrate backward, switching costs and resale buying.
To enable marketers come up with effective model to
affect consumers’ pressure in food industry, they need to understand the
correlation behind the factors. It is an agenda of every competitive
enterprise, whether small or large, to create collaborative relationship with
customers who are likely to cause increase in income and growth (LEE; CARTER, 2009).
This relationship forms a great competitive advantage for
enterprises in a globally hyper-competitive market. Kenyan micro and small
enterprises (MSE) are not exceptional either. Because of their role to economic
development especially through agriculture that is: high contribution to Gross Domestic
Product (GDP), employment creation and rural development; it is paramount that
their customers’ buying behavior in relation to the revenue performance
parameters be studied.
United Nations Food and Agriculture Organization (FAO)
observed persistent poor food productivity and insecurity in sub-Saharan
Africa, subjecting estimated 1.5 million Kenyans to relief food. This crisis
has caused serious focus for the government, policy makers and actors in food
value chain to up their game to save the human population from hunger and
starvation. Micro and small entrepreneurs involved in food manufacturing play a
pivotal role in the value chain.
They create value by improving on nutrition content,
variety and place utility for the buyer.
Micro and small enterprises (MSE) in Kenyan context, is defined as
businesses with annual sales of under Kshs. 1 million and 50 or fewer workers.
The MSE’s contribution to a country’s GDP cannot be underestimated. In India,
they remarkably contributed to employment, production of new products, export
and wealth creation (MOHANTY; GAHAN, 2012).
According to the Capital Market Authority of Kenya (CMA),
MSE sector has delivered over 7.5 million jobs to Kenyans, accounting for 80%
of employment and 45% of the GDP. This
makes Kenya the largest economy in East Africa and fifth in sub-Saharan Africa
with GDP of about US$ 61 billion. Though, the country has a growing
entrepreneurial middle class, its Human Development Indicators (HDI) rank
extraordinarily low at 147 out of 187. The population below poverty line is 43%
and unemployment standing at 40% (KENYA NATIONAL
BUREAU OF STATISTICS, 2016).
These conditions make Kenya a low middle income country.
Its economic mainstay is agriculture and micro and small enterprises sectors. However,
the Economic Survey 2015 found out that there was a decelerated increase rate
of 3.5% in agricultural value added product prices due to climate change and
overreliance on primary goods.
This has made both national
and county governments focus on catalyzing and accelerating growth of micro,
small and medium manufacturers in agriculture sector, agriculture being its
economic mainstay. The two levels of government acknowledge the fact that
raising the performance of micro and small scale agro-food processors is one of
the strategies to bring down poverty and pangs of hunger among the poor in
Kenya contemplated in the sustainable development goals.
The government of Kenya has
put in place structural frameworks for the promotion of manufacturing activities
by MSEs in agricultural sector through Micro
and Small Enterprises Act of 2012 (Ther
Republic of Kenya, 2012), Agricultural
Sector Development Strategy 2010-2020 (THE
REPUBLIC OF KENYA, 2010) and the Kenya
Vision 2030 (THE REPUBIC OF KENYA, 2007).
1.1.
Research
Objectives
The main research objective is to determine the influence
of buyers bargaining power influenced income of small food manufacturers.
Specific objectives entail:
a) To determine the influence of buyers’ price
sensitivity on incomes of small food manufacturers
b) To measure the influence of buyers’ knowledge level
on incomes of small food manufacturers
c) To find out how buyers’ union influenced incomes of
small food manufacturers
d) To investigate the buyers’ ability to integrate
backward influenced small food manufacturers income
e) To measure the influence of buyers switching costs
on small food manufacturers income
f) To find out the effect of buyers’ resale buying on
incomes of small food manufacturers
1.2.
Study
Hypotheses
In 2010, Farrugia, Petrisor
and Bhandari advised that hypothesis should follow the primary objective in an
evidence-based study. In this respect, the study hypotheses are:
2. LITERATURE REVIEW
Punch (2014) defined literature review as a synthesis of
empirical evidence and theoretical contexts relevant to the topic. This
section, therefore, shall endeavor to search and review what is known and not
known about the research questions above. Secondly it shall identify gaps and
inconsistencies in the evidence that this study seeks to address. Finally it
will dig into relevant theories that have relevant ideas and information that
would answer the research questions.
2.1.
Theoretical
Literature
Theoretical literature is about searching and reviewing
relevant concepts and theories to the topic. In this context the study found
five contemporary theoretical models that relevantly explained the buyers
buying behavior bargaining power. They include Howard–Sheth Model,
Engel-Kollat-Blackwell Model, Nicosia Model, Stimulus-response model (JISANA, 2014), and Michael Porter’s Five
Force Model (PORTER, 1980).
Howard–Sheth Model (1969) explained buyers’ behavior in
the market as a stimulus-response phenomenon. Information about the products
attributes such as quality, price, distinctiveness, services and availability
stimulated the buyer. The buyer reacted by paying attention and comprehending
the product. He consequently developed attitude, intention and actually
purchased the product. This process of course depended on the way the buyer
perceived and responded to information and also his motives.
Engel-Kollat-Blackwell Model (1978) explained the buyers’
behaviors as a conscious learning and decision-making process that entailed
active information seeking and price evaluation. It is a process of recognizing
need, searching information, evaluating alternatives and making a choice.
Nicosia Model explained buyer’s behavior as a link
between the firm and the consumer that was determined by compatibility of consumers’
and firms attributes, consumer’s evaluative understanding, actual buying and
use of the product. Stimulus-Response Model explained the buyer behavior as a
response to the marketing stimuli and other environmental factors.
Whereas the marketing stimuli entailed product, price,
place and promotion; environmental factors entailed economic technological,
political and cultural factors. Further, this model stressed that buyer’s
character determined his perception and ultimately his buying decision.
Porter (1980) propagated a Five Force Model that
explained any industry competitiveness. Buyers’ bargaining power was one of the
forces that determined an enterprise’s success. According to him the buyer’s
behavior entailed ability to switching to other products, ability to integrate
backwards and availability of substitutes.
These cues of buyer’s behavior advantaged the buyer to
bring down prices at the market. Of the five models, it is only Porter’s five
force model that described buyer’s power. The rest looked at the buyer as a
consumer and what prompted him to pick or not pick a product from the shelves.
However, porter’s model has been observed for failing to address contemporary
issues of information age, globalization and technology.
Faced with this deficiency in the contemporary
theoretical literature, the study finds it worth to combine Porters cues of
buyer’s bargaining power with other cues that have repeatedly been conceived to
predict incomes of small agro-food manufacturers in Kenya. They are price
sensitivity, knowledge level, unions, backward integration, switching costs and
resale market as end use of the processed products. These cues from the
conceptual model to shape the relationship of the bargaining power of buyers
and the small manufacturers’ income.
2.2.
Empirical
Review
Empirical review entails finding out what empirical
evidence there is in answering the research questions (PUNCH, 2014). Based on the previous research, empirical review
will unravel what is known-and not known- about relationship between buyers
bargaining power and income of food manufacturers.
Income is a quality of product or enterprise performance.
It is about yielding favorable financial returns or profits. Customer behavior
that cause increase income make the enterprises earn positive economic profits.
Enterprises in agro-food manufacturing industry, equally, struggle to up their
income by way of managing buying behaviors of customers as a competitive
strategy. In this context buyer bargaining power is tested on how it influences
micro and small agro-food manufacturers’ income.
In studying supermarkets and supplier, supermarkets being
buyers for resale influenced incomes of suppliers depending on customers’ level
of knowledge of products (NICHOLSON, 2012).
In addition, Porter (1980) observed buyers’ sensitivity to prices, knowledge
ability, unions/alliances, ability to integrate backwards, switching costs,
buyer group concentration and resale market as defining factors of buyers
bargaining power.
However the Michael Porter model was generic; applicable
to all firms and industries. In 2014, Al-Mamun,
Rahman and Robel critically reviewed the concept of buyers price sensitivity
and observed a 21st century buyer as rational whose decision
to pick or drop a product is informed by driving maximum value for money and
time. In other words, they are price sensitive and prices must reflect value
propositions of a product (SHRIVASTAVA; PARE;
SINGH, 2015).
In the manufacturer’s eye it influences profitability (AL-MAMUN; RAHMAN; ROBEL, 2014). Demand is
elastic when changes in price cause great effect on the buyer’s purchasing
behavior and inelastic when the changes caused are insignificant.
On one hand, buyer’s level of knowledge refers to his
degree of awareness of product attributes. The attributes include the quality,
price, availability, efficiency among others. It is believed that buyers
without knowledge of the product attributes will have no intension of purchase (YASEEN et al. 2011).
Greater product awareness can influence not only the
consumers but also the retailers or resellers purchase decision In entrepreneurial global perspectives, it
was found that the level of information a buyer had on a product price, cost of
making, comparative attributes and seller’s negotiation strategies leveraged
his power (NTEERE, 2012).
On the other hand, buyers union and alliances
refers to when customers are organized and coordinated in large numbers. Under
such circumstances they are advantages of joint efficiency, distribution payoffs
and enforcement of their demand at the market place. The more buyers are
unionized the greater the pressure they command. In addition unions create peer
pressure on members not to lower demands on the price, quality, competition and
terms of purchase of the products.
Backward integration is a vertical supply chain strategy
that makes an enterprise either own or increase control over its former
suppliers. When suppliers are unreliable, costly and unable to supply inputs in
required quantity and quality; backward integration is recommended (SHARMA; KHATRI; MATHUR, 2014).
A good example of backward integration is contract
farming. Under contract farming, the entrepreneur engages the farmer to produce
a product and the entrepreneur buys the product under agreed conditions. The
integration gives yield to two foreclosures: downstream and upstream. In 1970s
Coke and Pepsi embraced a downstream foreclosure strategy by acquiring
independent bottlers which neither allowed bottling nor marketing the competitors’
beverages.
Equally independent bottlers that were acquired
conditioned Coke and Pepsi not to sell their carbonated soft drinks to
rivals-upstream foreclosure (SPIEGEL, 2011).
This strategy frustrated Dr. Pepper, Crush and Schweppes performance at the marketplace
and increase Coke and Pepsi income through sales.
Switching costs refers to relationship, time, effort and
knowledge buyers invest in product that inhibits customers to change to
competitor’s product. When the switching costs are cheap the customer is more
ready to walk away from a deal and go elsewhere.
According to Klemperer
(1995), switching costs mean brand loyalty. Empirical evidence have
shown that in a framework of a networked environment, switching cost was a
critical underlying factor of buyer’s bargaining power and offers competitive
advantage to enterprises (HESS; RICART, 2002).
Enterprises compete to capture buyers and lock-in the
buyers ex post. Enterprises retain ex post market power by hindering buyers
from changing in response in efficiency (FARRELL;
KLEMPERER, 2007). When the switching cost is high entrepreneurs enjoy a
lot of ex post market power and brand loyalty from the buyers.
Switching costs does not only help entrepreneurs compete
aggressively for new customers, but also softens entrepreneurs on already
captured customers hence becoming less price elastic (SOMAINI; EINAV, 2013). It predicts the enterprise’s future
profitability (KLEMPERER, 1995).
Last cue of buyers bargaining power is customer buying
goods for resale. Products are either bought for consumption or resale. Resale
market refers to large scale buyers for either sale or value addition before
sale. As observed by Mohanty and Gahan (2012), they play a crucial role in
circumstances where the seller is a Micro, Small and Medium Enterprise. Resale
market increased allocative efficiency by allowing products reach high-value
from lower value-buyers (LESLIE; SORENSEN, 2014).
It is a welfare-stimulating that brokers underpriced products to the advantage
of both the seller and the buyer.
2.3.
Gaps
and Inconsistencies Identified in the Empirical Review
Despite varied studies done on the cues of buyers
bargaining power, the empirical evidence doesn’t specifically address the
issues of such customer pressure and advantage in the context of food
manufacturing among small firms in Kenya.
Porter 1980, for example, postulates buyers bargaining power in the
generic sense.
Buyers’ level of knowledge was done in the context of
supermarkets, switching costs in context of industrial organization and
framework of networked environment and backward integration in the context of
beverages – Pepsi and Coke. Resale market studies were done in Indian
manufacturing sector and ticket markets.
Finally, price sensitivity was done as critical review (AL-MAMUN; RAHMAN; ROBEL, 2014; SHRIVASTAVA; PARE;
SINGH, 2015).
This leaves unanswered questions on
how the buyers bargaining power would influence the income of the small food
manufacturers in kenya. Secondly, the previous studies reviewed don’t
demonstarte the extent the cues of buyers burgaining power(customers’ price sensitivity, knowledge level,
union, ability to integrate backward, switching costs and resale buying) contribute to the competitiveness, price reduction or
quality of products. Hence leaving research gap for this study to address.
2.4.
Conceptual
Framework
Conceptual framework is a logical configuration showing
the interactions of major variables under manipulatable conditions. In this
respect, Figure 1 is a visual depiction of the interaction of predictor
variable buyers bargaining power (price sensitivity, knowledge level, unions,
backward integration, switching costs and resale market) and how they correlate
and cause change in the dependent or criterion variable(income of small food
manufacturers.
Mugenda (2008) recommends conceptual framework for social
science research for its importance to both the researcher and the reader. To
the former it is a vintage point through which he sees the problem clearly and
improves the understanding about the study. To the later it enhances the
understanding of what the researcher is up to (MUGENDA,
2008).
Figure 1: Buyers Bargaining
Power Influence on Small Food Manufacturers’ Income
Figure1 shows the six cues of buyers bargaining power as
whole and as individual different components of a system of independent
variable that is likely to cause change in incomes of MSEs in agro-food
industry in Kenya. According to the visual depiction, advantage of buyers would
be if they suffered no penalty for switching to substitutes, if they had
ability to integrate backwards.
In addition, the buyers’ pressure would cause change in
incomes of food manufacturers if they were sensitive to profits if they were
fully aware of the products, had collective power in form of union or alliances
and if they bought the products not for consumption but for resale. The amount
of variation that each of the six cues and as a whole park would cause in the
income of MSEs in agro-food processing is main the concern of the study.
3. RESEARCH DESIGN AND METHODS
This study adopted a nomothetic causal design that
structured an inquiry to determine the amount of variations caused by
independent variable (buyer bargaining power) on the criterion variable
(income). The design also helped answer questions validly, objectively,
accurately and economically by minimizing variance and laying logistical
details of the journey of research (KUMAR, 2011).
The design allowed the researcher use open and
closed-ended questionnaires and to scientifically measure perceptions of
sampled small agro-food manufacturers with statistical precision. The temporal
considerations for the survey were between August 2015 and May 2016.
The targeted populations were all possible members of
agro-food manufacturing MSEs, as defined by Micro and Small Enterprises Act
2012. The Act defined MSE in manufacturing sector as enterprises that employed
between ten and fifty people and with total assets and financial investment of
between 10 and 50 million shillings (REPUBLIC OF
KENYA, 2012).
To avoid biases, the survey picked a rural county with
sparsely populated and a city county with densely populated such enterprises
which were Busia and Nairobi, respectively. The population of such
characteristics was gotten from the sampling frames which were the business
permit registers of the two county governments. The two sampling frames gave
2096 manufacturing MSEs (Busia, 26 MSEs and Nairobi, 2070 MSEs). A sample size
was determined so as to reduce the cost and test hypothesis effectively (KIM; SEO, 2013).
Though there were numerous formulas for calculating the
sample size, this study preferred fisher formula (n =Z2pqD/d2) for Nairobi County because of
the large population and the formula’s strength exhibited in exact tests. The
formula generated 146 MSEs out of the 2070 from the Nairobi sampling
frame. Busia being a rural county with
sparsely populated firms its sampling frame gave 26 agro-food manufacturing
MSEs. Therefore the study resorted to non-probabilistic techniques of sampling
called snowballing that yielded 42 enterprises that met the criteria for study.
In total the sample size was 188 MSEs.
The study adopted ordinal scale to measure the feelings
of small food manufacturers on buyers bargaining power and their income.
Specifically, Likert type scale was used to rate a series of items which were
responded to. Though 0-10 or 1-9 scales are recommended, the difficulty
encountered by most respondents in discriminating among the many points caused
the study resort to the scale of 1-7 (FISCHER;
CORCORAN, 2007).
A semi-structured questionnaire was used. The
questionnaire was piloted in Kisumu County and its reliability tested. An excellent reliability was found at
Cronbach alpha 0.97. During the main study, 132 out of 188 small manufacturers
sampled were successfully interviewed, making a 70% response rate. According to Babbie (2010), 70 percent
response rate was very good for analysis.
Strategy to analyze data after collection was an amalgam
of both qualitative and quantitative approaches. The two approaches traded off
the weakness and strengthen of each other’s approach in answering the research
questions. On one hand, a qualitative approach employed descriptive statistics
technique to test central tendencies, frequency distributions and the mean.
The means of two different groups of respondents which
were close to each other in opinion (near to the median of 4) were compared
using two sample t-tests. On the other hand, quantitative approach used
inferential statistics techniques by means of multiple linear regression
analysis that predicted models and determined the relationship between the
small agro-food manufacturers’ income and the buyers bargaining power six cues.
These methods were done on collected data using Statistical Package for the
Social Sciences (SPSS) computer program at confidence level of 95% or P-value
of 0.05 significance levels.
4. FINDINGS AND DISCUSSIONS
4.1.
Determining
small Manufacturers’ Income
Small manufacturers income in Kenya is the estimated income
a firm makes depending on the extent of pressure buyers exert on the market to
bring down prices of foods. The study examined this variable by measuring the
perceptions of the small food manufacturers about the products contribution to
the firm’s revenue, customer satisfaction, customer attraction, repeat buying,
production costs, sales turnover and profit margins as indicators income.
By asking how they would rate the product’s contribution
in terms of revenue, they responded (mean=5.90) on 1 to 7 individual rating
scale, majority of respondents n=122(92.4%) agreed that the products’
contribution to their MSEs’ revenues were very high. The study also measured
the dispersion of probability distribution and found a coefficient variation of
0.179 meaning that the standard deviation was about 18% of the mean, meaning
that they were homogeneous.
4.2.
Buyers’
Bargaining Power Effect on Small Food Manufacturers’ Income
Buyers
bargaining power refers to the ability of customers to obtain favorable terms
from the MSEs engaged in agro-food processing than those offered now. The
ability is characterized by customers being more powerful than suppliers,
sensitive to product prices, informed of the product, unionized, end users, and
able to integrate backwards.
Other
characteristics of buyer bargaining power include customers’ ability to reduce
selling price of goods and switching costs. If an agro-food manufacturing SME
would be powerful at the market place, it has to have an ability to profitably
maintain prices above competitive levels. This ability is often threatened by
the buyers’ concerted agitation for lower prices.
The
study wanted to know if the customers had ability to reduce prices of products
of SME manufacturers in Kenya. Respondents were asked if
the buyers could reduce prices below the selling price. It was revealed by most
of small agro-food processor mean = 4.4961 and n=68(51.1%) indicate that buyers
had ability to reduce price below the
profitable selling price.
The
means were compared using the independent sample t-test. On average, the mean
of buyers who reduced price below selling price (4.496 ± 1.55) were not
statistically significantly different from the buyers who did not (4.50 ± .71),
t(125) = -0.004, p = 0.997, sig >
0.05, 2 tailed. It is worth concluding that the difference of means in
write between buyers who reduce price profitably below selling price and those
that don’t reduce price profitably below selling price was 0.
The
implications were that the buyers had a stronger bargain at the market than the
food manufacturers. The cumulative
consideration of the buyers bargaining power cues above showed that
n=117(88.6%) respondents agreed that buyers bargaining power was strong.
After
descriptive analysis, the study used Ordinary Least Square (OLS) to establish
if correlation existed between the variables, if the independent variables
predicted well the variables and the extent of the effect of the buyer
bargaining power variables on the incomes of small food manufacturers. In OLS
econometrics, the SPSS model summary output always has the R that shows the
correlation between the predictor and criterion variables and R squared is used
to estimate discrepancy between the model and sample data. R squared measures
the model’s goodness of fit too.
They
are always presented as coefficients that must fall between 0 and 1. This study
had an R and R squared values of 0.393 and 0.155 respectively.
It means that a relationship between buyers bargaining power and income of
small food manufacturers do exist. It exists at 0.393. The R squared
establishes that 15.5% of the variability in buyers bargaining power cues
accounted for change in incomes of small food manufacturers. In other words
buyers’ sensitivity to prices, unions, level of awareness, ability to integrate
backwards and end resale buying predicted well the incomes of small food
manufacturers in Kenya. Therefore the model is good.
The
study therefore goes ahead to measure if the means of all the six variables
were relatively the same or if they were significantly different from one
another. This is done 1 Way Between Subjects using ANOVA technique as shown in Table
1.
Table 1: ANOVA for
All Variables
Model |
Sum of Squares |
df |
Mean Square |
F |
Sig. |
|
1 |
Regression |
.218 |
8 |
.027 |
2.377 |
.022b |
Residual |
1.192 |
104 |
.011 |
|
|
|
Total |
1.410 |
112 |
|
|
|
|
a. Dependent Variable: Y1 |
This test shows a
(F=2.377 p= 0.022, sig <0.05, 1 way). This values help the study
determine if condition means were relatively the same or if they were
significantly different from one another. Put differently, this value will help
you determine if buyers’ bargaining power had an effect. In this example, the
Sig. value is 0.022.
The p value is lower the set level of significance of
0.005. If the Sig value is less than 0.05, it is concluded that there is
statistically significant difference between the six conditions of buyers’
bargaining power. It is a clear indication that the differences between
condition Means are likely due to manipulation of buyers’ bargaining power and
not due to chance.
The study further tested the hypothesis linear regression
as shown in Table 2.
Table 2: Linear Regression Table
Coefficientsa |
||||||
Model |
Unstandardized
Coefficients |
Standardized Coefficients |
t |
Sig. |
||
B |
Std. Error |
Beta |
||||
1 |
(Constant) |
.724 |
.087 |
|
8.294 |
.000 |
The customers are very sensitive on product
prices |
.011 |
.013 |
.092 |
.871 |
.386 |
|
The customers are informed on what they need |
-.006 |
.009 |
-.073 |
-.702 |
.484 |
|
The buyers have a customer union and alliances |
.013 |
.008 |
.189 |
1.657 |
.101 |
|
Buyers ability to process their own foods
(backward integration) |
.005 |
.007 |
.093 |
.804 |
.423 |
|
It is likely to cost customers to switch
suppliers |
-.008 |
.006 |
-.126 |
-1.290 |
.200 |
|
Buyers end use of the product |
.010 |
.008 |
.171 |
1.295 |
.198 |
|
a. Dependent Variable: Y |
This test shows a (t=8.294, p=
0.00, sig <0.05, 2 tailed). The p value is lower the set level of
significance. According to Gall, Gall, and Borg (2007) lower p values should be
interpreted as higher level of significance. This means that the null
hypothesis is rejected and the alternative accepted. At confidence level of 95%
or P-value of 0.05 significance levels, the findings shows that buyers’
bargaining power has a statistically significant effect on the incomes of small
food manufacturers in Kenya.
After
testing the hypothesis the study estimated the incomes by regressing the buyer
bargaining power cues as follow:
Small
agro-food manufacturers incomes(Y) = 0.724 + 0.011*buyer’ price sensitivity -0.006*buyers level of knowledge + 0.013*buyers union + 0.005*buyers
ability to integrate backwards - 0.008*buyers
switching costs + 0.010*buyers end use of the
product + 0 .087.
Using
the information in variables in the equation and table 4.2, the study shows
that if all buyers bargaining power predictor variables were rated 0, income of
small food manufacturers in Kenya would increase by Kshs. 0.724.
4.3.
Buyers’
Price Sensitivity Effect on Incomes of Small Food Manufacturers
This
section focuses on the consciousness customers have on the prices of
agro-processed products. Respondents were asked whether their customers were very sensitive on product prices. Almost unanimously,
n=124(93.9%) at a mean of 6.36, the respondents agreed customers were sensitive
on product prices.
It
implies that consumers are vigilant and want to see value for their money at
every product purchase. The study further sought to determine the effect of the
price sensitivity on the income of the small food manufacturers. Using Ordinary
Least Square linear regression, the study found that small
food manufacturers received an increase in income of Kshs. 0.011 for every
one-unit increase in price sensitivity by the buyers, all other factors held
constant.
The study went further to test the
hypothesis: Ho1: Buyers’ price sensitivity has
no significant effect on incomes of small food manufacturers. It was revealed that at confidence level of 95% or
P-value of 0.05 significance levels, the findings shows p = 0.386, sig <0.05, 2 tailed. The null hypothesis
upheld. It means that at lower prices the buyers bought more and the profit
margins went up.
Though price sensitivity had a positive, effect on the
incomes of small food manufacturers, there was no enough evidence to warrant
significant change on incomes of small food manufacturers. However, Al-Mamun, Rahman and Robel ( 2014) found otherwise.
The difference could be that small manufacturers in kenya hardly produce
products whose prices don’t reflect the value proposition at the market (SHRIVASTAVA;
PARE; SINGH, 2015).
4.4.
Buyers’
Knowledge Level Effect on Incomes of Small Food Manufacturers
In
a market-oriented economy, it does not matter how an agro-food processor thinks
of his innovation, it is the customers’ opinion of on the product that matter.
The study therefore asked the respondents whether their current and potential
customers knew of their product. The finding were n=110, (83.4%) and
(mean=6.10) of the respondents showing that it is true that the customers are
informed on what they need from the processors of agro-products.
On
the cause-effect relationship of buyers’ knowledge level and small food
manufacturers’ income, the study found that for every unit increase in the
knowledge of the buyers, the small food manufacturers in Kenya suffered a
decline in income of 0.006. This again is quite
insignificant and so the null hypothesis was upheld. Buyers’
knowledge level has no significant effect on incomes of small food
manufacturers.
Despite
the fact that enough evidence was not found to support buyers’ knowledge having
significant influence on the incomes, contemporary theoretical models strongly
finds a buyer bargaining strength grounded on information (NICHOLSON, 2012;
JISANA, 2014) observed in that
contemporary buyer.
Recent
trends have shown an increase in availability of sophisticated customers and according
to the findings, the customers of the MSEs in agro-food industry are highly
informed. This calls for more tactful and strategic skills for the MSEs to
understand the customers’ point of pain, frustrations and unmet needs and
eventually offer customers more efficient and effective products that they
currently sell. It means that the agro-food processors must have the capacity
to handle vast amount of customers’ input and use it build products that would
attract greater income.
4.5.
Buyers’
Union Effect on Incomes of Small Food Manufacturers
When
customers are unionized, they yield social benefits which are often used to
counter the market power of agro-food manufacturers. The exercise of this power
prevents agro-food manufacturers from exploiting their market status as fully
as they could if they were faced with un-unionized buyers.
This
prompted an enquiry into experiences of micro and small agro-food processors
with customers’ alliances in Busia and Nairobi. Respondents were asked if their
buyers had customer union and alliances. According to
the results most of the respondents at a mean=3.57, n=60(44.6%)
perceived no customer union and alliances.
Because
the mean is close to 4, an independent sample t-test was done to compare means
of the customers that were in union and those that were not in union. On
average, the mean of customers that are unionized (3.5952 ± 1.66) are not
different than those who are not in union (2.50 ± 0.71), t(126) = 0.926, p = .356, sig > .05, 2 tailed.
The
difference between means of the customers who were unionized and not unionized
was 0. It implies that most customers, having no union, had weaker ability to
obtain from the agro-food processors more favorable terms than those available
under normal expected terms. In other word the small agro-food manufacturers
were little threatened by customer unions and had the ability, therefore to
profitably maintain prices above competitive levels.
Other
factors held at constant, the study sought to infer the effect of the unions on
the incomes. It was revealed that for every unionizable buyer who was not
unionized, the small food manufacturer gained by 0.013 units. This mark-up again is quite insignificant as shown in
Table 4.2 as p = .101, sig >0.05, 2 tailed. The null hypothesis was upheld, therefore. Buyers’
union has no significant effect on incomes of small food manufacturers.
Faced with these facts, it means that buyers of food products in Kenya are
uncoordinated, don’t enjoy joint efficiency and can hardly enforce their rights.
4.6.
Buyers’
Ability to Integrate Backwards Effect on Small Food Manufacturers’ Incomes
Backward
integration is a form of strategy through which MSE customers gained ownerships
and increased control over the agro-food processors. This buyer’s capability
would reduce MSEs in agro-food processing income and make them less
competitive. The respondents were asked if most
customers had the ability to process their own foods (backward integration).
The
findings revealed that most customers mean >
4.2 and n= 66(50%) had ability to process their products hence able to
integrate backwards as shown in Plate 1. The findings further revealed a
coefficient of variation of 0.45. This indicates a slightly above average
congruence and below average dispersion in the sample data.
Plate 1: Backward
Integration: A case of Busia Nakumatt Supermarket Bakery Products
The
above picture shows Nakumatt Supermarket one of the biggest buyers of SMEs in
bakery is integrating backwards buy buying raw materials, baking, packing and
putting the bread and cakes on the shelves for sell. This implied that most
buyers sought to save costs and wanted efficient products. Backward integration
is sought by Nakumatt to reduce cost, and improve efficiency for the buyers.
Consequently, the MSEs processing food were likely to suffer thinner profit
margin and stiffer competition.
Other
factors held constant, how much extra income do small food manufacturers
receive if they had one more buyer integrate backwards? Small food
manufacturers made 0.005 units for every buyer who
integrated backwards. This meant that it was cheaper to for small food
manufacturers who sold semi-finished products than finished products in Kenya.
Sharma, Khatri and Mathur (2014)
in their study of supply chain managemnt found the same to be true that buyers
integrating backward yield a cheper process. On testing the null hypothesis, the study revealed
as p = 0.423, sig >0.05, 2 tailed meaning that no enough
evidence was gotten by the study to negate the null hypothesis. It follows
therefore; buyers’ ability to integrate backwards has no significant effect on
incomes of small food manufacturers in Kenya.
Spiegel
(2011) confirmed this insignificance in the ultimate income when he observed
when Coke and Pepsi resolved to integrate backwards. Two opposite foreclosures
were realized: downstream and upstream which brought setoffs on both the
supplier and buyer’s bargaining powers.
4.7.
Buyers’
Switching Costs Effect on Incomes of Small Food Manufacturers
Customer
switching costs are negative psychological, physical and economic experiences
buyers face for changing from one business relationship with an agro-processor
to another. It is a critical determinant in an MSE’s ability to acquire, keep
customers and realize competitive advantage.
The
study sought to understand if the customers of micro and small agro-food
manufacturers in Kenya incurred such costs. After asking how unlikely it was
for customers to switch suppliers, majority of respondents (mean=5.15, n=94(71.2%)
agreed that it was unlikely. This implied that the MSEs in agro-food
manufacturing enjoyed customers’ brand loyalty and repeat-buying which are
renowned contributors to increased revenue and survival.
Results
given by Table 2 show that every one buyer who switched to alternative product,
the small food manufacturer in Kenya lost an income of 0.008 units, all other
buyers bargaining power factors held at constant. According to the p = 0.200, sig >0.05, 2 tailed the evidence is below the bar to reject the
null hypothesis. Therefore buyers’ switching costs has no significant effect on incomes of small
food manufacturers.
In
contrast, studies by Hess and Ricart (2002) as well as Farrell Klemperer (2007)
observed that switching costs under normal circumstances significantly
influence income of a firm. Now that it does not under small food manufacturers
in Kenya, it means that the food entrepreneurs have not build brand loyalty
among the buyers and therefore they are not bothered to resist buyers from
leaving. These possess a high risk of danger in a competitive market (Somani
& Einav, 2013).
4.8.
Buyers’
End Use of the Product Effect on Incomes of Small Food Manufacturers
The
study also sought to understand whether the customers of the micro and small agro-food
manufacturers bought the products for resale or for home use. The respondents
were asked if their customers buy the products for
resale. With a mean >4.4 and n=69(52%) most of respondents revealed
that customers bought products for resale. The coefficient of variance was
0.41.
It
means that the variable was less dispersed and the strength of congruence was
slightly above average. It shows that most of the customers for the micro and
small agro-food manufacturers were brokers who increase welfare by enhancing
locative efficiencies. The firms ought
to be ready to produce in large quantities to address stock needs of the
retailers and wholesalers (brokers).
Inferential
statistics were used to measure the causal relationship between the end purpose
of the product by the buyers and the small manufacturers’ income. The findings
in table 4.2 indicate that for every one buyer who bought the products for
resale, the small manufacturers made an extra income of 0.010 units. This
implies that the resale buying was more profitable than consumption buying in
Kenya.
This
is because resale buying bought in large quantities and reduced distribution
costs for the manufacturers. On testing the hypothesis, the p = 0.198, sig >0.05, 2 tailed was evident. The null hypothesis was
consequently retained because of greater p
value. Buyers’ resale buying has no significant
effect on incomes of small food manufacturers.
The
findings in this study disagree with other studies that observed higher
significance (MOHANTY; GAHAN, 2012; LESLIE; SORENSEN, 2014). Perhaps it is
because the small manufacturers have not produce in large quantities to address
stock needs of the retailers and wholesalers. Hence not enjoying allocative
efficiency at the market place.
5. CONCLUSION
The study sought to fill the gap in
knowledge about customers’ behaviors that could bring down incomes in small
entrepreneurial food industries. Using predictive design the researchers
surveyed 132 small industries in agro-food processing and found that buyers bargaining
power had a nomothetic causal effect to the incomes of small food manufacturers
in Kenya (t=8.294, p= 0.00, sig <0.05, 2 tailed).
The amalgam of the six cues measured
(price sensitivity, buyers union, backwards integration, resale buying, buyers’
knowledge level and switching costs caused significant variance in the incomes
of the small entrepreneurs in Kenya. Specifically the study found out the
following:
a)
Buyers’
price sensitivity has no significant effect on incomes of small food
manufacturers. As a standalone factor, no enough evidence could be found to
support that customer’s reactions to prices caused low sales and profit for
small food industrialists. However the little evidence showed that the more the
more the buyers were conscious about the prices the greater the profits for the
small industrialists. This implies that consumers were ready to buy foods at
any price.
b)
Buyers’
knowledge level has no significant effect on incomes of small food
manufacturers. Though awareness of customers on did not significantly influence
the industrialist customers, the study found that the more they became aware of
the product the lower the profits. Customers’ knowledge of the foods influenced
them not to buy. It implied that either the Jua kali food products in Kenya did
not meet the demand of the customer or the customers preferred the imported
foods stuffs. Whichever way, the industrialists should improve their products
to match the competitors and delight the customers at the market.
c)
Buyers’
union has no significant effect on incomes of small food manufacturers. Based
on this finding, customers in food industry are both disintegrated and have
high appetite for food. This explains the intermittent supply of food and
comparative high demand in the market that cause shooting of food prices in
Kenya.
d)
Buyers’
ability to integrate backwards has no significant effect on incomes of small
food manufacturers in Kenya. However, the more customers bought into the supply
chain they increased the more they increased the income of the industrialists.
It means the small food industrialists in Kenya made more income in less
processed goods than the more finished goods. It also meant that the customers
preferred preparing final products to their unique tastes.
e)
Buyers’
switching costs has no significant effect on incomes of small food
manufacturers. The little evidence available showed that the more buyers
changed to another food supplier the lesser incomes realized among the small
food industrialists. It implies that the buyers did not likely switch from one
small to another small entrepreneur, rather they moved to bigger multinational
or imported products. Large multinationals and imports are likely to smoother
the small food industries in Kenya. The government needs to intervene,
therefore, to protect small food firms.
f)
Buyers’
resale buying has no significant effect on incomes of small food manufacturers.
Many of the customers of small food industrialists bought goods for
consumption. They bought them neither for industrial nor resale purposes. The
industrialists need to market themselves to large multinationals to sale their
semi-processed food stuffs which they are competent in for survivability and
escaping competitive incompetence.
6. SUGGESTION FOR FURTHER RESEARCH
A structural equation modeling to find the right mix of
factors that would require critical attention by marketers need to be studied.
Secondly, Kenyan food market suffers acute food shortage and increased demand
for food, a better perspective of buyers bargaining power would be gotten where
the study covers a market that has enough supply of food. Finally medium and
large food manufacturers equally play a big role in economic development to
warrant better understanding on how their customer’s pressures influence their
performance in Kenyan context.
REFERENCES
AHMAD, H. (2010) Personality traits among entrepreneurial and
professional CEOs in SMEs. International
Journal of Business and Management, p. 203-213.
AL-MAMUN, A.; RAHMAN, M. K.; ROBEL, S. D. (2014) A critical review of
consumers' sensitivity to price: managerial and theoretical issues. Journal of International Business and
Economies, p. 01-09.
ANDAE, G. (2017) Maize flour prices surge again after April drop. Business Daily, p. 1-4.
BABBIE, E. (2010) The practice of social research.
12th Editition.
Belmont: Wadsworth.
BANDURA, A. (1982) Self-efficacy mechanism in human agency. American Psychologist, p. 122-147.
BIRD, B. (1989) Entrepreneurial
behaviour. London: Scott & Foresman & Company.
BRAUN, J. V. (2007) The world food situation: New driving forces and
required actions. IFPRI's bi-annual
overview of the world food situation presented to the CGIAR general meeting.
Beijing: IFPRI.
BULA, H. O.; TIAGHA, E.; WAIGUCHU, M. (2014) An empirical analysis of
entrepreneurship scorecard and performance of small scale women entrepreneurs
in urban - Kenya. International Journal
of Humanities and Social Science, p. 208-215.
CAMPO, J. L. (2010) Analysis of the influence of self-efficacy on
entrepreneurial intentions. Prospect,
p. 14-21.
CHAVEZ, J. (2016) The personality
characteristics of entrepreneurs and their effects on the performance of a new
business venture. Unpublished bachelors thesis. Helsinki: Helsinki
Metropolia University of Applied Sciences.
DEVAUX, A.; VELASCO, C.; JAGER, M. (2016) Integrating agricultural
innovation and inclusive value-chain development. In: Devaux, A.; Torero, M.; Donovan,
J.; Horton, D. Innovation for inclusive
value-chain development. Success
and challenges. Washington, DC:
International Food Policy Research.
FAGBOHUNGBE, B. O.; JAYEOBA, I. F. (2012) Locus of control, gender and
entrepreneurial ability. British Journal
of Arts and Social Sciences, p. 74-85.
FARRELL, J.; KLEMPERER, P. (2007) Coordination and lock-in: Competition
with switching costs and network effects. In M. Armstrong, & R. Porter, Handbook of industrial organisation (p.
1967-2072). Washington DC: Elservier.
FARRUGIA, P.; PETISOR, B. A.; FARROKHAR, F.; BHANDARI, M. (2010)
Research Questions, Hypothesis and Objectives. Canadian Journal of Surgery.
FISCHER, J.; CORCORAN, K. (2007) Measures
for clinical practice and research. New York: Oxford University Press.
GALL, M. D.; GALL, J. P.; BORG, W. R. (2007) Educational research. An
introduction. Boston: Pearson Education, Inc.
HESS, M.; RICART, J. E. (2002) Managing
customers switching costs: A framework for competing in the networked
environment. Barcelona: University of Navarra.
JISANA, K. T. (2014) Consumer behavior models: An overview. Sai Om Journal of Commerce and Management,
p. 34-49.
KENYA NATIONAL BUREAU OF STATISTICS. (2016) Economic Survey 2015. Nairobi : Government Printers.
KIM, J.; SEO, B. S. (2013) How to calculate sample size and why. Clinics in Orthopedic Surgery, p. 235-242.
KLEMPERER, P. (1995) Competition when consumers have switching costs: An
overview with applications to industrial organization, macroeconomics and
international trade. The Review of
Economic Studies, p. 515-539.
KOTHARI, C. (2004) Research
methodology: Methods and techniques.
New Delhi: New Age International Publishers.
KUMAR, R. (2011) Research
methodology: Step-by-step for beginners.
London: Sage Publishers Limited.
LEE, K.; CARTER, S. (2009) Global
marketing management. Changes, new challenges, and strategies. New York: Oxford University Press.
LESLIE, P.; SORENSEN, A. (2014) Resale and rent-seeking: An application
to ticket markets. Review of Economic
Studies, p. 266-300.
MISHRA, D.; MIN, J. (2010) Analyzing the relationship between dependent
and independent variables in marketing: A comparison of multiple regressions
with path analysis. Innovative Marketing,
p. 113-120.
MITRA, J. (2012) Entrepreneurship,
innovation and regional development.
New York: Routledge.
MOHANTY, M. K.; GAHAN, P. (2012) Buyer supplier relationship in
manufacturing industry: Findings from Indian manufacturing sector. Business Intelligence Journal, p. 319-332.
MUGENDA, A. G. (2008) Social
Science Research. Nairobi: Applied Research and Training Services.
NICHOLSON, C. (2012) The
Relationship between Supermarkets and Suppliers. What are the implications
for consumers? Stockholm: Bob Young.
NTEERE, K. K. (2012) Entrepreneurship.
A Global Perspective. Nairobi:
Kenhill Consultants.
PEFFERS, K.; TUUNANEN, T.; ROTHENBERGER, M. A.; CHATTERJEE, S. (2007) A
design science research methodology for information systems research. Journal of Management Information Systems, p. 45-77.
PINSTRUP-ANDERSEN, P.; PANDYA-LORCH, R.; ROSEGRANT, M. W. (1997) The world food situation: Recent
developments, emerging issues and long-term prospects. Washington DC.: The
International Food Policy Research Institute.
PORTER, M. (1980) Competitive
Strategy. Washington DC: Free Press.
PUNCH, K. F. (2014) Introduction
to social research. Quantitative and qualitative approaches. New Delhi: Sage Publications Ltd.
REDDY, N.; ACHARYULU, G. V. (2008) Marketing research.
New Delhi: Excel Books.
REPUBLIC OF KENYA. (2007) Kenya
vision 2030. Nairobi: Government
Printers.
REPUBLIC OF KENYA. (2010) Agricultural
sector development strategy 2010-2020. Nairobi: Government of Kenya.
REPUBLIC OF KENYA. (2012) Micro
and small enterprises act no. 55 of 2012. Nairobi: National council for law
reporting.
REPUBLIC OF KENYA. (2017) Economic
survey 2017. Nairobi: Government
Printers.
ROCHA, V. C. (2012) The
entrepreneur in economic theory: From an invisible man toward a new
research field. Porto: University of
Porto.
ROTTER, B. (1966) Generalized expectancies for internal versus external
control reinforcement. Psychological
Monograph, p. 609.
SHANE, S.; VENKATARAMAN, S. (2000) The promise of entrepreneurship as a
field of research. Academy of Management
Review, p. 217-226.
SHARMA, D.; KHATRI, A.; MATHUR, Y. B. (2014) Backward integration of
supply chain management: A case study. International
Journal of Emerging Technology and Advanced Engineering, p. 867-869.
SHIBIA, A. G.; BARAKO, D. G. (2017) Determinants of micro and small
enterprises growth in Kenya. Journal of
Small Business and Enterprise Development, p. 105-118.
SHRIVASTAVA, A.; PARE, S. K.; SINGH, S. (2015) A study to understand the
price sensitive buying behavior of consumers. Pacific Business Review International, p. 63-73.
SIREC, K.; MOCNIK, D. (2010) How entrepreneurs' personal characteristics
affect SMEs' growth. Original Scientific
Papers, p. 3-12.
SOMAINI, P.; EINAV, L. (2013) A model of market power in customer
markets. The Journal of Industrial
Economics, p. 938-986.
SORENSEN, P. L. (2014) Resale and rent-seeking: An application to ticket
markets. Review of Economic Studies, p. 266-300.
SPIEGEL, Y. (2011) Backward
integration, forward integration, and vertical foreclosure. Tel Aviv: Recanti Graduate School of
Business Administration, Tel Aviv University.
THE REPUBIC OF KENYA. (2007) Kenya
vision 2030. Nairobi: Government Printers.
THE REPUBLIC OF KENYA. (2010) Agricultural
sector development strategy 2010-2020. Nairobi: Government Printers .
THE REPUBLIC OF KENYA. (2012) Micro
and small enterprises act of 2012.
Nairobi: Government Printers.
VESKOVIC, N. (2014) Aspects of
Entrepreneurial Risk. Finiz, p. 115-117.
WILSON, F.; KICKUL, J.; MARLINO, D. (2007) Gender, entrepreneurial
self-efficacy, and entrepreneurial career intentions: Implications for
entrepreneurship education. Entrepreneurship
Theory and Practice, p. 1042-2587.
WORLD FOOD PROGRAM. (2016) Comprehensive
food security and vulnerability survey: Summary report Kenya. Nairobi: WFP.
XIE, C. (2014) Why do some people choose to become entrepreneurs? An
integrative approach. Journal of
Management Policy and Practice, p. 25-38.
YANG, K.; TREWN, J. (2003) Multivariate
statistical methods in quality management. Madrid: McGraw-Hill.
YASEEN, N.; TAHIRA, M.; GULZAR, A.; ANWAR, A. (2011) Impact of brand
awareness, perceived quality and customer loyalty on brand profitability and
purchase intention: A reseller view. Interdisciplinary
Journal of Contemporary Research in Business, p. 833-839.
ZHAO, X.; LYNCH, J. G.; CHEN, Q. (2010). Reconsidering Baron and Kenny:
Myths and truths about mediation analysis.
Journal of Consumer Research, p. 197-206.