45. SPSS AMOS - Moderation Analysis with Full Structural Model - Mixed Model Method

Role ambiguity significantly changes how collaborative culture translates into organizational performance—and the moderation effect shows up in a mixed structural approach that blends latent constructs with composite moderator terms. Using the mixed model method, the analysis treats collaborative culture and organizational performance as latent variables (with their indicators), while role ambiguity enters as a composite moderator and the interaction is built from mean-centered composite scores. The key outcome is a statistically significant interaction: role ambiguity moderates the CC → OP relationship, with the interaction effect remaining significant in the baseline model but weakening once the moderator is examined at different levels.

The workflow starts with mean centering. Collaborative culture (CC) and role ambiguity (RA) are mean-centered in SPSS by subtracting each variable’s sample mean from individual respondent values. An interaction term is then created by multiplying the centered CC and centered RA values (e.g., “CC into RA”). In the mixed model setup, the model includes latent independent and dependent constructs with their measurement indicators, while role ambiguity is inserted as a composite variable and the interaction term is also composite—meaning the model is not a “pure” full structural test because it mixes latent unobservable constructs with composite moderator components.

Model comparison highlights the tradeoff. The composite-based moderation model and the mixed model produce different estimates: the CC → OP relationship shifts slightly, weakening in the mixed model but improving in terms of accounting for measurement error in the latent constructs. The interaction term’s effect on organizational performance also weakens yet stays significant. In practical terms, the moderation still holds: RA meaningfully alters the strength of the CC → OP link, even though the approach is not a fully latent interaction test.

After establishing significance, the analysis probes the interaction by testing simple slopes at low, average, and high levels of role ambiguity. The method uses ±1 standard deviation around the mean-centered moderator. First, the standard deviation of RA is computed (reported as 1.36595). A “low RA” moderator value is created by shifting the centered RA by +1.36595 (to represent the low level in the coding used), then a new interaction term is formed by multiplying centered CC with this low-RA value. Under low role ambiguity, the CC → OP effect strengthens substantially: the unstandardized regression weight rises from 0.553 (baseline) to 0.813, and the relationship remains significant.

At the average level, moderation remains significant. At high role ambiguity, the CC → OP relationship weakens and the moderation effect becomes insignificant—indicating that collaborative culture boosts organizational performance most strongly when role ambiguity is low, and that this benefit largely disappears when role ambiguity is high.

Finally, reporting guidance ties the statistical interaction to interpretation: the moderation is negative and significant, and simple slope results show steeper positive slopes for low RA compared with flatter or non-significant slopes at high RA. A slope visualization is generated using the Jim Gaskin stats tool package, with the figure reflecting stronger CC → OP gains under low role ambiguity and diminished gains under high role ambiguity.