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Table 5.11: Construct reliability, convergent, and discriminant validity
CR FCI SMT SMC DMC AMC MP ET
FCI .964 .707
SMT .962 .213** .716
SMC .952 .326** .238** .715
DMC .906 .190** .226** .440** .706
AMC .947 .069** .095** .137** .233** .620
MP .897 ..272** .169** .228** .176** .323** .688
ET .846 .060** .011* .000 .000 .147** .056** .592
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
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exogenous and endogenous variables. The model is established using the observable variables directly by averaging the items that constitute the construct. This path analysis model is an alternative to the latent variable model when the CFA produced high construct reliability (Stephenson & Holbert 2003).
Besides, previous scholars suggest that the experience and size of the firm affect its performance and capabilities (Teece et al. 1997; Guo et al. 2018; Morgan & Slotegraaf 2012). Thus, these two variables were included as control variables for the research model. The covariance between the errors of the different marketing capabilities constructs has a theoretical reason. Previous literature underlines correlations between these capabilities (Guo et al. 2018; Kachouie, Mavondo & Sands 2018).
The model explains 35% of the variance in firm performance, and Table 5.12 shows the goodness of fit indices that suggest the acceptance of the research proposed structural model. Hence, the study proceeds to path analysis and hypotheses testing.
Table 5.12: Structural model summary of goodness of fit tests
CMIN CMIN/DF SRMR TLI CFI RMSEA
16.704 p=.033 2.088 .0.0428 .886 .967 .088
150 Figure 21: Research structural model visual diagram
5.9.1 The Relationships between FCI, SMC, DMC, and AMC
The findings of path analysis indicate that firm cultural intelligence relates positively and significantly to static marketing capabilities (β= 0.38, t-value= 5.112, p < .001) and dynamic marketing capabilities (β= 0.24, t-value= 3.026, p= .002). However, this relationship was not significant with adaptive marketing capabilities (β= 0.15, t-value= 1.764, p = .078). Thus, hypothesis 1: Firm cultural intelligence is positively related to static marketing capabilities, and hypothesis 2: Firm cultural intelligence is positively related to dynamic marketing capabilities were supported. However, hypothesis 3: Firm cultural intelligence is positively related to adaptive marketing capabilities was rejected.
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5.9.2 The relationships between SMT, SMC, DMC, AMC, and FCI
The analysis of path estimates highlights that social media technologies relate positively and significantly to static marketing capabilities (β= 0.30, t-value=4.001, p < .001), dynamic marketing capabilities (β= 0.32, t-value= 4.069, p < .001), adaptive marketing capabilities (β= 0.25, t-value= 2.818, p= .005), and firm cultural intelligence (β= 0.42, t-value= 5.588, p < .001). Thus, hypothesis 4: Firm social media technologies are positively related to static marketing capabilities, hypothesis 5: Firm social media technologies are positively related to dynamic marketing capabilities, hypothesis 6: Firm social media technologies are positively related to adaptive marketing capabilities, and hypothesis 7:
Firm social media technologies are positively related to firm cultural intelligence were supported.
5.9.3 The relationships between SMC, DMC, AMC, and MP
The relationships between static marketing capabilities (β= 0.23, t-value= 2.672, p= .008), adaptive marketing capabilities (β= 0.43, t-value= 5.506, p < .001), and firm performance were positive and significant. However, the relationship between dynamic marketing capabilities and firm performance was not significant (β= 0.05, t-value= 0.555, p= .579). Thus, hypothesis 8: Static marketing capabilities are positively related to firm performance was supported. Hypothesis 9: Dynamic marketing capabilities are positively related to firm performance was not supported. Besides, hypothesis 10: Adaptive marketing capabilities are positively related to firm performance was supported.
5.9.4 The relationships between SMC, DMC, AMC, and MP under low and high turbulence
This research attempts to explain the low and high levels of environmental turbulence moderation effects on the relationships between marketing capabilities and performance. To test these hypotheses, the variable environmental turbulence was recoded using SPSS to a new categorical variable. The values below the median of ET distribution were recoded to 1, and represent the low turbulence group 1. The
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values above 5 were recoded to 2, and represent the high turbulence group 2. This procedure is followed by a multi-group path analysis using AMOS. This approach is suitable for testing the study hypotheses by comparing specific path parameters across the two groups of high and low environmental turbulence (Stephenson, Holbert & Zimmerman 2006).
Figure 22 and 23 shows the research structural model and path estimates under low and high environmental turbulence consecutively. Table 5.13 proposes that the moderated model has satisfactory goodness of fit indices.
Table 5.13: Multi-group moderation model summary of goodness of fit tests
CMIN CMIN/DF SRMR TLI CFI RMSEA
27.467 p=.037 1.717 .0492 .846 .956 .071
Figure 22: Low turbulence moderated model visual diagram
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Figure 23: High turbulence moderated model visual diagram
The multi-group path analysis indicates that static marketing capabilities relate positively and significantly to performance under low ET (β= 0.40, t-value= 3.046, p < .01). However, this relationship is not significant under high ET (β= 0.13, t-value= 1.096, p= .279). Thus, hypothesis 8a: The relationship between static marketing capabilities and firm performance is weaker when the level of environmental turbulence is high than when it is low was supported.
On the other hand, the relations between dynamic marketing capabilities and firm performance were insignificant under both low (β= -0.02, t-value= -0.128, p= .903) and high (β= 0.10, t-value= 0.823, p=
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.416) environmental turbulence. Therefore, hypothesis 9a: The relationship between dynamic marketing capabilities and firm performance is not moderated by the level of environmental turbulence was supported.
Finally, the associations between adaptive marketing capabilities and firm performance were positive and significant under both low (β= 0.38, t-value= 3.023, p < .01) and high (β= 0.35, t-value= 3.335, p <
.001) environmental turbulence. Thus, hypothesis 10a: The relationship between adaptive marketing capabilities and firm performance is stronger when the level of environmental turbulence is high than when it is low was not supported. The findings of the models’ path analysis and hypotheses tests are summarised in table 5.14.
Table 5.14: Summary of the research hypothesis test results Hypothesis Standardised
estimate
Standard Error Critical Ratio Results
H1 FCI to SMC 0.377 0.064 5.112*** Supported
H2 FCI to DMC 0.235 0.071 3.026** Supported
H3 FCI to AMC 0.154 0.069 1.764 Not Supported
H4 SMT to SMC 0.296 0.068 4.001*** Supported
H5 SMT to DMC 0.317 0.074 4.069*** Supported
H6 SMT to AMC 0.246 0.073 2.818** Supported
H7 SMT to FCI 0.425 0.080 5.588*** Supported
H8 SMC to MP 0.231 0.087 2.672** Supported
H9 DMC to MP 0.051 0.088 0.555 Not supported
H10 AMC to MP 0.430 0.086 5.506*** Supported
H8a SMC to MP (Low) 0.399 0.132 3.046** Supported
H8a SMC to MP (High) 0.134 0.115 1.096
H9a DMC to MP (low) -0.02 0.127 -0.128 Supported
H9a DMC to MP (High) 0.100 0.124 0.823
H10a AMC to MP (low) 0.381 0.139 3.023** Not Supported
H10a AMC to MP (High) 0.345 0.122 3.335***
***. Correlation is significant at the 0.001 level
**. Correlation is significant at the 0.01 level
*. Correlation is significant at the 0.05 level
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