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Understanding Consumer Behavior through Intention Modelling and Recognition (Advert Reference: RDF22/BL/MOS/TANG)

   Faculty of Business and Law

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  Dr Jing Tang  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

Capturing consumers’ intents plays an important role in improving their experiences with digital businesses. In general, by analysing consumers’ online behaviours, we can discover their intentions and adapt the marketing and digitalization strategy to engage more consumers in an e-commerce platform. For example, the analysis of how consumers navigate through the Amazon platform and finally buy or not buy a product would help re-design the platform layout and functionalities, improving customers’ experiences, and eventually lead to more purchases. State-of-art research on intention modelling still resorts to data analysis or machine learning techniques and focuses on improving the intention recognition accuracy. The techniques do not explicitly model how consumers’ intentions are influenced by different factors, e.g. personal profiles, e-commerce platform function design, cognitive heuristics, etc. Consequently, the analysis results do not provide sufficient knowledge and insight to understand consumers’ behaviours and their intentions in a digital business platform.

This project aims to develop an intention recognition model for consumers in an e-commerce platform by analysing available data of their behaviours through a set of sophisticated statistical and machine learning methods. Particularly, we are planning to use a more explainable method, e.g. Bayesian model, to structure relations among a number of intentions and relevant business behaviour predictors. The new intention model will provide understanding of online consumers’ behaviours in an e-commerce platform and what potential intentions the consumers are holding in business engagement. It can be used to predict consumers’ intentions, which adds business value into an e-commerce platform on improving consumers’ experiences. In addition, this work will investigate more implicit human activities or features, e.g. eye movement, fixation length and fixation count, that could contribute to analysing personal intentions. The new features will enrich the intention modelling as so to improve recognition accuracy. Finally, this work will study the new intention model and recognition techniques in a specific e-commerce platform through subject study. A group of participants will be invited to join the model verification phase. A specific purchasing setting will be organized to record how the subjects act in the purchasing process. During this real-time study, we will use the learned model to predict whether the subjects succeed or fail in the purchasing, evaluating the model’s accuracy.

This project will develop multi-disciplinary research from digital business, artificial intelligence, and behavioural sciences. The project outcomes will contribute to the research on intention modelling in digital business as well as human behaviour study in cognitive science.

This project will conduct the case study of modelling intentions by using our eye-tracking tools and equipment, namely Gazepoint eye tracking system Technology[1] in our Behavioural Sciences Lab at Decisions and Analytics Research Interest Group (DA-RIG) in MOS department. This project has an opportunity of collaborating with local business on the e-commerce platform design and development which will lead to potential economic and societal impact on the digital businesses development as well.

Eligibility and How to Apply:

Please note eligibility requirement:

  • Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
  • Appropriate IELTS score, if required.
  • Applicants cannot apply for this funding if currently engaged in Doctoral study at Northumbria or elsewhere or if they have previously been awarded a PhD.

For further details of how to apply, entry requirements and the application form, see

Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. RDF22/BL/MOS/TANG) will not be considered.

Deadline for applications: 18 February 2022

Start Date: 1 October 2022

Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community.

Principal Supervisor Dr Jing Tang

Funding Notes

Each studentship supports a full stipend, paid for three years at RCUK rates (for 2021/22 full-time study this is £15,609 per year) and full tuition fees. UK and international (including EU) candidates may apply.
Studentships are available for applicants who wish to study on a part-time basis over 5 years (0.6 FTE, stipend £9,365 per year and full tuition fees) in combination with work or personal responsibilities.
Please also read the full funding notes which include advice for international and part-time applicants.


Ma, B., Tang, J., Chen, B., Pan, Y., Zeng, Y.:
Tensor optimization with group lasso for multi-agent predictive state representation. Knowl. Based Syst. 221: 106893 (2021) (Impact factor: 8.038)
Pan, Y., Tang, J., Ma, B., Zeng, Y., Ming, Z.:
Toward data-driven solutions to interactive dynamic influence diagrams. Knowl. Inf. Syst. 63(9): 2431-2453 (2021) (Impact factor: 2.822)
Huo, Y., Chen, B., Tang, J., Zeng, Y.:
Privacy-preserving point-of-interest recommendation based on geographical and social influence. Inf. Sci. 543: 202-218 (2021) (Impact factor: 6.795)
Hou, Y., Ong, Y., Tang, J., Zeng, Y.:
Evolutionary Multiagent Transfer Learning With Model-Based Opponent Behavior Prediction. IEEE Trans. Syst. Man Cybern. Syst. 51(10): 5962-5976 (2021) (Impact factor: 11.356)
Pan, Y., Huo, Y., Tang, J., Zeng, Y., Chen, B.:
Exploiting relational tag expansion for dynamic user profile in a tag-aware ranking recommender system. Inf. Sci. 545: 448-464 (2021) (Impact factor: 6.795)
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