An exploration of consumer resistance to innovation and its antecedents

Although firms are faced by a large number of market introduction failures, research into a major driver of these failures, customer resistance to innovation, is surprisingly scarce. While most authors have investigated positive adoption decisions, this paper focuses instead on consumer resistance to innovation. The current study presents a conceptual framework which explicates the major components of consumer resistance: (1) rejection, (2) postponement, and (3) opposition, and discusses two main groups of antecedents to consumer resistance: (1) degree of change required and (2) conflicts with the consumer’s prior belief structure. This framework is explored with both a literature review and a qualitative focus group study. These joint efforts result in the formulation of a model of consumer resistance. Finally, the authors discuss several relevant theoretical and strategic implications, and point out directions for future research.

A comparative study of bayesian models for unsupervised sentiment detection

This paper presents a comparative study of three closely related Bayesian models for unsupervised document level sentiment classification, namely, the latent sentiment model (LSM), the joint sentiment-topic (JST) model, and the Reverse-JST model. Extensive experiments have been conducted on two corpora, the movie review dataset and the multi-domain sentiment dataset. It has been found that while all the three models achieve either better or comparable performance on these two corpora when compared to the existing unsupervised sentiment classification approaches, both JST and Reverse-JST are able to extract sentiment-oriented topics. In addition, Reverse-JST always performs worse than JST suggesting that the JST model is more appropriate for joint sentiment topic detection.