QUEUE EFFICIENCY IN NIGERIA BANKS.
A COMPARATIVE ANALYSIS OF OLD AND NEW
GENERATION BANKS
BY
KASUM, A. S.,
ABDULRAHEEM, A.
AND
OLANIYI, T. A.
DEPARTMENT OF ACCOUNTING AND FINANCE
UNIVERSITY OF ILORIN, ILORIN.
ABSTRACT
Banks provides financial services to individuals and organizations. Nigeria
being an essentially cash based economy, in term of transaction payment
system, left the bank with the problem of having to contend with huge
volume of transaction which directly translate to long queue for services of
banks. It now becomes one of the challenges for banks, to be able to manage
the time spent by customers in the banking hall to remain competitive.
The objective of the study is to evaluate the effect of queue efficiency on
customer perception and hence, patronage of banks.
Primary data were collected, through a well structured questionnaire that
were administered on customers of selected banks. The data captured in the
questionnaire, which are on perception of banks queue management
practice and consequent effects, were analysed using percentages, mean and
standard deviation, which then facilitated inferential statistical analysis
using chi-square and Z scores tests.
The study reveals that more time is spent in the old generation banks than it
is in new generation banks. It also showed that quick service delivery could
influence choice of banks by customers. Based on these, the study suggest
that old banks should improves on time management practices and new ones
sustain their current practice and not to take customers above their
capacity.
Background to The Study
Every relationship is a game and banker-customer relationship is not
an exception. The corporate objective of any bank which is maximization of
shareholders’ wealth can only be achieved if customers are retained and
satisfied. This is in line with Kotler’s (1999) perception that the key to
successful marketing of financial services is identification and packaging of
customers’ needs to their satisfaction.
The competition in Nigerian banking sector is getting more intense,
partly due to regulatory imperatives of universal banking and also due to
customers’ awareness of their rights. Bank customers have become
increasingly demanding, as they require high quality, low priced and
immediate service delivery. They want additional improvement of value
from their chosen banks (Olaniyi, 2004). Service delivery in banks is
personal, customers are either served immediately or join a queue (waiting
line) if the system is busy. A queue occurs where facilities are limited and
cannot satisfy demand made against them at a particular period. However,
most customers are not comfortable with waiting or queuing (Olaniyi, 2004).
The danger of keeping customers in a queue is that their waiting time may
amount to or could become a cost to them (i.e. bank customers). According
to Elegalam (1978), customers are prepared not to spend more cost of
waiting / queuing. The time wasted on the queue would have been
judiciously utilized elsewhere (the opportunity cost of time spent in
queuing).
Conceptual Issues
A queue for the purpose of this study is the aggregation of customer
awaiting a service function, it is an everyday occurrences and results when
the number of calling units exceed the number of available service center
(Olaniyi, 2004). These has become an integral of any service which refers to
the whole time from arrival of inputs to their departure.
The variants of queuing models that can be applied to address the
customers’ needs according to Ashley (2000) include a simple system,
multiple-channel system, constant service and limited population models.
A simple system is a single line and a single service system which
consists of items forming a single queue which is served by a single facility.
Other models are one queue multi-server system, multiple queue and multiserver system and of course multiple server single-queue system. On the
other hand the constant service time model provides service to customers
instead of experimental distributed times. The limited population model
provides a dependent relationship between the length of the queue and the
arrival rate.
The Queue Structure
Fig. 1
Input or
Source
Queue
Service
mechanism
Service unit
or exit
Customer selection, according to
Queue Discipline.
Source ; Olaniyi, T.A.(2004)
Queue Discipline
This involves priority rules by which customers are served. Barry and
Jay (1993) gave the following categories of queue discipline:
a)
Preemptive Priority: This is common in emergency situations and it
allows customers that arrive at any time to replace customers that are
being served e.g. in-patient treatment in hospitals.
b)
Non-Preemptive priority: Queue is arranged in such a way that item
with the highest priority in the system is served first and there is no
displacement of items in service. Such methods include:
i) FIFO (First-in-First Out): This method allows the first item to
enter the system to be served first.
i) LIFO (Last-in-First-Out): In this type the last item on
queue that enters the system is served first.
A simple queue is a single channel with variables arrival following a
poison distribution while service time is exponentially distributed(Olaniyi,
2002). It adopts in FIFO queue discipline with no backing or reneging.
A multi-server has all the features of simple queue and in addition
assumed no limit to the permissible length of the queue, while one they
serves assumed to perform at the same rate. Banks are generally defined by
the functions they perform. However, for example Imala (2003) defined
bank by identifying the basic functions of banks in an economy, this
function which includes the acceptance of deposits, advancing banks, credit
creation, financing foreign trade, agency services to allocate available
resources by mobilizing fund from non-productive channels to finance
investment activities in productive sectors. A cross observation of Nigerian
banks adopt a multi-channel system but practically the performance of the
servers are not the same.
As laudable as these functions are, they cannot be provided without
high level customers base as this provide the basis to measuring the level of
patronage or otherwise of banks. This customers patronage could be describe
as the backbone of a highly liquid Financial institutions.
Hart (1981) defined banking as the business of receiving money and
collecting drafts for customers, subject to the obligation of borrowing
cheque drawn upon them from time to time by the customers, to the extent
of the amount available in their current account. Essang and Olayide (1974)
defined a commercial bank as a monetary institution owned by either
government or private businessmen for the purpose of profit.
Profit maximization objective may not be easy to achieve in banking,
without a good level of customer base, as this customer base enhances the
effectiveness and efficiency of the services rendered to the customers. In
order words, the faster they get attended to, the more the customer would be
encouraged to keep their money with a bank.
Methodology
Primary data were used for the study. They were collected from the
selected bank through a questionnaire structured along the inverted funnel
method, which was administered on the customers of the banks. This implies
that, the customers of the bank are the sources of our data. Questions there in
ranges from personal data to general observation about happenings in the
banks, which actually are the relevant data for the study. Our samples are
simple random selection of arriving and willing customers. to the banks. A
total of about six hundred and five(605) respondent returned our
questionnaire in old generation banks while five hundred and ninety eight
(598) did in new generation banks. This is out of a total no of six hundred
and forty (640) and six hundred and thirty (630) administered respectively.
Originally, questionnaires were distributed in a total of six banks, three in
new generation and three in old generation banks. Before the conclusion of
the study one of the new generation banks cease operation, as it could not
meet the N25b shareholders fund requirement of Central bank of Nigeria.
For the analysis to be balanced, it was decided that one of the old generation
banks should also give way. The old generation bank that has been
technically taken over by another bank was dropped. To ensure further
equity, the minimum number of questionnaire returned from one of the
banks one hundred and ninety five(195) was taken as the bench mark for all
the banks. Hence a total of three hundred and ninety (390) responses were
used for each of the generations.
Simple percentages were used to represent all our data for easy
statistical analysis and interpretation. Means and standard deviations were
also computed where they could add to explanations of data generated. For
analysis of data that are useful for hypotheses testing, however, two tools of
inferential statistical analysis were used. ‘Z’ score was used to analyse data
that are relevant to hypothesis one while chi-square was used for hypothesis
two. Responses to question 10 and 12 were the data used for testing
hypotheses one and two respectively.
Z is given as
XI – X2
SE
Where
XI = Mean of sample set 1
XI = Mean of sample set 2
SE = Standard errors of the difference of means.
While,
Chi-square (X2) is given as
X2 = ∑ (E – O)2
E
Where
E = Expected frequency
O = Observed frequency
Statement of hypotheses
Hypothesis 1
H0
–
There is no statistically significant difference between the mean
of time spent on the queue in old generation banks and new
generation banks.
HI
–
There is statistically significant difference between the mean
of time spent on the queue in old generation banks and new
generation banks.
Hypothesis 2
H0
–
There is no statistically significant relationship between the age
grouping of banks and queue efficiency of Nigerian banks.
HI
–
There is a statistically significant relationship between the age
grouping of banks and queue efficiency of Nigerian banks.
Decision rules
The null hypothesis is accepted in any case where the calculated value
is greater than table value and we fail to accept null where the reverse is the
case.
THE DATA
Detail
1
2
3
5
6
New Banks
Resp.
%
Resp.
%
Gender: Male
177
45.38
216
55.38
Female
213
54.62
174
44.62
Age: Below 30
102
26.15
101
25.89
31 – 40
243
62.31
165
42.31
41 & above
45
11.54
114
29.23
Marital Status: Single
75
19.23
132
33.85
315
80.77
258
66.15
3
0.78
Married
4
Old Banks
Level of Education
Non-formal
-
Primary
6
1.54
15
3.85
Secondary
36
9.74
12
3.08
Post secondary
300
76.92
240
61.54
Professional
48
12.30
72
18.46
Occupation:
30.77
Civil Servant
192
49.23
120
58.46
Business / Trading
195
50%
228
6.15
Artesian
3
0.77
24
4.62
Others
-
-
18
Less than N7,500
18
4.62
27
6.92
N7,500 – N15,000
39
10
87
22.31
N15,000 – N22,500
151
38.71
174
44.62
N22,500 and above
192
49.32
102
26.15
Income range per month:
7
8
Years with the Bank:
Less than 2 years
27
6.92
78
20
2 – 5 years
30
7.69
111
28.46
6 – 10 years
240
61.54
201
51.54
above 10 years
93
23.85
-
-
Daily
80
20.51
105
26.92
Weekly
75
19.23
104
29.49
For thoughtly
80
20.51
66
26.67
Monthly & above
155
39.75
115
16.92
Payment of salary
142
36.40
123
30.04
Nature of Business
132
15.4
114
26.15
Commitment
60
33.85
102
29.23
Others
54
13.85
51
13.08
0 – 10min
3
0.77
78
20
11 – 20min
9
2.31
186
47.69
21 – 30min
111
28.46
78
20
31 – 60min
189
48.46
36
9.74
1hr and above
78
20
12
3.08
198
50.77
99
25.38
Frequency of transaction
with the bank:
9
Determinant of the
frequency:
10
Time spent on each
transaction:
11
Perceived Determinant
of the time spent:
Number of customers
12
Nature of transaction
87
22.31
141
36.15
Staff efficiency
84
21.54
114
29.23
Availability of Materials
21
5.38
-
-
Others
-
-
36
9.74
Efficient
72
18.46
162
41.54
Moderate
181
46.41
213
54.62
Not efficient
137
35.13
15
3.85
Yes
192
49.32
186
47.69
No
198
50.68
204
52.31
Yes
336
86.15
186
47.69
No
54
13.85
204
52.31
No of customer
81
20.77
99
25.38
Nature of transaction
75
19.23
150
38.46
Staff efficiency
219
56.15
84
21.54
Availability or mat
15
3.85
-
-
Others
-
-
57`
14.62
Customers rating of the
bank:
13
Efficiency affects
queuing paid:
14
Would you prefer a bank
with shorter que:
15
In what area do you feel
efficiency should be
improved:
Source: Field survey 2005
Result
Our respondents under the two categories fairly represent all the
interest personal variable. Male and females, fairly educated to well
educated, various occupations and also income brackets.
23.85% has been banking with the old generation banks for more than
10 years while the remainder are for less than that. All our respondents for
new generation banks have been banking for less than 10 years. More of our
respondents banks on monthly basis in both categories. Daily and weekly
banking is more in new generation banks than in old generation banks.
Nature of business and payment of salaries are the more prominent reasons
for going to banks. While customers at old generation bank opined that
number of customer and staff efficiency mostly determine time spent in
Banks, nature of service requires is what is added to staff efficiency as most
determinant of time spent in new generation banks.
For that purpose ‘Z’ score was calculated giving 24.46 at 95%
confidence limit. This is greater than table value of 1.96. This suggests that
the alternative hypothesis could be accepted. The meaning is that there exists
statistically significant difference between times spent in the two categories
of banks.
On close examination of data presented, it would be seen that more
time is spent in old generation banks than in new generation banks. Chisquare calculated is related to hypothesis two. The result is 11.62 which is
greater than 5.991 obtainable on X2 table at the degree of freedom of 1 and
level of significance of 5%. This suggests the acceptance of the alternative
hypothesis also and means that statistically significant relationship exists
between generational grouping and efficiency in Nigerian banks. On the
table where data were presented, 35.13% of old generation customers’
scores their bank inefficient, while only 3.85% did the same in the new
generation banks.
Conclusion
As evidenced by the result of our analysed data, this study concludes
as follows;
-
that time spent on queue for services in old generation bank is in
aggregate longer than in the new generation
-
that new generation bank are more efficient in timely service delivery
than the old generation banks. and,
-
that timely service delivery could be an attraction to bank customers.
Based on our conclusions, the study suggest that old banks should improves
on their service delivery to customer especially in relation to timely service
delivery to reduce the probability of losing them (customers) to new
generation banks. And to new generation banks, the study recommends that
they strive to sustain their timely delivery of services by not taking more
customer from old banks than their capacity.
The two categories should also strive to maximize the benefits offered
by information communication technology (ICT) to deliver timely services
to their customers.
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