Research Paper

AN ASSESSMENT OF THE INFLUENCE OF TECHNOLOGY AND INNOVATION ON PERFORMANCE OF WOMEN SMALL SCALE ENTREPRENEURS (SSES) IN NAIROBI, KENYA

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An Assessment of the Influence of Technology and Innovation

on Performance of Women Small Scale Entrepreneurs (SSEs)

in Nairobi, Kenya†

Bula Hannah Orwa*, Edward Tiagha** and Muruku Waiguchu***

* Senior Lecturer of Human Resource Management, Entrepreneurship and General Management, Department of Human Resource Management, School of Business, Kenyatta University, Nairobi City, Kenya. E-mail: bula.hannah@ku.ac.ke; hannah.bula2014@gmail.com

* * Emeritus Professor of Engineering and Dean, University of Douala, Carrefour Ange Raphaël, P. O. Box 2701, Douala, Cameroon. E-mail: atiagha@gmail.com

* * * Emeritus Professor of Management, The William Paterson University, #300 Pompton Rd, Wayne, NJ 07470, United States of America, and Chairman, Doctoral Association of Eastern Africa, Viewpark Towers 2nd Floor, P.O. Box 42220-00200, Nairobi, Kenya. E-mail: waiguchum@gmail.com; waiguchu@wpunj.edu; guchu@daea.or.ke

INTRODUCTION

Research acknowledges the importance of knowledge as an important asset to venture creation and success. Research also appreciates that knowledge also known as human capital is an agent of enterprise success. However, other agents of a business enterprise

Small Scale Entrepreneurs (SSEs) play a significant role in Kenya’s Economy. Technology is fast growing and its usage has been embraced by most businesses to turn them around. It is imperative to study the contribution of Technology and Innovation (T&I) on the performance of women-run SSEs in Kenya. Women engage a lot in SSEs to earn their livelihoods as they contribute immensely to the country’s economy. The objective of this research study was to establish the effect of technology and innovation by women entrepreneurs on the performance of SSEs in urban Kenya. This research study was carried out on a random sample from SSEs in the city council wards in Nairobi East. Performance was the dependent variable, while T&I were the independent variables as the baseline factors controlled the relationships. Descriptive research design was used to explain the effect of the independent variables on the dependent variables. Findings indicated that there was a positive and significant relationship between technology and innovation and performance of women SSEs in urban Kenya.

Key Words: Human capital, Mobile telephony, Small Scale Enterprises, Technology and Innovation, Women entrepreneurs

† An earlier version of this paper was presented at the 12th SIMSR Global Marketing Conference on New Age Customer-centric Marketing, organized by and held at K. J. Somaiya Institute of Management Studies and Research (KJSIMSR), Mumbai, India, from February 9-10, 2017.

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exist such as the aspect of venture capital espoused by Schumpeter (1934) (Becker, 1993) in the theory where the entrepreneur is considered as being an economist and the role that money plays in starting and sustaining an entrepreneurial venture. Mises (1959) puts emphasis in human action in entrepreneurship innovation (Bula, Tiagha, and Waiguchu, 2014),

A technological system consists of various components which have to exist in a combined interface (Carlsson and Elliasson, 2003). The synergy of all the components of technology such as the ‘human capital’, ‘venture capital’ and the ‘actors’ result in the formation of innovation. In this study, all the disjointed theories on technology and innovation have been considered in an endeavor to ascertain how women entrepreneurs conduct themselves to guarantee their businesses success (Bula, Tiagha, Waiguchu, 2014).

Current technological developments, such as adoption of information technology, seem to favor small-scale production through affordable goods, reduced minimum costs for adjustable specialization (Loveman and Sengenberger, 1991). Developments in Information Technology (IT) have produced better access to information and communication devices that may assist small enterprises and increase the competitiveness of reputable small businesses (Audretsch and Thurik, 2001).

In Kenya, Government reports and statistics give emphasis to technological advancement as the huddle facing economic growth and propose transfer of technology from overseas investors to local investors and from large to small ventures through subcontracting dealings and joint undertakings (GOK, 2005). Technology transfer to SSEs improves the production volume because of the many SSEs, thus increasing the number of new products and service in the economy. With capacity exploitation and resource use, small enterprises continue to offer large firms a substitute for capacity boost, large firms turn to smaller ones to enhance supply in order to cushion the supply discrepancy. Smaller firms are also seen to guarantee proper utilization of resources to reduce wastages (CBS, 2004 and Bula, Tiagha and Waiguchu, 2013).

The focus of technology innovation was on the use of mobile telephony to enhance marketing and to keep an inventory of their customers and to enhance money transfers in their business operations and use of innovation research from clients or customers. This paper examined the technocrats who apply the technology created as other agents in technology and innovation. Technology and innovation is therefore a social aspect which involves many players.

STATEMENT OF THE PROBLEM

Eighty (80%) percent SSEs in Kenya do not celebrate their 5th birthday. There are issues of women disempowerment making it hard for them to embrace T&I in marketing which translates to poor business performance. There is an empirical gap that this study sought to help fill. Some case studies indicate that innovations do impact

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organizational functions and thus performance while other studies seem to give divergent views on the effect of innovations and performance (Glor, 2014). This study therefore sought to establish if the women SSEs have embraced and adopted technology and innovation in their businesses and what impact the T&I have on the performance of their enterprises.

RESEARCH OBJECTIVES

The general objective of this study was to establish if there is a relationship between Technology and Innovation (T&I) and performance of the women owned and/or managed SSEs in Nairobi, Kenya.

The specific objectives are:

i. To establish if baseline characteristics of women SSEs in Nairobi significantly influence the performance of their business enterprises.

ii. To examine if there is a significant relationship between Technology and Innovation (T&I) and performance of women owners and/or managers of SSEs in Nairobi, Kenya.

RESEARCH HYPOTHESES The hypotheses posed in this study were as follows:

H 1 : Baseline characteristics have a significant influence over Technology and Innovation

(T&I) and performance of women SSEs in Nairobi, Kenya.

H 2 : There is a significant relationship between Technology and Innovation (T&I)

and performance of women owned and/or managed SSEs in Nairobi, Kenya.

LITERATURE REVIEW

THEORETICAL FRAMEWORK

Two theories were used to underpin this study. The first theory is the Theory of Innovation by Schumpeter (1934) and Resource Based View (RBV) by Penrose (1959).The theory of innovation asserts that when an entrepreneur innovates at that point the original equilibrium is destroyed and a new equilibrium is established leading to abnormal profits for the enterprise giving the entrepreneur a leverage over other businesses in the same industry. Schumpeter argues that any entrepreneur who wants profits must innovate. “Entrepreneurship replaces today’s Pareto optimum with tomorrow’s different new thing”(Bula, 2012). Similarly the resource base view alludes to have a resource in an organization that is valuable, rare inimitable and non- subsumable which when used in an enterprise can accord the firm competitive advantage leading to improved firm performance.

This study applied the two theories to ground its hypotheses and also put together all the fragmented theories on technology and innovation in an attempt to establish how women entrepreneurs behave in ensuring their businesses succeed.

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TECHNOLOGY AND INNOVATION

Innovation has been found to be a factor in the survival/mortality/survival of a community or of a population. Innovations could affect people within an organization, and it could impact the amount of organizational functioning. People are the main driving factor in organizations, although little attention is given on the impact of innovation on them. Innovations and organizations are created by employees of organizations. If this involves the size of the organization, it may not be clear what size of innovation could influence the outcomes of an organization (Glor, 1997 and 2000). The size at which an innovation begins could affect the outcomes of an organization (Glor, 2014).

Transfer of technology translates into value addition for the business enterprise then the outcome can be considered to be an innovative venture which has resulted into appropriate technology transfer (Austin, Stevenson and Wei-Skillern, 2006).

Baseline Factors: Extant research has mentioned that belonging to a particular community can have certain disadvantages compared to other communities with respect to access to resources (Peredo and Chrisman, 2006 and Bula, Tiagha and Waiguchu, 2014). It has been established that 90% of SSE products particularly the rural enterprise products are marketed directly to rural households.

Small scale enterprises provide excellent opportunities for the entrepreneurial and managerial talent to reduce the critical shortage of entrepreneurs which is often a great handicap to economic development, and support industrialization policies that promote rural- urban balance. (CBS, 2004; and Bula, Tiagha and Waiguchu, 2014). The study sought to answer the following questions:

1. Have the entrepreneurs adapted the use of handset cellular phones in keeping customer contacts and mobile money transfer such as Orange money (from Orange mobile service provider), Airtel (from ‘airtel’ mobile service provider) and M-Pesa’ money (from ‘Safaricom’ mobile service provider) ‘M’kesho-family Bank, Pesa ‘PaP’?

2. How many discoveries are made within the lifespan of an SSE?

3. How many managers actually support the springing of ideas from their employees?

4. How many owners and/or managers of SSEs actually experiment on new ideas founded by themselves or the ‘intrapreneurs’-innovative employees?

5. How many SSEs have an innovative department or a research team in their organization?

Small enterprises are the engines that can drive Vision 2030 and beyond, further; it is observed that SSES play an integral role in changing and rejuvenating market competition because of their large numbers in a given market, and also by the intensity of their activity and hence contributing to entrepreneurial activity in an economy. Small enterprises are also seen to overcome the negative effects of monopoly in an

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economy as they are numerous in number and occasionally deal in related products thus encouraging stiff competition amongst them. From Kenya’s perspective on SSE development, the major benefits of the small enterprises are their contribution to the economy in terms of output of goods and services, creation of jobs at relatively low capital cost, especially in the fast growing service sector, development of a pool of skilled and semi-skilled workers who are the base for future industrial expansion, instrumental in strengthening forward and backward linkages among socially, economically and geographically diverse sectors of the economy by creating demand as well as supply (CBS, 2003; and Bula, Tiagha and Waiguchu, 2014). The theoretical framework used in this study is shown in Figure 1 below.

This paper attempted to establish the interactions of all the parties and stakeholders in technology creation such as owners, managers, employees and customers’ role in enhancing technology and innovation of the women SSEs. Other questions were further asked such as:

1. Ethnicity: Does belonging to a particular ethnic or community make an entrepreneur more alert to innovation? Is the gene of entrepreneurship innovation wired in a particular ethnic group?

2. Education: What is the role of education on uptake of technology and innovation? 3. Age: How is age related to Technology innovation uptake? 4. Marital Status: What is the relationship between marital status and Technology

and Innovation?

Figure 1: Conceptual Framework: The Effect of Technology and Innovation on Performance of Women SSEs

Source: Authors’ Analysis

Technology and Innovation

• Mobile telephony products

• Number of New discoveries made

• Existence of Innovative depart- ment/teams

• Management support of mew ideas

Baseline Characteristics

• Ethnicity • Marital Status

• Education

• Age

Performance of Women Small Scale Enterprises

• Sales growth • Assets growth

• Capital growth

• Increase in Number of Employees

• Growth in Profits after Tax

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RESEARCH METHODOLOGY

RESEARCH DESIGN

Descriptive correlation research in form of surveys was used. Technology and Innovation and Baseline Characteristics were measured through a Likert-type scale of five using 5 for strongly agree and 1 for and strongly disagree (Kothari, 2006).

TARGET POPULATION

The population of the study consisted of small scale enterprises in Nairobi County. These are: Retailing (56,177); Education (1,538); Food industry (4,993) and Personal and Professional services (5,815) which are 68,523 in total number (Nairobi City Council Business register, 2010). Nairobi was chosen because it is the capital city of Kenya and it can be representative of Kenya’s urban areas where a lot of commercial activities take place.

SAMPLING DISTRIBUTION

A stratified random sample was used to select the four industry sectors in Nairobi East which consists of four divisions and 33 council’s wards then a simple random sampling was used to identify the 384 respondents from all the council’s wards in Nairobi East. The population of study was divided into business type such as Retail (314); Food industry (28); Personal and Professional services (33) and Education (9). This samples calculation is based on n = z2 pq/e2 (Kothari, 2006) (see Table 1).

DATA COLLECTION PROCEDURES

The major data source was primary data that was generated by use of a questionnaire through a drop and pick method collected from the owners and or managers of the small scale enterprises.

Sectors

City Council Council’s Personal & Agriculture Divisions Wards Retail Professional & Food Education Total

Services Industry

Makadara 7 67 7 6 2 82 (21%) (21.2%) (21.43%) (21.2%) (21.4%)

Embakasi 11 105 11 9 3 128 (33.3%) (33.3%) (33.3%) (33.3%) (33.3%)

Kamkunji 6 57 6 5 2 70 (18.2%) (18.18%) (17.85%) (18.18%) (18.2%)

Kasarani 9 85 9 8 2 104 (27.2%) (27.27%) (28.57%) (27.27%) (27.1%)

Total 33 314 33 28 9 384%

Table 1: Sampling Distribution of Selected Sample Size in Nairobi East

Source: Authors’ Calculations

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DATA ANALYSIS

Descriptive statistical analyses were carried out to analyze the frequencies and percentages of enterprise characteristics and performance indicators and to determine the means and standard deviations of the variables under study. Exploratory and Regression analysis were used to test for collinearity, normality, skewness and heteroscedascity to get the data that can be regressed. Regression analysis was therefore done on performance indicators’ index as the dependent variables against Technology and Innovation and the baseline characteristics.. p  0.05 (probability) values were also acceptable and Adjusted R values were used to determine the existence of a relationship between the variables under study.

RETURN RATE OF THE QUESTIONNAIRES

A total of 384 questionnaires were given out to women owners of small scale enterprises in Nairobi, of these, 354 questionnaires were returned giving a response rate of 92.1%.

RELIABILITY ANALYSIS

Table 2 states the results of the reliability analysis. It involved questionnaires from 354 respondents. Cronbach’s alpha was calculated by applying the following formula in SPSS.

cNv cN

).1( . 



Cronbach’s alpha is a measure of internal consistency, that is, how closely related a set of items are as a group. The findings of the pilot test showed that the calculated Cronbach’s reliability alpha was 0.765. In conclusion, the instruments had an acceptable reliability coefficient and were appropriate for the study (see Table 2).

Table 2: Cronbach’s Reliability Statistics Results of the Instrument

Variable Cronbach’s alpha No. of Items

Technology and Innovation 0.765 4

Source: Authors’ Calculations

EDUCATION LEVEL AND TECHNOLOGY AND INNOVATION

From the results shown in Table 3, all categories of levels of education had applied Technology and Innovations in the business operations. Results also indicate that 31.25% of respondents with education below secondary level strongly agreed to have applied Technology and Innovations in the business operations, 48% of those having secondary education, 13.8% of those with college education and 40% of those with university education also strongly agreed to the same. 100% of the respondents with post university education strongly agreed to have applied Technology and Innovations in the business operations (see Table 3).

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Table 4 indicates that 56% of the respondents in the food and agriculture sector strongly agreed to applying T&I in business, 24.24% of the respondents in the personal and professional services, 33.3%% of those in education and 31% of respondents in retail also strongly agreed to applying T&I for business success. The food sector out- performed all the categories of businesses in being innovative. This performance could be explained by the fact that the entrepreneurs allowed the employees to suggest the type of goods the customers liked and also by ensuring that the entrepreneurs were able to use their cell-phones to keep customer contacts enhancing customer loyalty (see Table 4).

Table 3: Education Level and Technology and Innovation

Education Using Technology and Sub-sectional Innovations (Frequency) Percentages

Below Secondary Education 15 31.25%

Secondary Education 71 48%

College education 19 13.8%

University Education 8 40%

Post University Education 1 100%

Total 114 32.2%

Source: Authors’ Calculations

Table 4: Analysis of Sectors by Technology and Innovations

Sector Used Technology and Sectoral Innovations in Business Percentage

Food and Agriculture 14 56%

Personal and professional 8 24.24%

Education 3 33.3%

Retail 89 31%

Total 114 32.2%

Source: Authors’ Calculations

EXPLORATORY DATA ANALYSIS

The data was subjected to exploratory data analysis to elicit the blame of assumption of normality, multi-collinearity, linearity, homoscedasticity and to detect multivariate outliers and influential statistic. Composite/ latent variables were created from already observed/ manifest variables from the survey tool which were well researched from literature, hence no need to perform exploratory factor analysis.

NORMALITY TEST

A normality test was conducted to check the distribution of variables before subjecting them to regression analysis, as the normality of variables involved enhances the solution.

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Multivariate normality is assumed when statistical inference is used to determine a number of factors. Among single variables, normality is assessed by using kutrosis and skewness (Tabachnick and Fidell, 2007). Hence, the distributions of the variable need to be examined for kurtosis and skewness. Table 5 gives the normality statistics of the variable (see Table 5).

Mean Std. Std. Variance Skewness Std. Error Kurtosis Std. Error Error of Deviation of of

Mean Skewness Kurtosis

TI 2.91 0.04 0.74 0.55 -0.56 0.13 0.46 0.26

Table 5: Normality Test

Source: Authors’ Calculations

TI PC

Kurtosis/ Std. Error 1.76 9.08

Table 6: Kurtosis

Source: Authors’ Calculations

KURTOSIS

Kurtosis can be understood as the extent to which data would be concentrated in the peak versus the tail. A positive value indicates that data is concentrated in the peak, while a negative value indicates data concentration in the tail. Dividing the kurtosis statistic by its standard error helps one determine if the values of the standard scores are significantly different from normality. A concern arises when the value of the kurtosis statistic divided by its standard error is greater than z = +3.29 (p < 0.001, two-tailed test) (Tabachnick and Fidell, 2007).

Table 6 indicates that almost Technology and Innovation does not deviate from normality apart from performance indicators (see Table 6).

Table 7 indicates that there is no multi-collinearity because the value of the Variance Inflation Factor (VIF) of technology and innovation has not exceeded 10. In this case the VIF value of 1.317 is quite low and hence eliminates the worry of multi-collinearity. The VIF is used to indicate whether there exists a strong linear relationship between a predictor variable and the remaining predictors. A predictor may also have only a moderate/weak association with other predictors when considered in terms of correlation, but yet have a high value of R when regressed against other predictors, as indicated by Walpole, Myers, Myers and Ye (2007). While there is no set rule of thumb on numerical values to compare VIF, it is generally believed that if any VIF exceeds 10, there is reason for at least some concern (see Table 7).

Table 7 also indicates the tolerance value for Technology and Innovation. Exploring the output on Table 7 above, under colinearity statistics there is tolerance values for

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the ‘Technology and Innovation’ variable. Tolerance refers to the degree to which a predictor can itself be predicted by the other predictors in the model. A higher value of tolerance implies lesser overlap with the other variables. While a higher tolerance value implies better usefulness of the predictor to the analysis, a smaller value means a higher degree of collinearity. For this case, the tolerance value was closer to 1, therefore a high value suggesting low degree of colinearity and therefore the more useful the predictor (Technology and Innovation) to the analysis. The tolerance value of 0.759 indicates that the model used in this study is quite stable. The tolerance values are expected to be high, closer to 1.0 (see table 7).

ANALYSES AND DISCUSSION

H 1

: Baseline characteristics have a significant influence over Technology and Innovation (T&I) and performance of women SSEs in Nairobi, Kenya.

The following model was used to test the first hypothesis

Y =  o

+ ( 1

X 1 + 

2 X

2 + 

i X

i ) + 

1 X

1 + 

2 X

2 + X

i + 

Y =  o + (

1 X

1 + 

2 X

2 + 

3 X

3 + 

4 X

4 + 

5 X

5 + 

6 X

6 + 

7 X

7 + 

8 X

8 )

+  1

X 1 + 

2 X

2 + 

3 X

3 + 

4 X

4 + 

5 X

5 + 

6 X

6 + 

7 X

7 + 

8 X

8 + 

where,

X 1

= Education level regressed together with other baseline characteristics on performance

X 2

= Marital status regressed together with other baseline characteristics on performance

X 3

= Ethnicity regressed together with other baseline characteristics on performance

X 4

= Age regressed together with other baseline characteristics on performance

X 5

= Education regressed individually on performance indicators

X 6

= Marital status regressed individually on performance indicators

X 7

= Ethnicity regressed individually on performance indicators

X 8

= Age regressed individually on performance indicators

When the four baseline characteristics (marital status, ethnicity, age and education) of women entrepreneurs were analyzed against the composite index of performance

B Std. Error Beta t Sig. Tolerance VIF

Technology & Innovation –19.825 7.963 –0.144 –2.489 0.013 0.759 1.317

Table 7: Colinearity Test

Source: Authors’ Calculations

Unstandardized Coefficients

Standardized Coefficients

Collinearity Statistics

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indicators (sales, assets capital and number of employees and profits growth), results indicated that there was no statistical significance between the baseline characteristics apart from for two individual constructs in the baseline characteristics and the components of performance as shown by the p-values of 0.345, 0.077, 0.010 and 0.595 in education, marital status, ethnicity and age respectively on the performance. However, when all the baseline characteristics were computed individually and regressed hierarchically against performance index (all components of performance treated as a composite variable) the results indicated that only one individual construct in the baseline characteristics was significant to performance as controlling for Technology and Innovation. Marital status (with a p-value of 0.076) had a significant effect on the relationship between the dependent and the independent variables. Therefore we could conclude that the marital status as control variable had a significant effect over and above the independent variables on the dependent variable. The rest did not have any significant impact on the relationship.

Further, when a composite index of all the baseline characteristics was computed and regressed against individual indicators of performance namely sales, assets capital number of employees and profits the results indicated were not significant to performance as shown by the following p-values of 0.152, 0.484, 0.433, 0.282 and 0.543 in the sales, assets, capital, number of employees and profits, respectively. Therefore, the aggregate effect of all the baseline characteristics together indicated that the baseline characteristics did not influence Technology and Innovation (T&I) and hence performance of women SSEs in Nairobi, Kenya.

Hypothesis 1 was rejected. Baseline characteristics had no significant influence over Technology and Innovation (T&I) and performance of women SSEs in Nairobi, Kenya (see Table 8).

B Std. Error Beta Lower Upper Bound Bound

Education 0.101 0.107 0.191 0.946 0.345 –0.075 0.277

Marital 0.184 0.104 0.262 1.774 0.077 0.013 0.355

Ethnicity –0.495 0.190 –0.522 –2.605 0.010 –0.808 –0.182

Age 0.061 0.115 0.079 0.532 0.595 –0.128 0.250

Education 0.146 0.147 0.277 0.991 0.322 –0.097 0.388

Marital 0.185 0.104 0.264 1.782 0.076 0.014 0.356

Table 8: Mediation/Controlling Effect of Baseline Characteristics on Technology and Innovation against Performance

Unstandardized Coefficients

Standardized Coefficients

90.0% Confidence

Interval for BModel t Sig.

Coefficientsa,b

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H 2

: There is a significant relationship between Technology and Innovation (T&I) and performance of women owned and/or managed SSEs in Nairobi, Kenya.

The following regression model was used:

Y =  0 + 

1 X

1 + 

The results generated were as follows:

Y = 2.147546 + 0.22237 X 1 + 

The Hypothesis was tested at a 10% level of significance.

DISCUSSION

The findings indicated that H 2 should not be rejected. Technology and Innovation

has a significant relationship to performance (see Table 9). The coefficient of Technology & innovation were found to be significant (at 5% level of significant) to performance.

B Std. Error Beta Lower Upper Bound Bound

Table 8 (Cont.)

Unstandardized Coefficients

Standardized Coefficients

90.0% Confidence

Interval for BModel t Sig.

Note: a. Dependent Variable: Performance indicators Factor b. Linear Regression through the Origin * ** *** 10 %, 5 % and 1 % levels of significance respectively.

Source: Authors’ Calculations

Coefficientsa,b

Ethnicity –0.222 0.221 –0.235 –1.008 0.314 –0.586 0.142

Age 0.016 0.121 0.020 0.128 0.898 –0.184 0.215

Technology & 0.004 0.061 0.004 0.057 0.954 –0.098 0.105 Innovation Factor

Variables Coefficient Std Error t-Statistic p-value

Technology & Innovation 0.22237 0.108046 2.05808 0.0453**

C 2.147546 1.029564 2.085879 0.0371

R-squared 0.710819

Adjusted R-squared 0.617868

F-statistic 1.997644

Prob. (F-statistic) 0.001309

Table 9: Direct Effect of Regression Results of the Independent Variable against Performance

Source: Authors’ Calculations

Note: * ** *** 10 %, 5 % and 1 % levels of significance respectively.

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Tables 5 to 9 indicate the adoption of new technology within enterprises is actually a crucial determinant to the success of small businesses. With an R square of 0.710819, Results in Table 9 shows that 71% of the changes in performance of women SSEs can be explained by the application of Technology and innovation of the same enterprises.

Technology also plays a crucial role in giving any business enterprise a competitive advantage in performance improvement and enhanced service delivery. Information technology in particular has become an indispensable ingredient in several strategic thrust that business have initiated to meet the challenges of change. Such strategic thrust include internet working, the internet, intranets and other types of networks, which have become the primary information technology infrastructure that supports the business operations. This is especially evident in electronic commerce.

CONCLUSION

Although women operating SSEs in urban Kenya do not have a research and innovation department in their organizations, they do support new ideas inclusion into their businesses. The owners allow their employees (intrapreneurs) an opportunity to bring on board innovative practices, but most often than not the entrepreneurs themselves are the initiators of the new ideas. They have also taken the advantage of handset cellular phones to improve their business operations. The cellular phones acted as their inventories for keeping customer contacts, marketing new products and means of payments by customers/clients. The women entrepreneurs also use the ‘M-pesa’ (money transfer service through cellular phones),‘airtel money’ and orange money as a source of banking facility and money transfer service to their suppliers and banks. Using information technology for business re-engineering frequently results in the development of information systems that help give a company a competitive advantage in the market place.

This study therefore validates existing theory on innovation and resource base view that innovation brings in a new equilibrium which enhances performance and increases profits of an organization which confirms Schumpeter’s (1934) theory of innovation. It also affirms that having valuable, rare non immutable and non substitutable resources can enhance performance of firms as posited by Penrose (1959). The study also contributes new thinking to the already existing body of knowledge by breaking the impasse on whether innovation enhances performance or not. This study’s results have indicated that Technology and innovation contributes more than 70% of the changes in performance of women SSEs.

LIMITATIONS, SCOPE AND SUGGESTIONS FOR FURTHER RESEARCH

This study was limited to use and adoption of Mobile Telephony and its effect on performance of women SSEs. The study’s context also was restricted to Nairobi East

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(urban Kenya). We therefore recommend that a similar study be conducted to include other forms of technology and innovations and their effects on performance of women SSEs. Other studies can also focus on male owned and/managed enterprises as well as the focus be directed to rural businesses which were not within the scope of this study.

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