The concept of customer orientation in salespeople is defined, a scale is developed to measure the degree to which salespeople engage in customer-oriented selling, and the properties of the scale are reported. A test of the nomological validity indicates the use of customer-oriented selling is related to the ability of the salespeople to help their customers and the quality of the customer-salesperson relationship.
The authors investigate whether it is necessary to include disconfirmation as an intervening variable affecting satisfaction as is commonly argued, or whether the effect of disconfirmation is adequately captured by expectation and perceived performance. Further, they model the process for two types of products, a durable and a nondurable good, using experimental procedures in which three levels of expectations and three levels of performance are manipulated for each product in a factorial design. Each subject's perceived expectations, performance evaluations, disconfirmation, and satisfaction are subsequently measured by using multiple measures for each construct. The results suggest the effects are different for the two products. For the nondurable good, the relationships are as typically hypothesized. The results for the durable good are different in important respects. First, neither the disconfirmation experience nor subjects’ initial expectations affected subjects’ satisfaction with it. Rather, their satisfaction was determined solely by the performance of the durable good. Expectations did combine with performance to affect disconfirmation, though the magnitude of the disconfirmation experience did not translate into an impact on satisfaction. Finally, the direct performance-satisfaction link accounts for most of the variation in satisfaction.
A simulation study of the effects of sample size on the overall fit statistic provided by the LISREL program indicates the statistic is well behaved over a wide range of sample sizes for simple models. However, this statistic is apparently not chi square distributed for more complex models when samples are relatively small, and will reject the hypothesized model too often. A set of additional measures suggested by various researchers for evaluating causal models also is examined. These statistics are well behaved for both models tested as they converge to the true value and their variance approaches zero as sample size increases.
In marketing applications of structural equation models with unobservable variables, researchers have relied almost exclusively on LISREL for parameter estimation. Apparently they have been little concerned about the frequent inability of marketing data to meet the requirements for maximum likelihood estimation or the common occurrence of improper solutions in LISREL modeling. The authors demonstrate that partial least squares (PLS) can be used to overcome these two problems. PLS is somewhat less well-grounded than LISREL in traditional statistical and psychometric theory. The authors show, however, that under certain model specifications the two methods produce the same results. In more general cases, the methods provide results which diverge in certain systematic ways. These differences are analyzed and explained in terms of the underlying objectives of each method.