In this guide, we introduce researchers in the behavioral sciences in general and MIS in particular to text analysis as done with latent semantic analysis (LSA). The guide contains hands-on annotated code samples in R that walk the reader through a typical process of acquiring relevant texts, creating a semantic space out of them, and then projecting words, phrase, or documents onto that semantic space to calculate their lexical similarities. R is an open source, popular programming language with extensive statistical libraries. We introduce LSA as a concept, discuss the process of preparing the data, and note its potential and limitations. We demonstrate this process through a sequence of annotated code examples: we start with a study of online reviews that extracts lexical insight about trust. That R code applies singular value decomposition (SVD). The guide next demonstrates a realistically large data analysis of Stack Exchange, a popular Q&A site for programmers. That R code applies an alternative sparse SVD method. All the code and data are available on github.com.
This research uniquely contributes to the marketing policy literature by illuminating the widespread yet seldom studied problem of online inaccessibility of retail websites affecting approximately 30 million disabled Americans. When a website is not designed to be navigated easily or is not compatible with assistive technology such as a screen-reader, these potential customers are not able to independently search for information and conduct transactions. Blind and low vision participants in an empirical study provide their opinions regarding accessibility policy issues and reveal that their frustrations with inaccessible retail websites may result, not only in avoidance of the retailer in its differ
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