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The past decade has witnessed a tremendous growth in the use of multi-item scales in consumer-related research. Concurrently, there is increasing concern about the quality of these measures. While the majority of articles now discuss the reliability of the scales administered, fewer address the issue of scale validity. One neglected scale validity issue which should be of particular concern in consumer behavior research is potential social desirability bias associated with scale measures. The purpose of this paper is to discuss the nature of such a bias, the means for testing for it, and ways it can and should be implemented in consumer-related research.

That music affects human beings in various ways has probably been presumed as long as people have played music. Many marketing practitioners already accept this notion, given that music is increasingly used as a stimulus in the retail environment as well as in radio and television advertising. Yet, fewer than 20 published empirical studies in marketing have music as their focus. The author reviews the small body of marketing literature, surveys relevant literature outside marketing, and provides research propositions to guide future studies.

In the last few decades, scholars and practitioners have increasingly tried to understand the factors that influence technology acceptance. Theories and models developed by scholars have tended to focus on the role of cognition and have rarely included affect. The few studies that have incorporated affect have tended to measure a single emotion rather than modeling it comprehensively. This research addresses that inadequacy in our understanding of technology adoption by merging two previously unrelated models: TAM (the Technology Acceptance Model) and PAD (the Pleasure, Arousal, and Dominance paradigm of affect). This study also examines an enhanced view of cognition. The product of this unified theoretical framework is referred to as the Consumer Acceptance of Technology (CAT) model. The results of a test using structural equation modeling provide empirical support for the model. Overall, the CAT model explains over 50% of the variance in consumer adoption intentions, a considerable increase compared to TAM. These findings suggest that

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