Algorithmic Price Negotiation and Perceived Fairness in Digital Retailing

About the Project

Project Summary

The successful candidate will be joining the Aston Business School and benefit from the lively research environment and opportunities to engage with researchers from diverse research centres. The project provides an exciting opportunity to pursue intellectual development and academic excellence under the supervision of Professor Vignesh Yoganathan from the Department of Marketing and Entrepreneurship and Dr Xingyi Liu from the Department of Economics and International Business.

Project Details

The advancement of technology has transformed how firms interact with customers in the retailing industry. Modern digital platforms enable consumers to provide feedback on service levels, quality, and even specific product features. Advances in artificial intelligence (AI) allow retailers to delegate price negotiation to algorithmic agents. Yet, little is known about how such algorithmic negotiation shapes consumers’ fairness perceptions, and subsequent decisions, such as transaction rates, purchase likelihood, and propensity to generate electronic word of mouth. Prior work on participatory pricing mechanisms such as Name Your Own Price (NYOP) shows that involving buyers in price setting can raise perceived fairness and satisfaction, but design details like access fees, hidden thresholds, and decision aids strongly affect participation and profitability (Stich et al., 2026). Our study proposes an interactive AI negotiation framework where algorithmic chatbots serve as virtual negotiators, allowing consumers to suggest and bargain for prices in real-time. The research aims to evaluate whether this participatory approach leads to a positive impact on downstream consequences in digital retailing. Grounded in the price fairness and utility theories, and the smart shopper hypothesis, the study utilises an experimental approach with robust econometrics analysis to investigate whether and when algorithmic negotiation is perceived as fair/exploitative, and how those evaluations drive downstream consequences.

Stich, L., Zeithammer, R., Spann, M., & Häubl, G. (2026). Profitability of two-part tariffs inname-your-own-price markets. Journal of Retailing.doi:10.1016/j.jretai.2026.01.002

Person Specification

The successful applicant should hold, or expect to achieve:

A First or Upper Second Class Honours undergraduate degree, and a Masters degree with Merit or Distinction, both in relevant subjects.

Qualifications from overseas institutions will be considered, but performance must be equivalent to that described above, and the University reserves the right to ascertain this equivalence according to its own criteria.

Desirable / Essential Skills or Experience

Required characteristics

• High-level understanding of microeconomics or marketing/consumer behaviour, especially pricing, discrimination, and welfare concepts, which are central to algorithmic pricing and fairness debates.
• Ability to critically engage with literature on algorithmic discrimination, personalization, and consumer protection in digital markets, and formulate research questions at the intersection of relevant topics.
• Evidence of research potential (e.g. dissertation, thesis, research experience).
• Strong statistical and econometric skills (e.g. causal inference, experiment design) to assess impacts of pricing algorithms on consumers and fairness perceptions.
• Proficiency in at least one data‑science language (Python or R) for implementing pricing algorithms, simulation, and empirical analysis of retail data.
• Strong written and oral communication skills, including the ability to explain technical models and fairness trade‑offs to non‑technical audiences (e.g. retailers, policymakers).
• Self‑motivation, independence, and time‑management suitable for a three‑ to four‑year PhD project.

Desirable characteristics

• Professional experience relevant to the proposed research.
• Keen interest in pursuing professional development towards a career in academia.
• Experience with large‑scale or panel data from e‑commerce or retail settings, and/or implementing or evaluating dynamic or personalized pricing algorithms.
• Ability to understand and work with machine‑learning models used in pricing and personalization (e.g. demand estimation, reinforcement learning, recommendation systems).
• Engagement with open‑science practices (reproducible code, data documentation) and awareness of transparency concerns in algorithmic systems.
• Further evidence of research experience (e.g. publications, research assistantships, etc.).

Submitting an application

We can only consider applications that are complete and have all supporting documents. Applications that do not provide all the relevant documents will be automatically rejected.Your application must include:

1. English language copies of the transcripts and certificates for all your higher education degrees, including any Bachelor degrees.
2. A Research Statement detailing your understanding of the research area, how you would approach the project, and a brief review of relevant literature. Be sure to use the title of the research project you are applying for. There is no set format or word count.
3. A personal statement which outlines any further information which you think is relevant to your application, such as your personal suitability for research, career aspirations, possible future research interests, and further description of relevant employment experience.
4. A Curriculum Vitae (Resume) which details your education and work history.
5. Two academic refereeswho can discuss your suitability for independent research. References must be on headed paper, signed and dated no more than 2 years old. At least one reference should be from your most recent University. You can submit your references at a later date if necessary.
6. Evidence that you meet the English Language requirements. If you do not currently meet the language requirements, you can submit this at a later stage.
7. A copy of your passport. Where relevant, include evidence of settled or pre-settled status.

Contact Information

For enquiries about this project, contact v.yoganathan8@aston.ac.uk

Location

This position will be based on the Aston Campus in Birmingham, UK. The successful candidate will need to be located within a reasonable distance of the campus, and will be expected to visit in person regularly.

Interviews

Interviews will be conducted online via Microsoft Teams. If you are shortlisted, you will be contacted directly with details of the interview.

Funding Notes

This project covers all tuition fees and includes an annual stipend.

Please note that the successful candidate will be responsible for any costs relating to moving to Birmingham and/or visiting the Aston campus. International students must meet the financial requirements for the visa, flights, and NHS Surcharge. Applicants should be confident that they can meet these costs before applying.