Factor analysis for commitment

The conceptual framework in this study illustrated affective, normative, and

continuance commitment as the three types of commitment hence the three factors were set as desired in the Factor analysis procedure. Consequently, factor analysis using Varimax

rotation for a maximum of 25 rotations was performed on the four commitment variables with a view to reduce them to three in line with the conceptual framework presented at section 2.4 in the second chapter. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett’s Test of Sphericity were invoked to test for the suitability of the data pertaining to the four commitment variables in detection of a structure. The KMO measure of sampling adequacy index (.507) slightly exceeded the recommended value (0.5), as

recommended by Hair et al. (2010). In addition, the value of the Bartlett’s test of Sphericity was considerably big (65.939, df= 6) and statistically significant (p= .000) as shown in Table 15 below. The implication of this result is the rejection of the null hypothesis postulating that the correlation coefficients’ matrix is an identity matrix. Therefore, the four variables were related and suitable for structure detection.

Table 16. KMO and Bartlett’s test for factor analysis on commitment Kaiser-Meyer-Olkin Measure of Sampling Adequacy .507

Bartlett’s Test of Sphericity

Approx. Chi-Square 65.939

df 6

Sig. .000

The communalities table showed that the initial communalities for the extraction was 1 for all variables. This is usually so because the complete factors’ set is specifically designed to explain the variability in the complete set of items (DeCoster & Claypool, 2004). The extraction communalities approximate the variance within each of the four variables that can be explained by the factors within the factor solution. Smaller values in the extraction communalities are an indication that a variable does not fit well within the factor solution, and perhaps ought to be dropped from the analysis (Hair et al., 2010). Relative Differentiation had the lowest extraction communality value (.586) as shown in Table 16 below although the value was acceptable for continuation in analysis.

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Table 17. Communalities for factor analysis on commitment

Variable Initial Extraction

Overall service provider impression (Affective Commitment) 1.000 .618

How service provider's post paid services meet specific needs (Normative

Commitment) 1.000 .980

Motivation/Likelihood to switch from current service provider (Continuance

Commitment) 1.000 1.000

Commitment: Relative Differentiation 1.000 .586

Extraction Method: Factor analysis.

The total variance explained table (Table 17) showed that the first three factors could account for over 79% (79.61%) of the variability within the original variables. This was an indication that three latent influences were associated with commitment although there was still room for considerable unexplained variation. The extraction of sums of squared loadings and the rotation sus of squared loadings’ sections of the table showed that there was no loss of variation accounted for by the initial solution because of latent factors that were unique to the original variables or variability that was unexplainable by the factor model. This argument is rooted in the understanding that the cumulative percent variance before and after rotation of squared loadings was equal for the three factors (79.61%). However, there were changes in all the three factors as shown in the extracted and rotated sums of squared loadings. For example, factor one was extracted at 1.199, but later rotated to 1.180, which was a reduction. Similarly, factor 2 had a slightly lower rotated sum of square loadings upon rotation (1.004) down from 1.047. However, factor 3 had a higher total sum of square loadings upon rotation (1.000) than before rotation (.938).

Table 18. Total variance explained for Al Jawal commitment factors

Component Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative % 1 1.199 29.976 29.976 1.199 29.976 29.976 1.180 29.496 29.496 2 1.047 26.176 56.153 1.047 26.176 56.153 1.004 25.111 54.607 3 .938 23.458 79.610 .938 23.458 79.610 1.000 25.003 79.610 4 .816 20.390 100.000

Extraction Method: Factor analysis.

The component matrix table shown in Table 18 below indicated that both overall service provider (Al Jawal) impression and Relative Differentiation loaded highest into the first component at .736 and .754 respectively. Motivation or likelihood to switch loaded highest into the second component (.723). Similarly, the commitment factor on the extent to

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which the service provider (Al Jawal) meets the specific needs of the subscribers loaded highest into the second component (.723).

Table 19. Component matrix for Al Jawal commitment factors

Variable Component

1 2 3

Overall service provider impression (Affective Commitment) .736 -.111 .253

How service provider's post paid services meet specific needs (Normative Commitment)

.233 .713 -.646

Motivation/Likelihood to switch from current service provider (Continuance Commitment)

-.186 .723 .666

Commitment: Relative Differentiation .754 .066 .117

Extraction Method: Factor analysis. a. 3 components extracted.

The rotated components matrix, whose rotation converged in four iterations, showed that overall service provider (Al Jawal) impression and commitment loaded highest into the first component with .780 and .755 respectively. Nonetheless, the loading factor value for both overall impression and Relative Differentiation increased from .736 and from .754 respectively. The factor on service provider (Al Jawal) meeting the specific needs of the consumer loaded highest into the second component with a rotated factor loading of .990 up from .713. Finally, motivation or likelihood to switch from current service provider recorded a higher rotated factor loading into the third component (.999) than the initial factor loading before rotation of .723 in factor 2. Table 19 below displays these results.

Table 20. Rotated component matrix for Al Jawal commitment factors

Component

1 2 3

Commitment: Overall service provider impression .780 -.091 -.029

Commitment: How service provider's post-paid services meet specific needs .026 .990 .021 Commitment: Motivation/Likelihood to switch from current service provider -.022 .021 .999

Commitment: Relative Differentiation .755 .128 .000

Extraction Method: Factor analysis.

Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 4 iterations.

The theorization and eventual naming of the three commitment types extracted from the various commitments items was based on literature by Jones et al. (2009, p.17) indicating that the three commitment dimensions can be viewed as “want to stay” (affective), “should stay” (calculative/continuous), and “have to stay” (normative). The overall provider impression and Relative Differentiation are types of commitments that make the subscriber

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want to stay. Consequently, the first component was renamed to Affective Commitment for this study’s subsequent analysis. Indeed, Colgate et al. (2007) argued that Affective Commitment originates from positive attitudes towards the company (Image).

The capacity of the post-paid services by a service provider to meet the specific needs of a subscriber relates to the commitment based on rationalized motives. Various authors (such as Bolton, Lemon, & Verhoeff, 2004; Colgate et al., 2007; Jones et al., 2009) describe this type of commitment as calculative/continuance. Thus, the second component was renamed to continuance commitment for the purposes of this study.

Finally, the lack of motivation or low likelihood to switch from the current service provider as represented in the questionnaire prompt and later in the factor analysis procedure denotes a “have to stay” type of commitment. Gustafsson, Johnson, and Roos (2005) contended that this commitment captures the social mutual exchange norm and referred to it as Normative Commitment. On this basis, the third extracted component following the factor analysis procedure on commitment was renamed to, and saved as, Normative Commitment for the purposes of further analysis in this study.