\r\n\t \r\n\tMoreover, the application of modern methods in the developing world will help attract new investments, whether domestic or international from developed countries to developing countries. These actions will create new jobs for young people in terms of exploration projects and thus increase the living standard of people living in poor countries and help increase national income. \r\n\t \r\n\tWith regard to the problem of desert reclamation in these countries, the results of the application of these methods can help in finding new groundwater sites that can be used for drinking and agriculture, and create new urban communities. \r\n\t \r\n\tFinally, it can be concluded that these methods play an important and essential role in combating poverty in developing countries in the world. At the end, the book chapters will help the readers to know about Geophysics and it's applications.
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\n\t\t\t
1. Introduction
\n\t\t\t
Net Promoter Score (NPS) is a popular metric used in a variety of industries for measuring customer advocacy. Introduced by Reichheld (2003), NPS measures the likelihood that an existing customer will recommend a company to another prospective customer. NPS is derived from a single question that may be included as part of a larger customer survey. The single question asks the customer to use a scale of 0 to 10 to rate their willingness and intention to recommend the company to another person. Ratings of 9 and 10 are used to characterize so-called ‘promoters,’ ratings of 0 through 6 characterize ‘detractors,’ and ratings of 7 and 8 characterize ‘passives.’ The NPS is calculated as the percentage of respondents that are promoters minus the percentage of respondents that are detractors.
\n\t\t\t
The idea behind the labels given to customers is as follows. Promoters are thought to be extremely satisfied customers that see little to no room for improvement, and consequently would offer persuasive recommendations that could lead to new revenue. The passive ratings, on the other hand, begin to hint at room for improvement and consequently the effectiveness of a recommendation from a Passive may be muted by explicit or implied caveats. Ratings at the low end are thought to be associated with negative experiences that might cloud a recommendation and likely scare off prospective new customers. Additional discussion on the long history of NPS can be found in Hayes (2008).
\n\t\t\t
Some implementations of NPS methodology use reduced 5-point or 7-point scales that align with traditional Likert scales. However it is implemented, the hope is that movements in NPS are positively correlated with revenue growth for the company. While Reichheld’s research presented some evidence of that, other findings are not as corroborative (Kenningham et al., 2007). Regardless of whether there is a predictive relationship between NPS and revenue growth, implementing policies and programs within a company that improve NPS is an intuitively sensible thing to do [see, for example, Vavra (1997)]. A difficult and important question, however, is how to identify key drivers of NPS. Calculating NPS alone does not do this.
\n\t\t\t
This chapter is an illustrative tutorial that demonstrates how a statistical classification model can be used to identify key drivers of NPS. Our premise is that the classification model, the data it operates on, and the analyses it provides could usefully form components of a Decision Support System that can not only provide both snapshot and longitudinal analyses of NPS performance, but also enable analyses that can help suggest company initiatives aimed toward lifting the NPS.
\n\t\t\t
We assume that the NPS question was asked as part of larger survey that also probed customer satisfaction levels with respect to various dimensions of the company’s services. We develop a predictive classification model for customer advocacy (promoter, passive or detractor) as a function of these service dimensions. A novelty associated with our classification model is the optional use of constraints on the parameter estimates to enforce a monotonic property. We provide a detailed explanation of how to fit the model using the SAS software package and show how the fitted model can be used to develop company policies that have promise for improving the NPS. Our primary objective is to teach an interested practitioner how to use customer survey data together with a statistical classifier to identify key drivers of NPS. We present a case study that is based on a real-life data collection and analysis project to illustrate the step-by-step process of building the linkage between customer satisfaction data and NPS.
\n\t\t
\n\t\t
\n\t\t\t
2. Logistic regression
\n\t\t\t
In this section we provide a brief review of logistic and multinomial regression. Allen and Rao (2000) is a good reference that contains more detail than we provide, and additionally has example applications pertaining to customer satisfaction modeling.
\n\t\t\t
\n\t\t\t\t
2.1. Binomial logistic regression
\n\t\t\t\t
The binomial logistic regression model assumes that the response variable is binary (0/1). This could be the case, for example, if a customer is simply asked the question “Would you recommend us to a friend?” Let \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tY\n\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tn\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t denote the responses from n customers, assigning a “1” for Yes and “0” for No. Suppose a number of other data items (covariates) are polled from the customer on the same survey instrument. These items might measure the satisfaction of the customer across a wide variety of service dimensions and might be measured on a traditional Likert scale. We let \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tx\n\t\t\t\t\t\t\t\t\t\t˜\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t denote the vector of covariates for the i-th sampled customer and note that it reflects the use of dummy variable coding for covariates that are categorical scale. For example, if the first covariate is measured on a 5-point Likert scale, its value is encoded into \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tx\n\t\t\t\t\t\t\t\t\t\t˜\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t by using five dummy variables\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tx\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\t\t\tj\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tj\n\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t5\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t, where \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tx\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\tj\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t if and only if the Likert response is j.
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The binomial logistic regression model posits that \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tY\n\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t is a Bernoulli random variable (equivalently, a binomial random variable with trial size equal to one) with success probability\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tp\n\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t, and further, that the success probability is tied to the covariates through the so-called link function\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tp\n\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\texp\n\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\tα\n\t\t\t\t\t\t\t\t+\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tβ\n\t\t\t\t\t\t\t\t\t\t′\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t˜\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tx\n\t\t\t\t\t\t\t\t\t\t˜\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t/\n\t\t\t\t\t\t\t\t[\n\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t+\n\t\t\t\t\t\t\t\texp\n\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\tα\n\t\t\t\t\t\t\t\t+\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tβ\n\t\t\t\t\t\t\t\t\t\t′\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t˜\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tx\n\t\t\t\t\t\t\t\t\t\t˜\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t, where \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tβ\n\t\t\t\t\t\t\t\t˜\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t is a vector of model parameters (slopes). Continuing with the 5-point Likert scale example above, there would be five slopes \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tβ\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\t\t\tj\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tj\n\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t5\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t associated with the five dummy variables \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tx\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\t\t\tj\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tj\n\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t5\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t used to code the first covariate.
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Model fitting for the binomial logistic regression model entails estimating the parameters \n\t\t\t\t\t\t\n\t\t\t\t\t\t\tα\n\t\t\t\t\t\t\n\t\t\t\t\t and \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tβ\n\t\t\t\t\t\t\t\t˜\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t via maximum likelihood. The likelihood function for this model is
and the maximum likelihood estimate (MLE) of \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\tα\n\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tβ\n\t\t\t\t\t\t\t\t\t\t˜\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t′\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t is the value, say\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tα\n\t\t\t\t\t\t\t\t\t^\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tβ\n\t\t\t\t\t\t\t\t\t\t\t˜\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t′\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t^\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t, that maximizes this function. Once the MLE is available, the influence of the covariates can be assessed by the magnitude (relative to their standard error) of the individual slopes. In particular, it can be ascertained which attributes of customer service have a substantial effect on making the probability of a \'Yes\' response high.
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2.2. Multinomial logistic regression
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Suppose now that the outcome variable has more than two categories. For example, suppose the responses \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tY\n\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tn\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\tare measured on the 11-point NPS scale. The multinomial logistic model seeks to represent the probability of each response as a function of the covariates. Since each response is an ordinal categorical variable taking on values in the set\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t0\n\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t…\n\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t10\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t, we consider a multinomial logistic regression model with 10 cumulative link functions:
where \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tα\n\t\t\t\t\t\t\t\t\t\t\tj\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tj\n\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t0\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t9\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t are intercept parameters and \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tβ\n\t\t\t\t\t\t\t\t˜\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t is again a vector of slope parameters. In order to affect the required non-decreasing behavior (relative to j) of the right hand sides of the link functions, the constraint \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tα\n\t\t\t\t\t\t\t\t\t0\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t≤\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tα\n\t\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t⋯\n\t\t\t\t\t\t\t\t≤\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tα\n\t\t\t\t\t\t\t\t\t9\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t is imposed on the intercept parameters. Starting with\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tPr\n\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tY\n\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t0\n\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\tPr\n\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tY\n\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t≤\n\t\t\t\t\t\t\t\t0\n\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t, and then differencing the above expressions in (2) as in\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tPr\n\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tY\n\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\tj\n\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\tPr\n\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tY\n\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t≤\n\t\t\t\t\t\t\t\tj\n\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t−\n\t\t\t\t\t\t\t\tPr\n\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tY\n\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t≤\n\t\t\t\t\t\t\t\tj\n\t\t\t\t\t\t\t\t−\n\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t, \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tj\n\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t…\n\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t10\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t, we obtain expressions for the individual probabilities of the response as a function of the intercept and slope parameters. Defining the responses \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tY\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\t\tj\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tj\n\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t0\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t10\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t to be one (zero) if and only if\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tY\n\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\tj\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t, the likelihood function for this model is
and the MLE of \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tα\n\t\t\t\t\t\t\t\t\t˜\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tβ\n\t\t\t\t\t\t\t\t\t\t˜\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t′\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t is the value, say\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tα\n\t\t\t\t\t\t\t\t\t\t^\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t˜\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tβ\n\t\t\t\t\t\t\t\t\t\t\t˜\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t′\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t^\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t, that maximizes this function. Once the MLE is available, the magnitudes of the slope estimates (relative to their standard errors) can be used to identify the covariates that push the distribution of the response towards 9s and 10s. We note that the constraint on the intercepts is a standard constraint. In the next section, we will discuss an additional and novel constraint that can optionally be imposed on the slope parameters.
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3. Case study
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Carestream Health, Inc. (CSH) was formed in 2007 when Onex Corporation of Toronto, Canada purchased Eastman Kodak Company’s Health Group and renamed the business as Carestream Health. They are an annual $2.5B company and a world leader in medical imaging (digital and film), healthcare information systems, dental imaging and dental practice management software, molecular imaging and non-destructive testing. Their customers include medical and dental doctors and staff and healthcare IT professionals in small offices and clinics to large hospitals and regional and national healthcare programs. A major company initiative is to create a sustainable competitive advantage by delivering the absolute best customer experience in the industry. Customer recommendations are key to growth in the digital medical space and no one has been able to do it consistently well. The foundation for taking advantage of this opportunity is to understand what\'s important to customers, measure their satisfaction and likelihood to recommend based on their experiences, and drive improvement.
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While descriptive statistics such as trend charts, bar charts, averages and listings of customer verbatim comments are helpful in identifying improvement opportunities to improve the Net Promoter Score (NPS), they are limited in their power. First, they lack quantitative measurements of correlation between elements of event satisfaction and NPS. As a consequence, it is not clear what impact a given process improvement will have on a customer’s likelihood to recommend. Second, they lack the ability to view multi-dimensional relationships – they are limited to single factor inferences which may not sufficiently describe the complex relationships between elements of a customer’s experience and their likelihood to recommend.
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This section summarizes the use of multinomial logistic regression analyses that were applied to 5056 independent customer experience surveys from Jan 2009 – Jan 2010. Each survey included a question that measured (on a 5-point Likert scale) how likely it would be for the customer to recommend colleagues to purchase imaging solutions from CSH. Five other questions measured the satisfaction level (on a 7-point Likert scale) of the customer with CSH services obtained in response to an equipment or software problem. Key NPS drivers are revealed through the multinomial logistic regression analyses, and improvement scenarios for specific geographic and business combinations are mapped out. The ability to develop a quantitative model to measure the impact on NPS of potential process improvements significantly enhances the value of the survey data.
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3.1. CSH Customer survey data
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The 5-point Likert response to the question about willingness to recommend is summarized in Table 1 below. CSH calculates a unique net promoter score from responses on this variable using the formula\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tNPS\n\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t∑\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t5\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tw\n\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tp\n\t\t\t\t\t\t\t\t\t\t^\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t, where \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tw\n\t\t\t\t\t\t\t\t\t˜\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t−\n\t\t\t\t\t\t\t\t1.25\n\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t−\n\t\t\t\t\t\t\t\t0.875\n\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t−\n\t\t\t\t\t\t\t\t0.25\n\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t0.75\n\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t1.0\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t′\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t is a vector of weights and where \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tp\n\t\t\t\t\t\t\t\t\t\t^\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t is the estimated proportion of customers whose recommendation score is i. Two interesting characteristics of the weight vector are, first, the penalty for a 1 (2) exceeds the benefit of a 5 (4), and second, the negative weight for a neutral score is meant to drive policies toward delighting customers.
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Recommendation
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Interpretation
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1
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Without being asked, I will advise others NOT to purchase from you
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2
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Only if asked, I will advise others NOT to purchase from you
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3
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I am neutral
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4
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Only if asked, I will recommend others TO purchase from you
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5
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Without being asked, I will recommend others TO purchase from you
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Table 1.
Meaning of Each Level of Recommendation Score
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Let \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tY\n\t\t\t\t\t\t\t\t∈\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t3\n\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t4\n\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t5\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t be a random variable denoting the willingness of a particular customer to recommend CSH. The values \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tp\n\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t5\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t represent the theoretical probabilities of the possible values for Y. That is,\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tp\n\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\tPr\n\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\tY\n\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t. The multinomial logistic regression model treats the customer demographic variables and the customer satisfaction ratings as the covariates\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tx\n\t\t\t\t\t\t\t\t\t\t˜\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t, linking their values to the probability distribution of Y such that their influence on the values\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tp\n\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t5\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t can be ascertained. Since the expected value of NPS is a function of these \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tp\n\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t5\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t values, the end result is a model that links NPS to what customers perceive to be important. This linkage can then be exploited to determine targeted pathways to improve NPS via improvement plans that are customer-driven.
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The demographic covariates include the (global) region code, country code, business code and the customer job title. The demographic covariates are coded using the standard dummy variable coding technique. For example, region code utilizes 7 binary variables \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\tR\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tC\n\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t7\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t where
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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tR\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tC\n\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tif customer falls in i-th global region \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t0\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\totherwise \n\t\t\t\t\t\t\t\t\t\t\t\t\t. \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\tE4
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Country code utilizes similar dummy variables, but because countries are nested within regions we use the notation\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\tC\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tC\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tj\n\t\t\t\t\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t7\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tj\n\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tn\n\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t, where \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tn\n\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t is the number of country codes within the i-th region and where
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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tC\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tC\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tj\n\t\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tif customer falls in j-th country within \n\t\t\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\t\t\t-th region \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t0\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\totherwise \n\t\t\t\t\t\t\t\t\t\t\t\t\t. \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\tE5
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In the data set we have\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tn\n\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t7\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t3\n\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t7\n\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t5\n\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t4\n\t\t\t\t\t\t\t\t,\n\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t. Business code utilizes two dummy variables \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\tB\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tC\n\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t where
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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tB\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tC\n\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tif customer is aligned with \n\t\t\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\t\t\t-th business code \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t0\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\totherwise \n\t\t\t\t\t\t\t\t\t\t\t\t\t. \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\tE6
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Finally, job title utilizes 10 dummy variables \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\tJ\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tT\n\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t10\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t where
\n\t\t\t\t
\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tJ\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tT\n\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tif customer has \n\t\t\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\t\t\t-th job title \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t0\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\totherwise \n\t\t\t\t\t\t\t\t\t\t\t\t\t. \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\tE7
\n\t\t\t\t
The customer satisfaction covariates are also coded using the dummy variable scheme. The data on these covariates are the responses to the survey questions identified as q79, q82a, q82b, q82d and q82f. These questions survey the customer satisfaction on ‘Overall satisfaction with the service event,’ ‘Satisfaction with CSH knowledge of customer business and operations,’ ‘Satisfaction with meeting customer service response time requirements,’ ‘Satisfaction with overall service communications,’ and ‘Satisfaction with skills of CSH employees,’ respectively.
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Survey questions q82c and q82e, which survey satisfaction with ‘Time it took to resolve the problem once work was started,’ and ‘Attitude of CSH employees’ were also considered as covariates, but they did not show themselves to be statistically significant in the model. Their absence from the model does not necessarily imply they are not important drivers of their overall satisfaction with CSH, but more likely that their influence is correlated with the other dimensions of overall satisfaction that are in the model. Each customer satisfaction covariate is scored by customers using a 7-point Likert scale (where ‘1’ indicates the customer is “extremely dissatisfied” and ‘7’ indicates “extremely satisfied”), and thus each utilizes 7 dummy variables in the coding scheme. We denote these dummy variables as\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\tq\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t79\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t7\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t, \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\tq\n\t\t\t\t\t\t\t\t\t\t82\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\ta\n\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t7\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t, \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\tq\n\t\t\t\t\t\t\t\t\t\t82\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tb\n\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t7\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t, \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\tq\n\t\t\t\t\t\t\t\t\t\t82\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\td\n\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t7\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t, and\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\tq\n\t\t\t\t\t\t\t\t\t\t82\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tf\n\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t7\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t, respectively, and they are defined as follows:
\n\t\t\t\t
\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tq\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t79\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tif customer response to q79 is \n\t\t\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t0\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\totherwise , \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\tE8
\n\t\t\t\t
\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tq\n\t\t\t\t\t\t\t82\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\ta\n\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tif customer response to q82a is \n\t\t\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t0\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\totherwise , \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\tE9
\n\t\t\t\t
\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tq\n\t\t\t\t\t\t\t82\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tb\n\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tif customer response to q92b is \n\t\t\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t0\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\totherwise , \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\tE10
\n\t\t\t\t
\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tq\n\t\t\t\t\t\t\t82\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\td\n\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tif customer response to q82d is \n\t\t\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t0\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\totherwise , \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\tE11
\n\t\t\t\t
\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tq\n\t\t\t\t\t\t\t82\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tf\n\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tif customer response to q82f is \n\t\t\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t0\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\totherwise \n\t\t\t\t\t\t\t\t\t\t\t\t\t. \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\tE12
\n\t\t\t\t
Assembling all of the covariates together, we then have a total of 77 covariates in\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tx\n\t\t\t\t\t\t\t\t˜\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t. Thus, the vector of slopes \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tβ\n\t\t\t\t\t\t\t\t˜\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t in the link equations has dimension\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t77\n\t\t\t\t\t\t\t\t×\n\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t. Combined with the 4 intercept parameters\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tα\n\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t4\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t, the model we have developed has a total of 81 parameters. We note it is conceivable that interactions between the defined covariates could be important contributors to the model. However, interaction effects based on the current data set were difficult to assess because of confounding issues. As the data set gets larger over time, it is conceivable the confounding issues could be resolved and interaction effects could be tested for statistical significance.
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3.2. Model fitting and interpretation
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The SAS code for obtaining maximum likelihood estimates (MLEs) for the model parameters \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tα\n\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t4\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t and \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tβ\n\t\t\t\t\t\t\t\t˜\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t is shown in Appendix A. Lines 1-4 are used to read in the data that is stored as a space delimited text file ‘indata.txt’ that is located in the indicated directory. All of the input variables on the file are coded as integer values. The PROC LOGISTIC section of the code (lines 5-10) directs the fitting of the multinomial logistic regression model. The class statement is used to specify that all of the covariate variables are categorical in nature, and the param=glm option specifies to use the dummy variable coding scheme that was defined in the previous section. Table 2 summarizes the portion of the SAS output that reports the maximum likelihood estimates for \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tα\n\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t4\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t and\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tβ\n\t\t\t\t\t\t\t\t˜\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t. Note that the zero for the slope of the last level of each covariate is a structural zero resulting from the non-full rank dummy variable coding used when fitting the model.
Maximum Likelihood Estimates of\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tα\n\t\t\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t4\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t and \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tβ\n\t\t\t\t\t\t\t\t\t\t˜\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t
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The section of the PROC LOGISTIC output entitled ‘Type-3 Analysis of Effects’ characterizes the statistical significance of the covariates through p-values obtained by referencing a Wald chi-square test statistic to a corresponding null chi-square distribution. Table 3 shows the chi-square tests and the corresponding p-values, and it is seen that all covariate groups are highly significant contributors in the model.
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One way to assess model adequacy for multinomial logistic regression is to use the model to predict Y and then examine how well the predicted values match the true values of Y. Since the output of the model for each customer is an estimated probability distribution for Y, a natural predictor of Y is the mode of this distribution. We note that this predictor considers equal cost for all forms of prediction errors. More elaborate predictors could be derived by assuming a more complex cost model where, for example, the cost of predicting 5 when the actual value is 1 is higher than the cost of predicting 5 when the actual value is 4. Table 4, the so-called confusion matrix of the predictions, displays the cross classification of all 5056 customers based on their actual value of Y and the model-predicted value of Y.
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\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Covariate Group
\n\t\t\t\t\t\t\t
Degrees of Freedom
\n\t\t\t\t\t\t\t
Wald Statistic
\n\t\t\t\t\t\t\t
p-value
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
RC
\n\t\t\t\t\t\t\t
6
\n\t\t\t\t\t\t\t
41.2
\n\t\t\t\t\t\t\t
< .0001
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
CC
\n\t\t\t\t\t\t\t
16
\n\t\t\t\t\t\t\t
40.9
\n\t\t\t\t\t\t\t
< .01
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
BC
\n\t\t\t\t\t\t\t
1
\n\t\t\t\t\t\t\t
7.9
\n\t\t\t\t\t\t\t
< .01
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
JT
\n\t\t\t\t\t\t\t
9
\n\t\t\t\t\t\t\t
43.7
\n\t\t\t\t\t\t\t
< .0001
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
q79
\n\t\t\t\t\t\t\t
6
\n\t\t\t\t\t\t\t
84.8
\n\t\t\t\t\t\t\t
< .0001
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
q82a
\n\t\t\t\t\t\t\t
6
\n\t\t\t\t\t\t\t
56.5
\n\t\t\t\t\t\t\t
< .0001
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
q82b
\n\t\t\t\t\t\t\t
6
\n\t\t\t\t\t\t\t
34.4
\n\t\t\t\t\t\t\t
< .0001
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
q82d
\n\t\t\t\t\t\t\t
6
\n\t\t\t\t\t\t\t
34.8
\n\t\t\t\t\t\t\t
< .0001
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
q82f
\n\t\t\t\t\t\t\t
6
\n\t\t\t\t\t\t\t
39.9
\n\t\t\t\t\t\t\t
< .0001
\n\t\t\t\t\t\t
\n\t\t\t\t\t
Table 3.
Statistical Significance of Covariate Groups
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\n\t\t\t\t\t\t
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\n\t\t\t\t\t\t
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\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Actual Y
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Predicted Y
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Total
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
1
\n\t\t\t\t\t\t\t
2
\n\t\t\t\t\t\t\t
3
\n\t\t\t\t\t\t\t
4
\n\t\t\t\t\t\t\t
5
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
1
\n\t\t\t\t\t\t\t
3
\n\t\t\t\t\t\t\t
2
\n\t\t\t\t\t\t\t
7
\n\t\t\t\t\t\t\t
4
\n\t\t\t\t\t\t\t
4
\n\t\t\t\t\t\t\t
20
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
2
\n\t\t\t\t\t\t\t
3
\n\t\t\t\t\t\t\t
8
\n\t\t\t\t\t\t\t
48
\n\t\t\t\t\t\t\t
22
\n\t\t\t\t\t\t\t
7
\n\t\t\t\t\t\t\t
88
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
3
\n\t\t\t\t\t\t\t
2
\n\t\t\t\t\t\t\t
3
\n\t\t\t\t\t\t\t
342
\n\t\t\t\t\t\t\t
486
\n\t\t\t\t\t\t\t
133
\n\t\t\t\t\t\t\t
966
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
4
\n\t\t\t\t\t\t\t
0
\n\t\t\t\t\t\t\t
0
\n\t\t\t\t\t\t\t
126
\n\t\t\t\t\t\t\t
1233
\n\t\t\t\t\t\t\t
723
\n\t\t\t\t\t\t\t
2082
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
5
\n\t\t\t\t\t\t\t
0
\n\t\t\t\t\t\t\t
0
\n\t\t\t\t\t\t\t
39
\n\t\t\t\t\t\t\t
705
\n\t\t\t\t\t\t\t
1156
\n\t\t\t\t\t\t\t
1900
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Total
\n\t\t\t\t\t\t\t
8
\n\t\t\t\t\t\t\t
13
\n\t\t\t\t\t\t\t
562
\n\t\t\t\t\t\t\t
2450
\n\t\t\t\t\t\t\t
2023
\n\t\t\t\t\t\t\t
5056
\n\t\t\t\t\t\t
\n\t\t\t\t\t
Table 4.
Confusion Matrix of Multinomial Logistic Regression Model
\n\t\t\t\t
A perfect model would have a confusion matrix that is diagonal indicating the predicted value for each customer coincided identically with the true value. Consider the rows of Table 4 corresponding to Y=4 and Y=5. These two rows account for almost 80% of the customers in the sample. It can be seen that in both cases, the predicted value coincides with the actual value about 60% of the time. Neither of these two cases predicts Y=1 or Y=2, and only 4% of the time is Y=3 predicted. The mean values of the predicted Y when Y=4 and Y=5 are 4.28 and 4.59, respectively. The 7% positive bias for the case Y=4 is roughly offset by the 11.8% negative bias for the case Y=5.
\n\t\t\t\t
Looking at the row of Table 4 corresponding to Y=3, we see that 86% of the time the predicted Y is within 1 of the actual Y. The mean value of the predicted Y is 3.77, indicating a 26% positive bias. Considering the rows corresponding to Y=1 and Y=2, where only about 2% of the customers reside, we see the model struggles to make accurate predictions, often over-estimating the actual value of Y. A hint as to the explanation for the noticeable over-estimation associated with the Y=1, Y=2 and Y=3 customers is revealed by examining their responses to the covariate questions. As just one example, the respective mean scores on question q79 (“Overall satisfaction with the service event”) are 3.8, 4.1 and 5.2. It seems a relatively large number of customers that give a low response to Y are inclined to simultaneously give favorable responses to the covariate questions on the survey. Although this might be unexpected, it can possibly be explained by the fact that the covariate questions are relevant to the most recent service event whereas Y is based on a customer’s cumulative experience.
\n\t\t\t\t
Overall, Table 4 reflects significant lift afforded by the multinomial logistic regression model for predicting Y. For example, a model that utilized no covariate information would have a confusion matrix whose rows were constant, summing to the row total. In sum, we feel the accuracy of the model is sufficient to learn something about what drives customers to give high responses to Y, though perhaps not sufficient to learn as much about what drives customers to give low responses to Y.
\n\t\t\t\t
\n\t\t\t\t\tFigure 1 is a graphical display of the slopes for each of the customer satisfaction covariates. The larger the coefficient value, the more detrimental the response level is to NPS. The y-axis is therefore labeled as ‘demerits.’
\n\t\t\t\t
Figure 1.
MLEs of Slopes for 7-Point Likert Scale Customer Satisfaction Covariates
\n\t\t\t\t
In view of the ordinal nature of the customer satisfaction covariates, the slopes, which represent the effect of the Likert scale levels, should decrease monotonically. That is, the penalty for a ‘satisfied’ covariate value should be less than or equal to that of a ‘dissatisfied’ covariate value. As such, it would be logical to have the estimated values of the slopes display the monotone decreasing trend as the response level of the covariates ascends. Figure 1 shows that the unconstrained MLEs for the slopes associated with the customer satisfaction covariates nearly satisfy the desired monotone property, but not exactly. The aberrations are due to data deficiencies or minor model inadequacies and can be resolved by using a constrained logistic regression model introduced in the next section.
\n\t\t\t
\n\t\t\t
\n\t\t\t\t
3.3. Constrained logistic regression
\n\t\t\t\t
Consider the situation where the i-th covariate is ordinal in nature, perhaps because it is measured on a k-point Likert scale. The CSH data is a good illustration of this situation, since all the customer satisfaction covariates are ordinal variables measured on 7-point Likert scale. Let the corresponding group of k slopes for this covariate be denoted by\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tβ\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\t\tj\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tj\n\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tk\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t. In order to reflect the information that the covariates are ordered, it is quite natural to impose the monotone constraint \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tβ\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t≥\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tβ\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t2\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t⋯\n\t\t\t\t\t\t\t\t≥\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tβ\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\tk\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t onto the parameter space. Adding these constraints when finding the MLEs complicates the required maximization of the likelihoods in (1) and (3). In this section, however, we will show how this can be done using SAS with PROC NLP.
\n\t\t\t\t
In order to simplify our use of PROC NLP, it is convenient to work with a full-rank parameterization of the logistic regression model. Because countries are nested within regions, a linear dependency exists between the dummy variables corresponding to regions and countries within regions. We can eliminate the linear dependency by removing region from the model and specifying country to be non-nested factor. The result of this model reparameterization is that instead of 6 degrees of freedom in the model for regions and 16 degrees of freedom for countries nested within regions, we equivalently have 22 degrees of freedom for countries. For the same purpose, we also redefine the dummy variable coding used for other categorical and ordinal covariates by using a full rank parameterization scheme. In particular, we use k-1 dummy variables (rather than k) to represent a k-level categorical or ordinal variable. With the full rank parameterization, the highest level of customer satisfaction has a slope parameter that is fixed to be 0. Lines 3-10 in the SAS code shown in Appendix B are used to set up the full rank parameterization of the logistic regression model.
\n\t\t\t\t
Figure 2.
Constrained MLEs of Slopes for 7-Point Likert Scale Customer Satisfaction Covariates
\n\t\t\t\t
Beginning with line 12 in the SAS code, PROC NLP is used to derive the MLEs of the parameters under the constrained parameter space. The ‘max’ statement (line 13) indicates the objective function is the log-likelihood function of the model and that it is to be maximized. The maximization is carried out using a Newton-Raphson algorithm, and the ‘parms’ statement (line 14) specifies initial values for the intercept and slope parameters. The SAS variables bqj, baj, bbj, bdj and bfj are used to symbolize the slope parameters corresponding to the j-th response level of the customer satisfaction covariates q79, q82a, q82b, q82d and q82f. Similarly, bccj, bbcj, and bj\n\t\t\t\t\tj are used to denote the slopes associated with different countries, business codes and job titles. The ‘bounds’ and ‘lincon’ statements (lines 15-21) jointly specify the monotone constraints associated with the intercept parameters and the slopes of the customer satisfaction covariates. Lines 22-29 define the log likelihood for each customer which, for the i-th customer, is given by
Unconstrained and Constrained Slope MLEs of Customer Satisfaction Covariates
\n\t\t\t\t
\n\t\t\t\t\tTable 5 provides a side-by-side comparison of the constrained and unconstrained MLEs for the slopes of the customer satisfaction covariates, and Figure 2 is a plot that shows the monotone behavior of the constrained estimates. There is very little difference between the unconstrained and constrained MLEs for the demographic covariates. Recall that for the unconstrained MLEs, the zero for the slope of the last level of each covariate is a structural zero resulting from the non-full rank dummy variable coding used when fitting the model. In the case of the constrained MLEs, the slopes of the last levels of the covariates are implied zeros resulting from the full-rank dummy variable coding used when fitting the model. Table 5 shows that incorporating the constraints do not lead to a substantial change in the estimated slopes. In an indirect way, this provides a sanity check of the proposed model. We will use the constrained estimates for the remainder of the case study.
\n\t\t\t
\n\t\t\t
\n\t\t\t\t
3.4. Model utility
\n\t\t\t\t
A purely empirical way to compute NPS is to use the observed distribution (based on all 5,056 survey responses) of Y for \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tp\n\t\t\t\t\t\t\t\t˜\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\tin the formula\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tNPS\n\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t∑\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t5\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tw\n\t\t\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tp\n\t\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t, and this yields 61.7%. Consider now filling out the covariate vector \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tx\n\t\t\t\t\t\t\t\t˜\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t with the sample frequencies for the observed demographic covariates and with the observed sample distributions for the sub-element covariates. Using this \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tx\n\t\t\t\t\t\t\t\t˜\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\twith the model yields a predicted NPS of 65.7%. The close agreement between the data-based and model-based NPS scores is additional evidence that the model fits the data well, and it also instills confidence in using the model to explore “What If?” scenarios as outlined in Figure 3. Figure 3 defines sixteen “What If?” scenarios, labels them with brief descriptions, and then shows expected NPS score if the scenario is implemented. Table 6 contains a longer description of how each scenario was implemented. Each scenario can be evaluated on the basis of how much boost it gives to the expected NPS as well as the feasibility of establishing a company program that could make the hypothetical scenario real.
\n\t\t\t\t
We illustrated potential pathways to improve the overall NPS score, but this can also be done with specific sub-populations in mind. For example, if the first region was under study, then one could simply adjust the demographic covariates as illustrated in section 3.4.2 before implementing scenarios adjustments.
\n\t\t\t\t
Figure 3.
Predicted NPS for Different Scenarios
\n\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Scenario
\n\t\t\t\t\t\t\t
Brief Description
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
1
\n\t\t\t\t\t\t\t
For each of q79, q82a, q82b, q82d and q82f, alter the distributions of their responses by reassigning the probability of a neutral response (4) equally to the probability of responses (5), (6) and (7)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
2
\n\t\t\t\t\t\t\t
Replace the response distribution for sub-elements q82a, q82b, and q82d with what was observed for q82f (which was the sub-element that had the most favorable response distribution)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
3
\n\t\t\t\t\t\t\t
Make the response distribution for each of q82a, q82b, q82d and q82f perfect by placing all the probability on response (7)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
4
\n\t\t\t\t\t\t\t
Improve the response distribution for each of q82a, q82b, q82d and q82f by placing all the probability equally on responses (6) and (7)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
5
\n\t\t\t\t\t\t\t
Improve the response distribution for q79 by placing all the probability on response (7)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
6
\n\t\t\t\t\t\t\t
Improve the response distribution for q82a by placing all the probability on response (7)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
7
\n\t\t\t\t\t\t\t
Improve the response distribution for q82b by placing all the probability on response (7)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
8
\n\t\t\t\t\t\t\t
Improve the response distribution for q82d by placing all the probability on response (7)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
9
\n\t\t\t\t\t\t\t
Improve the response distribution for q82f by placing all the probability on response (7)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
10
\n\t\t\t\t\t\t\t
Improve the response distribution for each of q79, q82a, q82b, q82d and q82f by distributing the probability of response (1) equally on responses (2)-(7)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
11
\n\t\t\t\t\t\t\t
Improve the response distribution for each of q79, q82a, q82b, q82d and q82f by distributing the sum of the probability of responses (1) and (2) equally on responses (3)-(7)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
12
\n\t\t\t\t\t\t\t
Improve the response distribution for each of q79, q82a, q82b, q82d and q82f by distributing the sum of the probability of responses (1), (2) and (3) equally on responses (4)-(7)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
13
\n\t\t\t\t\t\t\t
Simulate making Business Code 2 as good as Business Code 1 by setting\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tBC=(1,0)\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
14
\n\t\t\t\t\t\t\t
Improve the response distributions of q79, q82a, q82b, q82d, and q82f by replacing them by the average across the different Region Codes, excluding the worst Region Code
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
15
\n\t\t\t\t\t\t\t
Improve the response distributions of q79, q82a, q82b, q82d, and q82f by replacing them by the average across the different Region Codes, excluding the two worst Region Codes
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
16
\n\t\t\t\t\t\t\t
Improve the response distributions of q79, q82a, q82b, q82d, and q82f by replacing them all by the observed, respective, distributions for Region Code 2 (which was the region that had the most favorable response distribution)
\n\t\t\t\t\t\t
\n\t\t\t\t\t
Table 6.
Implementation Detail for Each Scenario
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\n\t\t
\n\t\t
\n\t\t\t
4. Discussion
\n\t\t\t
Alternative measures to NPS of customer advocacy include customer satisfaction (CSAT) and Customer Effort Score (CES) (Dixon et al., 2010). CES is measured on a 5-point scale and is intended to capture the effort required by a customer to resolve an issue through a contact-center or self-service channel. (Dixon et al., 2010) compared the predictive power of CSAT, NPS and CES on service customers\' intention to do repeat business, increase their spending, and speak positively about the company. They concluded that CSAT was a relatively poor predictor, while CES was the strongest. NPS ranked in the middle.
\n\t\t\t
The choice of which customer advocacy measure to use depends on many factors such as the type of company-to-customer relationship, the degree to which recommendations (for or against a company) influence a purchase decision, and whether the measures will be complemented by other customer feedback. To gain an in-depth understanding of customers\' experiences and how to improve them may require multiple indicators. In the end, it is the action taken to drive improvements that customers value that is most critical.
\n\t\t\t
Our case study validates the feasibility for using a multinomial logistic regression model as a means to identify key drivers of NPS, though it is clear that the same methodology could be employed with alternative measures of customer advocacy. Improvement teams at CSH have used this model to prioritize projects relative to their expected impacts on NPS. A novel aspect of our model development was the implementation of monotone constraints on the slope parameters of the ordinal covariates. Our illustrative SAS code showing how to impose the constraints on the maximum likelihood estimates should be of significant help to practitioners interested in doing the same thing.
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\n\t\t
\n\t\t\t
5. Appendix A
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data indata;
infile \'C:\\CarestreamHealth\\indata.txt\';
input RC CC BC JT Y q79 q82a q82b q82d q82f;
proc logistic data=indata;
class RC CC BC JT
q79 q82a q82b q82d q82f/param=glm;
model Y = RC CC(RC) BC JT
q70 q79 q82a q82b q82d q82f;
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\n\t\t
\n\t\t\t
6. Appendix B
\n\t\t\t
data indata;
set indata;
array cc{23} cc1-cc23; do i=1 to 23; if CC=i then cc{i}=1; else cc{i}=0;end;
if BC=1 then bc1=1;else bc1=0;
array jt{9} jt1-jt9; do i=1 to 9; if JT=i then jt{i}=1; else jt{i}=0;end;
array q{6} q1-q6; do i=1 to 6; if q79=i then q{i}=1; else q{i}=0;end;
array a{6} a1-a6; do i=1 to 6; if q82a=i then a{i}=1; else a{i}=0;end;
array b{6} b1-b6; do i=1 to 6; if q82b=i then b{i}=1; else b{i}=0;end;
array d{6} d1-d6; do i=1 to 6; if q82d=i then d{i}=1; else d{i}=0;end;
array f{6} f1-f6; do i=1 to 6; if q82f=i then f{i}=1; else f{i}=0;end;
\n\t\n',keywords:null,chapterPDFUrl:"https://cdn.intechopen.com/pdfs/19338.pdf",chapterXML:"https://mts.intechopen.com/source/xml/19338.xml",downloadPdfUrl:"/chapter/pdf-download/19338",previewPdfUrl:"/chapter/pdf-preview/19338",totalDownloads:6959,totalViews:1531,totalCrossrefCites:2,totalDimensionsCites:4,hasAltmetrics:0,dateSubmitted:"October 17th 2010",dateReviewed:"February 25th 2011",datePrePublished:null,datePublished:"September 9th 2011",dateFinished:null,readingETA:"0",abstract:null,reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/19338",risUrl:"/chapter/ris/19338",book:{slug:"efficient-decision-support-systems-practice-and-challenges-from-current-to-future"},signatures:"Daniel R. Jeske, Terrance P. Callanan and Li Guo",authors:[{id:"26781",title:"Dr.",name:"Daniel",middleName:null,surname:"Jeske",fullName:"Daniel Jeske",slug:"daniel-jeske",email:"daniel.jeske@ucr.edu",position:null,institution:null},{id:"32979",title:"Dr.",name:"Terrance",middleName:"P.",surname:"Callanan",fullName:"Terrance Callanan",slug:"terrance-callanan",email:"terry.callanan@carestreamhealth.com",position:null,institution:null},{id:"32980",title:"Ms.",name:"Li",middleName:null,surname:"Guo",fullName:"Li Guo",slug:"li-guo",email:"lguo001@ucr.edu",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Logistic regression",level:"1"},{id:"sec_2_2",title:"2.1. Binomial logistic regression",level:"2"},{id:"sec_3_2",title:"2.2. Multinomial logistic regression ",level:"2"},{id:"sec_5",title:"3. Case study ",level:"1"},{id:"sec_5_2",title:"3.1. CSH Customer survey data",level:"2"},{id:"sec_6_2",title:"3.2. Model fitting and interpretation",level:"2"},{id:"sec_7_2",title:"3.3. Constrained logistic regression",level:"2"},{id:"sec_8_2",title:"3.4. Model utility",level:"2"},{id:"sec_10",title:"4. Discussion",level:"1"},{id:"sec_11",title:"5. Appendix A",level:"1"},{id:"sec_12",title:"6. Appendix B",level:"1"}],chapterReferences:[{id:"B1",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tAllen\n\t\t\t\t\t\t\tD. R.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tRao\n\t\t\t\t\t\t\tT. R. N.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2000\n\t\t\t\t\tAnalysis of Customer Satisfaction Data, ASQ Quality Press, 978\n\t\t\t\t\t0873894531Milwaukee.\n\t\t\t'},{id:"B2",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tDixon\n\t\t\t\t\t\t\tM.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tFreeman\n\t\t\t\t\t\t\tK.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tToman\n\t\t\t\t\t\t\tN.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2010\n\t\t\t\t\tStop Trying to Delight Your Customers, Harvard Business Review, July-August, http://hbr.org/magazine.\n\t\t\t'},{id:"B3",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tHayes\n\t\t\t\t\t\t\tB. E.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2008\n\t\t\t\t\tMeasuring Customer Satisfaction and Loyalty, 3rd edition, ASQ Quality Press, 978\n\t\t\t\t\t0873897433Milwaukee.\n\t\t\t'},{id:"B4",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tKeiningham\n\t\t\t\t\t\t\tT.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tCooil\n\t\t\t\t\t\t\tB.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tAndreassen\n\t\t\t\t\t\t\tT.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tAksoy\n\t\t\t\t\t\t\tL.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2007A Longitudinal Examination of Net Promoter and Firm Revenue Growth, Journal of Marketing,\n\t\t\t\t\t71\n\t\t\t\t\t3July 2007) 39-51, 0022-2429.\n\t\t\t'},{id:"B5",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tReichheld\n\t\t\t\t\t\t\tF.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2003The One Number You Need to Grow, Harvard Business Review, 81\n\t\t\t\t\t12December 2003) 46-54.\n\t\t\t'},{id:"B6",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tVavra\n\t\t\t\t\t\t\tT. G.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t1997\n\t\t\t\t\tImproving Your Measurement of Customer Satisfaction: Your Guide to Creating, Conducting, Analyzing and Reporting Customer Satisfaction Measurement Programs, ASQ Quality Press, 978\n\t\t\t\t\t0873894050Milwaukee.\n\t\t\t'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Daniel R. Jeske",address:"",affiliation:'
Professor and Chair, Department of Statistics,University of California, Riverside, CA, USA
'},{corresp:null,contributorFullName:"Terrance P. Callanan",address:null,affiliation:'
Customer Experience Manager, Six Sigma Champion,Carestream Health, Rochester, NY, USA
The Nigerian rumour mill is usually on overdrive. It is an industry that is resilient and immune from the vagaries of the Nigerian political and economic climate (Odunfa [1]). The affordances of digital technology have, in no small measure, contributed in weaponizing the Nigerian rumour mill. Now, rumours, misinformation, disinformation and half-truths travel at the speed of light and reach a wider audience. This is the case of xenophobic attacks against Nigerians living in South Africa in 2017 and 2019 in which rumours, mis-and disinformation resulted in reprisal attacks on South African businesses in Nigeria. A typical example of how the Nigerian rumour mill works is the story of a Nigerian, who was among those that attacked South African-owned Shoprite at The Palms Mall in Lagos, Nigeria. When the young man was asked why he joined others to attack Shoprite and other South African businesses in Nigeria, he responded that xenophobia, a Nigerian living in South Africa had been killed by South Africans. Hence, attacking South African businesses was the only way he could get even with the death of xenophobia in South Africa. Even though this story has not been verified, it goes to show the potency of mis-and disinformation and how it could have adverse impact on the government-to-government as well as people-to-people relations between two countries. For instance, mis-and disinformation has been identified as factors responsible for reprisals attacks against South African businesses in other African countries (Osuagwu & Elebeke [2]; Somdyala [3]). Chenzi [4], p. 3 explained that “foreign nationals residing in South Africa share content with their relations who reside outside South Africa about their xenophobic experiences.” He emphasised that the social media content these foreigners often share about xenophobic violence in South Africa at times, turn out to be misinformation, leading to reprisal attacks [4]. However, there is limited academic investigation into the motivation for sharing mis-and disinformation concerning xenophobic attacks against Nigerians living in South Africa on social media. This chapter provides an insight into factor (s) influencing Nigerians to share mis-and disinformation concerning xenophobic attacks on Nigerians living in South. The finding has both academic and policy relevance for the literature on mis-and disinformation as well as South Africa-Nigerian relations.
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2. Background
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Xenophobia has attracted widespread attention within and without the academic community. The reason is obvious; it is considered an anomaly that foreigners, particularly Africans, would face the magnitude of hatred that Xenophobia spews. The first major xenophobic outbreak in post-1994 South Africa happened on 11 May 2008 and there have several other outbreaks of violence against foreigners in South Africa. While recent incidents may not be of the same magnitude as the incident of 11 May 2008, its recurrence points to a troubling relationship between locals and foreigners. For instance, on 30 May 2013, 25-year-old Abdi Nasir Mahmoud Good, was stoned to death in Port Elizabeth, Eastern Cape, South Africa. The violence was captured on a mobile phone and shared on the Internet.1 Again, on 7 June 2014, a Somali national was stoned to death and two others were seriously injured when an angry mob of locals attacked their shops in Mamelodi, South Africa. Three more Somalis were wounded by gunshots and their shops were looted.2 In April 2015, there was an upsurge in xenophobic attacks throughout the country. The attacks started in Durban and spread to Johannesburg. Zulu King Goodwill Zwelithini was accused of igniting the attacks with his remarks that foreigners’ resident in the country “should go back to their countries.”3 In October 2015 the serene town of Grahamstown in the Eastern Cape recorded an outbreak of xenophobic attacks on foreigners. According to the news report, more than 500 people were displaced and more than 300 shops and homes looted. Muslims were identified as the target of the attacks.4 While South Africa witnessed a modicum of peace after the Grahamstown xenophobia outbreak, it ruptured in February 2017 when another xenophobia outbreak was recorded in Pretoria, South Africa. The incident took a new turn on 24 February 2017 when a large-scale anti-immigration protest suspected to have official approval, according to the Nelson Mandela Foundation, erupted in the capital city of Pretoria.5 The Protesters marched to the Foreign Ministry and handed a petition to government representatives. Protesters accused immigrants of taking jobs from South Africans, causing crime and complained that “they are arrogant and they don’t know how to talk to people, especially Nigerians.” The February xenophobia outbreak affected many Nigerians resident in Pretoria, who the locals accused of being “arrogant and don’t know how to talk to people.”6 However, xenophobic attacks on Nigerians in South Africa escalated in 2019 resulting in the repatriation of more than 600 Nigerians, who were flown home aboard Nigeria’s Air Peace.7 Both incidents resulted in reprisal attacks in Nigeria but it was more fatal in 2019 resulting in the closure of the South African High Commission, South African-owned Telco, MTN, Shoprite and other South African businesses in Nigeria.8 Even though the repatriation of Nigerians from South Africa incited reprisal attacks, it was mis-and disinformation on social media that worsened it and stoked tensions between South Africa and Nigerian relations. Hassan and Hitchen [5], p. 3 show that in Nigeria, disinformation is most effective when it draws on existing narratives and contexts to sharpen existing social divides and aimed at “delegitimising institutions, groups or personalities, glorifying a leader or, during elections, confusing voters, instigating apathy or marginalising women and other vulnerable groups.”
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3. Xenophobia or Afrophobia?
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The occurrence of xenophobic violence has become a feature of the lived realities of foreigners who reside in South Africa. Xenophobia has been defined as a hatred for foreigners (Steenkamp [6]; Williams [7]). However, Harris [8], p. 2, contends that Xenophobia extends beyond hatred; rather, it should be redefined to “incorporate practice” because it is not just a “dislike or fear of foreigners but a violent practice that results in bodily harm and damage” [8].
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The South African Migration Project (SAMP) identified several factors responsible for the outbreak of xenophobic violence. Some of these include isolation, relative deprivation, Nation Building, Public and official discourse, Legislative and policy Frameworks, Human Rights Education, Data and Information, Public and Officials Discourse, Opportunities for Interaction, Going beyond the Debate(s), Peer Education, and Establishing a Network (Crush et al. [9]). For his part, Harris [8] identified three hypotheses responsible for the occurrence of xenophobia in South Africa including the Scapegoating hypothesis; the isolation hypothesis and the biocultural hypothesis. However, the perception of African migrants as the economic threats has been identified as a major causative factor in the occurrence of xenophobic violence [9]. This was highlighted in the SAMP survey of 2006, which noted that attitudes towards foreigners as a national threat to the economic well-being of South Africans have hardened. For instance, the survey result showed that “the proportion of South Africans arguing that foreign nationals use up resources grew by 8% from 59% in 1999 to 67% in 2006. Furthermore, the survey also highlighted that migrants are being increasingly associated with crime rising from 45% in 1 1999 to 67% in 2006 as did the idea that migrants bring disease 24% in 1 1999 to 49% in 2006” [9], p. 3.
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On the other hand, xenophobia does not apply to all categories of foreigners in South Africa. Black Africans bear the brunt of xenophobic violence outbreak in South Africa. Steenkamp [6], p. 442 explains that xenophobia is carried out along certain categories. The major targets of xenophobic attacks in South Africa are black migrants, who are perceived as economic threat to black South Africans. Therefore, the incidences of xenophobia in South Africa could be majorly considered as “Afrophobia” because it is majorly targeted at African migrants’ resident in the country.
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Unfortunately, this contradicts the role of other Africans nations during the apartheid era when South African exiles were accommodated and educated in other African countries [6]. During this period, several high profile South African political leaders took shelter in other African countries, where they were accommodated, supported and encouraged to sustain the struggle against the Apartheid government in South Africa. For instance, Thambo Mbeki was posted to Nigeria as Deputy head of the ANC’s Department of Information and Propaganda. Additionally, many Nigerians were taxed to provide assistance to the ANC-led struggle against apartheid. In fact, it is estimated that the Nigerian government provided an annual subvention of $5million to the ANC and the Pan Africanist Congress (PAC) to aid the struggle against the Apartheid regime at the time.
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4. Theoretical framework
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Prinsloo [10], p. 130, explains that narrative is universal because it is “recognised as crossing all social and cultural divides.” Narrative is, therefore, a sense-making mechanism for understanding the real world (Fiske [11]; Wigston [12]). This study draws on structuralist approach to narrative analysis, which combines the syntagmatic and paradigmatic. Prinsloo [10], p. 133, explains that “the syntagmatic type of analysis identifies and describes the structure of formal organisation of the text following the chronological order or the linear sequence of the narrative while a paradigmatic type of analysis seeks to identify the deeper latent patterns or organisations which underpin the story.” Hence, a combination of syntagmatic and paradigmatic “approaches enable questions of positioning and power to be posed” (Prinsloo [10], p. 157). Also, structuralism “relies on binary opposition in order to transfer meanings in the easiest way because we make sense of concepts and ideas by contrasting them with their opposites [12], p. 142. Levi-Strauss explains that when “two characters are opposed in binary structure, their symbolic meaning is virtually forced to be both general and easily accessible because of the simplicity of the difference between them” [12], p. 152. Explaining further, Wigston [12], p. 154, pointed out that Levi-Strauss is not “interested in the sequential development of the narrative, but rather in the relationships between the various characters and their settings and hence, binary opposition then provide us with a means of undertaking a paradigmatic analysis of a text where we can determine these relationships.” Therefore, by analysing the binary oppositions in a text we can “uncover the ideological and manipulative structure of the values embodied in that message” as well as “reveal deep structures below the surface of the message that are not immediately clear but are implied in every opposition” [12], p. 172.
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This study draws on Propp and Todorov narratives theories. Propp’s model like Todorov’s provides a means of identifying the conventional narrative structure in a text. While Propp and Todorov models are useful for comparing text, other analytical devices such as Levi-Strauss binary opposition is needed to gain deeper insight into the structure of a text [13]. Hence, this study combines Propp and Todorov narrative theories to analyse the framing of the February 2017 and August 2019 xenophobic attacks on Nigerians living in South Africa on social media. This chapter is concerned with the narrative of xenophobia on social media and how it influences emotion, rational and sentiments among Nigerians [10], p. 130.
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4.1 Social media and fake news
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The affordances of digital technology have resulted in the rise of mis-and disinformation with dire consequences for truth and credibility of the media (Wasserman [13]; Schifferes et al., [14]). Wasserman and Benequista [15], p. 35, explain that the “ease with which media can be produced online has made it possible for rumours, untruths, and disinformation to spread and threaten the credibility of the news media.” The use of social media to manipulate public opinion has become a global challenge and sub-Saharan Africa is not an exemption. The role of Cambridge Analytica in election manipulation in Kenya and Nigeria is a pointer to this reality (Granville [16]). For instance, it is estimated that “organized social media manipulation has more than doubled since 2017, with 70 countries using computational propaganda to manipulate public opinion” (Howard and Bradshaw [17]). This has adversely impacted the average level of trust in the news across all countries (Nielsen et al. [18]). Even leading religious leaders such as Pope Francis of the Catholic Church has acknowledged fake news is a “very serious sin.”9 Chenzi [4] explained that “fake social media content about the xenophobic violence in South Africa is one of the triggers of reprisal attacks against South African businesses and nationals in other parts of Africa [4]. This is because most the content they share with their loved ones back in their home countries are likely to be “fake news leading to reprisal attacks” (Chenzi, [4], p. 3). This study examines the motivations for sharing mis-and disinformation concerning Xenophobic attacks against Nigerians living in South Africa on social media.
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5. Methodology
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This study is a netnograhpy involving a thematic analysis and focus group discussions. It uses narrative theories, thematic content analysis and focus group discussions with undergraduates and postgraduate students, adults under 40 and adults above 40. The two complementary methodologies enabled the author to collect comprehensive information on the motivation for sharing mis-and disinformation concerning xenophobic attacks on Nigerians living in South Africa (Sade-Beck [19], p. 49). In a netnograhpy three categories of data are recognised including archival data, elicited data, and fieldnote data (Kozinets [20], p. 4). This study uses archival data from messages posted on xenophobic attacks against Nigerians living in South Africa on Twitter and Facebook. First, the study developed a codebook for categorising Propp’s seven characters (see Table 1). Second, a total of 581 Facebook posts were harvested manually in relation the xenophobia outbreak of February 2017. Furthermore, the study focused on Facebook accounts with more than 2000 followers. This reduced the number of Facebook accounts to 100. These accounts were further coded using Google form. The Twitter analysis focused on the tweets of @JJ Omojuwa, who is regarded as a social media influencer in Nigeria. @Omojuwa is a known public opinion moulder, who also works as a consultant and runs a foundation. He is highly regarded within the Nigerian social media space. His Twitter account is also verified evincing his status as a social media celebrity in Nigeria. @Omojuwa has 560,000 followers on Twitter, which makes him a major influencer of events in Nigeria and Africa at large. The Twitter analysis is focused on the debate ignited on Twitter by @Omojuwa’s tweet on the February 2017 Xenophobia outbreak. @Omojuwa’s tweets were also selected because he travelled to South Africa within the period of the February 2017 xenophobia outbreak and was asked for detailed identification by South African border police, which resulted in his being delayed for a few hours. Rather than frame his tweet on this incident as an immigration issue, @Omojuwa framed it as a continuation of xenophobic attacks on Nigeria.
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Entries
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Categorisation
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South Africans are evil, south Africans are jealous of Nigerians, South Africans are lazy, they are ungrateful for the role Nigerians played in ending apartheid
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Villain
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Nigerians are victims of South African wickedness, jealousy
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Princess
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Seeks peace and reconciliation between Nigeria and South Africa
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Hero
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Portrays themselves as the hero seeking for the betterment of Nigerians but they are fuelling the conflicts by posting fake news
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False hero
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Calls on the Nigerian government to rescue Nigerian citizens leaving in South Africa
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Dispatcher
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Table 1.
Code book.
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Second, A total of eight focus group discussions were held across the three dominant regions of Nigeria. Each of the focus group discussions lasted between 50 and 90 minutes and were conducted in English. In instances where the Nigerian colloquial English was used it was translated to English language. A total of 68 participants joined the discussion in groups ranging from 8 to 10. The discussion guide was structured around five sections, each containing a list of key questions to be asked in all focus groups, and a set of additional questions to be asked at the discretion of discussion facilitator.
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6. Analysis
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An analysis of the Facebook posts and tweets suggest there are five broad themes that dominated social media framing of xenophobic attacks on Nigerians living in South Africa. The themes are: Retaliate, Lazy and ungrateful, Jealous and inhuman, Irresponsible government, South Africans are barbaric.
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7. Thematic analysis
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The thematic analysis focused on a few accounts on Facebook and Twitter. The accounts were selected for relevance and reach. Three accounts were selected purposively including @Engr.Chris Moore, AY COMEDIA, and YAW on Facebook and @JJ Omojuwa on Twitter.
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\nEngr. Chris Moore’s profile on Facebook describes him as a former system administrator, who lives in Milan, Italy. He is followed by 2138 people on Facebook. On 18 March 2017, he posted the following message on his Facebook wall:
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Fellow Nigerians please, stay away from South Africa because of their xenophobic attacks on Nigerians in their country. Please, travel to countries that can at least protect your human right. Please, keep sharing to keep our citizens from travelling to such country.
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This post was accompanied by five gory pictures. Four of the pictures were from the 2008 xenophobic violence in South Africa. The fifth picture was from a scene of necklacing\n10 in Nigeria. This post was shared by 1596 people, liked by 82 people and attracted 124 comments on Facebook. While responding to comments to this message, @Engr.Chris Moore sustained this narrative with another post that “South African government is not doing anything to arrest the situation” but this is not true because about 136 South11 Africa protesters were arrested by the South African police on February 24 during the anti-immigrant protest in Pretoria.
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The post by @ Engr. Chris Moore ignited widespread debate on Facebook about xenophobic attacks on Nigerians resident in South Africa. However, most of the debates were informed by alternative facts and stereotypes. This is because most of the participants in the debate are not resident in South Africa (see Figure 1) and relied on secondary sources. This was highlighted by some of the respondents, who pointed out that the pictures posted by @Engr. Chris Moore were old pictures that had been used elsewhere. For instance, @Ehis Ese confirmed that one of the pictures was that of an armed robbery perpetrator that was necklaced in Warri, Delta State, Nigeria: “Dis guy was a thief and was burnt last year in estate roundabout in Warri, Delta state. I mean this particular guy but I don’t know about the rest” (Sic). @Oscarpedro Osagie also noted: “Wrong. This photograph was on before this incident”. Another respondent, @Mountain Omobaorun Ododo Omoleye, who is resident in South Africa also pointed that the pictures were not a true reflection of the incident of February 2017: “Hello people this post and the pictures, I have not seen this picture and the news here in South Africa, pls which path of South Africa did this happen” (Sic). A South African @Mapie Mhlangu queried why @Engr. Chris Moore was spreading fake news on Facebook: “But Why are you spreading fake news?”
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Figure 1.
Geographic location of social media handles.
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\nAY COMEDIAN is a celebrated Nigerian comedian, whose Facebook account is a community. AY COMEDIAN lives in Lagos Nigeria and his Facebook community is followed by 4, 718,434 people. On 23 February 2017 AY COMEDIAN posted a picture of a man being mobbed by an angry crowd with the following message: “Xenophobia: Angry South Africans Mob Brutally Attack a Nigerian Man in SA.” The man that was being attacked cannot be identified through the picture because his face was not visible. How AY COMEDIAN knew that the man being attacked was a Nigerian demystifies human knowledge but the picture helped promote the narrative that South Africans were attacking Nigerians that are resident in South Africa. The source of this picture was given as gistvilla.co but this is a non-existent news site. This particular post was shared by 433 people, liked by 1400 and attracted 1200 comments. Another post by YAW, a leading Nigerian comedian and radio host also promoted the same narrative of xenophobia. YAW is a community on Facebook followed by 241, 455 people. On 27 February YAW posted a video on Facebook with the following message: “Another video of South African youths shooting a young man to death in the xenophobic attack has emerged. Watched the video below.” The source of the video was given as http://yawnaija.tv/xenophobic-attacks-south-africa-2017/. First, the video in question was not produced in South Africa because the actors in the video spoke the Arabic language, which suggests it could have been in any of the Arabian countries in Africa. Secondly, the source of the video was traced to Yaw’s online news site which goes by his own name, http://yawnaia.tv. However, this video was used to promote the xenophobic narrative that South Africans were attacking Nigerians resident in their countries. This post was shared by 46 people, liked by 54 and attracted 20 comments. Furthermore, YAW posted another three pictures with the message: “We are all foreigners somewhere.” Two of the pictures were from the 2008 xenophobic outbreak in South Africa but were used to frame the incident of February 2017.
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On Twitter, the analysis is focused on @JJ Omojuwa’s tweet in response to the outbreak of xenophobia in South Africa. Omojuwa tweeted about the February xenophobia outbreak in South Africa on March 2 to his followers: “Xenophobia: Stop being ingrates-Thabo Mbeki.” This was former President Thambo Mbeki’s message cautioning South Africans to replicate the friendliness that other African countries showed to South Africans during apartheid. The tweet attracted 62 retweets and 29 likes. After this post was made on March 2, @Omojuwa travelled to South Africa on March 4 and was asked by South African border Police to provide further details about his trip, which he could have done without attracting attention but it presented him an opportunity to promote the xenophobic narrative about South Africans. However, there were a few of @Omojuwa’s followers who saw through the attempt to frame an immigration issue as xenophobia. One of his followers with the handle @LuamboJT replied that it was all about Omojuwa’s arrogance: “my man, your arrogance will put you in danger some other days, last time I checked, no Nigerian has been killed in SA.” In another tweet, @LuamboJT cautioned @ Omojuwa that “the big issue to be addressed is your attitude my man, you don’t go to a foreign country and say things you were saying.”
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8. Retaliate
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The responses in this category urge Nigerians to retaliate against South Africans by either attacking their businesses in Nigeria such as MTN, Shoprite or attacking South Africans that are resident in Nigeria. A Facebook post by @Aniebonam Rusky Lotachukwu captures the majority of framing in this category. He responded to AY COMEDIAN’s post with the following message: “I swear the war has just begun.....Nigerians gonna burn down any shop or business owned by South Africans since South Africans seem to be wise.....soon South Africa will fall like an ass and will be one of the poorest country because no country now is happy with their devilish behaviour” (Sic). Another Facebook response by @Juliet Nganwaozuzu reproduced the same narrative: “We should start killing all the South African people that are in Nigeria without mercy please enough is enough” (Sic). This category comprised 38.7% (see Figure 2) of social media posts on the February xenophobia outbreak in South Africa. This suggests that Nigerians are in support of a reprisal attack in the face of xenophobic violence against Nigerians resident in South Africa.
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Figure 2.
Words used to describe south Africans.
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8.1 Lazy and ungrateful
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Messages in this category suggest that South Africans are ungrateful for the help rendered to them by the Nigerian government during apartheid, when some of their citizens were sheltered in Nigeria. For instance, @Omoabass Kafayat Anifowose responded to AY COMEDIAN’s post that: “Just South Africans are ingrate so easy to forgetting the favour Nigeria did for them. We stood by their side and we fought for them. So to kill Nigerians is what to pay them back?” (Sic). Messages in this category comprise 13.3% (see Figure 2) of social media posts on the xenophobic violence of February 2017.
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8.2 Jealous and inhumane
\n
In this category, South Africans are framed as being jealous of Nigerians because they are hardworking and attract the attention of South African women. A Facebook post by @Allan Donann is representative of the framing in this category. He posted in response to @AY COMEDIAN’s post that: “I just can’t believe how a fellow black man will treat his fellow black man like this. This is absurd and inhuman. The lazy mind is a frustrated mind. Why blame your failures on hardworking people” (Sic). Another Facebook post by @Jayne Bahanack Betow responded thus: “I will say this loud and clear without mincing words and in all confidence....These very lazy South Africans are super jealous of hardworking Nigerians....I am a Cameroonian and I witness how hardworking, consistent, smart and intelligent Nigerians are when it comes to business and other essential and lucrative things....And we Cameroonians admire and respect them for that. Guess what? Many Cameroonians are beginning to learn from them.....so please South Africans enough of this hatred and jealousy instead try to learn from them” (Sic). This category comprises 2.7% (see Figure 2) of social media post on the xenophobic attack of February 2017.
\n
\n
\n
8.3 Irresponsible government
\n
The responses in this category frame the South African government as is responsible and doing nothing to arrest the attacks on foreigners living in the country. For instance, @Olufemi Obembe posted that: “I feel that South African authorities are not responsible. They are supposed to protest lives and properties of everyone living in their territory, most especially foreigners” (Sic). This category comprises 29.3% (see Figure 2) of social media posts on the xenophobic outbreak of February 2017. This suggests that many Nigerians think that the South African government tacitly support xenophobic violence on Nigerians resident in their country.
\n
\n
\n
8.4 South Africans are barbaric
\n
In this category, South Africans are framed as barbaric and wicked. For instance, @Tee Jay responded to AY COMEDIAN’s post that: “Being confronted by two South Africans before on the street and they said all Nigerians must die without saying anything to them; had to run for my life because they hold knife”. In the same vein, @YemmyOcean Ige responded to @Engr. Chris Moore thus: “Why are d South Africans bad minded as dis, it’s not right oooo, let all Nigerians there come back home oooo, Abeg” (Sic). This category comprises 16% (see Figure 2) of social media posts on the xenophobic violence of February 2017.
\n
\n
\n
\n
9. Interview analysis
\n
\n
9.1 Southwest under 40
\n
The majority of participants in this category are likely to share misinformation on social media platforms if it involves the mistreatment of Nigerians in the diaspora as a means of group solidarity. This is evinced in the xenophobia outbreak in South Africa in 2019 when videos from the 2008 xenophobia attacks in South Africa were shared as that of 2019. This resulted in reprisal attacks on South African business interests in Nigeria. One participant in Lagos, Nigeria mentioned that she felt that sharing misinformation about xenophobic attacks on Nigerians living in South Africa amounted to doing good. This suggests that Nigerian youths are likely to share mis-and disinformation about xenophobic attacks on Nigerians living in South Africa is a way of expression national solidarity with Nigerian victims.
\n
\n
\n
9.2 Northeast under 40
\n
In the northern part of Nigeria, Participants under the age of forty are also likely to share fake news to show solidarity with Nigerians being maltreated in the Diaspora. One participant said he shared fake videos on the 2019 xenophobic attacks against Nigerians living in South Africa to make Nigerians know what was happening to fellow Nigerians in South Africa.
\n
\n
\n
9.3 Northeast above 40
\n
Also, for participants above 40 years of age in the northeast of Nigeria did not share misinformation on Xenophobic attacks against Nigerians because they felt it could result in reprisal attacks. So, most of the people in the age bracket of 40 years and above in the northern part of Nigeria are not likely to share such videos.,
\n
\n
\n
9.4 Southeast above 40
\n
In the southeast, the view that Nigerians are likely to share mis-and disinformation concerning xenophobic attacks on Nigerians living in South Africa as a form of national solidarity is valid. Some participants said they shared videos from the xenophobia outbreak of 2008 when it reoccurred in 2019 leading to reprisal attacks in parts of Nigeria. One participant mentioned that he shared misinformation concerning Xenophobic attacks on Nigerians living in South Africa but pulled it when he found out it was false:
\n\n
I have been a victim of sharing false news/information from people like the xenophobia in South Africa. I got pictures of events that happened before that xenophobia. I went as far as sharing it, but when I came back to verify the authenticity of the news, I discovered that those events had already happened long ago. So, it is false from my side, I pulled it down.
\n\n
\n
\n
9.5 Nigerian students
\n
Nigerian undergraduate students are also likely to share trending misinformation that has elements of patriotism and emotions. For example, participants shared fake news on the July 2019 xenophobic attacks on Nigerians resident in South Africa without verifying it. This is because the xenophobia outbreak in South Africa was the trending news on social media especially Twitter at the time. So, for undergraduate students sharing videos of xenophobic attacks from 2008 as that of 2019 was part of trending on Twitter and garnering more followers. One participant said he did not care to verify the videos he shared on social media when he saw someone that someone was being burnt alive; it angered him and he shared it on social media but he later realised that it was from the 2008 xenophobic attacks in South Africa:
\n\n
When I saw the video of someone who was burning; and there was someone adding fuel to the fire, I was angered and has to share it on Twitter. Later, I started seeing on Twitter that the video was from the 2008 xenophobic attacks. I deleted it and put up the link to where they said it is fake news.
\n\n
This student was one out of many undergraduate students who found it responsible to delete a misinformation posted on social media and also put up link showing it was fake news. This could be considered an isolated instance not the norm among undergraduate students in Nigeria. Another undergraduate student regurgitated a Nigerian stereotyping of South Africans of being lazy people who are jealous of hardworking Nigerians. This stereotyping happens on both sides of the divide. While South Africans tar Nigerians with the label of drug peddlers, Nigerians retort that South African men are lazy and jealous of Nigerian men, who are winning over their women:
\n\n
If you look at a typical South African man, he is lazy, no offence…he is actually a very lazy person and people come into your country to help you, just like Chinese people come into our country to help us build airport, did we kill them?
\n\n
This view correlates with one of the themes identified in the framing of xenophobic attacks on Nigerians living in South Africa on social media, Lazy and ungrateful. There is a perception among Nigerians that South Africans are lazy ad ungrateful for the role Nigeria played in bringing an end to apartheid in South Africa.
\n
On the other hand, for postgraduate students, most of them said that they only share contents that are verified. Hence, they did not share fake videos on xenophobic attacks on Nigerians because they were unable to verify the authenticity of such content. The findings in this segment suggest that Nigeria youths are more likely to share mis-and disinformation concerning xenophobia as a form of national solidarity especially when it involves the ‘mistreatment’ of Nigerians in the diaspora such as the incident of Xenophobic attacks on Nigerians living in South Africa in 2017 and 2019.
\n
\n
\n
\n
10. Conclusion
\n
This chapter answers the research question: What is the motivation for sharing mis-and disinformation on xenophobic attacks on Nigerians living in South Africa on social media? The chapter answered this question through an analysis of social media-Facebook, Twitter framing of both incidents as well through focus group discussions across the three dominant regions of Nigeria. The findings suggest that the framing of the February 2017 xenophobic outbreak in South Africa was based mis-and disinformation and reproduction of stereotypes on social media platforms (see Figure 3). Alternative facts accounted for 65% of the framing while 35% was based on fact. For instance, 80% of the posts on social media-Facebook and Twitter- framed South Africans as villains while 16% were framed as the false hero (see Figure 4). An analysis of the geographic location of the social media accounts-Facebook and Twitter-suggests that majority of them are domiciled in Nigeria (see Figure 1). For instance, 75% of the social media accounts are resident in Nigeria and relied on secondary sources, to frame xenophobic attacks on Nigerians living in South Africa; 13% are resident in South Africa and 7% are resident in America (see Figure 1). Consequently, the framing of the xenophobic attacks on Nigerians living in South Africa in 2017 and 2019 were based on rumours, untruths, mis-and disinformation emanating from the Nigerian rumour mill. Also, analysis of interview suggests that there is a general consensus among Nigerians that sharing mis-and disinformation about xenophobic attacks on Nigerians living in South Africa amount to some form of national solidarity; a show of patriotism to save Nigerians from the dangers of living in South Africa. This point is validated across the three dominant regions of Nigeria as well as among undergraduate students. Consequently, this results in reprisal attacks and diplomatic tension between the Nigerian and South African governments. For instance, the Senior Special Adviser to the President on International Affairs, Honourable Abike Dabiri rebuked South Africa’s Minister of Home Affairs, Malusi Gigaba’s that “it appears that Mr. Gigaba would rather dwell on and entertain himself with diplomatic niceties when the welfare of Nigerians resident in South Africa are at stake now more than any time in recent history. His response to the xenophobic attacks, which has now become a recurring decimal on Africans, most especially Nigerians living peacefully in their host country of South Africa was, indeed, unfortunate.”12 Additionally, the Nigerian Ministry of Foreign Affairs followed up with a summon on the South African High Commissioner to Nigeria: “The High Commissioner of South Africa to Nigeria is being summoned to the Ministry of Foreign Affairs during which government’s concerns on this matter will be brought to his attention.”13 The findings correlate with Chenzi’s [4] that mis-and disinformation about xenophobia on social media is a leading factor for reprisal attacks on South African businesses in Nigeria and diplomatic tension between Nigeria and South Africa.
\n
Figure 3.
Category of social media posts.
\n
Figure 4.
Categorisation of social posts.
\n
\n\n',keywords:"Xenophobia, national solidarity, social media, Nigeria, South Africa, reprisal attacks, tensions",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/74142.pdf",chapterXML:"https://mts.intechopen.com/source/xml/74142.xml",downloadPdfUrl:"/chapter/pdf-download/74142",previewPdfUrl:"/chapter/pdf-preview/74142",totalDownloads:84,totalViews:0,totalCrossrefCites:0,dateSubmitted:"June 29th 2020",dateReviewed:"September 18th 2020",datePrePublished:"November 21st 2020",datePublished:null,dateFinished:null,readingETA:"0",abstract:"This study examines mis-and disinformation concerning xenophobic attacks on Nigerians living in South Africa in 2017 and 2019. The study uses narrative theories and netnograhpy involving thematic content analysis and focus group discussions with undergraduate university students, youths and adults across the three dominant regions of Nigeria. The study answers the question: what motivates Nigerians to share mis-and disinformation concerning xenophobic attacks against Nigerians living in South Africa on social media? The findings of this study suggest that national solidarity is an overriding motivation for sharing mis-and disinformation about xenophobic attacks on Nigerians living in South Africa on social media.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/74142",risUrl:"/chapter/ris/74142",signatures:"Emeka Umejei",book:{id:"9967",title:"Fake News Is Bad News - Hoaxes, Half-truths and the Nature of Today's Journalism",subtitle:null,fullTitle:"Fake News Is Bad News - Hoaxes, Half-truths and the Nature of Today's Journalism",slug:null,publishedDate:null,bookSignature:"Associate Prof. Ján Višňovský and Dr. Jana Radošinská",coverURL:"https://cdn.intechopen.com/books/images_new/9967.jpg",licenceType:"CC BY 3.0",editedByType:null,editors:[{id:"196996",title:"Associate Prof.",name:"Ján",middleName:null,surname:"Višňovský",slug:"jan-visnovsky",fullName:"Ján Višňovský"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Background",level:"1"},{id:"sec_3",title:"3. Xenophobia or Afrophobia?",level:"1"},{id:"sec_4",title:"4. Theoretical framework",level:"1"},{id:"sec_4_2",title:"4.1 Social media and fake news",level:"2"},{id:"sec_6",title:"5. Methodology",level:"1"},{id:"sec_7",title:"6. Analysis",level:"1"},{id:"sec_8",title:"7. Thematic analysis",level:"1"},{id:"sec_9",title:"8. Retaliate",level:"1"},{id:"sec_9_2",title:"8.1 Lazy and ungrateful",level:"2"},{id:"sec_10_2",title:"8.2 Jealous and inhumane",level:"2"},{id:"sec_11_2",title:"8.3 Irresponsible government",level:"2"},{id:"sec_12_2",title:"8.4 South Africans are barbaric",level:"2"},{id:"sec_14",title:"9. Interview analysis",level:"1"},{id:"sec_14_2",title:"9.1 Southwest under 40",level:"2"},{id:"sec_15_2",title:"9.2 Northeast under 40",level:"2"},{id:"sec_16_2",title:"9.3 Northeast above 40",level:"2"},{id:"sec_17_2",title:"9.4 Southeast above 40",level:"2"},{id:"sec_18_2",title:"9.5 Nigerian students",level:"2"},{id:"sec_20",title:"10. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'\nOdunfa , S. (2009, December 2). Lies, politics and Nigeria’s great rumour mill. BBC News. Retrieved from http://news.bbc.co.uk/2/hi/africa/8389020.stm\n\n'},{id:"B2",body:'\nOsuagwu, P., & Elebeke, E. (2017). Nigeria: Xenophobia - How reprisal attacks, infrastructure vandalization affect Nigeria’s $38 billion telecoms market. All Africa. Retrieved from https://allafrica.com/stories/201703010139.html\n\n'},{id:"B3",body:'\nSomdyala, K. (2019). Fake news about xenophobia on social media aimed at ruining brand SA - govt. news24. Retrieved from www.news24.com/SouthAfrica/News/fake-newsabout-xenophobia-on-social-media-aimed-at-ruining-brand-sa-govt-20190403\n\n'},{id:"B4",body:'\nChenzi, V. (2020). Fake news, social media and xenophobia in South Africa. African Identities, 1-20.\n'},{id:"B5",body:'\nHassan, I and Hitchen, J. (2020, April 15). Driving Division? Disinformation and the New media landscape in Nigeria. Center for Democracy and Development. Retrieved from https://www.cddwestafrica.org/driving-division-disinformation-and-the-new-media-landscape-in-nigeria/\n\n'},{id:"B6",body:'\nSteenkamp, C. (2009). Xenophobia in South Africa: What does it say about trust? The Round Table, 98(403), 439-447.\n'},{id:"B7",body:'\nWilliams V. Xenophobia in South Africa: overview and analysis. Cape Town: The Perspective. 2008.\n'},{id:"B8",body:'\nHarris, B. (2002). Xenophobia: A new pathology for a new South Africa. Psychopathology and social prejudice, 169-184.\n'},{id:"B9",body:'\nCrush, J., McDonald, D., Williams, V., Lefko-Everett, K., Dorey, D., Taylor, D. and la Sablonniere, R. (2008). The Perfect Storm: The Realities of Xenophobia in Contemporary South Africa. Migration Policy Series, 50, http://www.queensu.ca/samp/sampresources/samppublications/\n\n'},{id:"B10",body:'\nPrinsloo, J. (2009). Textual Analysis: Narrative and Argument. In Fourie, P. (ed). Media Studies Volume 3: Media Content and Media Audiences, pp 204-253. Cape Town: Juta.\n'},{id:"B11",body:'\nJohn, F. (1987). Television culture. London: Methuen.\n'},{id:"B12",body:'\nWigston, D. (2001). Narrative analysis. In Fourie, P. (ed). Media Studies Volume 2: Media Content and Media Audiences, pp 204-253. Cape Town: Juta.\n'},{id:"B13",body:'\nWasserman, Herman. (2017). Fake news from Africa: Panics, politics and paradigms. Journalism 1-14.\n'},{id:"B14",body:'\nSchifferes, S, Newman,N, Thurman,N, Corney, D, Göker, A and Martin, C. (2014). Identifying and verifying news through social media: Developing a user-centred tool for professional journalists. Digital Journalism 2(3): 406-418.\n'},{id:"B15",body:'\nWasserman, Herman and Nicholas Benequista. (2017). Pathways to Media Reform in Sub-Saharan Africa: Reflection from a regional consultation. Centre for international Media Assistance. Accessed on 20 June. https://www.cima.ned.org/wp- content/uploads/2017/12/CIMA-Media-Reform-in-SSA_web_150ppi.pdf.\n'},{id:"B16",body:'\nGranville, K. (2018, March 19). Facebook and Cambridge Analytica: What you need to know as fallout widens. New York Times online. Retrieved from: https://www.nytimes.com/2018/03/19/technology/facebook-cambridge-analytica-explained.html\n\n'},{id:"B17",body:'\nHoward, P & Bradshaw, S. (2019, September 4). The Global Disinformation Order: 2019 Global Inventory of Organised Social Media Manipulation. Oxford Internet Institute. Retrieved from https://www.oii.ox.ac.uk/news/releases/use-of-social-media-to-manipulate-public-opinion-now-a-global-problem-says-new-report/?fbclid=IwAR0YWpWz8HxPnbFga1iab71izh7rq3xpJTfjzzB_j7d2rJgbYtegC-MBWkM\n\n'},{id:"B18",body:'\nNielsen, R. K., Newman, N., Fletcher, R., & Kalogeropoulos, A. (2019, June 13). Reuters Digital News report 2019. Reuters Institute for the Study of Journalism. Retrieved from https://reutersinstitute.politics.ox.ac.uk/sites/default/files/2019-06/DNR_2019_FINAL_0.pdf\n\n'},{id:"B19",body:'\nSade-Beck, L. (2004). Internet Ethnography: Online and Offline. International Journal of Qualitative Methods, 3(2), 45-51.\n\n\n'},{id:"B20",body:'\nKozinets, R. V. (2015). Netnography. The international encyclopedia of digital communication and society, 1-8.\n\n\n'}],footnotes:[{id:"fn1",explanation:"\nhttps://www.aljazeera.com/news/africa/2013/06/2013638213294187.html\n"},{id:"fn2",explanation:"\nhttp://www.worldbulletin.net/news/138539/two-dead-in-xenophobic-attacks-in-s-africa\n"},{id:"fn3",explanation:"https://www.bbc.com/news/world-africa-32372501\n"},{id:"fn4",explanation:"https://www.dailymaverick.co.za/article/2015-10-27-where-poverty-meets-xenophobia-grahamstown-a-city-in-crisis/#.WzJ0a9IzbIU\n"},{id:"fn5",explanation:"https://www.news24.com/SouthAfrica/News/anti-immigration-march-is-march-of-hatred-\nnelson-mandela-foundation-20170224\n"},{id:"fn6",explanation:"\nhttps://www.theguardian.com/world/2017/feb/24/south-african-police-use-force-anti-immigration-\nprotest-pretoria\n"},{id:"fn7",explanation:"https://www.aljazeera.com/news/2019/09/nigeria-begins-repatriate-nationals-south-africa-190911074310949.html\n"},{id:"fn8",explanation:"https://www.voanews.com/africa/nigerians-attack-south-african-businesses-retaliation\n"},{id:"fn9",explanation:"https://www.csmonitor.com/World/Europe/2016/1208/Why-Pope-Francis-says-fake-news-is-a-sin\n"},{id:"fn10",explanation:"Necklacing. This is an approach to summary execution and torture by forcing tyre round the neck of victims. It is a common practice in Nigeria. For further detail, See https://www.sowetanlive.co.za/news/world/2012-11-28-necklacing-murder-of-students-shocks-world/\n"},{id:"fn11",explanation:"\nhttps://www.news24.com/SouthAfrica/News/p2-136-arrested-in-pretoria-during-xenophobic-\nviolence-phahlane-20170224\n"},{id:"fn12",explanation:"\nhttp://www.pulse.ng/news/local/xenophobia-abike-dabiri-erewa-slams-sa-minister-id6287420.html\n"},{id:"fn13",explanation:"\nhttp://www.pulse.ng/news/local/xenophobia-foreign-ministry-summons-south-african-envoy-over-attacks-on-nigerians-id6255692.html\n"}],contributors:[{corresp:"yes",contributorFullName:"Emeka Umejei",address:"mosieds@gmail.com",affiliation:'
Department of Communication and Multimedia Design, American University of Nigeria, Yola, Adamawa State, Nigeria
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Until 2010, she was a referee for CINECA and for several scientific journals in the field of microbiology. 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Many forms of resistance can spread with remarkable speed and cross international boundaries. \nWorld health leaders are devoting efforts to the problem by planning strategies for monitoring the effectiveness of public health interventions and detecting new trends and threats. This volume focuses on the problem from different perspectives, taking into consideration geographical dissemination (soil and water), human medicine (methicillin-resistant Staphylococcus aureus and Klebsiella pneumoniae) and veterinary (Enterococcus spp.) impact and molecular analysis. The purpose of this volume is to provide a useful tool for control and prevention and to discuss useful epidemiological data concerning ways of obtaining an accurate picture of resistance in different communities.",editors:[{id:"80691",title:"Prof.",name:"Maria Cristina",surname:"Ossiprandi",slug:"maria-cristina-ossiprandi",fullName:"Maria Cristina Ossiprandi"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",title:"Edited Volume"}}],chaptersAuthored:[{title:"Antimicrobial Susceptibility of Enterococcal Species Isolated from Italian Dogs",slug:"antimicrobial-susceptibility-of-enterococcal-species-isolated-from-italian-dogs",abstract:"Monitoring planes of the occurrence of antimicrobial resistance among bacteria isolated from both animals and humans should be considered essential and strategic for preserving not only human health but also animal welfare (well-being). Moreover, the use of antimicrobial in companion animals (pets) received little attention and is not currently regulated in comparison with what happens in livestock; for this reason, the prevalence of antibiotic resistance in 165 different Enterococcus strains isolated from dogs (subjected to previous antibiotic treatment(s) or not) was determined. For each strain, the minimum inhibitory concentration (MIC) against 9 different antibiotics was assessed. While all isolated strains were susceptible to vancomycin, high resistance frequency toward erythromycin, rifampicin, enrofloxacin, and tetracycline was detected. Enterococcus faecium strains isolated from the previously treated dogs demonstrated more resistance to tetracycline compared to the control ones. 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As this section deals with legal issues pertaining to the rights of individual Authors and IntechOpen, for the avoidance of doubt, each category of publication is dealt with separately. Consequently, much of the information, for example definition of terms used, is repeated to ensure that there can be no misunderstanding of the policies that apply to each category.
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Copyright is the term used to describe the rights related to the publication and distribution of original Works. Most importantly from a publisher's perspective, copyright governs how Authors, publishers and the general public can use, publish, and distribute publications.
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IntechOpen only publishes manuscripts for which it has publishing rights. This is governed by a publication agreement between the Author and IntechOpen. This agreement is accepted by the Author when the manuscript is submitted and deals with both the rights of the publisher and Author, as well as any obligations concerning a particular manuscript. However, in accepting this agreement, Authors continue to retain significant rights to use and share their publications.
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HOW COPYRIGHT WORKS WITH OPEN ACCESS LICENSES?
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By accepting the agreement terms Authors retain their copyright on their Work but grant broad publishing and distribution rights to the publisher.
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Depending on the type of publication (Chapter or Long Form Monograph/Compacts; see definitions below), IntechOpen applies a Creative Commons license to the publication, allowing readers to use and share it freely.
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IntechOpen makes the publication available online under an appropriate license.
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Agreement samples are listed here for the convenience of prospective Authors:
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Author - in order to be identified as an Author, three criteria must be met: (i) Substantial contribution to the conception or design of the Work, or the acquisition, analysis, or interpretation of data for the Work; (ii) Participation in drafting or revising the Work; (iii) Approval of the final version of the Work to be published.
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Compilation - a collection of Works distributed in a Book that IntechOpen has selected, and for which the coordination of the preparation, arrangement and publication has been the responsibility of IntechOpen. Any Work included is accepted in its entirety in unmodified form and is published with one or more other contributions, each constituting a separate and independent Work, but which together are assembled into a collective whole.
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IntechOpen - Registered publisher with office at 5 Princes Gate Court, London, SW7 2QJ - UNITED KINGDOM
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IntechOpen platform - IntechOpen website www.intechopen.com whose main purpose is to host Monographs in the format of Book Chapters, Long Form Monographs, Compacts, Conference Proceedings and Videos.
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Video Lecture – an audiovisual recording of a lecture or a speech given by a Lecturer, recorded, edited, owned and published by IntechOpen.
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TERMS
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All Works published on the IntechOpen platform and in print are licensed under a Creative Commons Attribution 3.0 Unported License, a license which allows for the broadest possible reuse of published material.
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Copyright on the individual Works belongs to the specific Author, subject to an agreement with IntechOpen. The Creative Common license is granted to all others to:
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Share — copy and redistribute the material in any medium or format
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Adapt — remix, transform, and build upon the material
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An Attribution, giving appropriate credit and providing a link to the license, with an indication as to whether changes to the original were made
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A commitment not to add additional restrictions. In effect, this prohibits the application of legal conditions or technological measures that legally restrict others from doing anything that the license permits.
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All Works are published under the CC BY 3.0 license. However, please note that book Chapters may fall under a different CC license, depending on their publication date as indicated in the table below:
Creative Commons Attribution 3.0 Unported (CC BY 3.0)
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5 October 2011 (2011-10-05)
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The CC BY 3.0 license permits Works to be freely shared in any medium or format, as well as the reuse and adaptation of the original contents of Works (e.g. figures and tables created by the Authors), as long as the source Work is cited and its Authors are acknowledged in the following manner:
Originally published in {full citation}. Available from: {DOI}
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Republishing – More about Attribution Policy can be found here.
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The same principles apply to Works published under the CC BY-NC-SA 3.0 license, with the caveats that (1) the content may not be used for commercial purposes, and (2) derivative works building on this content must be distributed under the same license. The restrictions contained in these license terms may, however, be waived by the copyright holder(s). Users wishing to circumvent any of the license terms are required to obtain explicit permission to do so from the copyright holder(s).
\\n\\n
DISCLAIMER: Neither the CC BY 3.0 license, nor any other license IntechOpen currently uses or has used before, applies to figures and tables reproduced from other works, as they may be subject to different terms of reuse. In such cases, if the copyright holder is not noted in the source of a figure or table, it is the responsibility of the User to investigate and determine the exact copyright status of any information utilised. Users requiring assistance in that regard are welcome to send an inquiry to permissions@intechopen.com.
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All rights to Books and all other compilations published on the IntechOpen platform and in print are reserved by IntechOpen.
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The copyright to Books and other compilations is subject to separate copyright from those that exist in the included Works.
Copyright to the individual Works (Chapters) belongs to their specific Authors, subject to an agreement with IntechOpen and the Creative Common license granted to all others to:
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Share — copy and redistribute the material in any medium or format
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Adapt — remix, transform, and build upon the material
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Under the following terms:
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There must be an Attribution, giving appropriate credit, provision of a link to the license, and indication if any changes were made.
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NonCommercial - The use of the material for commercial purposes is prohibited. Commercial rights are reserved to IntechOpen or its licensees.
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No additional restrictions that apply legal terms or technological measures that restrict others from doing anything the license permits are allowed.
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The CC BY-NC 4.0 license permits Works to be freely shared in any medium or format, as well as reuse and adaptation of the original contents of Works (e.g. figures and tables created by the Authors), as long as it is not used for commercial purposes. The source Work must be cited and its Authors acknowledged in the following manner:
Originally published in {full citation}. Available from: {DOI}
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All Book cover design elements, as well as Video image graphics are subject to copyright by IntechOpen.
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Every reproduction of a front cover image must be accompanied by an appropriate Copyright Notice displayed adjacent to the image. The exact Copyright Notice depends on who the Author of a particular cover image is. Users wishing to reproduce cover images should contact permissions@intechopen.com.
Share — copy and redistribute the material in any medium or format
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Under the following terms:
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Attribution — give appropriate credit, provide a link to the license, and indicate if changes were made.
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NonCommercial use only - you may not use the material for commercial purposes. Commercial rights are reserved to IntechOpen or its licensees.
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No additional restrictions — you may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
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Users wishing to repost and share the Video Lectures are welcome to do so as long as they acknowledge the source in the following manner:
Users wishing to reuse, modify, or adapt the Video Lectures in a way not permitted by the license are welcome to contact us at permissions@intechopen.com to discuss waiving particular license terms.
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All software used on the IntechOpen platform, any used during the publishing process, and the copyright in the code constituting such software, is the property of IntechOpen or its software suppliers. As such, it may not be downloaded or copied without permission.
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Unless otherwise indicated, all IntechOpen websites are the property of IntechOpen.
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All content included on IntechOpen Websites not forming part of contributed materials (such as text, images, logos, graphics, design elements, videos, sounds, pictures, trademarks, etc.), are subject to copyright and are property of, or licensed to, IntechOpen. Any other use, including the reproduction, modification, distribution, transmission, republication, display, or performance of the content on this site is strictly prohibited.
Copyright is the term used to describe the rights related to the publication and distribution of original Works. Most importantly from a publisher's perspective, copyright governs how Authors, publishers and the general public can use, publish, and distribute publications.
\n\n
IntechOpen only publishes manuscripts for which it has publishing rights. This is governed by a publication agreement between the Author and IntechOpen. This agreement is accepted by the Author when the manuscript is submitted and deals with both the rights of the publisher and Author, as well as any obligations concerning a particular manuscript. However, in accepting this agreement, Authors continue to retain significant rights to use and share their publications.
\n\n
HOW COPYRIGHT WORKS WITH OPEN ACCESS LICENSES?
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By accepting the agreement terms Authors retain their copyright on their Work but grant broad publishing and distribution rights to the publisher.
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Depending on the type of publication (Chapter or Long Form Monograph/Compacts; see definitions below), IntechOpen applies a Creative Commons license to the publication, allowing readers to use and share it freely.
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IntechOpen makes the publication available online under an appropriate license.
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Agreement samples are listed here for the convenience of prospective Authors:
The following definitions apply in this Copyright Policy:
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Author - in order to be identified as an Author, three criteria must be met: (i) Substantial contribution to the conception or design of the Work, or the acquisition, analysis, or interpretation of data for the Work; (ii) Participation in drafting or revising the Work; (iii) Approval of the final version of the Work to be published.
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Work - a Chapter, including Conference Papers, and any and all text, graphics, images and/or other materials forming part of or accompanying the Chapter/Conference Paper.
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Monograph/Compacts - a full manuscript usually written by a single Author, including any and all text, graphics, images and/or other materials.
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Compilation - a collection of Works distributed in a Book that IntechOpen has selected, and for which the coordination of the preparation, arrangement and publication has been the responsibility of IntechOpen. Any Work included is accepted in its entirety in unmodified form and is published with one or more other contributions, each constituting a separate and independent Work, but which together are assembled into a collective whole.
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IntechOpen - Registered publisher with office at 5 Princes Gate Court, London, SW7 2QJ - UNITED KINGDOM
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IntechOpen platform - IntechOpen website www.intechopen.com whose main purpose is to host Monographs in the format of Book Chapters, Long Form Monographs, Compacts, Conference Proceedings and Videos.
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Video Lecture – an audiovisual recording of a lecture or a speech given by a Lecturer, recorded, edited, owned and published by IntechOpen.
\n\n
TERMS
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All Works published on the IntechOpen platform and in print are licensed under a Creative Commons Attribution 3.0 Unported License, a license which allows for the broadest possible reuse of published material.
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Copyright on the individual Works belongs to the specific Author, subject to an agreement with IntechOpen. The Creative Common license is granted to all others to:
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\n\t
Share — copy and redistribute the material in any medium or format
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Adapt — remix, transform, and build upon the material
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And for any purpose, provided the following conditions are met:
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An Attribution, giving appropriate credit and providing a link to the license, with an indication as to whether changes to the original were made
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A commitment not to add additional restrictions. In effect, this prohibits the application of legal conditions or technological measures that legally restrict others from doing anything that the license permits.
\n
\n\n
All Works are published under the CC BY 3.0 license. However, please note that book Chapters may fall under a different CC license, depending on their publication date as indicated in the table below:
Creative Commons Attribution 3.0 Unported (CC BY 3.0)
\n\t\t\t
\n\t\t\t
5 October 2011 (2011-10-05)
\n\t\t\t
\n\t\t\t
Currently
\n\t\t
\n\t\n
\n\n
The CC BY 3.0 license permits Works to be freely shared in any medium or format, as well as the reuse and adaptation of the original contents of Works (e.g. figures and tables created by the Authors), as long as the source Work is cited and its Authors are acknowledged in the following manner:
Originally published in {full citation}. Available from: {DOI}
\n\n
Republishing – More about Attribution Policy can be found here.
\n\n
The same principles apply to Works published under the CC BY-NC-SA 3.0 license, with the caveats that (1) the content may not be used for commercial purposes, and (2) derivative works building on this content must be distributed under the same license. The restrictions contained in these license terms may, however, be waived by the copyright holder(s). Users wishing to circumvent any of the license terms are required to obtain explicit permission to do so from the copyright holder(s).
\n\n
DISCLAIMER: Neither the CC BY 3.0 license, nor any other license IntechOpen currently uses or has used before, applies to figures and tables reproduced from other works, as they may be subject to different terms of reuse. In such cases, if the copyright holder is not noted in the source of a figure or table, it is the responsibility of the User to investigate and determine the exact copyright status of any information utilised. Users requiring assistance in that regard are welcome to send an inquiry to permissions@intechopen.com.
\n\n
All rights to Books and all other compilations published on the IntechOpen platform and in print are reserved by IntechOpen.
\n\n
The copyright to Books and other compilations is subject to separate copyright from those that exist in the included Works.
Copyright to the individual Works (Chapters) belongs to their specific Authors, subject to an agreement with IntechOpen and the Creative Common license granted to all others to:
\n\n
\n\t
Share — copy and redistribute the material in any medium or format
\n\t
Adapt — remix, transform, and build upon the material
\n
\n\n
Under the following terms:
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There must be an Attribution, giving appropriate credit, provision of a link to the license, and indication if any changes were made.
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NonCommercial - The use of the material for commercial purposes is prohibited. Commercial rights are reserved to IntechOpen or its licensees.
\n\n
No additional restrictions that apply legal terms or technological measures that restrict others from doing anything the license permits are allowed.
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The CC BY-NC 4.0 license permits Works to be freely shared in any medium or format, as well as reuse and adaptation of the original contents of Works (e.g. figures and tables created by the Authors), as long as it is not used for commercial purposes. The source Work must be cited and its Authors acknowledged in the following manner:
Originally published in {full citation}. Available from: {DOI}
\n\n
All Book cover design elements, as well as Video image graphics are subject to copyright by IntechOpen.
\n\n
Every reproduction of a front cover image must be accompanied by an appropriate Copyright Notice displayed adjacent to the image. The exact Copyright Notice depends on who the Author of a particular cover image is. Users wishing to reproduce cover images should contact permissions@intechopen.com.
Share — copy and redistribute the material in any medium or format
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Under the following terms:
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Attribution — give appropriate credit, provide a link to the license, and indicate if changes were made.
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NonCommercial use only - you may not use the material for commercial purposes. Commercial rights are reserved to IntechOpen or its licensees.
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Distribution of remixed or transformed material building on the original termed derivatives is not permitted.
\n\t
No additional restrictions — you may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
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\n\n
Users wishing to repost and share the Video Lectures are welcome to do so as long as they acknowledge the source in the following manner:
Users wishing to reuse, modify, or adapt the Video Lectures in a way not permitted by the license are welcome to contact us at permissions@intechopen.com to discuss waiving particular license terms.
\n\n
All software used on the IntechOpen platform, any used during the publishing process, and the copyright in the code constituting such software, is the property of IntechOpen or its software suppliers. As such, it may not be downloaded or copied without permission.
\n\n
Unless otherwise indicated, all IntechOpen websites are the property of IntechOpen.
\n\n
All content included on IntechOpen Websites not forming part of contributed materials (such as text, images, logos, graphics, design elements, videos, sounds, pictures, trademarks, etc.), are subject to copyright and are property of, or licensed to, IntechOpen. Any other use, including the reproduction, modification, distribution, transmission, republication, display, or performance of the content on this site is strictly prohibited.
\n\n
Policy last updated: 2016-06-08
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I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. 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After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\r\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. He is an expert in structural, absorptive, catalytic and photocatalytic properties, in structural organization and dynamic features of ionic liquids, in magnetic interactions between paramagnetic centers. The author or co-author of 3 books, over 200 articles and reviews in scientific journals and books. He is an actual member of the International EPR/ESR Society, European Society on Quantum Solar Energy Conversion, Moscow House of Scientists, of the Board of Moscow Physical Society.",institutionString:null,institution:{name:"Semenov Institute of Chemical Physics",country:{name:"Russia"}}},{id:"62389",title:"PhD.",name:"Ali Demir",middleName:null,surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62389/images/3413_n.jpg",biography:"Dr. Ali Demir Sezer has a Ph.D. from Pharmaceutical Biotechnology at the Faculty of Pharmacy, University of Marmara (Turkey). He is the member of many Pharmaceutical Associations and acts as a reviewer of scientific journals and European projects under different research areas such as: drug delivery systems, nanotechnology and pharmaceutical biotechnology. Dr. Sezer is the author of many scientific publications in peer-reviewed journals and poster communications. Focus of his research activity is drug delivery, physico-chemical characterization and biological evaluation of biopolymers micro and nanoparticles as modified drug delivery system, and colloidal drug carriers (liposomes, nanoparticles etc.).",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"61051",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"100762",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"St David's Medical Center",country:{name:"United States of America"}}},{id:"107416",title:"Dr.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Texas Cardiac Arrhythmia",country:{name:"United States of America"}}},{id:"64434",title:"Dr.",name:"Angkoon",middleName:null,surname:"Phinyomark",slug:"angkoon-phinyomark",fullName:"Angkoon Phinyomark",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/64434/images/2619_n.jpg",biography:"My name is Angkoon Phinyomark. 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