A Teaching License Case Study - SurveyEngine GmbH

A Teaching License Case Study

Using AI Technologies at Self-Checkouts

Author: Isaak Collie, Student
Supervised by: Dr Thomas Magor, Lecturer in Marketing
Course: Business Research Strategy Course, July – Nov 2023
Number of students: 75
Institution: University of Queensland Business School, Australia
Technical Course Sponsor: SurveyEngine GmbH

Dr. Thomas Magor from the University of Queensland used the SurveyEngine software to teach choice modelling methods and to design choice experiments during his Business Research Strategy course. The course was attended by 75 students, who learned how to easily change a design and update it by adding or removing attributes and levels as well as basic modelling (MNL) that SurveyEngine provides automatically on the collected data. Many thanks to Dr. Thomas Magor for using SurveyEngine and choosing the best case study from his class, summarized below:

Research Context
The use of self-checkouts has been contentious amongst customers and media, with various large grocery stores pushing for a self-checkout driven experience claiming ease-of-use and the cost savings that automation can bring. Critics cite job concerns and the fact that the cost savings of automation do not appear to be passed on to customers. Although interestingly, experts in consumer marketing state that self-checkouts will not reduce jobs and the grocery industry has also expanded staff numbers in the past few years. The use of AI technologies at self-checkouts has also raised privacy concerns, especially on the back of serious data hacks that affected other large Australian firms such as Optus and Medibank. Informal sentiment surrounding the use of AI and growing push to a self-checkout orientated experience has been incredibly negative, with consumers often expressing their disdain over social media or to staff directly.

Choice Experiment Editor: SurveyEngine Software

The Business Problem
At a high level, the strategic business research problem is finding a balance between grocery stores improving their internal efficiencies and reducing theft but also allows customers to have a convenient experience. To be more specific, large grocers are trying to influence an outcome of reduced theft, reduced costs from wages, and an automated checkout process that is convenient for customers. All these factors will lead to a larger profit. Ideally, a convenient check-out process will also create higher customer satisfaction which will promote not only more customers shopping at specific grocers but also customers performing longer shops, which leads to more revenue per customer.

Currently, the drive to a primarily self-checkout experience is obvious, with various stores shutting down more of their manned checkouts, more self-checkouts are being given conveyer belts to accommodate larger shops, and the growing use of machine learning AI to detect the various products that are passed through the self-checkouts.

The survey research aims to help formally quantify consumer sentiment towards the self-checkouts and the use of AI as well as provide suggestions on how large grocery changes could change the self-checkout experience. The research should also uncover how different demographics of consumers react to the use of self-checkouts and AI.

Summary of Findings
The findings ultimately showcase that consumers do prioritise convenience in their shopping experience, however despite the convenience that self-checkouts bring, they are still apprehensive around perceived job loss concerns and the use of AI to monitor actions at self-checkouts. It was found that an individual’s demographic had no real bearing on their sentiment towards self-checkouts, meaning these findings could be applied broadly to the entire population, although more research with larger sample sizes is needed to truly confirm this idea.

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