impact of information and communication technology on productivity of small and medium enterprises...

EXCEL International Journal of Multidisciplinary Management Studies ________________ ISSN 2249- 8834

EIJMMS, Vol.3 (10), October (2013)

Online available at zenithresearch.org.in

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IMPACT OF INFORMATION AND COMMUNICATION TECHNOLOGY

ON PRODUCTIVITY OF

SMALL AND MEDIUM ENTERPRISES

IN OYO STATE, NIGERIA

ADEWOYE, JONATHAN OYERINDE PH.D.*;

ADEBAYO, NATHANIEL ADEYEMI PH.D. **

*ASSOCIATE PROFESSOR,

DEPARTMENT OF MANAGEMENT AND ACCOUNTING,

LADOKE AKINTOLA UNIVERSITY OF TECHNOLOGY, OGBOMOSO, NIGERIA

**SENIOR PRINCIPAL LECTURER,

DEPARTMENT OF BUSINESS ADMINISTRATION AND MANAGEMENT STUDIES,

THE POLYTECHNIC, IBADAN, NIGERIA

ABSTRACT

The study assesses impact of Information and Communication Technology (ICT) on productivity

of Small and Medium Enterprises in Oyo State, Western Nigeria. It also determines if

Productivity Paradox holds among the Small and Medium Enterprises.

The study sample consisted of 420 Small and Medium Enterprises drawn across the three

geopolitical zones of Oyo State, Western Nigeria. Primary data, collected through a carefully

drawn Questionnaire were analysed using Logistic Regression Analysis.

With exp. (β) =1.254 the logistic regression results indicated that ICT had about 25%

impact on productivity. This implies positive but weak impact of ICT on the productivity of the

Small and Medium Enterprises. Non-ICT positive correlates of productivity are entrepreneur‟s

ability (EABT) 12.18, staff quality (SQUA) 5.45, banking and credit facilities (BACF) 3.09 and

government support (GOSP) 1.41. However it is only EABT and SQUA that are significantly

different from zero at 1% and 5% levels respectively. Competition (COMP) is both negative and

not significant. Other results of the study showed that Productivity Paradox did not hold among

the Small and Medium Enterprises. Recommendations made in line with the findings, were to

encourage greater adoption of ICT and boost SMEs productivity.

KEYWORDS: Information and communication Technology (ICT), Productivity Paradox, Small

and Medium Enterprises, Socio-economic Development.

______________________________________________________________________________

1.0 INTRODUCTION The pivotal role of Small and Medium Enterprises (SMEs) in socio-economic

development and poverty reduction cannot be over-emphasised. This role from available studies

is not restricted to just a group of countries. On the contrary, the positive impact of Small and

Medium Enterprises is felt across the developed as well as developing countries. For instance in

Europe, micro, small and medium enterprises have been found to play significant role in the

“enlarged European Union of 25 countries; some 23 million SMEs, provide around 75 million

jobs and represent 99% of all enterprises” (European Commission, 2005).




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These findings reinforce the belief that SMEs can fast-track economic development,

especially poverty reduction which is a top priority on Development Agenda of developing

countries, Nigeria being no exception. The bridge that the establishment of SMEs and their

delivery of the mandate of socio-economic development and poverty reduction can be no other

thing than productivity.

Productivity generally and among Small and Medium enterprises in particular can be

enhanced by Information and Communication Technology (ICT). Empirical studies have been

carried out that indicate productivity drive can be successfully achieved if Information and

Communication Technology is adopted and applied by small and medium entrepreneurs. Past

productivity studies have engendered concept of „Productivity Paradox‟, a term which implies

negative relationship between ICT investment and organisational productivity. (Zachary, 1991;

Metcalfe 1992; Brynjoifsson 1994; Battles Mark and Ryan, 1996; Brynjolfsson and Yang, 1996).

With „Productivity Paradox‟ making the wave and consistent call to Small and Medium

entrepreneurs to adopt ICT for productivity improvement, more empirical studies are required to

chart the appropriate direction. This study is therefore a contribution in this regard.

Since ICT has been identified as a vehicle that can drive SMEs‟ productivity, further

studies in this area are required for sustainable, stable and expansive Small Scale Enterprises

(SSE) sector.

In addition, a review of previous studies indicates that most of them concentrated on

impact of ICT on socio-economic development while a number of others merely investigated

barriers to adoption and use of ICT by Small and Medium Enterprises (Irefin et al 2012;

Rahman et al 2013). Studies on direct impact of ICT on SMEs productivity are scarce. The

aim of this study is therefore to fill this identified gap. To do this, the study asks and answers the

following research questions.

(1) Does the use of ICT significantly improve productivity of Small and Medium

Enterprises?

(2) What factors inhibit adoption and usage of ICT by Small and Medium Enterprises?

The paper reviews literature in section 2 while methodology of the study is explained in

section 3. Results of the data analysed are presented and discussed in section 4.

Conclusions are contained in section 5.

2.0 LITERATURE REVIEW

Over the years, conflicting definitions have been given of Small and Medium Enterprises

across the globe. In this study, we settled for definitions given by Nigeria‟s National Council of

Industry. According to Udechukwu (2003) the National Council of Industry defined a Small

Enterprise as a business with total employed of over N1.5million but not more than N50million

including working capital but excluding cost of land or a labour size of 11-100 workers. A

medium scale business on the other hand is defined as one with a total capital employed of over

N50million but not more than N200million, including working capital but excluding cost of land

or a labour size of 101 to 300 workers. Three major reasons informed the choice of this

definition. Firstly, National Council of Industry is the final authority in Nigeria on industrial

matters and since this study is on Nigeria, it imperative that its definition be used. Secondly, this




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definition accommodates the dominant parameters that are common in most definitions; capital

size and workforce size. Finally, the definition is about the most current in the lines of definitions

in Nigeria.

It remains a challenge how micro, small and medium enterprises can accelerate the

poverty reduction process and deliver on the mandate of fast-tracking economic development.

This is because the productivity capability of SMEs is constantly in doubt The extent to which

Information and Communication (ICT) can assist small and medium enterprises increase their

productivity therefore demands urgent and necessary attention.

2.1 IMPACT OF MICRO, SMALL AND MEDIUM ENTERPRISES; THE LINK

WITH PRODUCTIVITY

A large number of studies has been carried out on socio-economic importance of Micro,

Small and Medium Enterprises (MSMEs). Several empirical studies in this regard are now

replete in literature. For instance in a study carried out by Small Enterprises Assistance Fund

(SEAF) in 2004, ten successful case studies of positive impact of Small and Medium Enterprises

were show cased. According to SEAF (2004) the “ten case studies demonstrate the multiple

paths by which these SMEs have affected their communities”.

The SEAF (2004) study revealed among other things that;

(a) The economic impact of investments in SMEs is significant. In specific terms the study

observed that every dollar invested in local currency.

(b) The greatest share of benefits from the investments gives to employees, followed by

governments.

(c) Two thirds of total employment in the sampled firms gives to low-skilled workers. This

finding is a reflection of the validity of the hypothesis that SMEs generate new jobs,

which are suitable for the poor.

(d) Employees‟ annual real wage growth can be as high as 28% for low-skilled and 34% for

high-skilled workers and

(e) The enterprises also provide non-salary benefits.

The positive impacts of Micro, Small and Medium Enterprises (MSMEs) from the

Shrestha (2004) study include job creation, significant increase in income and the meeting of

basic needs. The study showed that the enterprises provided total employment for about 8,139

persons, resulting in an average of about 1.3 persons per entrepreneur. Using basic needs

approach Shrestha (2004) found that households that were incapable of meeting basic needs

before the MEDEP intervention, were financially empowered through micro-entrepreneurship to

do so.

To enable Small and Medium Enterprises play these roles effectively, productivity should

be enhanced. Productivity studies have shown that Information and Communication Technology

is a strong driver of productivity generally.




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2.1.2 MEASUREMENT OF PRODUCTIVITY

Productivity has many definitions. A common definition is that productivity is a ratio of

a volume measure of output to a volume measure of input used (OECD, 2001). According to

Mole (2002), a firm‟s productivity measures how much input is needed for the firm‟s output.

Measurement of productivity is not however as simple as its definition. The complexity

of productivity measurement arises not from the „output‟ but from the „input‟ angle of the

definition. Since there are different inputs in production, there are also different measures of

productivity. As a result, productivity can be in terms of factor inputs such as labour, capital etc.

Labour productivity measures the output per unit of labour, while capital productivity

measures the output per unit of capital. Using either labour or capital productivity will always

result in partial productivity measurement. While each may have economic implications,

comprehensive productivity measurement is captured by Total Factor Productivity (TFP). TFP

takes into consideration all factor (inputs) in production at the same time.

It is obvious that Total Factor Productivity will give more realistic and “more rounded

picture of firm productivity, but can be difficult to estimate” (Mole, 2002; 3). As a result of the

difficulty caveat, this study measured firm productivity, using a dichotomous variable, Small and

Medium Enterprises Productivity (SMEP). For this purpose firm was scored „1‟ if the

entrepreneur opined that ICT improved his firm‟s productivity and „0‟ if otherwise.

2.2 INFORMATION AND COMMUNICATION TECHNOLOGY (ICT)

The term Information and Communication Technology (ICT) consists of three different

words; Information, Communication and Technology. Simply put, information is message

through which knowledge of a situation, place, products, a new person or environment is

acquired. This may be in written, audio, visual or audio-visual form. Communication on the

other hand is the mode of transferring the message to others, through a medium. This implies

that there must a sender, a receiver and clarity of the message being sent. Technology is the

process that produces medium for message transmission. According to Rahman, Abdullah,

Haroon and Toohen ,(2013) Information and Communication Technology (ICT) can be defined

as “the use of modern technology to aid the capture, processing, storage and retrieval, and

communication of information, whether in the form of numerical data, text, sound or image”. .

ICT within a very short period of time has been found to exert profound positive

influence on the planet earth. Its impact is felt in almost all facets of human endeavour. It is in

the light of this that we examine in subsequent paragraphs, impact of ICT on economic growth,

socio-economic development, business productivity, in manufacturing as well as service

industries.

2.2.1 ADOPTION AND IMPACT OF ICT ON PRODUCTIVITY OF SMES

There are a number of studies that discuss adoption and impact of ICT in developed

countries (love et, al., 2004; Schubert and leimstoll , 2006 and 2007) Despite the importance of

ICT and emphasis by various governments to encourage SME‟s to adopt ICT, it has been

reported that SMES has been slow in adopting ICT for various reasons .(Houghton and




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winklhofer ,2004; Lawson et. al., 2003) They indentified the characteristics of SME‟s ,limited

financial and human resources and lack of ICT skills and knowledge in SMEs as some of major

challenges faced by all European countries.

There are few studies on ICT adoption and impact on developing countries. ( Mutula et,

al., 2007; Yeh et, al., 2007 ) . Lai (2007) investigating adoption of ICT in Nigeria found that

one of the major factors inhibiting ICT diffusion and intensive utilisation is poor physical

infrastructure.

The impact of adopting ICT on the growth of SMEs in developing countries is reported in

Esselaar et al. (2007). They found that ICTs are significant input factors for SMEs and

contribute positively to revenue generation and also increases labour productivity. However

SMEs are yet to reap these benefits due to their inability to meet up with the prerequisite level of

access to and effective utilisation of ICT (Chacko and Harris, 2005). It is imperative that there is

a need for Nigeria SMEs to make good use of the latest ICT and ideas as this may likely assist

them stay competitive and therefor productive (Lai, 2007)

However adoption of ICT by Nigeria SMEs is very slow (Apulu and latharn, 2009).

Olajide (2013) opined that the benefits of ICT many business enterprises have not adopted and

integrated ICT into their operations and that there is dearth of study on the role of ICT in

business enterprises in Nigeria.

2.2.2 FACTORS IN AND BARRIERS TO ADOPTION AND USE OF ICT

In developing countries some of the ICT adoption challenges include legal and regulatory

issues, weak ICT strategy, lack of R and D, excessive reliance on foreign technology and

ongoing weaknesses in ICT implementations (Dutta et, al.2003) Some studies have found out

that in spite of its potential benefits, micro, small and medium enterprises in developing

countries lagged behind in adoption and use of ICT. Examples include Ndyali (2013) on

Tanzania and Ifefin et al. (2012) on Nigeria. The slow adoption of ICT in business is however

not limited to developing countries. Even in Canada SMEs were found to lag behind their larger

counterpart in the adoption of both basic and advanced ICT applications, (Martins and Milway,

2007)

According to Martins & Milway (2007) assessing slow adoption of ICT by SMEs

requires a systematic view of the „Innovation System‟. The „Innovation System‟ is said to be

based on the interaction of three elements; supply of innovation, demand of innovation and the

financing of innovation. According to the study, it is the interaction of support and pressure of

these three elements that reduced the rate of adoption of ICT by SMEs in Canada.

3.0 RESEARCH METHODOLOGY

3.1 STUDY AREA

The choice of Oyo State for this study is informed by two major factors. First is that the

state is situated in the South West, the zone with lowest poverty indices. Using the Dollar per

Day measure, extreme poverty in 2010 in the zone was 50.1%, compared to 56.1% in South

South, 59.2% in South East, 59.7% in North Central, 69.1% in North East and 70.4% in North




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West (NBS, 2012). Secondly, outside Lagos (former Federal Capital), Oyo State has the highest

concentration of Micro, Small and Medium Enterprises. (NBS, 2010).

Oyo State is administratively divided into three; Ibadan/Ibarapa, Oyo Central and Oyo

North. Respondents for this study have therefore been drawn from all the three

administrative/geopolitical zones. These are Ibadan metropolis for Ibadan/Ibarapa, Ogbomosho

for Oyo Central and Saki for Oyo North.

3.2 METHOD OF DATA COLLECTION

The study used primary data collected through a carefully drawn questionnaire. The

questionnaire was divided into three sections. Section one was on the bio data of the Small and

Medium entrepreneurs. Section two was on business history while questions relating to

adoption, use and impact of Information and Communication Technology (ICT) were raised in

section three. In section three also, information on barriers to adoption of ICT was elicited. The

Likert style was used as measuring scale in most cases.

The instrument of data collection was administered through Research Assistants, who

were given initial training. Most of them were students of The Polytechnic, Ibadan, Nigeria at

the institution‟s Ibadan and Saki campuses. Others were drawn from Ladoke Akintola

University, Ogbomoso, Nigeria. The questionnaire was administered for a period three months;

January to March, 2013.

3.3 TOOLS OF DATA ANALYSIS

The main statistical tool of data analysis was Logistic Regression. This was necessary

because the dependent variable Small and Medium Enterprises Productivity (SMEP) was binary,

where a respondent was scored „1‟ if the entrepreneur felt that ICT had improved productivity

and „0‟ if otherwise. The explanatory or independent variables were both binary and continuous.

In addition, the study adopted the counterfactual model of impact assessment which is the

current fashionable trend. The counterfactual model identified productivity of SMEs before the

adoption of ICT and after. The difference between the two was recognised as the impact.

In line with the foregoing, the general model of this study was

SMEP = β0 β1AICT + β2DICT +β3 (AICT * DICT) + βjXj +e………. (1)

where

SMEP = Small and Medium Enterprises Productivity which is scored „1‟ if ICT has enhanced

productivity and „0‟ if otherwise.

AICT = Adoption of ICT. This is scored „1‟ if SMEs has adopted ICT and „0‟ if otherwise.

DICT = Duration since using of ICT.

Xj = Vector of other factors that influence productivity.

The predictor of impact was exp (β3).




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For this study therefore, explanatory variables of productivity used are entrepreneurs‟

ability (EABT); staff quality (SQUA) which is defined in terms of qualifications, expertise and

competence; banking and credit facilities (BACF), competition (COMP) and government support

(GOSP).

As a result of the foregoing, the explicit model for this study is

SMEP = β0 + β1AICT + β2DICT + β3(AICT X DICT) + β4EABT + β5SQUA + β6BACF +

β7COMP + β8GOSP + e -------------------------------------------- (2)

where SMEP, AICT and DICT are as previously defined.

EABT = Entrepreneurs ability

SQUA = Staff quality

BACF = Availability of banking and credit facilities

COMP = Degree of competition in the industry and

GOSP = Availability of government support.

4.0 DATA PRESENTATION, RESULTS AND DISCUSSION

4.1 DATA PRESENTATION

4.1.1 DISTRIBUTION AND RETRIEVAL OF QUESTIONNAIRE

A total of 540 copies of the questionnaire was prepared and distributed across the three

locations in Oyo State; Ibadan metropolis (for Ibadan/Ibarapa), Ogbomoso (for Oyo Central) and

Saki (for Oyo North/Oke Ogun). We were however able to retrieve 430 copies of the

questionnaire. Table 5.1 shows the distribution in this regard.

From table 4.1, 77.8% of the distributed questionnaire was retrieved. Further analysis

indicates that 81.8% of the distributed questionnaire in Ibadan was retrieved. The figures for

Ogbomoso and Saki were respectively 81.2% and 68.8%. The retrieval rates were not surprising,

considering the fact that Ibadan and Ogbomoso are more cosmopolitan than Saki.

Table 4.1: Spread and Distribution of Questionnaire

Locations No of

Questionnaire

Distributed

No of Questionnaire

Retrieved

Percentage

retrieved

Percentage of

the sample

Ibadan 220 180 81.8 42.9

Ogbomoso 160 130 81.3 31.0

Saki 160 110 68.8 26.1

540 420 77.8 100

Source: Field Study 2013




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4.1.2 RESPONDENTS BY NATURE, AGE AND SIZE OF BUSINESS

Almost half (47.6%) of the Small and Medium Enterprises were into Distribution

followed by Service Industry (29.1%). This implies that Distribution and Service Industries

accounted for about 76.7% of the sample. This is not a good omen as the Manufacturing Sector,

which is known as a better driver of economic growth and poverty reduction accounted for a

mere 23.3% of the sample.

From table 4.2 also, Small and Medium Enterprises under five years old were 12.6% of

the sample, those between five and ten years, 68.1% and above ten years 19.3%. This means that

Small and Medium Enterprises that have been in existence for above five years, constituted

87.4% of the sample. This is good for a study which focused is on impact of ICT on

productivity. This is because young businesses might still be struggling for survival and unable

to raise enough money to acquire ICT tools and equipment for application to business.

In addition table 4.2 gives insight into the sizes of business. Most of the respondents

were Small Scale entrepreneurs, using the benchmark in Udeckuwu (2003). As mentioned

earlier, the number – of – employees criterion rather than the size of capital has been adopted in

this study. Medium Scale Enterprises were 28.3% while micro enterprises were 3.6%. Small

and Medium Enterprises which are the focus of this study, accounted for 96.4% of the sample.

With this, the distribution and number may be said to be adequate for the study to come up with

findings that may be valid.

Table 4.2: Distribution of Respondents by Nature Age and Size of Business

Nature of Business Frequency Percentage Cumulative

Manufacturing

Distribution

Service

Others

98

260

122

-

420

23.3

47.6

29.1

-

100.0

23.3

70.9

100.0

Age of Business

Under 5 years

Between 5 and 10 years

Above 10 years

53

286

81

420

12.6

68.1

19.3

100.0

12.6

80.7

100.0

Size of Business

Micro enterprises

Small enterprises

Medium enterprises

15

286

119

420

3.6

68.1

28.3

100.0

3.6

71.7

100.0

Source: Field Study (2013)




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4.1.3 RESPONDENTS BY ENTREPRENEURS’ SEX, AGE,

EDUCATIONAL/PROFESSIONAL QUALIFICATIONS AND EXPERIENCE

As shown on table 4.3 three-quarters of Small and Medium entrepreneurs were male,

with the remaining one-quarter being female. This is good for the study, considering the fact that

using ICT is sometimes considered as a form of adventure. Over the years men are known

generally to want to engage in one form of adventurism or another.

Adults who usually are between 36 years and 60 years, accounted for half (50%) of the

study sample. This group is a followed by youths (between 18 and 35 years), 32.8%. Those

above 60 years of age and under 18 years were 14.3% and 2.9% respectively. From observations

youths and adolescents are attracted more new inventions and ICT products than those above

60years. Consequently, a sizeable number of the respondents (82.8%) of the sample fell into this

category.

Table 4.3: Distribution of Entrepreneurs by Sex, Age, Educational/Professional

Qualification and Experience

Frequency Percentage Cumulative

Sex

Male

Female

315

105

75.0

25.0

75.0

100.0

420 100.0

Age

Under 18 years

28 – 35 years

36 – 60 years

Above 60 years

12

138

210

60

2.9

32.8

50.0

14.3

2.9

35.7

85.7

100.0

420 100.0

Educational Qualifications

Primary Education

Secondary Education

Tertiary Education

10

220

190

2.4

52.4

45.2

2.4

56.8

100.0

420 100.0

Professional Qualifications

Yes

No

113

307

26.9

73.1

26.9

100.0

420 100.0

Experience

Yes

No

118

302

28.1

71.9

28.1

100.0

420 100.0





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4.1.5 RESPONDENTS BY ADOPTION, DURATION OF USE AND IMPACT OF ICT

From table 4.4 almost three quarters (71.9%) of the sample had not adopted any form of

ICT in their enterprises. Only 28.1% of the entrepreneurs had been applying ICT in their

businesses. Ironically 135 small and medium business entrepreneurs of the study agreed that

ICT improved productivity as opposed 118 who claimed to have adopted ICT in the running of

their enterprises. The implication of this is quite clear; some entrepreneurs who had not adopted

ICT had faith in its positive effect on productivity.

Out of the micro and small business entrepreneurs who had adopted ICT in their business

operations, 78% of them had been doing so for five years and above, 16% for four years, 10%

for three years, 7% for two years and 4% for one year. Only 3% of those who had adopted ICT

had less than one year experience in doing so.

Table 4.4 Respondents by Adoption, Duration of Use and Impact of ICT

Adoption/Use of ICT Frequency Percentage Cumulative

Yes

No

118

302

28.1

71.9

27.4

100.0

420 100.0

ICT Improves Productivity

Agree

Don‟t know

Disagree

135

245

40

32.1

58.3

9.6

32.1

90.4

100.0

420 100.0

Duration of Using ICT

Less than 1 year

1 year

2 years

3 years

4 years

5 years

More than 5 years

3

4

7

10

16

54

24

2.5

3.4

5.9

8.5

13.6

45.8

20.3

2.5

5.9

11.8

20.3

33.9

79.7

100.0

118 100.0


4.1.5 DISTRIBUTION OF RESPONDENTS BY TYPE OF ICT APPLICATION

ADOPTED

A review of literature indicates that common basic ICT applications are as listed on table

4.5. From the table the most popular ICT application was the internet. 110 of the 118

entrepreneurs, who claimed to have adopted ICT application in their business operations, used

the internet. This was closely followed by users of E-mail (102) Electronic Point of Sales

(EPOS) (83) presentation Packages, Purchase Online, Word Processing, Spread Sheets (63),




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Photocopiers (60) and Smart Phones (60). Less than half of the 118 used the remaining ICT

applications.

Table 4.5: Respondents By Type of ICT Applications Adopted

Type of ICT Application Frequency

Personal Computers

Work processing

Spreadsheets

Databases

Presentation packages

75

63

15

81

Servers

Mainframe

E-mail

Internet

Purchase Online

Selling Online

Video Conferencing

EPO (Electronic Point of Sales)

Photocopiers

-

-

102

110

80

15

10

82

60

Mobile Devices

Smart phones

Tablets

60

45


4.1.6 RESPONDENTS BY OTHER FACTORS OF PRODUCTIVITY

Factors other than ICT identified in this study, as capable of influencing productivity are

entrepreneurs‟ ability, staff or employee quality, availability of banking and credit facilities,

government support and competition. Table 4.6 presents data in this respect. The strongest of

these factors is banking and credit facilities, where about 92% of the sample agreed that it is an

agent of productivity. This is followed by government support (84.8%), entrepreneurs‟ ability

(75%) and staff quality (68.1%).




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Table 4.6: Distribution of Respondents by Impact of Other Factors on Productivity

Entrepreneurs Ability Frequency Percentage Cumulative

Agree

Don‟t know

Disagree

315

20

85

75.0

4.8

20.2

75.0

100.0

420 100.0

Staff Quality

Agree

Don‟t Know

Disagree

286

104

30

68.1

24.8

7.1

68.1

92.9

100.0

420 100.0

Banking and Credit Facilities

Agree

Don‟t Know

Disagree

386

5

29

91.9

1.2

6.9

91.9

93.1

100.0

420 100.0

Competition

Agree

Don‟t Know

Disagree

103

272

45

24.5

64.8

10.7

24.5

89.3

100.0

420 100.0

Government Support

Agree

Don‟t Know

Disagree

356

10

54

84.8

2.3

12.9

84.8

87.1

100.0

420 100.0


4.1.7 RESPONDENTS BY ACCESS TO BANKING/CREDIT FACILITIES, DEGREE

OF COMPETITION AND GOVERNMENT SUPPORT

The study tried to evaluate accessibility of the small and medium business entrepreneurs

to banking and credit facilities and government support. It equally attempted evaluating

awareness of the entrepreneurs to the degree of competition. Table 4.7 captures entrepreneurs‟

opinions on these.

With 73.6% not having access to banking and credit facilities, it is certain that the

entrepreneurs had been denied an essential platform of productivity. Follow-up interview on this

revealed that the high interest rate on bank loans was mostly responsible for poor patronage of

banks‟ loan/credit facilities. Similarly, with 79.8% of the entrepreneurs not having access to




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government support, agencies of government charged with responsibilities for Business

Development and Support Services (BDSS), like National Directorate of Employment (NDE)

and Small and Medium Enterprises Development Agency of Nigeria (SMEDAN) have not been

doing enough. About 53% of the entrepreneurs, were not aware of competition in the industries

in which they operate. The remaining - about 47% - knew and experienced competition.

Table 4.7: Distribution of Respondents by Access to Banking/Credit Facilities, Degree of

Competition and Government Support

Access to Banking and

Credit Facilities

Frequency Percentage Cumulative

Yes

No

111

309

26.4

73.6

26.4

100.0

420 100.0

Competition

Yes

No

198

222

47.1

52.9

47.1

100.0

420 100.0

Government Support

Yes

No

85

335

20.2

79.8

20.2

100.0

420 100.0


4.1.8. RESPONDENTS BY FACTORS INFLUENCING ADOPTION OF USE OF ICT

Table 4.8 presents reasons for adoption and use of ICT in business by the entrepreneurs.

The most important of these reasons is request by employees (74) closely followed by desire to

increase revenue (72) and pressures from competitors (70). Other reasons in ranking are need to

be more competitive (56), request by customers (51), desire to reduce cost (45) and suppliers‟

request (28).

Table 4.8: Distribution of Respondents by Factors Influencing Adoption and Use of ICT

Factors Frequency

To be more competitive 56

To increase revenues 72

To reduce cost 45

Customers requested it 51

Suppliers requested it 28

Pressures from competitors 70

Employees requested it 74





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4.2 DATA ANALYSIS

4.2.1 LOGISTIC REGRESSION IMPACT OF ICT ON PRODUCTIVITY OF SMES IN

OYO STATE

The result of the logistic regression is shown in table 4.8. With Standard Error ranging

from 0.035 to 0.063, the various co-efficients of this study are well estimated, as a result of

which we have a fairly precise estimate.

Conceptually, the Wald statistic is very much like t-statistic and it is a test of the null

hypothesis. In the main null hypothesis for this study can be stated as follows:

ICT does not have significant impact on Small and Medium Enterprises productivity.

Going by the logistic regression in table 5.10, where the p-values for AICT is 0.001, for DIET is

0.005, then the β coefficients of these variables are not equal to zero. This implies that (AICT

*DICT) after controlling for AICT and DICT predicts the response variable, SMEP better than

chance alone. Similarly because 0.001<P<0.01 in this study, there is strong evidence against the

null hypothesis in favour of the alternative.

Similarly, the predictor of impact of ICT on SMEs productivity is Exp (β), and in this

case is 1.254. This implies that ICT has about 25% impact on productivity of Small and Medium

Enterprises.

Table 4.8 Logit Regression of Impact of ICT on Productivity of SMEs in Oyo State

Β S.E Wald Df Sig Exp (β)

AICT 0.524 0.035 10.128 1 0.001 1.689

DICT 0.918 0.063 12.064 1 0.005 2.504

AICT*DICT 0.226 0.045 11.131 1 0.003 1.254

Constant 5.862 0.056 28.75 1 0.000 0.16

Variable(s) entered on step 1: AICT, DICT, (AICT*DICT)

4.2.2 MULTIVARIATE LOGISTIC REGRESSION OF IMPACT OF NON-ICT

FACTORS ON PRODUCTIVITY OF SMEs

Results of multivariate logistic regression analysis of impact of non-ICT factors on

productivity of Small and Medium enterprises are shown in table 5.10. From this table

Entrepreneurs‟ ability (EABT), Staff quality (SQUA), access to banking credits and facilities

(BACF) have positive signs. It is only competition (COMP) that has negative sign. Consequently

from the table the 2-values for the non-ICT productivity factors are entrepreneurs‟ ability

(12.18), staff quality (5.45), banking and credit facilities(3.09), competition (- 0.48) and

government support (1.41).

It is however observed from this table also that only entrepreneurs ability (EABT) and

staff quality (SQUA) are both positively correlated with productivity at different levels of

significance.




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Table 4.9 Multivariate Logistic Regression of Impact of Non-ICT Factors on

Productivity of SMEs in Oyo State, Nigeria

Factor Co-efficient Z-value

EABT 2.112 * 12.18

SQUA 1.523 ** 5.45

BACF 3.412 3.09

COM - 0.987 - 0.48

GOSP 3.124 1.41

Diagnostics

Pseudo R2 = 0.3015

LR Chi2 = 4375.81

Prob. > Chi2 = 0.0000

Log likelihood = -7605.1438

N = 402

* - significant at 1%

** - significant at 5%

4.3 DISCUSSION

It is evident from this study that ICT is a potential driver of productivity in Small and

Medium Enterprises. With about 25% that impact however, the driving force of ICT on

productivity is weak. This is not cheering considering the fact that 78% of the study sample

(table 4.4) had used ICT for five years and above.

On the other hand however, the weak impact of ICT on productivity in this study might

not have come as surprise, considering a number of factors. First is the business size of the study

sample. From table 4.2, most dominant in the study were micro and small business enterprises

accounting for 71.7%. This size of business lacks appropriate capacity for adoption and use of

ICT especially in Nigeria.

Another factor is nature of business. A large number of the study sample was in

Distribution and Service sectors. This from table 4.2 accounts for 76.6%. Impact of ICT in

these sectors has been adjudged as difficult to measure compared to the real sector. See Bloom,

Draca., Kretschmer and Sadum (2010).

Age of the entrepreneurs is also another factor. More than 60% of the study sample was

in the 36 years and above category (table 4.3). This age bracket is not well known to be ICT-




16

compliant. As a result, ability to understand the ICT language and dexterity in its use will be

low. Since these are major factors in impact assessment, the result cannot be expected to be in

anyway different from what has been obtained for the study.

The type of ICT in use is another factor in the weak impact of ICT on productivity of the

SMEs. From this study, the most popular type of ICT in use amongst the small and medium

entrepreneurs were e-mail and the internet (see table 4.4). These types would not be expected to

generate strong impact, particularly when most of the businesses were in the Distribution and

Service Sector.

From this study poor funding has been found to be the bane of adoption, usage and

impact of productivity among the small and medium entrepreneurs. The study showed that most

of the enterprises had low access to banking and credit facilities and government support. More

than 70% of the study sample did not have access to banking and credit facilities and more than

79% of the study sample did not receive government support (see table 4.6). This is a highly

limiting factor when it is realised that low start-up capital and high financial incapacitation are

common constraints among small and medium entrepreneurs in the country.

This study has also proved wrong the proponents of „Productivity Paradox‟, who posit

that there is inverse relationship between ICT investment and productivity. This is crystallised in

this study by the fact ICT had positive although weak impact on productivity. This finding is in

conformity with results in Brynjolfsson and Hitt, 1996, and Greenwood and Yorukoglu, 1996.

Multivariate logistic results in respect of non-ICT factors of productivity slow that they

are positive correlates of productivity except competition (COMP) which is negative (-0.987).

the Z-value for value for each of these factors is 2.112 for entrepreneurs ability (EABT), 1.523

for staff quality (SQUA), 3.412 for banking and credit facilities (BACF) and 3.412 for

government support (GOSP). With access to banking and credit facilities and government

support being positive correlates of productivity, it is clear that these two factors can play

significant roles in boosting productivity of Small and Medium enterprises. Both factors are not

however significant at 1% and 5% levels in this study. This is in tandem with the earlier

discussed descriptive statistics. This trend may continue for a long time to come if interest on

bank loans and credit facilities continue to be high. The Federal Government of Nigeria has

agencies that are expected to give micro, small and medium enterprises support. These include

National Directorate of Employment (NDE), National Poverty Eradication Programme

(NAPEP), Small and Medium Enterprises Development Agency of Nigeria (SMEDAN). With

government support (GOSP) in this study not being significant at 1% and 5% levels, it means

that their lines of support are either weak or they are not reaching out well enough.

That competition (COMP) is a negative correlate of productivity in this study is not

surprising. Small and Medium Enterprises (SMEs) operate in the informal sector of the

economy. In this sector competition especially on price basis is usually very weak. One reason

for this is the fact the market is perfectly competitive. Another reason is that most of the

enterprises belong to one association or another, where prices are highly regulated and violation

is heavily punished.




17

From the result of multivariate logistic regression of non-ICT factors on productivity of

SMEs (table 4.9), entrepreneur‟s ability (EABT) and staff quality (SQUA) are not only positive

but significant at 1% and 5% levels respectively. This is perfect alignment with the descriptive

statistics of this study.

5.0 RECOMMENDATIONS

The recommendations here are made pursuant to the findings discussed in the foregoing

sections.

Whereas most of the small and medium entrepreneurs in this study indicated that other

non-ICT factors like entrepreneurs‟ ability, staff quality, banking and credit facilities,

competition and government support also have positive impact on productivity (table 4.6), it is

necessary to isolate government support from the rest. Government should always take the lead

in the provision of Business Development and Support Services (BDSS). For the purpose of

raising impact of ICT on productivity of small and medium enterprises, government especially

through National Directorate of Employment (NDE) and Small and Medium Enterprises

Development Agency of Nigeria (SMEDAN), should provide both training and support that

would change entrepreneurs‟ orientation towards the use of ICT in their operations. Such

training can be organised on zonal trade association basis.

Secondly, most students in secondary schools and tertiary institutions are more ICT –

compliant than older adults. Ironically most of the youths in these educational institutions are

not in business yet while the adults in business are mostly ICT – averse. It is therefore

recommended that ICT in school curricula should be specific and emphatic on ICT application in

business operations. This will guarantee a large crop of the ICT – compliant future business

executives.

Thirdly, government and trade associations should explore possibility of deploring high –

speed/connectivity internet facilities for the use of small and medium entrepreneurs. One way

through which this can be done is by encouraging establishment of industrial parks. This will

enable business organisations to cluster appropriately to use ICT facilities. In addition,

government should make plans to reduce cost of access to the internet facilities by the

entrepreneurs. This may be achieved through a pragmatic cost – sharing formula between

government and trade associations and among entrepreneurs that operate in the Industrial Parks.

Fourthly, efforts should be made by government to liberalise access to bank loans and

credit facilities. It is very clear that ICT especially the hardware aspect is very expensive.

Although costly in the short-run, the long-run benefits of higher productivity level and increased

revenue will ultimately lead to higher profit. This will not only enhance business survival but

will also reduce poverty amongst entrepreneurs.

Finally, government should put greater investment in the real sector, since ICT impact on

productivity is more pronounced there than in Distribution and Service Sectors. Encouragement

recommended may come through robust incentive policies which may include tax holiday,

liberal export policy, import – substitution policy, provision of infrastructural facilities among

others.




18

6.0 CONCLUSIONS

From this study, it is established that ICT is a potential driver of productivity

growth among small and medium entrepreneur. The study also proves wrong the Productivity

Paradox which states that there is negative relationship between ICT investment and

organisational productivity.

The study nevertheless observed that ICT impact on productivity of small and medium

enterprises is currently weak. This may be improved if government promotes training, provides

business development and support services, deplores high speed internet connectivity in

appropriate locations where the entrepreneurs can access same. If and when these and other

recommendations are taken and implemented, productivity in the SME sector will rise, more

revenue would be generated while cost of operation and production would, in the long-run

reduce. In the final analysis, business survival would be enhanced, more earnings would be

made by the entrepreneurs and consequently there would be increase in productivity among

small and medium entrepreneurs.

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