Table 3.

Hypotheses and structural model estimates

Hypotheses and structural relationship	Coefficients	t value
H1: Good ICR → PA	0.337	4.481**
H2: Bad ICR → NA	− 0.223	− 2.657**
H3: High ISP → PA	0.535	7.216**
H4: Low ISP → NA	− 0.396	− 4.615**
H5: PA → Increasing customer retention	0.638	9.134**
H6: NA → Decreasing customer retention	− 0.125	− 2.051*
The variance explained Customer Retention (R2) = 0.498 Positive Affect (R2) = 0.653 Negative Affect (R2) = 0.336	Total impact on customer retention PA = 0.638; NA = − 0.127 ICR = 0.242; SP = 0.388 Moderation formula Y = i + aX + bM + cXM + E The interaction of X and M or coefficient c measures the moderation effect Here path a measures the simple effect of X, sometimes called the main effect of X, when M equals zero	Indirect impact βICR-PAandNA-customer retention = 0.242 βSP-PAandNA-customer retention = 0.388

Annotation 1: *p < 0.05; **p < 0.01; Goodness-of-fit statistics: χ² = 272.219; df = 113; χ²/df = 2.409; p = 0.001; CFI 0.967, IFI 0.968, TLI 0.958, RMSEA = 0.080

Annotation 2: ICR Insurance company reputation, ISP Insurance service performance, PA Positive affect, NA Negative affect