Manipulation Checks

To conduct a manipulation check, we used the average scores of the involvement items and the think/feel items. Regarding the level of involvement, we found significant differences between soft drink and day cream (M_{soft drink} = 4.26, M_{day cream} = 4.94, p < 0.001) as well as between toothpaste and DVD player (M_toothpaste = 4.14, M_{DVD player} = 4.72 , p < 0.001). With respect to the type of offering (think vs. feel), significant differences were found between soft drink and toothpaste (M_{soft drink} = 4.91 , M_toothpaste = 4.39 , p < 0.001) as well as between day cream and DVD player (M_{day cream} = 4.76 , M_{DVD player} = 3.99 , p < 0.001).

Research Objective 1 Analytical Approach

Analog to multiple regression analysis, predictive ability was evaluated by means of the multiple correlation coefficient , which is defined as the correlation between the actual value of the dependent variable ( ) and the predicted value of the dependent variable ( ). Thus,

Assessing research objective 1 (“to assess and to compare the performance of different commonly used customer value measurement methods with regard to their predictive ability of customer satisfaction, repurchase intentions and word-of-mouth.”) involves testing the following set of hypotheses.

H_A: at least one is different

The letters refer to the value measurement methods of Dodds et al. (1991), Gale (1994), Woodruff and Gardial (1996) and Holbrook (1999) respectively. The variable ( ) represents the actual (predicted) value of satisfaction, repurchase intentions or word-of-mouth.

As each respondent filled out a questionnaire containing only one of the different value measurement methods under study, the four relevant correlation coefficients can be considered independent of one another. Thus, testing the null hypothesis involves testing whether four independent sample correlation coefficients are statistically equal. For this purpose Zar (1996) proposes the test presented in equation 1.

(1)

Where

	=	Fisher z-transformation of correlation coefficient
	=	sample size on which is based
	=	number of independent correlation coefficients

If the null hypothesis of equal independent correlation coefficients is rejected, it is of interest to determine which of the correlation coefficients are different from which others. Therefore, we used pairwise comparisons based on a Tukey type test. This procedure implies that for each pair of correlation coefficients, and , the following null hypothesis is tested.

To test this null hypothesis we used the following test

(2)

With

The statistic has a known distribution (see Table B5 of Zar [1996], which lists the critical values of the accompanying distribution [i.e., ]).

Parameter Estimation

Partial Least Squares (PLS) path modeling played a prominent role in the assessment of our empirical data. The reasons to opt for PLS path modeling are as follows. First, in line with our objective to evaluate predictive ability of the different value measurement approaches, an estimation approach that ensures optimal prediction accuracy was desirable. Second, PLS path modeling allowed us to estimate measurement models that include both formative and reflective indicators. This is particularly relevant as the literature indicates that value measurement models include both types of measurement (Ruiz et al., 2008). Third, PLS path modeling allowed us to calculate latent variable scores, which are crucial in assessing and comparing the predictive ability of the different value measurement methods under study.

To assess the statistical significance of the parameter estimates, we constructed percentile bootstrap confidence intervals based on 5000 samples (Preacher and Hayes, 2008).