Browsing by Author "Dawit, G. Michael"

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    The Effect of Innovation on Manufacturing Firms’ Performance in Ethiopia: Evidence from Garment Firms’ in Addis Ababa
    (A.A.U, 2020-01) Dawit, G. Michael; Dr. Ethiopia, Legese
    Innovation is a key factor for manufacturing firms’ competitive advantage in this competitive global economy with an embedded purpose of performance improvement, which in turn, leads to economic development. The purpose of this study is to investigate the effect of innovation types (product, process, marketing & organizational innovations) on garment firms’ performance by using BSC approach in Addis Ababa, Ethiopia. Even though it has not been adequately studied in Ethiopia, previous empirical studies from abroad revealed that proper implementation of innovation activities have a significant positive impact for economic development in general and firms’ performance improvement in particular. The study employed quantitative research approach and an explanatory research design. A survey is conducted by using structured close ended questionnaires. Out of 222 garment firms in Addis Ababa, as innovation is more pertinent to medium and large firms, from 69 questionnaires administered to these firms located in Addis Ababa, 60 questionnaires were completely filled and received. The analysis of the data is done with the help of the SPSS v24 and multiple regression models with OLS technique are used to investigate effect of innovation types on the firm’s performance. The result of the research revealed that only process & marketing innovation have a significant and positive effect on most of the dimensions of firm performance. Product innovation has no statistically significant effect at 5% significance level. However, organizational innovation has a negative and significant effect in the four dimensions of firm performance. Therefore, the firms should give special emphasis for process and marketing innovations to assure better performance and further researches are needed to be conducted on multi-sectors with large sample size