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Business Analytics in the Internet of things (IoT) (Advert Reference: SF19/BL/MOS/QU3)

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

The proliferation of the Internet of things (IoT) has introduced new challenges of data efficiency, security and dynamic interface and communication for business users. Firms are in urgent need for new scientific knowledge on how the data that has been generated by millions of devices can be retained, configured and used with coherent interface and communication mechanisms in different managerial roles within and across firms. This PhD project will look into this area to explore, for example, a potential firm-level multi-device data architecture and permutations of data communication interface, which optimize the efficiency in responding to market situations and business decision making tasks at different organisational levels. Academic research in this particular area is scarce despite vast advances in IoT and information system research. This research will benefit businesses in upgrading/re-constructing information infrastructure and optimal data use to sustain a competitive position in the IoT driven, fast changing market. The analysis will involve different optimization techniques to produce simple and actionable business processes. The candidate may develop different business models, technologies and frameworks to achieve the objective of the project that will make important scientific contributions to the disciplinary area and benefit business and industries with actionable models and operational guidelines.

This PhD project is seeking one applicant who has some background knowledge in data analytics, business models, business intelligence, optimization, and statistical techniques. The PhD supervision team will consist of Dr Yi Qu who is a Lecturer and active researcher specialising in optimisation modelling and Artificial Intelligence, and Professor Charles Cui who specialises in marketing, behavioural analysis and quantitative modelling.

Indicative bibliography

Sena, V., Bhaumik, S., Sengupta, A. & Demirbag, M. (2019), Big data and performance: What can management research tell us? British Journal of Management, 30, 219-228.

This project is supervised by Dr Yi Qu and Professor Charles Cui.

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.

For further details of how to apply, entry requirements and the application form, see: https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply/

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. SF19/…) will not be considered.

Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community. The University holds an Athena SWAN Bronze award in recognition of our commitment to improving employment practices for the advancement of gender equality.

Funding Notes

Please note this is a self-funded project and does not include tuition fees or stipend.

Related Subjects

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