Data-driven multi-criteria decision making with marketing applications
PhDs and postgraduate research
Self-funded PhD students only
Business and Management
Applications accepted all year round
The work will look at:
- developing a state-of-the art review of existing data-driven multi-criteria decision methods
- developing a data-driven multi-criteria decision method based on sentiment analysis
- validating the method in a marketing context
It is well known that optimal decisions are fundamentals for companies but also for individuals; so it's not surprising that multi-criteria decision analysis has undergone a considerable expansion in recent years (Figueira et al. 2016), as a large number of methodologies have been developed and used in multiple contexts (lshizaka and Nemery 2013).
However, these methods require a direct input from the decision-maker. But if a decision-maker is unavailable, the multi-criteria decision method cannot be used; and if the decision applies to a large group of people (e.g. which product to launch on the market), it is unlikely that a single decision-maker can represent all the parties. It is, therefore, important to develop new data-driven techniques based on secondary data, which do not require a direct input of a decision-maker.
To this end, we propose to use sentiment analysis to extract consumer preferences from social networks. Previous research has investigated how multi-criteria ratings allow consumers to assess whether or not a product or a service matches their expectations.
However, previous research has rarely investigated how companies may employ such ratings to develop new products or services (Lin 2018). In particular, very little effort has been made so far to assess whether consumer emotions, as expressed through text, images, or videos, on the social media, may provide guidance for multi-criteria decision making related to the development of new products and services (Jannach et al. 2014).
This research aims to enhance the field of multiple criteria decision analysis by incorporating sentiment analysis from social media, and the new methodology will be validated with a marketing case study. Secondary data will be collected from social media, which has two advantages: i) individuals can express themselves freely, and ii) data is available in large quantities.
Multi-criteria decision-making literature (Bous et al. 2010) shows that consumers consider a variety of attributes in a holistic manner to arrive at a decision compatible with their preferences. Thus, because multiple attributes shape consumer decisions to adopt or resist new products, companies could benefit from a decision method that considers these attributes and their relative importance. Such a method may, in fact, help them design or improve different aspects of their offerings.References
- Ishizaka, A., and Nemery, P. (2013). Multi-Criteria Decision Analysis. Chichester (United Kingdom), John Wiley & Sons Inc.
- Jannach, D., Zanker, M., and Fuchs, M. (2014). "Leveraging multi-criteria customer feedback for satisfaction analysis and improved recommendations." Information Technology & Tourism 14(2): 119-149.
- Lin, K. (2018). "A text mining approach to capture user experience for new product development." International Journal of Industrial Engineering 25(1): 108-121.
Fees and funding
Funding availability: Self-funded PhD students only.
PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only).
2020/2021 fees (applicable for October 2020 and February 2021 start)
Home/EU/CI full-time students: £4,407 p/a*
Home/EU/CI part-time students: £2,204 p/a*
International full-time students: £15,100 p/a*
International part-time students: £7,550 p/a*
*All fees are subject to annual increase
- You'll need a good first degree from an internationally recognised university (minimum second class or equivalent, depending on your chosen course) or a Master’s degree in an appropriate subject.
- In exceptional cases, we may consider equivalent professional experience and/or Qualifications.
- English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.
Successful candidates should demonstrate a significant interest in social media marketing and Internet technologies.
If you have project specific enquiries, please contact Professor Alessio Ishizaka (email@example.com) to discuss your interest before you apply, quoting the project code.
How to apply
When you are ready to apply, you can use our online application form and select ‘Business and Management’ as the subject area. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV.
Please also include a research proposal of 1,000 words outlining the main features of your proposed research design – including how it meets the stated objectives, the challenges this project may present, and how the work will build on or challenge existing research in the above field.
Our ‘How to Apply’ page offers further guidance on the PhD application process.
To be considered for this self-funded PhD opportunity quote project code BUSM4510219 when applying.