Effect of Dynamic Capabilities on Competitive Advantage of Manufacturing Firms in Nairobi, Kenya

Previous studies on manufacturing firms particularly in less developed countries have revealed that a third of these firms have failed to become operational as well as additional failures or closures of firms in previous years due to lack of sensing, seizing and reconfiguring of firm’s capabilities leading to competitive advantage. There are no empirical studies in Kenya that show the effect of dynamic capabilities and competitive advantage of manufacturing firms which this study strives to achieve. The main objective of the study was to determine the effect of dynamic capabilities on competitive advantage of manufacturing firms in Kenya. The specific objectives of the study were to assess the effect of sensing, seizing and reconfiguration capabilities on competitive advantage of manufacturing firms in Nairobi, Kenya. The study adopted explanatory research design and data was collected using survey approach on a target population of 762 manufacturing firms registered under Kenya Association of Manufacturers. A sample size of 321 firms was selected based on Yamane formula of determination in selecting respondents to be served with the questionnaires. Pearson correlation was used to test the linear. relationship of variables while multiple regression model was used to analyze data in order to test the hypothesis for the study Descriptive and inferential statistics were used in data analysis and the study findings revealed that there was a positive and significant effect of sensing capabilities and competitive advantage (β=.392, p=.000); seizing capabilities and competitive advantage (β=.194, p=.000); reconfiguration capabilities and competitive advantage (β =.174, p=.001) with all p-value being less than .05. The study recommends that managers and industry practitioners should put more emphasis on, and appreciate the role of the leader in the deployment of dynamic capabilities by sensing, seizing, reconfiguring their capabilities in order to achieve competitive advantage in the ever changing contemporary operating environment.


Introduction
The attainment of competitive advantage is a priority for strategists, regulators and policymakers as it occupies a pivotal position in strategic management studies [1][2][3][4]. Every business framework should endeavor to put up strategies to match the key success factors for operating in its market and hence exceeding those of its competitors [5]. Dynamic capabilities has been researched widely and scholars have acknowledged that it increases or enhances competitive advantage thus long-term profitability of the firm guaranteed or assured [6][7][8]. The goal of every organization is to outperform its rivals and attract potential buyers to its products and services while at the same time retain current customers in the dynamic, volatile business functional environment [9]. Dynamic capabilities has been viewed by scholars as the most significant organizational capability that aids attainment of sustainable competitive advantage over competitors as well as profit realization [10] Firms in Nairobi, Kenya Manufacturing and service industries are majorly concentrated in various clusters of the country like Nairobi, Eldoret, Kisumu, Mombasa, Nakuru and Thika because of the basic infrastructure [11] with approximately 80% located in Nairobi County. The sector is the third biggest industrial sector after agriculture and transport and communication [12]. Globally, manufacturing has acted as a growth escalator for economies that have succeeded in eventuating high incomes and those countries that have achieved rapid industrialization have done so by putting in place deliberate policies that promote and encourage value addition and diversification of manufactured goods [13].
Economic Survey results for the periods 2010 to 2014 by the Kenya National Bureau of Statistics further indicate that some major sectors of the Kenyan economy has witnessed intermittent higher growth, though the manufacturing sector has consistently decelerated in growth rates [14] because of high cost of production, stiff competition from imported goods, high cost of credit and political shock leading to firms exiting Kenya hence spelling doom to an economy that was expected to recover.
Further statistics from Kenya Association of Manufacturers have shown that certain firms announced plans to shut down their plants and shift operations to Egypt and other countries as a result of reduced profits, competition, and government policies [15] hence the basis this study is seeking to determine the effect of dynamic capabilities, on competitive advantage of manufacturing firms in Nairobi, Kenya.

Methodology
The study employed explanatory design where the unit of analysis was 321 CEOs drawn from a target population of 762 firms which are members of Kenya Association of Manufacturers [15]. The data was collected through census on CEOs of manufacturing firms in Nairobi County.

The Data
Primary data were gathered from the respondents using the questionnaires and keyed into SPSS package version 23 for analysis. In order to test for reliability, the researcher used the internal consistency technique by employing Cronbach Alpha value of α>0.7. The questionnaire was constructed based on measures, scales and items from previous literature and further checks done through pilot study which was done in manufacturing firms in Eldoret town hence enabling the researcher to know the extent to which data collected and analysis procedures yielded consistent findings thus providing assurance that the same results could be expected on any other subsequent similar occasions [16].

Model Specification
Pearson correlation coefficients were used to determine the degree or strength of relationship that exists between the independent (dynamic capabilities) and the dependent variables (competitive advantage). Multiple regression model was used to analyze the data in order to determine the significance of the hypotheses of the study. In order to achieve objectives 1 to 3, the direct effects, linear regression models were tested for purposes of H o 1 -H 0 3. The test statistics that were computed and derived included the coefficients of determination (R 2 ); the beta coefficient (β) and the p-values. The effects both for controls (age and size of the firm) and the direct effects were statistically processed using the specified linear equations below: Y = β 0 + β 1 size β 2 age + ε (1) Y = β 0 + C + β 1 X a + β 2 X b + β 3 X c + ε 1 (2) Where: Y: dependent variable (competitive advantage) C: control variables (age and size of the firm) β 0: constant X 1 : Sensing capabilities X 2 : Seizing capabilities X 3 : Reconfiguration capabilities β 1 -β 3 : The effect of slope coefficients denoting the influence of the associated independent variables over the dependent variable coefficient of regression ε: Error terms

Demographic Information of the Respondents
This covers type of company, the department, size of the firm and the age or number of years the firm has been in operation in Kenya. Results showed that product firms were 262 (82.1%) while service industries 57 (17.9%). Marketing department were 24 (7.5%) while production and or operations had 295 (92.5%). Firms with below 300 employees had the highest percentage of 57.1%, followed by employees' range of 301-600 at 24%, while above 900 employees at 10.2% and finally 601-900 employees range at 8.6% with 194, 71, 21, and 7 employees respectively. Firms that have been in operation for above 30 years had the highest percentage of 45.1% followed by those in 11-20 years range (21.6%), then less than 10 years at 18.2% and lastly 21-30 years range at 15.1% with 144, 69, 58 and 48 years respectively as shown in Table 2.

Factor Analysis
Factor analysis was done so as to identify the latent variables in the data constructs and to prepare it for regression [17].

KMO Results for Dynamic Capabilities
Factorability of the data was assessed using Bartlets test of sphericity and Kaiser-Meyer-Olkin measure of sampling adequacy where Bartlets test of sphericity should be statically significant at ρ< 0.05, KMO index should range from 0 to 1. KMO measure was greater than .5 (.87) and Barlett's test findings were significant (X 2 (528) = 4373.95, p-value <.001 [18]. confirming that all the changes in the three components of sensing, seizing, learning and reconfiguration capabilities can significantly be relied upon to assess the majority of the changes in the dynamic capabilities.

Total Variance Explained Results for Dynamic
Capabilities Factor analysis was carried out on dynamic capabilities and the factors were extracted using principal component analysis and rotation done using varimax with Kaiser Normalization [19]. PCA was chosen as the most convenient method as it revealed the set of factors which accounted for all common and unique variances [17]. Table 4 showed that sensing capability accounted for 27.42% of variation in dynamic capability while seizing 36.01%; and reconfiguration 42.69% of the changes in the dynamic capabilities. Factors with Eigen values greater than 1 were chosen but three items only were considered for dynamic capabilities variables.

Rotated Component Matrix Results
The threshold for retaining an item as a measure of a given variable was a minimum factor loading of .5, and Eigen value of not less than 1.0 [19,20]. Table 5 above showed that all the components were above .5 which is the cut-off for factor loading with the lowest being .520 and the highest .713 implying that these factors were retained for data transformation and the factors that did not load were removed.

Correlation Analysis Results
The purpose of conducting correlation analysis was to measure the possibility of any existing linear relationship between the dependent variable and the other variables through determining the magnitude and direction of the possible relationships considering that both variables are at interval level of measurement and the data is parametric in nature. Pearson correlation coefficient was used to measure the relationships between the variables [20,21] as shown in Table 6 below: Results in Table 6 showed that there is positive and significant correlation between; sensing capabilities and competitive advantage (r = 0.534, p-value < 0.01), seizing capabilities (r = .414, p < 0.01) and reconfiguration capabilities (r = 0.411, p < 0.01).

Hypothesis Testing
A regression test to determine the effects of both the control and the independent variables (direct effect) was done and the findings revealed that 36.0% variation of competitive advantage is predicted by sensing, seizing and reconfiguration (R 2 = 36.0). Their joint prediction was significant as shown by F-change (35.27), p (.000) and Durbin Watson (1.908). The results showed that all the three variables -sensing capabilities (β=.392, p=.000), seizing capabilities (β=.194, p=.000) and reconfiguration capabilities (β=.174, p=.001); have significant and positive effect on competitive advantage. The variables when combined contributed 36% (R 2 =.360) of the variance in competitive advantage which is an improvement from the first set of control variables 'contribution, by 2.1% (∆R 2 = .021) as shown in Table 7. H 0 1 stated that sensing capabilities had no significant effect on competitive advantage. The findings in the table showed that sensing capabilities had coefficients of estimate which was positive and significant (β 1 = .392, p =.000) which is less than (.05) implying that there was .392 (39.2%) unit increase in competitive advantage for each unit increase in sensing capabilities. This therefore led to null hypothesis being rejected and researcher concluded that sensing capabilities had a significant and positive effect on competitive advantage.
H 0 2 stated that seizing capabilities had no significant effect on competitive advantage. The study findings showed that seizing capabilities had a positive and significant effect on competitive advantage based on the β 2 = .194 with a p = .000 which is less than (.05) implying that seizing capabilities had a positive and significant effect on competitive advantage This implies that there was .194 (19.4%) unit increase in competitive advantage for each unit increase in seizing capabilities hence null hypothesis rejected.
H 0 3 of the study stated that reconfiguration capabilities had no significant effect on competitive advantage. The study findings showed that reconfiguration capabilities had coefficients of estimates which were positive and significant (β 3 = .174; p = .001) which is less than (.05) thus null hypothesis was rejected confirming that reconfiguration capabilities had a positive and significant effect on competitive advantage. Results showed that there was .174 (17.4%) unit increase in competitive advantage for each unit increase in reconfiguration capabilities.

Discussions of the Study
The research findings indicated that objective 1 of the study was to determine the effect of sensing capabilities on competitive advantage of manufacturing firms in Nairobi, Kenya and was hypothesized that there was no significant effect of sensing capabilities on competitive advantage (H 0 1). The results showed that there was positive and statistically significant effect of sensing capabilities on competitive advantage (β = .392, p= .000) implying that sensing capabilities which comprise constant scanning, searching, identifying opportunities, threats, changes and also competitor's possible responses to the focal enterprise actions in firms [3] affect competitive advantage.
Objective 2 of the study was to examine the effect of seizing capabilities on competitive advantage of manufacturing firms in Nairobi, Kenya which was hypothesized that there was no significant effect seizing capabilities on competitive advantage (H 0 2). The findings showed that seizing capabilities had a positive and statistically significant effect on competitive advantage (β = .194, p = .000) implying that seizing capabilities which comprise of correcting decisions and executing them so that they simultaneously align with the enterprises' assets and strategic goals [3] by capturing value from opportunities through mobilizing existing resources towards these new innovative goals [22].
Objective 3 was to establish the effect of reconfiguration capabilities on competitive advantage of manufacturing firms in Nairobi, Kenya. The hypothesis was that there was no significant effect of reconfiguration capabilities on competitive advantage of manufacturing firms in Kenya. The study findings (β = .174, p = .001) supported this objective leading to null hypothesis being rejected.

Conclusion of the Study
Empirical findings of this study confirmed the significant and positive relationship between dynamic capabilities and competitive advantage of manufacturing firms. Based on the hypothesis of dynamic capabilities and competitive advantage the findings agreed with reviewed literature.
The study concludes that firms with a stronger commitment to deploying dynamic capabilities (sensing, seizing and reconfiguration) are more successful hence firms need to continuously deploy all firm-relevant capabilities in line with the Dynamic Capabilities View and Resource-Based View because ignoring deployment of a single dynamic capability can negatively affect the deployment of other dynamic capabilities since they are correlated and interwoven together.

Recommendations for Further Research
Follow-up studies could focus on a deeper investigation of each dynamic capability, especially on the paths and positions affecting the development of dynamic capabilities. Secondly, a longitudinal research would also be valuable since the results of deploying and developing dynamic capabilities usually cannot be seen in the short term but in the long-term. Thirdly, the same or a similar study could also be conducted in other industries or a cross-industry analysis could reveal commonalities and diversities in deploying dynamic capabilities across industries. Finally, future studies exploring the dynamic capabilities field should involve other qualitative approaches such as focus groups or observation methods.