David T. Ory

Shopping-related attitudes: a factor and cluster analysis of Northern California shoppers

This paper presents an analysis of general shopping and travel-related attitudes collected from a custom-designed Internet-based survey conducted in the spring of 2006, of randomly selected residents of two communities in Northern California. These and other data collected in the survey will eventually lead to models of shopping mode (channel) choice, intention, and frequency among other analyses. In this early examination of the data (N = 966), exploratory factor analysis is performed to identify the conceptual constructs underlying a group of forty-two general shopping-related attitudinal statements, with which respondents agreed or disagreed on a five-point Likert-type scale. From the nineteen potential constructs identified in the design stage, thirteen dimensions emerged empirically: shopping and store enjoyment, price and time consciousness, impulse buying, materialism, trust, caution, trendsetting, and opinions with respect to credit card usage, technology, exercise, and the environment. Cluster analysis is then conducted to identify seven market segments having different attitudinal profiles: store shopaholics (15%), bichannel shopaholics (14%), time-starved worriers (16%), nonmaterialistic greens (16%), unwired antishoppers (14%), practical and leisure-oriented (13%), and technoconservatives (11%). These segments differ significantly, in logical ways, on a number of sociodemographic and other characteristics, including shopping channel choices. Thus, more detailed investigations of choice behavior using these market segments should prove fruitful.

Exploring the Cognitive and Affective Mechanisms Behind Subjective Assessments of Travel Amounts

Travel demand models focus on explaining how much individuals actually travel but offer no insight into how much individuals think they travel. The authors propose that the latter is an important determinant of traveler behavior, and that actual mobility is refracted through a variety of filters that magnify or diminish those subjective evaluations of travel amounts. Linear regression models of subjective mobility measures provided by 1,358 San Francisco Bay Area commuters were estimated earlier; the focus of this article is on identifying the potential cognitive and affective mechanisms that influence subjective mobility upward or downward, after controlling for objective mobility. The authors find three major types of mechanisms: awareness-heightening, affective, and comparison-inducing. Recurring patterns of effects in these three categories are analyzed in the light of psychological and marketing research concepts including the availability heuristic, social comparison, relative deprivation, autobiographical memory, and motivation theory.

Structural Equations Models

When one variable is an effect as well as a cause of another variable, single-equation regression coefficients will be biased and inconsistent. Structural equation modeling (SEM) explicitly allows multiple directions of causality through simultaneously estimating the parameters of multiple interconnected equations. It is then possible to distinguish the direct effect of variable A on variable B from its total effect on B, which includes all pathways from A to B through other variables. The direct and total effects can differ in magnitude and even sign. SEMs are estimated through covariance structure analysis, minimizing the discrepancy between the covariance matrix estimated directly from sample data and the one implied by the model. Not all hypothesized model structures are estimable, and so tests of identifiability should ideally be conducted before data collection. The important assumption that the data are multivariate normally distributed should also be checked. Several strategies are available in the (frequent) event that the data fail this assumption, including transforming variables, discarding outliers, using more robust estimators, and using distribution-free estimation. A number of goodness-of-fit measures are available; members of several different families of measures should be evaluated, and competing model structures should be compared.