Investigating Importance Weighting of Satisfaction Scores from a Formative Model with Partial Least Squares Analysis

Abstract

This study introduced a formative model to investigate the utility of importance weighting on satisfaction scores with partial least squares analysis. Based on the bottom-up theory of satisfaction evaluations, the measurement structure for weighted/unweighted domain satisfaction scores was modeled as a formative model, whereas the measurement structure for global satisfaction scores was modeled as a reflective model according to top-down theory. The purpose was to see if the predictive effect of importance-weighted domain satisfaction scores is stronger than unweighted domain satisfaction scores in predicting global satisfaction scores. Three datasets in life, self, and job satisfaction were analyzed. In the life satisfaction dataset, 237 undergraduates at Central Taiwan University of Science and Technology voluntarily provided their responses. The mean age of respondents was 20.80 years (SD = 1.05). In the self-satisfaction dataset, 269 undergraduates at National Taiwan University provided their responses. The mean age of respondents was 19.78 years (SD = 1.44). Finally, in the job satisfaction dataset, 557 staff members in seven Taiwan provincial hospitals provided their responses. The mean age of respondents was 35.87 years (range from 21 to 65, SD = 8.60). Three measures of domain satisfaction, domain importance, and global satisfaction were collected in each dataset. Partial least squares analysis was used in model estimation. All the results revealed that unweighted domain satisfaction scores have a stronger predictive effect for global satisfaction measures than importance-weighted domain satisfaction scores, indicating that importance weighting on satisfaction scores did not have an empirical benefit.