Authors
Ross Metusalem
JMP
Muralidhara A
JMP
Objective
Apply exploratory factor analysis to uncover latent factor structure in an online shopping questionnaire.
Background
Online shopping has influenced the world of marketing significantly. Various online retailers across the globe sell a multitude of products and services, and consumers often prefer the convenience of shopping from home. While it provides an opportunity to shop 24/7, online shopping also exposes customers to increased risk. They can fall victim to fraud through fake websites set up to steal personal information or through the theft of their information from a seller’s database. Even without fraud, the relatively lower overhead of setting up an online store gives easy access to the marketplace to sellers who lack experience or skill, creating the risk of incorrect, incomplete, or lost orders. Therefore, to promote sales, online sellers have a strong incentive to encourage customers to view them as trustworthy.
The Task
Anna is a market researcher for an online retailer who wanted to identify the underlying factors driving customers’ trust in online sellers. She adopted a primary method of data collection and prepared a set of 20 statements related to features of the online shopping experience that might influence customers’ trust in the seller. In a questionnaire, respondents were asked to rate on a scale of 0 to 5 how strongly each feature would increase their sense of trust in the seller, with 0 being “not at all” and 5 being “very much.” The 20 questions and their corresponding short abbreviations are provided in Exhibit 1 (see PDF).
Anna used exploratory factor analysis (EFA) to analyze the trust ratings data. With EFA, Anna can summarize the information from the 20 questionnaire items using a smaller number of latent constructs or factors. She then can apply her domain expertise to interpret the latent factors in terms of potential drivers of customer trust.
Anna collected data from 445 respondents, which can be found in consumer-fa.jmp. The Column Properties of each column include a note containing the full text of the corresponding questionnaire item. Hover your pointer over the column header to quickly view the full text.