How does green suit me? Consumers mentally match perceived product attributes with their domain‐specific motives when making green purchase decisions

Nowadays, environment‐friendly products are widely accepted by society. However, market share of sustainable technologies demanding higher investments or behavioral changes is rather small, indicating that consumers seem to generally approve of sustainable products, but rarely perceive them as being compatible with their needs. The present research introduces a model emphasizing the importance of consumers perceiving the product as being suitable to match their motives relevant to the product domain. In investigating a sample of 531 car drivers, the hypothesized model was tested in the realm of a highly relevant sustainable product: electric vehicles (EVs). Findings based on structural equation modeling showed that participants' perceived matching with EV attributes was related to their domain‐specific motives (hedonic, freedom, ecological, and financial motives) and a global preference towards the product. Specific to sustainable products, ecological motives strongly influenced participants' matching processes by positively affecting perceived matching with all product attributes. Participants' purchase intentions, in turn, were strongly affected by their perceived matching of the product with their motives. The added value of the model with respect to self‐image theories and implications for sustainable product marketing are examined through discussion. Copyright © 2014 John Wiley & Sons, Ltd.     

Correcting Too Much or Too Little? The Performance of Three Chi-Square Corrections

This simulation study investigates the performance of three test statistics, T₁, T₂, and T₃, used to evaluate structural equation model fit under non normal data conditions. T₁ is the well-known mean-adjusted statistic of Satorra and Bentler. T₂ is the mean-and-variance adjusted statistic of Sattertwaithe type where the degrees of freedom is manipulated. T₃ is a recently proposed version of T₂ that does not manipulate degrees of freedom. Discrepancies between these statistics and their nominal chi-square distribution in terms of errors of Type I and Type II are investigated. All statistics are shown to be sensitive to increasing kurtosis in the data, with Type I error rates often far off the nominal level. Under excess kurtosis true models are generally over-rejected by T₁ and under-rejected by T₂ and T₃, which have similar performance in all conditions. Under misspecification there is a loss of power with increasing kurtosis, especially for T₂ and T₃. The coefficient of variation of the nonzero eigenvalues of a certain matrix is shown to be a reliable indicator for the adequacy of these statistics.