satisfaction: Satisfaction dataset

In plspm: Partial Least Squares Path Modeling (PLS-PM)

Satisfaction dataset

Description

This data set contains the variables from a customer satisfaction study of a Spanish credit institution on 250 customers.

Format

A data frame with 250 observations and 28 variables. Variables from 1 to 27 refer to six latent concepts: 1) IMAG=Image, 2) EXPE=Expectations, 3) QUAL=Quality, 4) VAL=Value, 5) SAT=Satisfaction, and 6) LOY=Loyalty. The last variable is a categorical variable indicating the gender of the individual.

IMAG: Includes variables such as reputation, trustworthiness, seriousness, solidness, and caring about customer's needs.
EXPE: Includes variables such as products and services provided, customer service, providing solutions, and expectations for the overall quality.
QUAL: Includes variables such as reliable products and services, range of products and services, personal advice, and overall perceived quality.
VAL: Includes variables such as beneficial services and products, valuable investments, quality relative to price, and price relative to quality.
SAT: Includes variables such as overall rating of satisfaction, fulfillment of expectations, satisfaction relative to other banks, and performance relative to customer's ideal bank.
LOY: Includes variables such as propensity to choose the same bank again, propensity to switch to other bank, intention to recommend the bank to friends, and sense of loyalty.

Source

Laboratory of Information Analysis and Modeling (LIAM). Facultat d'Informatica de Barcelona, Universitat Politecnica de Catalunya.

Examples

  data(satisfaction)
  satisfaction

plspm documentation built on June 22, 2024, 11:29 a.m.