This chapter provides information of the results of the data analysis and discussion of the outcomes. SPSS software was used to calculate mean, standard deviation and correlations between variables. SmartPLS software was used to test hypotheses.
Correlation Analysis
According to table 4.1, there is a significant correlation between the size of the business with the age of the business(r=0.261, p<0.01). This might be due to the fact that almost all MSMEs have more full time employees compared with the beginning or start up of the business. This might indicate that these businesses have experienced a level of growth through time as they needed to hire more full time employees to attend the demand of the product or service they provide. Table 4.1 also showed that there is a negative correlation between the size of the business and the network assistance the incubator program provides to the SME(r= -0.298, p<0.01). This means that companies with larger number of employees seem to receive fewer networking assistance. This could signify that the incubators in Nicaragua might prioritize those micro enterprises to help them to establish connections with appropriate networks such as other MSMEs, vendors, fairs etc. because micro and small business might need more support in this area compared to medium size enterprises.
There was as well, a negative correlation between the age of the business and the capacity to respond to the market demand (r= -0.253, p< 0.05). This might indicate that the older the business the less the capacity of the business to respond the market demand or vice versa. It was expected by the researcher that the older the MSMEs, the higher their capacity to respond to market demand. This was expected because those MSMEs that have been on
the market for a longer time, are supposed to know their customers and the market behavior and therefore be more competitive (Abimbola, 2001). However, the result of this study contradicts that thought.
In the case of technology assistance, the Table 4.1 showed significant correlation with the financial assistance (r= 0.276, p<0.01) and the training assistance (r=0.309, p<0.01).
Nevertheless, because the coefficients are not too large; there is no serious concern of multicollinearity. Correlation between technology assistance and formal organization administration was also identified on the table (r= 0.333, p<0.01). This might indicate that those MSMEs that received technology assistance might be more likely to perform better in the formal organization administration of their business. The implementation of technology influences MSMEs competitiveness and contributes to effective reduction of costs and standardization of product/service among other benefits. The introduction of internet and web base information technologies also enables MSMEs to improve organizational and management capabilities such as automation of clerical procedures etc. (Mowery, 1988;
OECD, 2000). In the case of financing assistance, no correlation with any of the indicators of performance was identified. This is an unexpected result because those SME that count with the financial resources are more likely to invest on their product/service, add feature to existing product/service, explore undiscovered niches, automate their clerical processes etc.
(OECD, 2006). For networking assistance, a significant correlation with formal organization administration was identified (r= 0.310, p<0.01). This might suggest that the contact with the appropriate networks may help MSMEs to perform better in the formal organization administration. According to Gibb (1997), the network channels include customers, suppliers, bankers, accountants, solicitors, agents, marketing channels, workers and regulatory authorities as well as (more intimately) acquaintances, friends and family. In the case of training assistance, a significant relation with innovation(r=0.339, p<0.01) and formal
organization administration was identified (r=0406, p<0.01). This could indicate that when training assistance is provided to MSMEs, innovation and formal organization administration is enhanced. According to Jennings and Banfield (1993), training is a powerful source of change. Those MSMEs that engage in the process of training are more likely to have the tools to overcome future internal and external difficulties and to insert in globalized and competitive economies. The power of information and knowledge is what makes businesses dynamic, creative and innovative. It was expected training assistance to be the service offering more likely to positively correlate with the performance of MSMEs. Those MSMEs that engage in the process of training may have a bigger chance to deal with all the external factors that may affect their businesses (Ellinger, Watkins, & Bostrom, 1999).
The indicators of performance also presented significant correlation between each other.
For example, the innovation of product/service significantly correlates with the quality of a product/service (r=0.246, p<0.05) and with the formal organization administration (r=0.319, p<0.01). Product innovation refers to any new product, process or service that has been implemented or developed and it can be categorized as technical (technologies, product and services) or administrative (new procedures) innovation depending on the type (Van de Ven, 1986). Also the quality of the product/service shows significant correlation with the formal organization administration (r=0.402, p<0.01). Reeves and Bednar (1994) use excellence, value, conformance to specifications and meeting consumer expectations to define quality because only by knowing customers expectations and needs, using quality control processes or practices and adding extra value to the product/service MSMEs offer, success, development and growing will be achieved. Most of the results of correlation were expected by the researcher. However, it was unexpected that the time the MSMEs spent in the incubator program did not show any significant correlation with any of the other indicators of performance such as innovation, quality and the formal organization administration. It was
expected that those MSMEs that have spent more time in an incubator program might be more likely to perform better in all these aspects. However, it seems that what matters is the quality of the assistance and not the time the MSME spend with the incubator or program of assistance.
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Table 4.1.
Correlation Analysis
Variable Mean S/D 1 2 3 4 5 6 7 8 9 10 11
1.Full time employees a. 1.51 0.59
2.Age of the Business b. 4.15 1.78 .261**
3.Time in the program c. 4.06 2.20 -.132 .180
4.Technology Assistance 1.10 1.38 -.079 -.035 .084 (0.95)
5.Finance Assistance 1.38 1.71 .045 .060 .056 .276** (0.90)
6.Networking Assistance 2.57 1.50 -.298** -.021 .193 .142 -.132 (0.96)
7.Training Assistance 2.15 1.75 -.072 -.120 .095 .309** -.003 .187 (0.96)
8.Innovation 3.35 0.89 .068 .191 .175 .162 .061 .058 .339** (0.80)
9.Quality 4.33 0.63 .192 .084 -.077 .188 .039 .046 .115 .246* (0.90)
10.Capacity to respond to market demand 2.98 0.89 -.143 -.253* -.101 .071 -.159 .041 .008 -.038 .012 (0.70)
11.Formal organization administration 3.41 0.86 -.040 -.032 -.071 .333** .077 .310** .406** .319** .402** -.003 (0.84) Note: Numbers in the brackets represent the Cronbach’s Alpha values of the variables
a.1=Micro (1-5), 2=Small (6-30) and 3=Medium (31-100).
b.1= more than 1 year, 2= more than 2 years, 3= more than 3 years, 4= more than 4 years, 5= more than 5 years and 6= more than 5 years
c. 1=1-6 months, 2=7-12 months, 3=13-18 months, 4=19-24 months,5= 25-30 months , 6=31-36 months and 7=graduated (>37 months ).
Model Testing in PLS
PLS software allowed the researcher to simultaneously test the relationship between the dependent and independent variables. This was done by duplicating the sample and t-value of the duplication through bootstrapping. For this study it was recommended to run bootstrapping at a 5000 sample (Hair, Ringle, & Sarstedt, 2011). PLS also allowed the researcher to review how much the variance of the indicators of performance (dependent variables) is explained by the variance of the service offerings (independent variables) incubators provide to MSMEs through the coefficient of determination (R2). The closer the R2 is to 1, the better, however; R2 values of 0.02, 0.13 and 0.26 can be used as conventional measures to assess weak, medium or strong R2 (Cohen, 1998).
On the other hand, the path coefficient indicates the relationship between the dependant and independent variable. This relationship can be interpreted as positive or negative, meaning by negative that when one variable increase the other one decrease.
According to Moore, McCabe, Duckworth and Alwan (2009), the critical t-value for the two-tailed test used in this model is considered weak at the level of 1.660 (*), moderate at 1.984 (**), and strong at 2.626 (***).
Table 4.2 shows the path coefficient, error, t-value and R square of the analysis for this research.
Table 4.2.
Path Coefficient, Error, T-values and R-square
Age of the business -> Formal organization administration 0.0453 0.0729 0.0538 0.0538 0.8421
Age of the business -> Innovation 0.2 0.2006 0.1 0.1 2.0013
Age of the business -> Marked demand -0.1832 -0.1905 0.0924 0.0924 1.9819
Age of the business -> Quality 0.0542 0.0921 0.069 0.069 0.7854
Size of the business -> Formal organization administration 0.1085 0.1197 0.082 0.082 1.3225
Size of the business -> Innovation 0.0309 0.0974 0.0718 0.0718 0.4304
Size of the business -> Marked demand -0.0344 -0.0961 0.0728 0.0728 0.4725
Size of the business -> Quality 0.254 0.2538 0.0917 0.0917 2.7686
Time in the program -> Formal organization administration -0.1552 -0.1644 0.0887 0.0887 1.7506
Time in the program -> Innovation 0.0968 0.1188 0.08 0.08 1.2091
Time in the program -> Marked demand 0.0424 0.1231 0.0898 0.0898 0.472
Time in the program -> Quality -0.1025 -0.126 0.0828 0.0828 1.2373
Effect of main variables
Technology -> Formal organization administration 0.2062 0.2141 0.0884 0.0884 2.3333
Technology -> Innovation 0.0549 0.0993 0.0746 0.0746 0.7361
Technology -> Marked demand 0.1181 0.1395 0.0985 0.0985 1.1984
Technology -> Quality 0.2347 0.2452 0.078 0.078 3.0106
Financial -> Formal organization administration 0.0566 0.1031 0.0743 0.0743 0.7615
Financial -> Innovation 0.021 0.1074 0.0781 0.0781 0.2695
Financial -> Marked demand -0.2644 -0.2701 0.1006 0.1006 2.628
Table 4.2. (continued)
Original Sample (O)
Sample Mean (M)
Standard Deviation (STDEV)
Standard Error (STERR)
T Statistics
(|O/STERR|) R Square
Financial -> Quality 0.0612 0.0967 0.0694 0.0694 0.8819
Networking -> Formal organization administration 0.3016 0.3046 0.0992 0.0992 3.0399
Networking -> Innovation 0.0691 0.1143 0.0832 0.0832 0.8309
Networking -> Marked demand -0.0301 -0.1079 0.081 0.081 0.3719
Networking -> Quality 0.1385 0.1533 0.0886 0.0886 1.5636
Training -> Formal organization administration 0.2832 0.29 0.096 0.096 2.9497
Training -> Innovation 0.2177 0.224 0.0997 0.0997 2.1843
Training -> Marked demand -0.0322 -0.0986 0.0732 0.0732 0.4395
Training -> Quality 0.029 0.0859 0.0636 0.0636 0.4566
Innovation of product/service 0.1279
Quality of product/service 0.1592
Capacity to respond to market demand 0.1006
Formal organization administration 0.3194
Figure 4.1 illustrates the hypothesis testing, level of significance of the relation, path coefficients and r square value.
The results showed that hypothesis 1 is partially supported (refer to table 4.3) because the technology assistance provided by business incubators positively affects only the quality of product/service (β=0.055, t>2.626) and formal organization administration (β=0.206, t>1.984). It was also expected by the researcher for technology to positively affect innovation and capacity to respond to the market demand. According to Mowery (1988), technology is crucial for MSMEs transition to innovation. Technology introduction to MSMEs functioning cannot only be interpreted in updated machinery but automation of processes and operations.
Updated machinery permits the standardization of product hence higher opportunities to enter international markets. Automation of processes allows MSMEs to be more efficient and effective, therefore; more able to respond to the market demand. Refer to figure 4.1.
Hypothesis 2 is rejected because the financial assistance provided by business incubators does not positively affects any of the indicators of performance hence is eliminated from the model. This is an unexpected result because financial resources provide MSMEs owners more chances to carry out strategies, innovate, invest, adopt technologies etc.
All these actions positively influence the business performance (Boden & Nucci, 2000).
Hypothesis 3 is also partially supported because the networking assistance provided by business incubators positively affects only the formal organization administration (β=0.302, t>2.626). It was expected by the researcher for networking assistance to positively affect innovation of product/service, quality of product/service and capacity to respond to market demand. According to the OECD (2004b), those MSMEs that count with a solid network of contacts have more chances to develop innovation and risk to explore domestic and international markets. This happens because through the right network of contacts
MSMEs owners are able to share information, experiences, ideas and strategies. Refer to figure 4.1.
Hypothesis 4 is partially supported as well because the training assistance provided by business incubators seems to positively affect the innovation of product/service (β=0-218, t>1.984) and the formal organization administration (β=0.283, t>2.626). Other studies have shown that there is a positive relationship between the good managerial performance and business success (Cooper, Woo & Dunkelberg, 1989; Stuart & Abetti, 1990). The researcher also expected training to positively affect quality of product/service and capacity to respond to the market demand. Feigenbaum (1982) described quality as the most significant factor that conducts businesses to economic development. Quality makes MSMEs competitive and allows them to adventure in unknown local and international markets. Being competitive for a MSME implies that they know their customer and market behavior. Those successful MSMEs know the product they offer, how they offer it, where they offer it and to whom they offer it. Therefore, there is a higher probability for them to see opportunities where other see threatens. (Wiklund & Sheperd, 2003).
The control variables that seem to have an effect on the indicators of performance are size of the business, age of the business and time in the program. The first one seems to have an effect on quality of product (β=0.254, t>2.626). The second one, appears to have a positive effect on the innovation of product/service (β=0.2, t>1.984) and a negative effect on the capacity to respond to the market demand (β=-0.183, t>1.984). And the third one has a negative effect on the formal organization administration (β=-0.155, t>1.660).
This could mean that those MSMEs with higher number of employees are more likely to pay attention to the quality of the product/service they offer. Those that have been in the market for longer time are more likely to be innovative but not necessarily to respond to the
market demand. For example, new MSMEs could have a high capacity to respond to market demand or older MSMEs could have less capacity to respond to the market demand. Other studies suggest that MSMEs grow through a learning process that allows them to adapt to the different conditions of the market and considered the relationship between the firm age and their capacity to respond and adjust to the market demand positive (Peña, 2004).
The effect of the time in the program and the formal organization administration indicates that those MSMEs that have stayed less time in an incubator program perform better than those that have stayed longer in terms of formal organization administration. It could also mean that the performance of the formal organization administration of those MSMEs that have stayed longer in an incubator program is poorer.
.
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Figure 4.1. Hypotheses testing, path coefficient and R squares
Note: The stars represent the level of significance or t-value, the number in parenthesis show the path coefficient and the number in the circle the R square. Refer to figure 4.2, 4.3 and 4.4 for the original models.
Figure 4.2. PLS model
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Figure 4.3. PLS algorithm result
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Figure 4.4. PLS bootstrapping result
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Table 4.3.
Overview of Hypotheses Testing
Hypotheses Results
H1: The technological assistance business incubator provide to MSMEs,
positively affects their performance. Partially supported
H1a: The technology assistance business incubator provided to MSMEs,
positively affects their product/ service innovation. Not supported H1b: The technology assistance business incubator provided to MSMEs,
positively affects their quality of the product/ service product/ service.
Supported
H1c: The technology assistance business incubator provided to MSMEs, positively affects their capacity to respond to market demand.
Not supported
H1d: The technology assistance business incubator provided to MSMEs, positively affects their formalization of internal administrative organization.
Supported
H2: The financial assistance business incubator provide to MSMEs, positively affects their performance.
Not supported
H2a: The financial assistance business incubator provide to MSMEs,
positively affects their product/service innovation. Not supported H2b: The financial assistance business incubator provide to MSMEs,
positively affects their quality of product/service. Not supported H2c: The financial assistance business incubator provide to MSMEs,
positively affects their capacity to respond to the market demand. Not supported H2d: The financial assistance business incubator provide to MSMEs,
positively affects their formalization of internal administrative organization.
Not supported
H3: The networking assistance business incubator provide to MSMEs, positively affects their performance.
Partially supported
H3a: The networking assistance business incubator provide to MSMEs, positively affects their product innovation.
Not supported
H3b: The networking assistance business incubator provide to MSMEs,
positively affects their quality of the product/service. Not supported (continued)
Table 4.3. (continued)
Hypotheses
Results
H3c: The networking assistance business incubator provide to MSMEs, positively affects their capacity to respond to the market demand.
Not supported
H3d: The networking assistance business incubator provide to MSMEs, positively affects their formalization of internal administrative organization.
Supported
H4: The training assistance business incubator provide to MSMEs,
positively affects their performance. Partially supported
H4a: The training assistance business incubator provide to MSMEs, positively
affect their product innovation. Supported
H4b: The training assistance business incubator provide to MSMEs, positively affect their quality of the product/service.
Not supported
H4c: The training assistance business incubator provide to MSMEs, positively affect their capacity to respond to the market demand.
Not supported
H4d: The training assistance business incubator provide to MSMEs, positively affect their formalization of internal administrative organization.
Supported
Post Hoc Analysis
A post hoc analysis was conducted to assess which one of the incubators provided better assistance in terms of technology, financing, networking and training. For this reason the seven incubators that participated in this study were categorized as follows:
• Incubator A: Dynamic Entrepreneurs (30 tenants).
• Incubator B: Prodef (30 tenants).
• Incubator C: The 5 remaining incubators (40 tenants).
ANOVA test allowed the researcher to run multiple comparisons between the groups in question by comparing their means (Kinnear & Gray, 2000). This test permitted the researcher to provide further suggestions and recommendations to those incubators that participated in this research and for future studies.
According to Table 4.4, there are significant differences between the level of technology assistance and networking assistance provided by the incubators in question.
Table 4.5 showed that Incubator A seemed to be better than Incubator C in the technology assistance provided to the MSMEs in Nicaragua. In the case of the networking assistance provided by these incubators, Incubator A seemed to be better than the others.
Overall speaking, Incubator A seems to outperform others in all four types of assistance. The other incubators that belong to the group of Incubator C should consider revising and improving the assistance they provide in terms of networking and technology.
Table 4.4 show the level of significance in mean differences of the service offerings of incubators A, B and C.
Table 4.4.
ANOVA Post hoc Analysis Level of Significance
ANOVA
Between the multiple comparisons among the Incubator A, B and C by using Scheffe test.
Dependent Variable (I) Incubator (J) Incubator Mean
Difference (I-J) Std. Error Sig.
Incubator B .44007 .34526 .447 Incubator A
Incubator C .87897* .32738 .031 Incubator A -.44007 .34526 .447 Incubator B
Incubator C .43890 .32438 .404 Incubator A -.87897* .32738 .031 Technology
Assistance
Incubator C
Incubator B -.43890 .32438 .404 Incubator B .58898 .43831 .409 Finance
Assistance Incubator A
Incubator C .50833 .41561 .476
Table 4.5.
ANOVA Post hoc Analysis: Scheffe Test
Incubator A -.58898 .43831 .409 Incubator B
Incubator C -.08065 .41181 .981 Incubator A -.50833 .41561 .476 Incubator C
Incubator B .08065 .41181 .981 Incubator B .05215 .37130 .990 Incubator A
Incubator C .93566* .35207 .033 Incubator A -.05215 .37130 .990 Incubator B
(*) The mean difference is significant at the 0.05 level.
Note: Incubator A: Dynamic Entrepreneur (30 tenants), Incubator B (30 tenants): Prodef and Incubator C: the other 3 incubators.