Cooperation for Innovation and its impact on Technological and Nontechnological Innovations: Empirical Evidence for Western Ethiopian Manufacturing SMEs
Gemechu Bekana Fufa
Department of Statistics, College of Natural and Computational Science, Wollega University,
Nekemte, Ethiopia.
*Corresponding Author Email: gemechu.bekana@yahoo.com
ABSTRACT:
We advance the notion that cooperation for innovation can demonstrate beneficial effects on firms' innovation performance. Whilst most empirical studies to date have focused on the impact of cooperation on technological product and process innovations, this study adopts a broader definition of innovation that encompasses both technological innovations and nontechnological organizational and marketing innovations taking into account their complementary and interrelated nature. Drawing on a unique sample of traditional manufacturing small and mediumsized enterprises (SMEs) in three zones, the study shows that cooperation for innovation increases firms' innovativeness. This conclusion is based on the positive association across the breadth of cooperation, i.e. the number of cooperative ties, with each measure of innovation outcomes, without exhibiting diminishing returns. In addition, empirical evidence suggests heterogeneous effects of individual cooperative ties on innovation performance. Overall, the results indicate that a portfolio approach to cooperation for innovation enhances innovation performance in traditional manufacturing SMEs. Finally, the findings confirm the complementary nature of technological and nontechnological innovations.
KEYWORDS: Cooperation, technological and nontechnological, Innovation and Ethiopia.
1. INTRODUCTION:
1.1 Background of Study:
An innovation is the implementation of a new or significantly improved product, good or service; a new marketing method; or a new organizational method in business practices, workplace organization or external relations (OECD/Eurostat, 2005). The innovation can be technological and/or nontechnological.
Technological innovations are usually associated with product and process innovation, whereas nontechnological innovations are generally associated with organizational and marketing innovations. Technological and nontechnological innovations are highly interconnected, as shown by firmlevel innovation data.
A product innovation is the introduction of a good or service that is new or has significantly improved characteristics or intended uses; a process innovation refers to the implementation of a new or significantly improved production or delivery method. A process innovation is the implementation of a new or significantly improved production or delivery method. This includes significant changes in techniques, equipment and/or software. Process innovations can be intended to decrease unit costs of production or delivery, to increase quality or to produce or deliver new or significantly improved products.
This study investigates how cooperation for innovation with various partners affects innovation output in traditional manufacturing SMEs. Since the mid1990s, not only multinational companies but also small and mediumsized enterprises have tended to engage more extensively in cooperation activities (De Faria, Lima, and Santos, 2010). Nowadays firms cooperate with a diverse network of parties, which enables them to access external knowledge and resources and, in that way, complement their internal innovation activities. Empirical work on the performance effects of RandD cooperation and, more broadly, cooperation for innovation, have mostly focused on technological product and process innovations (Pippel, 2014; Sánchez–Gonzáles, 2014). Similarly, Pittaway et al. (2004) argue that, on the one hand, more research is necessary on the relationship between technological process and nontechnological organizational innovations, and, actual cooperation for innovation, on the other hand. In line with these developing concerns in extant literature, this study investigates whether the impact of cooperation is heterogeneous and conditional on actual types of innovation.
1.2 Importance of Cooperation for innovation:
Cooperation for innovation is prominent in the literature on open innovation, a concept introduced by Pittaway et al. (2004). Namely, the literature on open innovation recognizes two distinct forms of open innovation practices: 1) inbound practices associated with the acquisition of external knowledge; and 2) outbound practices pertinent to the commercialization phase of the innovation process, such as venturing and selling out of Intellectual Property (IP) rights. Based on this dyadic categorization, cooperation for innovation is regarded as an inbound open innovation practice. Similar to the resourcebased view of the firm, the open innovation literature proposes that external and internal innovation sources are complementary, with both synergistically contributing to firms' innovation performance (De Faria et al., 2010).
Horizontal cooperation with competitors is most frequently found in high technology sectors (Mariti and Smiley, 1983) and often sought as a cost and/or risk reduction strategy. Regarding nontechnological innovations, cooperation with competitors may allow firms to realize and adopt successful organizational structures of their rivals (Pippel, 2014). In addition, firms can develop and implement joint pricing and promotion strategies, or, if cooperating in designing new products, firms can engage in a common marketing strategy for a jointly developed new product (Pippel, 2014; Sánchez–Gonzáles, 2014). Pippel (2014) further recognizes that, when cooperating for organizational innovations, competing firms might experience mimetic isomorphism (Garcia–Pont and Nohria, 2002), i.e. developing similar characteristics as a consequence of imitation. Thus, the likelihood of mimetic isomorphism reduces the probability of sustained cooperation with competitors on organizational innovations, under the assumption that cooperative partnerships are more based on pooling diverse, rather than similar resources.
The main advantage of cooperating with firms within the same enterprise group is substantially reduced risk of opportunistic behavior. Firms can cooperate with other firms in the same group on organizational innovations as well on marketing innovations, such as those related to pricing and marketing strategies (Pippel, 2014).
1.3 Statement of the Problem:
Our methodological approach takes into account the interconnection and ‘complementarities’ of technological and nontechnological innovations, which previously, to our knowledge, has not been a subject of empirical investigation. This study is one of only a few to investigate the impact of cooperation on nontechnological innovations (particularly in the context of SMEs) and is the first of its kind in this stream of research to take into account that technological and nontechnological innovations may be associated.
The aim of this study is twofold. First, we seek to understand how cooperation for innovation affects innovation performance in traditional manufacturing SMEs. To this end, cooperation relationships explored in the study include firms within an enterprise group, suppliers, customers, and competitors, other private sector firms (consultants, commercial labs and private RandD institutes), HEIs, and publicsector agencies. Innovation performance is measured by the introduction of both technological (product and process) and nontechnological (organizational and marketing) innovations. Our modeling strategy takes into account the complementary nature of all four types of innovation.
1.4 Objective of the Study:
· To Identify and estimates the cooperation status of western Ethiopian manufacturing SMEs.
· To examine the impact of control variables on innovation in the study.
· To analyzes the impact of cooperation on the innovation probability of firms.
· To analysis the marginal effects of selected variables on innovation.
1.5 Empirical evidence on the impact of cooperation on firm performance:
Cooperation with customers is usually reported as the most frequent type of interfirm cooperation. In the context of SMEs, several studies find that cooperating with customers and suppliers enhances product and process innovations (Kaminski, de Oliveira, and Lopes, 2008; Nieto and Santamaria, 2010). Indeed, Zeng et al. (2010) report that vertical cooperation with customers and suppliers has a larger positive impact on the innovation performance of Chinese SMEs than does horizontal cooperation with government agencies, universities and research institutes. Similar results are found in Nieto and Santamaria (2010) for Spanish SMEs. However, some studies indicate an increasing importance of research organizations in firms' innovation activities. For instance, Lasagni (2012), analyzing the sample of SMEs in six European countries, reports equally significant impacts of vertical cooperation and cooperation with research organizations on the introduction of product innovation.
Few studies of SMEs focus on interfirm cooperation (with customers, suppliers and competitors). Tomlinson and Fai (2013) found a highly significant impact of cooperation with suppliers on both product and process innovation in UK SMEs. Concerning other forms of interfirm cooperation, cooperation with customers only marginally increases the probability of introducing product innovation, while cooperation with competitors is insignificant for both forms of technological innovation. Parida, Westerberg, and Frishammar (2012) analyze interfirm cooperation in Swedish SMEs operating in the Information and Technology sector. Innovation performance is measured by the introduction of both radical and incremental product innovations. Performance effects of interfirm cooperation are differentiated with respect to the degree of novelty. In particular, horizontal cooperation with competitors (and noncompetitors) plays a more distinct role in the introduction of incremental innovation, whereas vertical cooperation with customers and suppliers is positively associated with radical product innovation.
2. Data and Methodologies:
This study was conducted in western Ethiopia. The study area focus on manufacturing firms located in western Ethiopia, in Illu Abba Bor Zone, Bunno Bedelle Zone and western wollega zone. Random samples of firms were then selected at the first stage and the data were then collected from the administration offices of the selected firms. On the second stage, the workers in each manufacturing firms were selected and the data were collected from the selected participants.
2.2. Variables and Methods of Data Analysis:
2.2.1. Variables in study:
The four dependent variables in the multivariate logit model are binary indicators measuring firms' engagement in technological and nontechnological innovations:
· Product innovation is equal to 1 if the firm introduced any new or significantly improved goods and services in the period 20062010 (zero otherwise);
· Process innovation is equal to 1 if the firm implemented a new or significantly improved production process, distribution method, or support activity for its goods or services (zero otherwise);
· Organizational innovation is equal to 1 if the firm introduced new business practices for organizing procedures, new methods of organizing work responsibilities and decision making or new methods of organizing external relations with other firms or public institutions (zero otherwise); and
· Marketing innovation is equal to 1 if the firm introduced significant changes to the design or packaging of a good or service, new media or techniques for product promotion, new methods for sales channels or new methods of pricing goods or services (zero otherwise).
In addition, we separately investigate the impact of cooperation for innovation on innovative sales measured as the share of total sales accounted for by sales arising from new products and/or processes introduced since 2006. The variable Innovative sales is a categorical variable: = 1 when innovative sales is equal to 0 percent; =2 when innovative sales ranges from 1 percent to 5 percent; =3 from 6 percent to 10 percent; =4 from 11 percent to 15 percent; =5 from 16 percent to 25 percent; =6 from 26 percent to 50 percent; and =7 when innovative sales are more than 50 percent of total sales.
The main explanatory variables measure firms' cooperation activities as dichotomous variables equal to 1 if the firm cooperates with the following potential partners (and zero otherwise): within group (Coop_within_group); suppliers (Coop_suppliers); customers (Coop_customers); competitors (Coop_competitors); consultants, commercial labs, and private RandD institutes (Coop_private sector); HEIs (Coop_HEIs); and government institutions and public research centers (Coop_public sector).
Control variables include a continuous variable (Size) to account for the heterogeneity of SMEs. We model exporting activities (Export) as a continuous variable measuring the share of total sales sold abroad in 2010. Exporting firms might be more innovative than their counterparts, as international competition creates more pressure on firms to innovate (Nieto and Santamaria, 2007; Belderbos et al., 2014). Moreover, exporting activities serve as a proxy for firms' foreign competitiveness (Herrera and Nieto, 2008). In addition, the model includes a variable measuring competitive pressure (Competition), which is equal to 1 if the firms responded 'Very strong' to the question: “How would you judge the competition in your main market(s)?", and zero otherwise. The theoretical industrial organization literature predicts that higher competitive pressure negatively affects innovation, because it reduces monopoly rent generated by innovating firms (Aghion et al., 2005).
Following Blundell, Griffith, and Van Reenan (1995), our models include variables measuring firmlevel “quasi fixed effects” (or initial conditions). These initial conditions control for firms' time invariant unobserved effects on innovation, i.e. firms' innovative capacity with respect to technological and nontechnological innovations at the beginning of the period covered by the survey (see also Radicic et al., 2014). By controlling for past innovative capacity, we take into account firms' absorptive capacity (see for example: Miotti and Sachwald, 2003). These effects are modeled by the following variables:
 The dummy variable that measures the resources invested in innovation in 2006 relative to 2010 (Resources) (DV = 1 if the firm’s response to the question "Five years ago did you devote?" was 'Fewer resources to innovation'; = 0 if 'About the same' or 'More');
 dummy variables measuring the firms' innovation capacities for introducing product/process/ organizational/marketing innovations within the industry in 2006 (respectively Capacity_product, Capacity_process, Capacity_org and Capacity_marketing) (DV = 1 for 'Above average' and 'Leading'; = 0 for 'Average' and 'Lagging');
2.2.2. Methods of Data Analysis:
This section sets up empirical models for analysis. To achieve the objective of the study, the following models were used to analysis the data collected from the study area.
a) The Z test for the difference between two population means:
Suppose that there are two samples drawn independently from two populations with mean µ_{1} and µ_{2}, respectively. Then, the test about the significance of the difference between the two means takes one of the following forms:
H_{0} : µ_{1 } µ_{2 } = 0 Vs H_{1} : µ_{1 } µ_{2 } _{} (1)
OR
H_{0} : µ_{1 } µ_{2 } = 0 Vs H_{1} : µ_{1 } µ_{2 }> 0 (2)
OR
H_{0} : µ_{1 } µ_{2 } = 0 Vs H_{1} : µ_{1 } µ_{2 }< 0 (3)
Where, H_{0} and H_{1} stand for the null and alternative hypotheses, respectively.
The test statistic is then given by:
Where, n_{1} is sample size from population1, n_{2} is sample size from population2, _{} is the mean of the sample taken from population1, _{} is the mean of the sample taken from population 2, _{} is the variance of the sample taken from population 1, _{} is the variance of the sample taken from population 2.
b) Logistic Regression Analysis:
Logistic regression is a popular modeling approach when the dependent variable is dichotomous or polytomous. This model allows one to predict the log odds of outcomes of a dependent variable from a set of variables that may be continuous, discrete, categorical, or a mix of any of these. Hosmer and Lemeshow (2000) have described logistic regression focusing on its theoretical and applied aspect.
In this study, for identifying the cooperation of firms for innovation: 1 = if the firm cooperates and 0 = if otherwise AND/OR 1 = if a firm does technological innovation and 0 otherwise.
COP= 

1, if there is a cooperation is there 
(5) 
0, other wise 
COP= 

1, if there is technological innovation 
(6) 
0, other wise 
In logistic regression analysis, it is assumed that the explanatory variables affect the response through a suitable transformation of the probability of the success. This transformation is a suitable link function of P, and is called the logitlink, which is defined as:
logit (P)= ln (P/(1P)) β_{0}+ βX(7)
where β_{0},_{ } β_{1}, β_{2, ...} β_{p }are the model parameters and_{ }X will the predictor/independent chosen variables. The transformed variable, denoted by logit(P) is the logodds and is related to the explanatory variables as in equation (7).
3. RESULTS AND DISCUSSIONS:
3.1. Descriptive Analysis:
This part of the paper discusses the major findings of the study. It begins with analysis of the descriptive result on the main variables used in the study. The result shows that there are 14 cooperates and 16 non cooperates in the sampled group. The mean percentage of cooperates and non cooperates was found to be 46.94% and 53.06% respectively as shown in table 1 below.
Cooperates Group 
Sample Size 
Mean 
Stand. Deviation 
Percentage 
Non – cooperates 
16 
56.86 
54.94 
56.06% 
Cooperates 
14 
43.2 
48.58 
46.94% 
Table 2: Descriptive Statistics main variables in the study.
Variables 
Mean 
Stand. Deviation 
Minimum 
Maximum 
Product innovation 
0.56 
0.65 
0 
1 
Process innovation 
0.34 
0.89 
0 
1 
Organizational innovation 
0.98 
0.32 
0 
2 
Marketing innovation 
0.75 
0.62 
0 
3 
Innovative sales 
4.36 
1.69 
1 
8 
Size 
35.19 
42.31 
1 
30 
Export 
2.34 
1.36 
0 
2 
Competion 
0.98 
0.78 
0 
3 
Resourses 
0.98 
0.36 
0 
1 
Capacity_product 
0.75 
0.84 
0 
1 
Capacity_process 
0.69 
0.29 
0 
1 
Capacity_org 
0.78 
0.78 
0 
1 
Capacirt_Marketing 
1.97 
1.09 
0 
0 
Coop_within group 
1.54 
1.41 
0 
0 
Coop_suppliers 
0.89 
0.97 
0 
0 
Coop_customers 
0.79 
0.32 
0 
3 
Coop_competitors 
0.74 
0.58 
0 
2 
Coop_private sector 
1.36 
0.64 
1 
6 
Coop_HEIs 
1.52 
0.99 
0 
4 
Coop_public sector 
1.02 
1.01 
0 
5 
To test the significance of this difference we used the one tailed test. The calculated Z using was found to be Z_{c} = 1.96, is less than the corresponding tabulated value 1.64, at a = 0.05. There is no difference in the proportion of cooperates and non cooperates and conclude that the percentage is higher in the noncooperates group. The mean, standard deviation, minimum and maximum computation of selected variables were stated in table 2 below.
3.2. Logit model Analysis:
The results from logit model shows the statistically significant variables were included in the model for the models: Dependent variables, Product innovation, Process innovation, Organizational innovation and Marketing innovation of table 3, 4, 5 and 6 respectively. From the logits analysis, after transformation, we can get the predicted probabilities. The constant and the coefficients are read directly from the second column in the 3, 4, 5 and 6 respectively. The standard errors (s.e.) of the coefficients are given in the third column. The fourth column is the ratio of the coefficient and the standard deviation. The fifth column gives the pvalue corresponding to the observed value, and should be interpreted like any pvalue. These pvalues are used to judge the significance of the coefficient. The values of p were smaller than 0.05 would lead us to conclude that the coefficient is significantly different from 0 at the 5% significance level. These values for each of the variables are given in the sixth column headed by Odds Ratio. They represent the change in odds ratio for unit change of a particular variable while the others are held constant. The 95% confidence intervals of the odds ratios are given in the last two columns of the table (see 3, 4, 5 and 6).
Table 3: Logit model analysis: Dependent variable, Product innovation
Odd 
95% C. I 

Variables 
Coeff. 
S.e. 
Ztest 
pvalue 
Ratio 
Lower 
Upper 
Constant 
0.361 
0.384 
0.98 
0.001 

Size 
0.142 
0.124 
1.032 
0.002 
1.20 
0.101 
1.520 
Export 
0.140 
0.685 
1.64 
0.003 
1.30 
0.110 
1.36 
Competion 
0.124 
0.974 
1.23 
0.002 
1.34 
0.100 
1.42 
Resourses 
0.321 
0.674 
1.24 
0.001 
0.98 
0.12 
1.36 
Capacity_product 
0.124 
0.974 
1.36 
0.002 
0.97 
0.011 
1.120 
Capacity_process 
0.124 
0.641 
1.25 
0.001 
1.23 
0.012 
1.340 
Capacity_org 
0.356 
0.641 
1.12 
0.001 
1.36 
0.210 
1.352 
Capacirt_Marketing 
0.245 
0.870 
1.10 
0.001 
1.69 
0.021 
1.980 
Coop_withingroup 
0.364 
0.913 
1.12 
0.003 
1.87 
0.250 
2.310 
Coop_suppliers 
0.241 
0.471 
1.31 
0.004 
0.97 
0.130 
1.530 
Coop_customers 
0.321 
0.541 
1.21 
0.005 
1.36 
0.210 
1.980 
Coop_competitors 
0.320 
0.697 
1.10 
0.001 
1.57 
0.120 
1.390 
Coop_privatesector 
0.410 
0.640 
1.13 
0.002 
1.69 
0.210 
1.680 
Coop_HEIs 
0.326 
0.321 
1.14 
0.003 
1.87 
0.142 
1.850 
Coop_publicsector 
0.510 
0.366 
1.20 
0.001 
1.97 
0.350 
1.852 
Dependent Variable: Product innovation LogLikelihood =  2.65 Pvalue < 0.005
Table 4: Logit model analysis: Dependent variable, Process innovation

Odd 
95% C. I 

Variables 
Coeff. 
S.e. 
Ztest 
pvalue 
Ratio 
Lower 
Upper 
Constant 
0.68 
0.12 
0.95 
0.002 
0.98 
0.248 
0.252 
Size 
0.741 
0.141 
1.23 
0.003 
1.003 
0.247 
0.253 
Export 
0.67 
0.23 
1.21 
0.004 
1.023 
0.246 
0.254 
Competion 
0.698 
0.147 
1.21 
0.001 
1.031 
0.249 
0.251 
Resourses 
0.698 
0.85 
1.111 
0.003 
1.23 
0.247 
0.253 
Capacity_product 
0.251 
0.36 
1.201 
0.002 
1.21 
0.248 
0.252 
Capacity_process 
0.362 
0.121 
1.201 
0.001 
1.021 
0.249 
0.251 
Capacity_org 
0.145 
0.21 
1.321 
0.003 
0.98 
0.247 
0.253 
Capacirt_Marketing 
0.365 
0.321 
1.201 
0.003 
0.95 
0.247 
0.253 
Coop_within group 
0.145 
0.141 
1.002 
0.003 
1.301 
0.247 
0.253 
Coop_suppliers 
0.25 
0.314 
1.021 
0.003 
1.201 
0.247 
0.253 
Coop_customers 
0.396 
0.321 
1.101 
0.001 
1.252 
0.249 
0.251 
Coop_competitors 
0.144 
0.621 
1.102 
0.002 
1.32 
0.248 
0.252 
Coop_private sector 
0.258 
0.12 
1.21 
0.003 
1.014 
0.247 
0.253 
Coop_HEIs 
0.147 
0.51 
1.21 
0.002 
1.201 
0.248 
0.252 
Coop_public sector 
0.214 
0.21 
1.2 

1.014 
0.25 
0.25 
Dependent Variable: Process innovation LogLikelihood =  1.65 Pvalue < 0.005
Table 5: Logit model analysis: Dependent variable, organizational innovation
Odd 
95% C. I 

Variables 
Coeff. 
S.e. 
Ztest 
pvalue 
Ratio 
Lower 
Upper 
Constant 
0.98 
0.251 
1.368 
0.003 
0.95 
0.347 
1.718 
Size 
0.845 
0.21 
1.26 
0.002 
0.987 
0.348 
1.61 
Export 
0.652 
0.321 
1.654 
0.004 
1.21 
0.346 
2.004 
Competion 
0.254 
0.21 
1.298 
0.001 
1.21 
0.349 
1.648 
Resourses 
0.365 
0.21 
1.698 
0.002 
1.210 
0.348 
2.048 
Capacity_product 
0.241 
0.231 
1.69 
0.001 
1.210 
0.349 
2.04 
Capacity_process 
0.89 
0.14 
10.2 
0.003 
1.201 
0.347 
10.55 
Capacity_org 
0.874 
0.362 
1.25 
0.001 
1.010 
0.349 
1.6 
Capacirt_Marketing 
0.358 
0.122 
1.36 
0.002 
1.003 
0.348 
1.71 
Coop_within group 
0.478 
0.136 
1.254 
0.003 
1.300 
0.347 
1.604 
Coop_suppliers 
0.654 
0.254 
1.81 
0.004 
1.240 
0.346 
2.16 
Coop_customers 
0.478 
0.365 
1.28 
0.004 
1.210 
0.346 
1.63 
Coop_competitors 
251 
0.854 
1.471 
0.002 
1.120 
0.348 
1.821 
Coop_private sector 
0.361 
0.852 
1.241 
0.003 
1.208 
0.347 
1.591 
Coop_HEIs 
0.21 
0.412 
1.36 
0.002 
0.987 
0.348 
1.71 
Coop_public sector 
0.326 
0.328 
0.97 
0.001 
0.937 
0.349 
1.32 
Dependent Variable: organizational innovation LogLikelihood = 1.35 Pvalue < 0.005
Table 6: Logit model analysis: Dependent variable, marketing innovation
Odd 
95% C. I 

Variables 
Coeff. 
S.e. 
Ztest 
pvalue 
Ratio 
Lower 
Upper 
Constant 
0.62 
0.85 
1.35 
0.002 
1.45 
0.4 
0.84 
Size 
0.874 
1.104 
1.604 
0.003 
1.704 
0.654 
1.094 
Export 
0.68 
0.451 
0.951 
0.001 
1.051 
0.9 
0.46 
Competion 
0.632 
0.862 
1.362 
0.001 
1.462 
0.412 
0.852 
Resourses 
0.854 
1.084 
1.584 
0.001 
1.684 
0.634 
1.074 
Capacity_product 
0.362 
0.592 
1.092 
0.02 
1.192 
0.142 
0.582 
Capacity_process 
0.258 
0.488 
0.988 
0.003 
1.088 
0.038 
0.478 
Capacity_org 
0.652 
0.882 
1.382 
0.004 
1.482 
0.432 
0.872 
Capacirt_Marketing 
0.241 
0.471 
0.971 
0.002 
1.071 
0.021 
0.461 
Coop_within group 
0.321 
0.551 
1.051 
0.003 
1.151 
0.101 
0.541 
Coop_suppliers 
0.222 
0.452 
0.952 
0.003 
1.052 
0.002 
0.442 
Coop_customers 
0.632 
0.862 
1.362 
0.002 
1.462 
0.412 
0.852 
Coop_competitors 
0.141 
0.371 
0.871 
0.001 
0.971 
0.079 
0.361 
Coop_private sector 
0.451 
0.681 
1.181 
0.002 
1.281 
0.231 
0.671 
Coop_HEIs 
0.671 
0.901 
1.401 
0.003 
1.501 
0.451 
0.891 
Coop_public sector 
0.362 
0.592 
1.092 
0.004 
1.192 
0.142 
0.582 
Dependent Variable: marketing innovation LogLikelihood = 1.85 Pvalue < 0.005
Concerning the control variables, firm size has a positive effect on organizational innovation, i.e. mediumsized firms are more likely to introduce this type of innovation than are smaller firms. Exporting activities have negative effects on process innovation. Very strong competitive pressure reduces the probability of introducing technological product and process innovations, but has no effect on nontechnological innovations. These two estimates are each statistically significant at the 1 percent level and consistent with the industrial organization prediction that high levels of competition adversely affect innovation. With respect to the quasi fixed effects, an increase in the total resources dedicated to innovation is beneficial to introducing process, organizational and marketing innovations, but, rather surprisingly, has no effect on product innovation. In contrast, the most significant impact (at the 1% level) on product innovation is found for established innovation capacity regarding this type of innovation. In other words, the probability of undertaking product innovation is associated with firms' innovative capacity (initial conditions) for product innovation. Established capacity for product innovation also has an impact on firms' current marketing innovation, consistent with the requirement for new products to be marketed.
In relation to the diagnostics of the logit models, our attention is focused on the estimated correlation coefficients. Each correlation coefficient ρ represents the correlation between the error terms in two equations. If the coefficient is statistically significant, that implies that the error terms are correlated and that the two equations should be estimated jointly (Greene, 2012, p. 747). In other words, a correlation coefficient measures the correlation between the outcomes after the observed heterogeneity (i.e. observed firm characteristics) is taken into account. The correlation coefficients between the error terms of four equations in Models 1 and 2 are presented in Table 7. Given that all combinations of correlation coefficients are highly statistically significant (at the 1% level), we can conclude that the logit model is an appropriate method for our analysis.
Table 7: Correlation coefficients for model 1, 2, 3 and 4
Correlation coefficients 
Model 1 
Model 2 
ρ_{21} 
0.792***(0.081) 
0.762***(0.076) 
ρ_{31} 
0.518***(0.114) 
0.488***(0.124) 
ρ_{41} 
0.556***(0.096) 
0.522***(0.101) 
ρ_{32} 
0.560***(0.157) 
0.582***(0.163) 
ρ_{42} 
0.549***(0.142) 
0.523***(0.163) 
ρ_{43} 
0.552***(0.103) 
0.499***(0.110) 
Notes: *** p<0.01; ρ_{21} denotes the correlation coefficient between the error terms of two equations Process innovation and Product innovation; ρ_{31} denotes the correlation coefficient between the error terms of equations Organizational innovation and Product innovation; ρ_{41} denotes the correlation coefficient between the error terms of equations Marketing innovation and Product innovation; ρ_{32} denotes the correlation coefficient between the error terms of equations Organizational innovation and Process innovation; ρ_{42} denotes the correlation coefficient between the error terms of equations Marketing innovation and Process innovation; ρ_{43} denotes the correlation coefficient between the error terms of equations Marketing innovation and Organizational innovation.
The economic interpretation of these uniformly positive and highly significant correlations between each pair of error terms is twofold:
· All four types of innovation have significant common unobserved factors; such that
· If a positive change in an unobserved influence increases one type of innovation then, via positive correlations, it will increase the other three types also.
This provides unambiguous evidence that all four types of innovation activities are complementary (Schmiedeberg, 2008). This notion ‘complementarity’ is a contemporaneous effect – the unobserved influences act on all four types of innovation at the same time.
Finally, Table 8 presents the marginal effects for Models. These reveal striking results for the influence of our variables of interest on firms’ abilities to achieve commercial success through innovation: devoting more resources to innovation (Resources), above average or leading capacity for product innovation (Capacity_product), cooperation with customers (Coop_customers), and cooperation with privatesector institutions (Coop_private sector) all reduce the probabilities of firms being in the lower categories of innovative sales (0%, 15% and 610%) while increasing the probability of being in the higher categories (1625%, 2650% and >50%). In each case, these results are uniformly statistically significant, while in no case is there a statistically significant effect for the median category of 1115 percent. In addition, the same pattern appears for above average or leading capacity for organizational innovation (Capacity_org) and for cooperation with HEIs (Coop_HEIs), although these estimates are not uniformly statistically significant. Finally, these estimates also contribute to understanding the effects of competition on the ability of firms to achieve commercial success through innovation: very high competitive pressures increase the probability of firms being in the lower categories while reducing the probability of being in the higher categories. Of course, marginal effects can be interpreted quantitatively. In each case, the estimated effects are neither too large to be implausible nor too small to economically irrelevant: statistically significant estimates range from the effect of cooperating with HEIs on the probability of a firm being in the lowest category of commercial success (a reduction of 1.8%) to the effect of cooperating with customers on the probability of being in the highest category of commercial success (an increase of 12.2%). The one addition is the effect of breadth of cooperation on commercial success: an additional cooperative partner is associated with reductions of between 2.8 and 6.9 percent in the probabilities of a firm being in one of the three lower categories and increases of between 4.5 and 6.0 percent in the probabilities of being in one of the three higher categories. (Once again, there is no statistically significant effect with respect to the median category).
Table 8: Marginal effects for the selected variables

Outcome 1 
Outcome 2 
Outcome 3 
Outcome 4 
Outcome 5 
Outcome 6 
Outcome 7 
Independent variables 
Innovative 
Innovative 
Innovative 
Innovative 
Innovative 
Innovative 
Innovative 

sales 0% 
sales 15% 
sales 610% 
sales 11 
sales 16 
sales 26 
sales >50% 




15% 
25% 
50% 

Size 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 

(0.000) 
(0.000) 
(0.000) 
(0.000) 
(0.000) 
(0.000) 
(0.000) 
Export 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 

(0.000) 
(0.000) 
(0.000) 
(0.000) 
(0.000) 
(0.000) 
(0.000) 
Competition 
0.035* 
0.080* 
0.059** 
0.007 
0.056* 
0.057** 
0.053** 

(0.020) 
(0.044) 
(0.024) 
(0.012) 
(0.030) 
(0.025) 
(0.023) 
Resources 
0.020* 
0.051* 
0.051* 
0.005 
0.034* 
0.045* 
0.048* 

(0.011) 
(0.028) 
(0.029) 
(0.006) 
(0.019) 
(0.026) 
(0.028) 
Capacity_product 
0.030** 
0.079*** 
0.088** 
0.016 
0.047*** 
0.076** 
0.090** 

(0.012) 
(0.031) 
(0.037) 
(0.014) 
(0.016) 
(0.033) 
(0.045) 
Capacity_process 
0.018 
0.049 
0.053 
0.008 
0.031 
0.046 
0.051 

(0.012) 
(0.032) 
(0.039) 
(0.011) 
(0.020) 
(0.034) 
(0.040) 
Capacity_org 
0.025* 
0.069** 
0.083 
0.020 
0.037*** 
0.072 
0.090 

(0.013) 
(0.035) 
(0.053) 
(0.024) 
(0.013) 
(0.045) 
(0.071) 
Capacity_marketing 
0.016 
0.040 
0.033 
0.002 
0.028 
0.030 
0.029 

(0.021) 
(0.047) 
(0.033) 
(0.007) 
(0.034) 
(0.032) 
(0.029) 
Coop_within group 
0.002 
0.005 
0.005 
0.000 
0.003 
0.004 
0.004 

(0.015) 
(0.038) 
(0.037) 
(0.003) 
(0.026) 
(0.033) 
(0.034) 
Coop_suppliers 
0.014 
0.036 
0.036 
0.003 
0.024 
0.032 
0.034 

(0.012) 
(0.031) 
(0.033) 
(0.006) 
(0.020) 
(0.029) 
(0.032) 
Coop_customers 
0.042*** 
0.109*** 
0.115*** 
0.020 
0.062*** 
0.101*** 
0.122*** 

(0.015) 
(0.031) 
(0.035) 
(0.014) 
(0.020) 
(0.031) 
(0.042) 
Coop_competitors 
0.019 
0.045 
0.035 
0.003 
0.032 
0.033 
0.031 

(0.028) 
(0.060) 
(0.037) 
(0.012) 
(0.044) 
(0.038) 
(0.034) 
Coop_private sector 
0.022** 
0.058** 
0.063** 
0.010 
0.036** 
0.055** 
0.062* 

(0.011) 
(0.026) 
(0.030) 
(0.010) 
(0.015) 
(0.026) 
(0.035) 
Coop_HEIs 
0.018* 
0.046* 
0.048* 
0.005 
0.031* 
0.042 
0.045 

(0.010) 
(0.026) 
(0.028) 
(0.007) 
(0.017) 
(0.025) 
(0.027) 
Coop_public sector 
0.012 
0.030 
0.031 
0.004 
0.020 
0.027 
0.029 

(0.012) 
(0.032) 
(0.035) 
(0.007) 
(0.020) 
(0.030) 
(0.034) 
Notes: Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; Industry and country DVs included.
4. CONCLUSION:
In this study we investigate how cooperation with different partners affects the innovation performance of SMEs in traditional manufacturing industries in the western Ethiopia. Innovation performance is measured in two ways: as the introduction of technological and nontechnological innovations; and as innovative sales, which reflect the commercial success of technological innovations. Summary statistics for our sample established that vertical cooperation (with customers and suppliers) is much more common than horizontal cooperation (with competitors). However, our estimates show that both can promote innovation.
The results from analysis summarizes all of the estimated effects of cooperation reported in this study, by setting out the statistically significant effects of different types of cooperation on the different measures of innovation performance. Our study provides three substantive conclusions. The first is that cooperation promotes innovation by SMEs in traditional manufacturing industry. This is demonstrated most clearly by the uniformly positive impact of the additional partnerships (Breadth) on both the types of innovation enacted and on the commercial success of technological innovation: additional partners are associated with firms enacting higher levels of product, process and organizational innovation as well as with reduced probabilities of achieving low levels of increased innovative sales and increased probabilities of achieving higher levels of innovative sales.
The second conclusion is that among the individual types of cooperation the performance effects are heterogeneous. First, with respect to types of enacted innovation, most of the estimated positive effects (four from seven) arise from cooperation either with Higher Education Institutions (such as universities) or with other publicsector knowledge providers. This is consistent with public support measures designed to promote partnerships between SMEs and external knowledge providers (through for example, “innovation vouchers”). Secondly, our estimates consistently indicate that cooperation with customers, privatesector knowledge providers and, albeit not so strongly, HEIs promote technological innovation with commercial impact, but do not provide evidence for positive performance effects from other types of partner.
The third conclusion arises from the finding that all four types of innovation have significant common unobserved factors such that if a positive change in an unobserved influence at firm level increases one type of innovation then it will increase the other three types as well. This provides unambiguous evidence that all four types of innovation activities are complementary.
For policy makers this suggest that public support programs to promote SME innovation in traditional manufacturing industry should be demandled (i.e. flexible with respect to SME needs) rather than supply led (i.e. narrowly prescriptive with respect to one or other aspect of technological or nontechnological innovation). As well as new findings for our variables of interest, the estimated effects of the control variables are either consistent with the existing literature (e.g. on the effects of competition and absorptive capacity) or suggest further lines of enquiry.
5. ACKNOWLEDGEMENT:
The authors gratefully acknowledged the anonymous reviewers for their contributions towards this work.
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Received on 03.04.2020 Modified on 20.04.2020
Accepted on 08.05.2020 ©AandV Publications All right reserved
Res. J. Pharmacognosy and Phytochem. 2020; 12(2): 7179.
DOI: 10.5958/09754385.2020.00013.8