Empirical results

Porter's generic strategies are applicable in the competitive environment; we have tested the competing environment of respondent firms. Table 3 shows the data for the competitive environment in which respondent firms operate. In the questions presented in Table 3, the participants had five scales to present their competing environment from 1 - not at all competing environment to 5 - extremely competing environment. From this table, it can be seen that the highest assessment by the respondent firms has taken the ascertainment "products/services are similar in the market" which is evaluated on average with 4.39 from 5 that was the maximal evaluation, while the lower evaluation has taken ascertainment "a small number of firms are dominant in the market" on average with 2.63 by 5 that was the maximal evaluation. By these results, the answer is found for the first research question: Are the respondent firms operating in the competitive industry? So, the environment where the respondent firms operate is a competitive environment, and these results provide the needed conditions to go further with hypotheses testing that derives by the third section of this study (Table 4).

Table 3 Firm's responses for competing environment.

Study of environment variables

Minimum

Maximum

Mean

Std. deviation

A small number of firms are dominant in the market

1

5

2.63

1.775

Products/services are similar in the market

1

5

4.29

1.151

A large number of firms offer similar products/services

1

5

4.26

1.144

In our industry, there is a decrease in requirement

1

5

3.48

1.617

Obstacles to get out of market are high

1

5

3.03

1.367

Table 4 Descriptive statistics of the study variables (n = 113).

Study variables

Minimum

Maximum

Mean

Std. deviation

LCS

2

5

3.91

0.713

DS

3

5

4.29

0.776

FS

1

5

3.75

0.770

FP

2

5

3.81

0.991

Descriptive statistics

Descriptive data are minimum, maximum, mean, and standard deviation, for all independent variables and dependent variable that are part of this research.

A "Cronbach's alpha" test was used to evaluate the reliability of the factors as suggested by Nunnally. Cronbach's alpha can be considered an adequate index of the inter-item consistency reliability of independent and dependent variables. Nunnally suggests that constructs should have reliability values 0.7 or greater. Table 5 shows the relationship between the items that are measured, deliberately to see which factors have the highest relationship, and that can be represented by a single variable. The reliabilities for each of the four constructs were adequate since the Cronbach's alpha values for each were significantly greater than the prescribed 0.7 threshold. So, in this study the values varied from 0.734 (focus strategy) to 0.894 (firm performance), showing that the instruments are sufficiently reliable. Variables LCS2, LCS4, FS1, and FS3 are moved from further analyses because they have reliability value lower than (< 0.7). In order to see which factors are included within each Porter's generic strategy, which enables us to test the hypotheses of this research paper, Cronbach's alpha test is performed for reliability (Table 5).

Table 5 Statistical highlights - Cronbach's alpha test for reliability

Low-cost strategy

Differentiation strategy

Focus strategy

Firm performance

Cronbach's alpha test for reliability

 0.760

0.779

0.734

0.894

Remaining items with loading values > 0.7

LCS1

0.767

DS1

0.750

FS1

0.754

PS1

0.866

LSC2

0.564x

DS2

0.750

FS2

0.621x

PS2

0.871

LCS3

0.715

DS3

0.700

FS3

0.619x

PS3

0.880

LCS4

0.597x

DS4

0.771

    

PS4

0.873

LCS5

0.767

DS5

0.793

    

PS5

0.875

LCS6

0.712

DS6

0.765

    

PS6

0.889
   

DS7

0.712

        

The first-order inter-items for reliability test by Cronbach's alpha found that items LCS2, LCS3, FS2, and FS3 are not related enough to put in their box of the question to test their strategy and are removed for further analysis. The differentiation strategy is represented by seven items, and all of them consisted of the level above 0.7 of the reliability test, Cronbach's alpha 0.779. The dependent variable "firm performance" is made by six questions in the first- and second-order inter-items; the reliability results have shown a Cronbach alpha value of 0.894, which is a high level of reliability. Based on the reliability test (Cronbach's alpha), all values were above 0.7.

In order to analyze the data and to test the hypotheses, the correlation and regression analyses were applied. To complete the regression and correlation analysis, IMB SPSS statistical software was used. In addition to correlation and regression analyses, descriptive statistics were presented to clarify more the fitness of used variables. Whereas empirical findings presented below show the results achieved by correlation matric and regression analyse.


Correlation analysis

Table 6 shows the Pearson correlation analysis for the independent variables that are taken as a prediction in finding (defining) dependent variable "firm performance," in order to measure the scale of the relationship between independent variables in this testing. It is presented the connection between low-cost strategy, differentiation strategy, and focus strategy. According to the results presented in the table, it is shown that the relation in between independent variable is inside the allowed borders (+, − 0.7). The results showed no potential multicollinearity among variables. The results shown in Table 6 allow us to continue with further analysis to test the regression analysis.

Table 6 Correlation matrix (n = 113).

Variables

Correlations

LCS

DS

FS

FP

LCS

Pearson's correlation

1

      

Sig. (two-tailed)

        

DS

Pearson's correlation

0.233*

1

    

Sig. (two-tailed)

0.074

      

FS

Pearson's correlation

0.527***

0.119

1

  

Sig. (two-tailed)

0.000

0.355

    

FP

Pearson's correlation

0.499***

0.337***

0.433***

1

Sig. (two-tailed)

0.000

0.007

0.000

  
  1. *Correlation is significant at the 0.10 level (two-tailed)
  2. ***Correlation is significant at the 0.01 level (two-tailed)


Regression analysis

In order to measure the impact of independent variables in dependent variable "firm performance," multivariate regression analysis has been used. Regression analysis is presented in Table 7. According to regression analysis, independent variables that enter in the analysis explain 63.2% of dependent variable "firm performance." F value is 9.976 (sig. 0.000), which means that the model is statistically important with the significance level α = 0.05. Independent variable "LCS" is positively connected with dependent variable "FP" by predicting it for 31.2% (b = 0.312 and p = 0.031), which means that for each 1% change in pursuing of the low-cost strategy the firm performance will change by 31.2%. Independent variable "DS" is positively related to dependent variable "FP" by predicting it for 43.9% (b = 0.439 and p = 0.019), which means that for each 1% change in application of the differentiation strategy the firm performance will change by 43.9%. As well, independent variable "FS" is positively related to dependent variable "FP" by predicting it for 31.5% (b = 0.315 and p = 0.028), which means that for each 1% change in application of the focus strategy the firm performance will change by 31.5%. If it is analyzed closely, Table 7 shows that independent variable "DS" has a higher impact in increasing firm performance compared to two other generic strategies. With these results, we answered the second research question: Which of the three Porter's generic strategies has more impact on firm performance?

Table 7 Regression analysis of dependent variable "Firm performance," n = 113.

Model

R2

ΔR2

β

b

S.E

F

t

p
 

0.671

0.632

      

9.976

    

(Constant)

      

0.448

0.800

  

0.560

0.038

LCS

    

0.245

0.312

0.141

  

2.207

0.031

DS

    

0.312

0.439

0.182

  

2.410

0.019

FS

    

0.246

0.315

0.163

  

1.934

0.028
  1. b, non-standardized coefficients; S.E, standard error of variables; β, standardized coefficients; t, t-statistic; p, significant level; R2, R square; ΔR2, adjusted R square
Porter stressed that if firms want to have a strategy in order to achieve a competitive advantage they should pursue three strategies, that he called generic strategies. In accordance with his result and based on the empirical results of this study, Eq. 6 is presented, which shows the participation of each strategy in firm performance.

\hat Y_{fp}=α+b_1LCS+b_1DS+b_1FS+ε_i→ \hat Y_{fp} =0.448+0.312∗LCS+0.439∗DS+0.315∗FS+ε_i

As shown in Eq. 6, all the variables that were tested have a positive impact on firm performance; Porter has shown that all of his three generic strategies have a positive impact on firm performance, if those are used in the right way. Equation 6 shows something more and tells that by pursuing low-cost strategy in conditions that all the other variables remain unchanged firm performance will be increased for 31.2%; by pursuing differentiation strategy in conditions that all the other variables remain unchanged firm performance will be increased for 43.9%; and by pursuing focus strategy in conditions that all the other variables remain unchanged firm performance will be increased for 31.5%. In this econometric model, exactly the impact of each generic strategy in firm performance is presented, and these results have a positive impact in managers' decision making and in enriching the strategic literature related on using Porter's generic strategies and their impact on firm performance.

See the "beta" column of Table 7. If we increase using low-cost strategy by 1 standard deviation, the firm performance will increase by 0.245 standard deviations, if we increase using differentiation strategy by 1 standard deviation, the firm performance will increase by 0.312, and if we increase using focus strategy by 1 standard deviation, the firm performance will increase by 0.246.