The main objective of this study was to explain top management perceptions of industry and firm effects on firm performance through the mediation of competitive advantage in financial service firms operating in a regulated industry in a developing Ethiopian economy. The resource-based and industry-based views, constituting the two main schools of thought explaining performance variations among firms, were used as theoretical foundation of this study. Porter’s five-forces framework was used during this process. The researcher employed a post-positivist paradigm using a cross-sectional survey design. A total of 27 financial service firms (15 banks and 12 insurance firms) that had functioned for three and more years were selected for the study. The unit of analysis was ‘firms’, while respondents were top level managers with a total target population of less than 300. A census survey rather than a sample survey was undertaken. A total of 287 survey questionnaires were distributed (banks 180 and insurance industry 107), of which 215 were collected from 26 firms (15 banks and 11 insurance firms). Of the questionnaires 206 were properly completed leading to a valid response rate of 71%. These were used for the data analysis. A variance-based PLS-SEM approach, which is relevant to evaluate the predictive effects of the industry and firm factors on firm performance, was used to explain the hypothesized model using SmartPLS 2.00 software as well as the Statistical Package for the Social Sciences program. The assessment of the hypothesized model indicated that the R2 result on firm performance variance due to the combined industry effects and firm effects was 39%, indicating a moderately significant predictive accuracy of the model. The relative direct effect size (f2) of the industry on firm performance was 3%, while firm had a direct effect size of 2%, which was small. The combined indirect relative predictive accuracy of industry and firm effect sizes on firm performance through competitive advantage was high at 27%. This was driven by the relative substantial predictive power of firm effect on competitive advantage (f2 = 65%). Furthermore, the predictive capability (Q2) assessment result of the model indicated that both industry and firm effects had a 23% relevant predictive power on firm performance. The direct relative measure of the predictive relevance (q2) value of industry effect (q2 = 0.02) on firm performance was relatively higher than that of the firm effect (q2 = 0.01). Competitive advantage had a relative predictive power of 0.12, which was driven by the direct relative predictive capability of firm effect (q2 = 0.25) on competitive advantage. The overall assessment results of the structural model revealed that the model had satisfactory statistical power to predict the hypothetical research model. The hypothesis that industry effects had an influence on the performance of financial service firms was not supported. The result indicated that industry effects had a positive and non-significant relationship with firm performance, which points to competitiveness in the financial services industry. These results were achieved against the tenets of Porter’s five-forces framework. The hypothesis that firm effects had a positive predictive effect on firm performance was also not supported, indicating that resources and capabilities do not directly lead to improved firm performance. The direct effect of competitive advantage on firm performance was supported. The mediating effect of competitive advantage between industry effects and firm performance was not significant, while the mediation of competitive advantage between firm effects and firm performance was highly significant. The findings of this study revealed that firm effects were relevant through the mediation of competitive advantage in explaining performance variances among financial service firms, operating in a strictly regulated industry. The relative predictive power of firm effect on competitive advantage was high. Firm resources, particularly intangible resources and dynamic capabilities, are the key predictors of firm performance indirectly through the mediation of competitive advantage. Such an advantage may not last long given the excessive supervision and regulations that exist and the fact that firms are being dictated to by the government to comply with its strategic direction as opposed to pursuing their own firm specific strategies. Such practice could encourage competing financial firms to converge and pursue similar types of strategies and encourage imitations to gain short term competitive advantage and superior performance. This finding contradicts the fundamental premise of the resource-based view and firm heterogeneity even though it tentatively supports the argument made by Foss and Knudsen (2003) who argue that heterogeneity is not a necessary condition to gain competitive advantage and superior firm performance. Financial service firms should not only develop and manage their resources and capabilities, but they should also monitor the changes in the industry. This finding highlights the fact that firms can create competitive advantage and enjoy superior performance in a closed and regulated industry. The findings of this research make a significant contribution to the existing debate on the resource-based and industry-based views in explaining the causes of firms’ performance variations specifically in a regulated environment. / Business Management / D.B.L.

Manufacturing performance measures the extent to which the manufacturing plant has built capabilities like low cost, high quality, delivery, and flexibility. The importance of identifying drivers of these capabilities has been underscored by many scholars although limited evidence exists so far regarding this issue. The available evidence is also primarily based on data obtained from manufacturing firms operating in developed and emerging economies and not from firms in developing economies. This study, therefore, bridges this gap by exploring key internal and external drivers of manufacturing performance taking evidence from the manufacturing sector of a developing economy - Ethiopia. A quant-emphasis mixed method approach was used along with cross-sectional survey design to gather data and answer the research questions in the study. The unit of analysis is the manufacturing plant, and hence primary data was collected using multidimensional questionnaires at plant level from 197 medium and large scale firms from Addis Ababa and its periphery. Secondary data was obtained from census reports, the country’s Growth and Transformation Plan (GTP), and report on the performance of the Ethiopian economy, which were analyzed qualitatively and the implications to manufacturing performance drawn in the study. A series of scale checks and analyses were made to test unidimensionality, reliability, and validity of measures and then structural equation modeling (SEM) was used to analyze hypothesized relationships. The main finding is that environmental dynamism significantly influences competitive priorities and firm’s strategic orientation, which in turn significantly influence manufacturing decisions. Structural and infrastructural manufacturing decisions eventually significantly influence manufacturing performance when firms place increased emphasis on quality or delivery. The competitive priorities also significantly influence external learning capability of the manufacturing plant, although the influence of strategic orientation on this variable was not significant even at the 0.1 level except in the delivery priority model. Both the competitive priorities and strategic orientation, however, play little role in guiding leadership practices of manufacturing managers. The study further indicates that government support directly influences manufacturing performance, though it does not significantly influence external learning capability. Based on the findings, it is suggested that manufacturing firms should give due attention to what is going on in their external environment and accordingly align their competitive priorities, strategic orientation, and investments in structural and infrastructural resources to enhance plant performance. They should exhaustively utilize the supports provided by government as well. / Business Management / DBL

Manufacturing performance measures the extent to which the manufacturing plant has built capabilities like low cost, high quality, delivery, and flexibility. The importance of identifying drivers of these capabilities has been underscored by many scholars although limited evidence exists so far regarding this issue. The available evidence is also primarily based on data obtained from manufacturing firms operating in developed and emerging economies and not from firms in developing economies. This study, therefore, bridges this gap by exploring key internal and external drivers of manufacturing performance taking evidence from the manufacturing sector of a developing economy - Ethiopia. A quant-emphasis mixed method approach was used along with cross-sectional survey design to gather data and answer the research questions in the study. The unit of analysis is the manufacturing plant, and hence primary data was collected using multidimensional questionnaires at plant level from 197 medium and large scale firms from Addis Ababa and its periphery. Secondary data was obtained from census reports, the country’s Growth and Transformation Plan (GTP), and report on the performance of the Ethiopian economy, which were analyzed qualitatively and the implications to manufacturing performance drawn in the study. A series of scale checks and analyses were made to test unidimensionality, reliability, and validity of measures and then structural equation modeling (SEM) was used to analyze hypothesized relationships. The main finding is that environmental dynamism significantly influences competitive priorities and firm’s strategic orientation, which in turn significantly influence manufacturing decisions. Structural and infrastructural manufacturing decisions eventually significantly influence manufacturing performance when firms place increased emphasis on quality or delivery. The competitive priorities also significantly influence external learning capability of the manufacturing plant, although the influence of strategic orientation on this variable was not significant even at the 0.1 level except in the delivery priority model. Both the competitive priorities and strategic orientation, however, play little role in guiding leadership practices of manufacturing managers. The study further indicates that government support directly influences manufacturing performance, though it does not significantly influence external learning capability. Based on the findings, it is suggested that manufacturing firms should give due attention to what is going on in their external environment and accordingly align their competitive priorities, strategic orientation, and investments in structural and infrastructural resources to enhance plant performance. They should exhaustively utilize the supports provided by government as well. / Business Management / D.B.L.