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Strategic Pathways to Success: Exploring the Effect of Generic Competitive Strategies on Competitive Advantage Cover

Strategic Pathways to Success: Exploring the Effect of Generic Competitive Strategies on Competitive Advantage

By:   
Open Access
|Sep 2026

Figures & Tables

Figure 1

Research model, created by the researcher.

(Source: Author’s own research)

Table 1

Reliability and validity coefficients of study variables.

Variables and symbolsCronbach’s alphaValidity
Cost leadership strategy (CLS) X 1 0.9890.990
Differentiation strategy (DS) X 2 0.9800.989
Focus strategy (FS) X 3 0.9890.995
Generic competitive strategies (GCS) X 0.9780.989
Competitive advantage (CA) Y 0.9770.995
Minimum value0.9770.989

(Source: Author’s own research)

Table 2

Shapiro–Wilk and Levene’s test results for normality and homogeneity.

Tests of normality and equal varianceShapiro–Wilk statistic P-value
CLS X 1 0.9970.059
DS X 2 0.9840.062
FS X 3 0.9930.342
GCS X 0.9970.537
CA Y 0.9870.532
Levene’s test0.9870.552

(Source: Author’s own research)

Table 3

Correlation matrix of independent and dependent variables.

Independent variables/dependent variableCA Y
CLS X 1 R 61.0%
Sig. value<0.001
DS X 2 R 93.2%
Sig. value<0.001
FS X 3 R 67.1%
Sig. value<0.001

(Source: Author’s own research)

Table 4

Correlation matrix of the main independent and dependent variable.

VariablesGCS X
CA Y R 87.8%
Sig.<0.001

(Source: Author’s own research)

Table 5

ANOVA summary for regression models.

ModelsDependent variableIndependent variables R 2 Sig.
Model 1: Y = f(X)CA Y GCS X 77.2%<0.001
Model 2: Y = f(X 1)CA Y CLS X 1 37.2%<0.001
Model 3: Y = f(X 2)CA Y DS X 2 86.8%<0.001
Model 4: Y = f(X 3)CA Y FS X 3 45.1%<0.001

(Source: Author’s own research)

Table A1

Coefficients of regression models, standard error (SE), t-statistic, 95% confidence intervals.

ModelsCoefficientSE T-statisticSig.LLUL
Intercept−0.0400.082−0.4850.628−0.2020.122
GCS (X)1.0310.02344.965<0.0010.9861.076
R-squared77.20%
Model 2: Y = f ( X 1 )
Intercept0.9240.1456.371<0.0010.6391.208
CLS (X 1)0.7240.03818.824<0.0010.6480.799
R-squared37.20%
Model 3: Y = f ( X 2 )
Intercept0.2870.0545.310<0.0010.1810.394
DS (X 2)0.9220.01562.693<0.0010.8930.951
R-squared86.80%
Model 4: Y = f ( X 3 )
Intercept1.7290.08819.691<0.0011.5571.902
FS (X 3)0.5710.02622.154<0.0010.5210.622
R-squared45.10%

(Source: Author’s contribution)

LL = Lower Limit of the 95% Confidence Interval; UL = Upper Limit of the 95% Confidence Interval.

Table A2

Analysis of variance of regression models for each independent variable.

SourceDF R²Sum of squaresMean square F-ratioSig.
ANOVA model 1: Y = f ( X ) common model for the independent variables
Model177.20%211.988211.9882021.851<0.001
GCS (X)177.20%211.988211.988<0.001
Error59822.80%62.6990.105
Total599100.00%274.687
ANOVA model 2: Y = f ( X 1 ) common model for the independent variables
Model137.20%102.206102.206354.355<0.001
CLS (X 1)137.20%102.206102.206<0.001
Error59862.80%172.4810.288
Total599100.00%274.687
ANOVA model 3: Y = f ( X 2 ) common model for the independent variables
Model186.80%238.413238.4133930.400<0.001
DS (X 2)186.80%238.413238.413<0.001
Error59813.20%36.2740.061
Total599100.00%274.687
ANOVA model 4: Y = f ( X 3 ) common model for the independent variables
Model145.10%123.822123.822490.808<0.001
FS (X 3)145.10%123.822123.822<0.001
Error59854.90%150.8650.252
Total599100.00%274.687

(Source: Author’s contribution)

DOI: https://doi.org/10.2478/fman-2026-0002 | Journal eISSN: 2300-5661 | Journal ISSN: 2080-7279
Language: English
Page range: 74 - 85
Submitted on: Nov 24, 2024
Accepted on: Sep 18, 2025
Published on: Sep 17, 2026
Published by: Warsaw University of Technology
In partnership with: Paradigm Publishing Services
JEL:

© 2026 Noha AHMED, published by Warsaw University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.