**4. Methodology**

The current study used Finscope MSME Survey Zimbabwe (2012) that used random selection of eligible small to medium enterprises. It was a comprehensive survey focusing on individual entrepreneurs and owners of micro, small and medium enterprises (MSMEs) and their general needs. This survey carried out 3222 face to face interviews with business owners across all provinces in Zimbabwe. Those interviewed were above 18 years, business owners or generating income through business activities and employing 75 people or less. The survey provided critical information allowing one to analyse determinants of SMEs operation and growth focusing on a number of characteristics. The survey generated information about the characteristics and statistics of informal and formal SMEs operations. The survey gives valuable information permitting different elements of business that have not been studied before, owing to lack of appropriate information at the national level.

To address the research objective that seeks to investigate the determinants of SMEs business growth and operations in Zimbabwe an econometric model was created. The study estimated linear probability model (LPM) to investigate the determinants of SMEs growth, operations and profitability. The dependent variable is profitability, the level of profit obtained in the last 12 months.

In this analysis the LPM take as the dependent variable, profitability, the level of profits obtained in the past year. The 25 independent variables are as below.

profitability = f (number of business units, travelling distance, education level, age, ownerun, business type, family business, ownership, use ICT, motivation of starting business, financial problem, business regulation problem, business planning problem, employee motivation, advertising, licenced, bank account, business insurance, business records, business advisory, education, corruption, tax, marital)

#### **4.1. Descriptive statistics**

Twenty six variables considered have their frequency, percentage, mean and standard deviation displayed in **Table 1** below.

**219**

Yes No

*The Factors Influencing SMEs Growth in Africa: A Case of SMEs in Zimbabwe*

**Characteristics of SMEs Frequency % Mean Std. Dev.**

Businessnum 1.262259 0.5354235

Travel time 7.053974 19.75407

Education level 4.22874 1.512077

Age 3.336127 1.389518

Ownerun 0.996586 0.0583389

Bustype 0.0145872 0.119912

fambus 0.4484792 0.4974157

44.85 55.15

1445 1777

*DOI: http://dx.doi.org/10.5772/intechopen.87192*

Total number of respondents 3222

1 2508 77.84 2 598 18.56 3 104 3.23 4 10 0.31 5 1 0.03 6 1 0.03

Less than 10 mins 494 29.30 11–20 366 21.71 21–30 278 16.49 31–60 214 12.69 60–120 116 6.88 120–180 59 3.50 >180 159 9.43

No schooling 134 4.16 Some primary education 347 10.77 Grade 7 complete 442 13.72 Some secondary incomplete 715 22.19 Secondary school compete 1246 38.67 A level 52 1.61 College 219 6.80 University degree 67 2.08

18–24 years 279 8.66 25–30 years 647 20.08 31–40 years 990 30.73 41–50 years 608 18.87 51–60 years 414 12.85 60 years and above 284 8.81

Yes 3211 99.66 No 11 0.34

Formal 3175 98.54 Informal 47 1.46


#### *The Factors Influencing SMEs Growth in Africa: A Case of SMEs in Zimbabwe DOI: http://dx.doi.org/10.5772/intechopen.87192*

*Regional Development in Africa*

gender and financial support.

ence to Zimbabwe.

**4. Methodology**

national level.

ruption, tax, marital)

**4.1. Descriptive statistics**

standard deviation displayed in **Table 1** below.

On a study focusing on business start-ups in Zimbabwe, Nyoni and Bonga [3] found that majority of SMEs in Zimbabwe were prompted by unprecedented a shrinking job market as many workers were laid off. Nyoni and Bonga argue that SMEs have a great potential to play a crucial position in addressing socio economic challenges such as poverty and unemployment in Zimbabwe. It is also argued that many developed economies trace their development from SMEs growth and development. The results of the study show that enterprises in Zimbabwe are mainly influenced by the following factors technology, start-up funding, marketing, management skills, education level, social networks, age of owner, government,

In sum extant literature reveals that factors influencing SMEs operation and growth include: technology, start-up funding, marketing, management skills, education level, social networks, age of owner, government, gender, financial support, lack of government support, local authority unfair treatment, stiff competition and research and development. There is no study that has considered or analysed the effect of these factors in a single study, The current study seeks to consider 26 variables in investigating the determinants of SMEs profitability with special refer-

The current study used Finscope MSME Survey Zimbabwe (2012) that used random selection of eligible small to medium enterprises. It was a comprehensive survey focusing on individual entrepreneurs and owners of micro, small and medium enterprises (MSMEs) and their general needs. This survey carried out 3222 face to face interviews with business owners across all provinces in Zimbabwe. Those interviewed were above 18 years, business owners or generating income through business activities and employing 75 people or less. The survey provided critical information allowing one to analyse determinants of SMEs operation and growth focusing on a number of characteristics. The survey generated information about the characteristics and statistics of informal and formal SMEs operations. The survey gives valuable information permitting different elements of business that have not been studied before, owing to lack of appropriate information at the

To address the research objective that seeks to investigate the determinants of SMEs business growth and operations in Zimbabwe an econometric model was created. The study estimated linear probability model (LPM) to investigate the determinants of SMEs growth, operations and profitability. The dependent variable

In this analysis the LPM take as the dependent variable, profitability, the level of

Twenty six variables considered have their frequency, percentage, mean and

profits obtained in the past year. The 25 independent variables are as below. profitability = f (number of business units, travelling distance, education level, age, ownerun, business type, family business, ownership, use ICT, motivation of starting business, financial problem, business regulation problem, business planning problem, employee motivation, advertising, licenced, bank account, business insurance, business records, business advisory, education, cor-

is profitability, the level of profit obtained in the last 12 months.

**218**


**221**

**Table 1.**

*Descriptive statistics.*

**4.2. Variables included in the model**

*Source: Finscope MSME Survey Zimbabwe (2012).*

*4.2.1 Number of businesses*

Variables considered in this study are those in accordance with the literature and availability of Finscope MSME Survey data may influence profitability of small to medium enterprise. Below is a brief description of the variables included in the

One with two or more businesses is likely to report better profits than relying on one. Two business units are likely to produce more profits. There are higher chances

Linear Probability Models (LPM) and the reasons for using them.

*The Factors Influencing SMEs Growth in Africa: A Case of SMEs in Zimbabwe*

No 2385 74.02

Yes 19 0.59 No 3203 99.41

Yes 2442 75.79 No 780 24.21

Refused 55 1.71 Less than USD\$100 334 10.37 USD\$100–USD\$199 181 5.62 USD\$200–USD\$299 201 6.24 USD\$300–USD\$399 197 6.11 USD\$400–USD\$699 297 9.22 USD\$700–USD\$999 207 6.42 USD\$1000–USD\$1399 237 7.36 USD\$1400–USD\$2499 1230 38.17 USD\$2500–USD\$4999 152 4.72 USD\$5000–USD\$7999 53 1.64 USD\$8000–USD\$10999 28 0.87 USD\$11000–USD\$15999 22 0.68 USD\$16000–USD\$29999 12 0.37 USD\$30000–USD\$39999 5 0.16 USD\$40000–USD\$49999 2 0.06 USD\$50000–USD\$59999 5 0.16 USD\$70000–USD\$99999 3 0.09 USD\$100000+ 1 0.03

**Characteristics of SMEs Frequency % Mean Std. Dev.**

Corruption 0.005897 0.0765768

Tax 0.0204842 0.1416714

Marital status 0.7579143 0.4284124

Profitability 41.84699 44.96179

2.05 97.95

66 3156

*DOI: http://dx.doi.org/10.5772/intechopen.87192*

Yes No


#### *The Factors Influencing SMEs Growth in Africa: A Case of SMEs in Zimbabwe DOI: http://dx.doi.org/10.5772/intechopen.87192*

**Table 1.** *Descriptive statistics.*

*Regional Development in Africa*

**Characteristics of SMEs Frequency % Mean Std. Dev.** Ownership 0.7846058 0.4111593

Use ICT 0.0391061 0.1938776

Motivations 0.3311608 0.4707038

Financial problem 0.3792675 0.4852801

Business regulation problem 0.0260708 0.1593705

Business planning problem 0.0021726 0.0465673

Employee motivation 0.1610801 0.3676619

Advertising 0.1387337 0.3457221

Licenced 0.1511484 0.358249

Bank account 0.1021105 0.3028406

Business insurance 0.016139 0.1260298

Business record 0.0121043 0.1093686

Business advisory 0.0912477 0.2880057

Expertise 0.2597765 0.43858

13.87 86.13

447 2775

Yes 2528 78.46 No 694 21.54

Yes 126 3.91 No 3096 96.09

Self-motivated 1067 33.12 Default 2155 66.88

Yes 1222 37.93 No 2000 62.07

Yes 84 2.61 No 3138 97.39

Yes 7 0.22 No 3215 99.78

Yes 519 16.11 No 2703 83.89

Yes 487 15.11 No 2735 84.89

Yes 329 10.21 No 2893 89.79

Yes 52 1.61 No 3170 98.39

Yes 39 1.21 No 3183 98.79

Yes 294 9.12 No 2928 90.88

Yes 837 25.98

**220**

Yes No
