A data frame with 250 observations and 28 variables. Variables from 1 to 27 refer to six latent concepts: IMAG=Image, EXPE=Expectations, QUAL=Quality, VAL=Value, SAT=Satisfaction, and LOY=Loyalty.

imag1-imag5

Indicators attached to concept IMAG which is supposed to capture aspects such as the institutions reputation, trustworthiness, seriousness, solidness, and caring about customer.

expe1-expe5

Indicators attached to concept EXPE which is supposed to capture aspects concerning products and services provided, customer service, providing solutions, and expectations for the overall quality.

qual1-qual5

Indicators attached to concept QUAL which is supposed to capture aspects concerning reliability of products and services, the range of products and services, personal advice, and overall perceived quality.

val1-val4

Indicators attached to concept VAL which is supposed to capture aspects related to beneficial services and products, valuable investments, quality relative to price, and price relative to quality.

sat1-sat4

Indicators attached to concept SAT which is supposed to capture aspects concerning overall rating of satisfaction, fulfillment of expectations, satisfaction relative to other banks, and performance relative to customer's ideal bank.

loy1-loy4

Indicators attached to concept LOY which is supposed to capture aspects concerning propensity to choose the same bank again, propensity to switch to other bank, intention to recommend the bank to friends, and the sense of loyalty.

gender

The sex of the respondent.

satisfaction_gender

Format

An object of class data.frame with 250 rows and 28 columns.

Source

The plspm package (version 0.4.9). Original source according to plspm: "Laboratory of Information Analysis and Modeling (LIAM). Facultat d'Informatica de Barcelona, Universitat Politecnica de Catalunya".

Details

This data set contains the variables from a customer satisfaction study of a Spanish credit institution on 250 customers. The data is taken from the plspm package. For convenience, there is a version of the dataset with the last column (gender) removed: satisfaction.