Black Box Thinking for Professional Firms
PhDs and postgraduate research
Self-funded PhD students only
Business and Law
Applications accepted all year round
The Faculty of Business and Law offers funding to attend conferences (currently £550), training (currently £450), and a work-based placement (currently a maximum of £3,000 tied up to the period of 12 weeks).
The work on this project will:
- Apply theories of organisational learning from High Reliability Organisations (HROs) to professional legal practice in order to create a culture of learning from error akin
to that adopted by aviation and medicine and thereby enhance resilience at both the level of the individual professional and the organisation.
- Focus on investigation of the extent to which professional negligence claims are impacted in legal professional firms following enhancing a culture of no blame and greater disclosure of error.
- Investigate multi-levels learning loops in the development of an organisational maturity grid aimed at mitigation against fear in response to errors among professional employees
This project takes its name from the book by Matthew Syed: Black Box Thinking.
This research project applies organisational learning from High Reliability Organisations (HRO) to a particular professional firm: solicitors’ private practice. Agwu & Labib (2019) have developed a framework to improve the reliability of organisations. The organisational reliability maturity model (ORM2) tracks maturity levels. We believe that this model has not been applied to Law firms. Solicitors are highly regulated professionals and must carry professional negligence insurance to compensate clients in the event of human error amounting to the tort of negligence. Disastrous impacts should not then fall upon clients but may do so upon the firm itself, the individual professional, and or upon the reputation of the legal profession.
Dealing with human error in a culture of denial and blame drives fear of consequences, and a response to deny and disguise the error, and prevents any form of learning. This situation has recently been examined by the High court in the case of SRA v Jones 2018 where three solicitors were struck off after lying as a result of fear following error. For the individuals in this case the consequences were catastrophic since not only were they struck off but they are also unable to seek restoration to the roll. Enhancing a culture of no blame will result in greater disclosure of error. This culture of learning should be applied to the disclosure of near misses because this holds the potential for increased reliability which has been experienced in the aviation industry and the medical profession.
High Reliability Organisations (HROs) usually refer to industries such as nuclear and aviation where they possess a high degree of reliability despite their hazardous environment. Previous work by Agwu (2018), and Agwu et al (2019), have shown that applying the framework of the five principles of organisational learning from HROs across diverse organisations in different industries could potentially reduce errors and major failures in organisations. It is envisaged that guided by these principles an organisational maturity grid can be developed to mitigate against fear in response to errors among professional employees. In doing so, we will also investigate multi-levels learning loops.
Agwu, A. E., Labib, A., & Hadleigh-Dunn, S. (2019). Disaster prevention through a harmonized framework for high reliability organisations. Safety science, 111, 298-312.
Agwu, A. E., & Labib, A. (2018). Safeguarding Process Plants through Higher Reliability.
Agwu, A. E. (2018). Towards a Harmonized Framework for High Reliability Organisations (Doctoral dissertation, University of Portsmouth).
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).
2021/2022 fees (applicable for October 2021 and February 2022 start)
PhD and MPhil
Home/EU/CI full-time students: £4,500 p/a*
Home/EU/CI part-time students: £2,250 p/a*
International full-time students: £16,300 p/a
International part-time students: £8,150 p/a
PhD by Publication
External candidates: £4,407*
Members of staff: £1,720
All fees are subject to annual increase. If you are an EU student starting a programme in 2021/22 please visit this page.
*This is the 2020/21 UK Research and Innovation (UKRI) maximum studentship fee; this fee will increase to the 2021/22 UKRI maximum studentship fee when UKRI announces this rate in Spring 2021.
Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.
You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in Risk Management, Strategic Operations Management and Decision Analysis or a related area. In exceptional cases, we may consider equivalent professional experience and/or Qualifications. English language proficiency at a minimum of IELTS band 7.0 with no component score below 6.5.
Knowledge of empirical research methods and mathematical modelling.
How to apply
We’d encourage you to contact Caroline.Strevens@port.ac.uk to discuss your interest before you apply, quoting the project code.
When you are ready to apply, you can use our online application form. 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. Our ‘How to Apply’ page offers further guidance on the PhD application process.
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.
When applying please quote project code: LLAW4611020