Enhancing e-Business Decision Making: An Application of Consensus Theory

Enhancing e-Business Decision Making: An Application of Consensus Theory

William J. Tastle (University of Iceland, Iceland) and Mark J. Wierman (Creighton University, USA)
DOI: 10.4018/978-1-60566-966-3.ch008
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Abstract

Statistical analysis is the universally accepted method by which sense is created from raw data. Successful requirements determination is often dependent upon the gathering customer data over the Internet, and it may be largely limited to collecting the responses such as Yes/No and Likert scale categories. These data are then analyzed to identify customer trends or other items of interest to management. The data can be useful, but key to their usage is the application of suitable mathematical tools. Traditionally little more than standard statistics has been used in the analysis of ordinal, or category, data. This chapter introduces measures of agreement and dissent to the field of e-business analysis and shows how ordinal data can be analyzed in meaningful ways.
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Introduction

Gathering data from customers is a common activity and much research has gone into design and planning (Parsons, 2007; Solomon, 2001), improving response rates (Cook, et al, 2000; Kaplowitz, et. al., 2004; Schmidt, et al, 2005), the study of privacy and ethics (Couper, 2000), mode of questionnaire delivery (Denscombe, 2006), the effect of subject lines of survey responses (Porter and Whitcomb, 2005), the analysis of web usage using traditional statistics (Korgaonkar and Wolin, 1999; Stanton, 1998) and but little has been written about the evolution of ordinal scale survey results, typical of Likert or Likert-like scale surveys. Acknowledging that getting respondents to answer surveys, either paper or digital, can be a challenge, and once the data is collected the effort to squeeze as much information from the data as possible begins.

Traditionally, data analysis is well founded in statistics, even though the same underpinnings of statistics recognize that there are limits to this branch of mathematics. Statistics are at home when dealing with ratio or interval data (Tastle and Wierman, 2006a), but once the scale shifts to ordered categories the use of statistics is circumspect, for what does it mean to say the average of “warm” and “hot” is reported as “warm-and-a-half“ (Jamieson, 2004). Ordinal scales of measurement typically consist of ordered category hierarchies such as: “strongly agree (SA),” “agree (A),” “neither agree nor disagree (N),” “disagree (D),” and “strongly disagree (SD)”; “very cold,” “cold,” “cool,” “tepid,” “warm,” “hot,” and “very hot.” The instrument typically used to collect this kind of data is called the Likert scale, though there are variations of this scale such as Likert-like, Likert-type, and ordered response scales. Researchers utilize this kind of instrument to collect data that cannot be ascertained using traditional measures, for the data being collected are feelings, perceptions, sensations, emotions, impressions, sentiments, opinions, passions, or the like. Unfortunately, the application of standard statistics to these data can be improper (Cohen, et al, 2000; Jamieson, 2004; Pell, 2005). This paper looks at the different kinds of scales and presents a new measure for analyzing ordinal scale data.

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