Albert R. Wildt

Price, Product Information, and Purchase Intention: An Empirical Study

Price, nonprice product information, and purchase intention, together with the intervening variables of perceived price, perceived quality, and perceived value, are empirically examined. The results indicate that perceived price is positively influenced by objective price and negatively influenced by reference price. They support the positive price-perceived quality relationship found in previous studies and, further, show that the influence of price on perceived quality is lessened in the presence of substantial direct product information. Finally, the results demonstrate that a trade-off between perceived price and perceived quality leads to perceived value, and perceived value is a primary factor influencing purchase intention.

Determinants of Scale Response: Label versus Position

Two determinants of scale response—the denotative meaning of the adjective labels used and the location of the labels in relation to scale endpoints—were investigated. On the basis of the procedures used, both label and location were found to have an impact on subject response. Estimation of a mixing parameter for the two factors permits comparison of their relative effects across different scales.

Consideration Set Measurement

The consideration set is a concept that is both intuitively appealing and practically useful. However, there is no standard operational definition of the construct, little reported research investigating alternative measures for empirically assessing the construct, and only limited investigation of interproduct differences in set characteristics (typically focusing on set size). Results of the experiment reported here indicate that situation-specific operational definitions yield smaller reported consideration sets that exhibit greater correspondence to reported purchases than do situation-neutral definitions, and that semantic variations within definition type have little impact on measured set characteristics. Further, findings indicate that much of the interproduct variation in consideration set size is related to awareness set size.

Impact of product information on the use of price as a quality cue

The use of price in subjective product quality evaluations is influenced by the nature and availability of other product information. Empirical investigation of this proposition indicated that the utilization of price as a quality cue diminished in relation to the quantity and quality of intrinsic attribute information. However, for one of the two products investigated this diminished influence reversed when a large amount of information was provided.

Approximating Confidence Intervals for Factor Loadings

Practical theoretic means for assessing the sampling variability of loadings estimated by exploratory factor analytic procedures have not been readily available in the absence of restrictive distributional assumptions. It has been necessary for researchers to interpret these point estimates (loadings) through the use of arbitrary rules-of-thumb. Under these conditions, loading interpretations may be problematic. A method is presented for exploiting information in the empirical data, collected for a studys primary goals, to approximate confidence intervals for factor loadings, The method appears generalizable across factor methods, numbers of extracted factors, and rotation criteria.

Assessing Sampling Variation Relative to Number-of-Factors Criteria

Employment of the bootstrap method to approximate the sampling variation of eigenvalues is explicated, and its usefulness is amplified by an illustration in conjunction with two commonly used number-of-factors criteria: eigenvalues larger than one and the scree test. Confidence intervals for eigenvalues are approximated for sample correlation matrices that have ones and squared multiple correlation coefficients on the diagonals. The results demonstrate the usefulness of the bootstrap method in providing information about the sampling variability of eigenvalues-knowledge that affords a basis for more informed decisions regarding the number of factors when employing common criteria. Further, this information can be obtained with little difficulty, and the approach avoids tenuous assumptions of symmetric confidence intervals.

Forecasting sales response for multiple time horizons and temporally aggregated data: A comparison of constant and stochastic coefficient models

Abstract Past research on time-varying sales-response models emphasized the application of different estimation techniques in examining variation in advertising effectiveness over time. This study focuses on comparing sales forecasts using constant and stochastic coefficients sales-response models. Selected constant and stochastic coefficient models are applied to six sets of bimonthly and one set of annual advertising and sales data to assess forecasting accuracy for time horizons of various lengths. Results show improved forecasting accuracy for a first-order autoregressive stochastic coefficient model, particularly in short-run forecasting applications.

Bias Approximations for Complex Estimators: An Application to Redundancy Analysis.

In applied research designed to address conceptual as well as program and policy formulation issues, it is often important to assess the bias present in estimators of association and prediction stemming from the use of multivariate analytical procedures. However, the theoretic sampling distributions of these estimators are often unknown in applied research contexts because of their complexity and the inability to satisfy highly restrictive assumptions. Consequently, in many instances the amounts of bias and subsequent bias corrections have eluded analytic determination. This paper presents and illustrates a method for approximating the amounts of bias in estimators having complex sampling distributions that are influenced by a variety of properties typical of applied research data. The method, which appears to have broad applicability to many multivariate procedures, is illustrated in the context of redundancy analysis.

Approximate Confidence Intervals for Estimates of Redundancy Between Sets of Variables.

Use of redundancy analysis would be enhanced by having available a procedure that (a) evaluates the statistical significance of the estimated proportion of variance in the criterion variable set extracted by a given predictor variate, Ry i (2), as well as by all predictor variates combined, R(y 2), and (b) is lenient with respect to assumptions about the distributions of redundancy estimates. A previous testing method leaves in doubt the assessment of redundancy attributed individually to each predictor variate, and it requires parametric assumptions about the distributions of the estimators. This paper describes and demonstrates the bootstrap methodology which yields approximations of the sampling variation of redundancy estimates while assuming little a priori knowledge about the distributions of these statistics. The procedure is applicable for evaluating the estimated proportions of criterion set variance explained solely by a given predictor variate and cumulatively by all predictor variates.