Factor Analysis in R: Measuring Consumer Involvement

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The first step for anyone who wants to promote or sell something is to understand the psychology of potential customers. Getting into the minds of consumers is often problematic because measuring psychological traits is a complex task. Consumer involvement is a measure of the attitude people have towards a product or service. This article introduces the concept of consumer involvement. An example using data from tap water consumers illustrates the theory. This article analyses the data collected from these consumers with factor analysis in R, using the psych package.

The most common method to measure psychological traits is to ask people a battery of questions. Analysing this data is complicated because it is difficult to determine how the survey responses relate to the software of the mind. While the answers given by survey respondents are the directly measured variables, we like to know the hidden (latent) states in their mind. Factor Analysis is a technique identifies latent variables within a responses set of data, such as a customer survey.

The basic principle of measuring consumer attitudes is that their state of mind causes them to respond in a certain way. Factor analysis reverses this causality by analysing patterns in the responses that are indicative of the consumer's state of mind. Using a computing analogy, factor analysis is a technique to reverse-engineer the source code by analysing the input and output.

What is Consumer Involvement?

Involvement is a marketing metric that describes the relevance of a product or service in somebody's life. Judy Zaichkowsky defines consumer involvement formally as “a person's perceived relevance of the object based on inherent needs, values, and interests”. People who own a car will most likely be highly involved with purchasing and driving the vehicle due to the money involved and the social role it plays in developing their public self. Consumers will most likely have a much lower level of involvement with the instant coffee they drink than with the clothes they wear.

Managerial Relevance

The level of consumer involvement depends on a complex array of factors. These factors are related to psychology, situational factors and the marketing mix of the service provider. The lowest level of involvement is considered a state of inertia which occurs when people habitually purchase a product without comparing alternatives.

From a managerial point of view, involvement is crucial because it is causally related to willingness to pay and perceptions of quality. Consumers with a higher level of involvement are willing to pay more for a service and have a more favourable perception of quality. Understanding involvement in the context of urban water supply is also important because sustainably managing water as a common pool resource requires the active involvement of all users.

Cult products have the highest possible level of involvement as customers are fully devoted to a particular product or brand. Commercial organisations use this knowledge to their advantage by maximising the level of consumer involvement through branding and advertising. This strategy is used effectively by the bottled water industry. Manufacturers focus on enhancing the emotional aspects of their product rather than on improving the cognitive elements. Water utilities tend to use a reversed strategy and emphasise the cognitive aspects of tap water, the pipes, plants and pumps, rather than trying to create an emotional relationship with their consumers.

Measuring Consumer Involvement

Asking consumers directly about their level of involvement would not lead to a stable answer because each respondent will interpret the question differently. The best way to measure psychological states or psychometrics is to ask a series of questions that are linguistically related to the topic of interest.

The most cited method to measure consumer involvement in the Personal Involvement Index, developed by Judy Zaichowsky. This index is a two-dimensional scale consisting of:

  • cognitive involvement (importance, relevance, meaning, value and need)

  • affective involvement (involvement, fascination, appeal, excitement and interest).

The survey instrument consists of ten semantic-differential items. A Semantic Differential is a type of a rating scale designed to measure the meaning of objects, events or concepts. The researcher translates the concept, such as involvement, into a list of synonyms and their associated antonyms.

In the involvement survey, participants position their views between two extremes, such as Worthless and Valuable or Boring and Interesting. The level of involvement is the sum of all answers, which is a number between 10 and 70. In more detailed analysis, each item in the scale can be of a different strength.

Personal Involvement Inventory semantic-differential scale
Personal Involvement Inventory semantic-differential scale (Zaichowsky 1994).

Exploratory Analysis

For my dissertation about customer service in water utilities, I measured the level of involvement that consumers have with tap water. 832 tap water consumers completed this survey in Australia and the United States.

This data set contains other information, and the code selects only those variable names starting with “p” (for Personal Involvement Inventory). Before we analyse any data, we remove customers who provided the same answers to all items, or did not respond to all questions. These responses are most likely invalid, which leaves 757 rows of data.

A boxplot is a convenient way to view the responses to multiple survey items in one visualisation. This plot immediately shows an interesting pattern in the answers. It seems that responses to the first five items were generally higher than those for the last five items. This result seems to indicate a demarcation between cognitive and affective involvement.

Responses to Personal Involvement Index by tap water consumers
Responses to Personal Involvement Index by tap water consumers.

Next step in the exploratory analysis is to investigate how these factors correlate with each other. The correlation plot below shows that all items strongly correlate with each other. In correspondence with the boxplots above, the first five and the last five items correlate more strongly with each other. This plot suggests that the two dimensions of the involvement index correlate with each other. The next section shows how to use factor analysis in R to check the significance of these correlation patterns.

Correlation matrix for the Personal Involvement Index
Correlation matrix for the Personal Involvement Index.

Factor Analysis in R

Researchers often confuse Factor Analysis with Principal Component Analysis. The outcomes of are very similar when applied to the same data set. Both methods are similar but have a different purpose. Principal Component Analysis is a data-reduction technique that serves to reduce the number of variables in a problem. The specific purpose of Factor Analysis is to uncover latent variables. The mathematical principles for both techniques are similar, but not the same and should not be confused.

One of the most important decisions in factor analysis is to decide how to rotate the factors. There are two types: orthogonal or oblique. In simple terms, orthogonal rotations seek to reduce the correlation between dimensions and oblique rotation allow for dimensions to relate to each other. Because of the strong correlations in the correlation plot and the fact that both dimensions measure involvement, this analysis uses oblique rotation. The visualisation below shows how each of the items how, and the two dimensions relate to each other.

Factor analysis in R with Psych package
Factor analysis in R with Psych package.

This simple factor analysis in R shows the basic principle of how to analyse psychometric data. The psych package has a lot more specialised tools to dig deeper into the information. This article has not assessed the validity of this construct, or evaluated the reliability of the factors. Perhaps that is for a future article.

The R Code



  ## Consumer Involvement
  library(tidyverse)
  library(psych)

  consumers <- read_csv("customers/customers_quan.csv") %>%
    select(starts_with("p"))
  dim(consumers)

  ## Data cleansing
  sdevs <- apply(consumers, 1, sd, na.rm = TRUE)
  incomplete <- apply(consumers, 1, function(i) any(is.na(i)))
  consumers <- consumers[sdevs != 0 & !incomplete, ]
  dim(consumers)

  ## Exploratory Analysis
  consumers %>%
    rownames_to_column(var = "Subject") %>%
    gather(Item, Response, -Subject) %>%    
    ggplot(aes(Item, Response)) + geom_boxplot(fill = "#f7941d") +
    theme_bw(base_size = 10) + 
    ggtitle("personal Involvement Index",
            subtitle = paste("Tap Water Consumers USA and Australia (n =",
                             nrow(consumers), ")"))
  ggsave("involvement-explore.png", width = 6, height = 4)

  ##png("involvement-correlation.png")
  corPlot(consumers)
  ##dev.off()

  ## Factor Analysis
  piiFac <- fa(consumers, nfactors = 2, rotate = "varimax")

  ##png("involvement-factors.png")
  fa.diagram(piiFac)
  ##dev.off()

Data Science for Water Professionals

If you like to know more about using R to analyse water data, then onsider following the course Data Science for Water Utility Professionals.

Data Science for Water Utility Professionals, LeanPub.

Managing reliable water services requires not only a sufficient volume of water, but also large amounts of data. This course teaches the basics of data science using the R language and the Tidyverse libraries to analyse water management problems.



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