Beyond Affect Transfer: Attribute Associations in Video-Game Brand Placements and Their Impact on Brand Attitudes

Figures & data

Table 1. Correlations and descriptive statistics.

	Attituderel	Speedrel	Healthinessrel	Brand attention	Game affinity	Game liking	Performance
Attituderel	–	0.482***	0.568***	−0.006	−0.027	−0.089	−0.078
Speedrel		–	0.382***	0.045	0.045	−0.072	−0.02
Healthiness			–	−0.028	−0.059	−0.127*	−0.085
Brand Attention				(.82)	0.337***	0.242***	0.06
Game Affinity					(.70)	0.535***	0.653***
Game Liking						(.93)	0.406***
Performance							–
M	0.11	0.15	0.26	4.11	4.75	4.07	9.17
SD	2.16	2.15	1.97	1.62	1.32	1.62	3.83

Note. Values in brackets represent Cronbach’s alpha. *p < .05 ***p < .001.

Figure 1. Screenshots from the game. (A) The general design of the labyrinths with the character, the destination area, and a robot firing a lightning ball. (B) The user interface and the moment the player receives an item. (C) The effects of the different items (i.e., speed-positive, speed-negative, healthiness-positive, healthiness-negative).

Figure 2. Brand ratings by conditioned attribute and conditioned valence. Error bars represent 95% confidence intervals. All values represent relative values compared to unshown brands. We provide means and standard deviations within each factorial cell on the OSF.

Figure 3. Regression coefficients from the mediation analysis. Values in parentheses refer to the full mediation model when controlling for the mediators. Conditioned valence (positive = 1; negative = –1) and conditioned attribute (speed = 1; healthiness = –1) were effect-coded, the other variables were standardized at the grand mean. *p < .05, **p < .01, ***p < .001.