Digital Transformation in Small and Medium-Sized Enterprises: an Automated Predictive Model for Forecasting and Measuring the Scope and Scale of Digital Change in Sociotechnical Systems.
Doctoral Training Grant Funding Information
This funding model includes a 36 month fully funded PhD Studentship, set in-line with UK Research & Innovation values. For 2025/6, this will be £20,780 per year. The tax-free stipend will be paid monthly. This PhD Studentship also includes a Full-Time Fee Scholarship for up to 3 years. The funding is subject to your continued registration on the research degree, making satisfactory progression within your PhD, as well as attendance on and successful completion of the Postgraduate Certificate in Research Practice.
All applicants will receive the same stipend irrespective of fee status.
Application Closing Date:
Midday (UK Time) on Wednesday 17th September 2025 for a start date of 2nd February 2026.
How to Apply
To apply, please follow the below steps:
- Complete the BCU Online Application Form.
- Complete the Doctoral Studentship Proposal Form in full, ensuring that you quote the project ID. You will be required to upload your proposal in place of a personal statement on the BCU online application form.
- Upload two references to your online application form (at least one of which must be an academic reference).
- Upload your qualification(s) for entry onto the research degree programme. This will be Bachelor/Master’s certificate(s) and transcript(s).
- International applicants must also provide a valid English language qualification. Please see the list of English language qualifications accepted here. Please check the individual research degree course page for the required scores.
Frequently Asked Questions
To help support you to complete your application, please consult the frequently asked questions below:
Project title: Digital Transformation in Small and Medium-Sized Enterprises (SMEs): an Automated Predictive Model for Forecasting and Measuring the Scope and Scale of Digital Change in Sociotechnical Systems.
Project Lead: Professor Sharon Cox
Project ID: 08 - 46384786
Project description:
Upgrading the technical capabilities of Small and Medium-Sized Enterprises (SMEs) is essential for sustainable industrialisation. SMEs often have limited technical knowledge and skills, underestimating the scope and scale of changes needed to implement new technologies or change existing IT systems. This results in incomplete, inefficient implementations with unintended and unanticipated consequences, which may digitally exclude staff, customers or suppliers.
This research aims to use machine learning techniques such as Support Vector Machines, decision trees, and deep neural networks to develop a predictive model that quantifies the scope and scale of sociotechnical changes in SMEs undergoing digital transformation.
Data will be collected from SMEs in a range of industries, at various stages of IT-led change. The data will focus on the sequence of changes triggered by implementing or updating IT systems (such as changes to other IT systems and processes). Patterns of organisational change may emerge that can be mapped to models such as the organisation architecture (Cox, 2014) and used to develop systems dynamics models to facilitate scenario simulation and analysis.
Maturity assessments will be conducted by applying maturity models before and after digital transformation in SMEs. The maturity models will include models of organisational maturity, systems maturity and process maturity, building on the work of previous PhD students. The maturity assessments will enable data to be captured relating to the scale of changes that have taken place throughout an SME during digital transformation projects.
The collected data will be processed using methods such as feature engineering, to form a dataset comprising organizational, technological, and social factors. The dataset will capture key features such as IT infrastructure maturity, workflow integration, and employee digital readiness, with corresponding impacts on sociotechnical transformation areas. The resulting model will output probabilistic forecasts of transformation impact areas to improve planning of digital transformation projects. The predictive model will enable decision-makers to anticipate challenges, allocate resources more effectively, and reduce the likelihood of unintended consequences stemming from poorly managed digital change.
The main stages of the research will comprise:
- Literature review to provide theoretical foundation, reviewing organisation architectures, maturity models and machining learning techniques.
- Data collection from SMEs to identify and measure the impact of IT-led change.
- Data analysis using systems dynamics and feature engineering, identifying patterns of change.
- Feedback from SMEs on patterns of change identified.
- Development of machine learning predictive change algorithm
- Testing and evaluation of the algorithm with SMEs.
Anticipated findings and contributions to knowledge:
he anticipated findings are that patterns of organisational change can be identified in the digital transformation of Small and Medium-sized Enterprises (SMEs). It is asserted that by identifying these patterns, it is possible to predict what changes will need to be made to the systems, processes and people in an SME to introduce new technology or change existing IT systems. It is anticipated that the scale of changes within each pattern will be influenced by the technical, process and organisational maturity of the SME. Identifying and measuring these patterns of digital transformation will lead to better planning of digital change in SMEs and enable potential unintended consequences to be anticipated.
The research addresses the knowledge gap of what changes are needed in an SME to successfully implement digital transformation. The lack of understanding of digital change in SMEs leads to IT systems that are not fully implemented as unanticipated changes are required to accommodate the systems. In addition, changes to digital systems can lead to unintended digital exclusion of staff, customers and suppliers.
The research builds on existing knowledge in three areas. It will validate conceptual models of technology-led change; integrate process maturity and system maturity models; and use machine learning to define patterns of digital change and develop a predictive algorithm.
The contribution to knowledge will include verified patterns of IT-led change, new SME maturity models, and an algorithm to predict the impact of digital change on SMEs. This research will impact responsible technical upgrading of SMEs.
Person Specification:
Entry Requirements:
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To apply for our Computing PhD Research Degree you should have, or expect to be awarded, a Master’s degree in a relevant subject area from a British or overseas university.
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Exceptional candidates without a Master’s degree, but holding a first class or upper second class Bachelor’s degree in a relevant subject area, may be considered.
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We also welcome enquiries from potential PhD researchers with appropriate levels of professional experience.
Essential Criteria:
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Knowledge of fundamental AI concepts and practical experience in developing and applying Artificial Intelligence and Machine Learning algorithms to deliver software solutions.
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Knowledge of digital transformation and practical experience in assessing the impact of digital transformation on organisations.
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Working experience in design and implementing data architecture and data cleansing to train AI models.
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Proficiency in software development programming languages such as Python, C#, in a commercial environment and machine learning tools (e.g., TensorFlow, PyTorch, scikit-learn)
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Knowledge of organisational structures and processes.
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Demonstrated ability to conduct independent research, including literature review, experimental design, and analysis.
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Excellent communication skills to express complex information effectively, both verbally and in writing, engaging the interest and enthusiasm of the target audience.
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Excellent analytical, problem-solving, and computational skills, adept at applying knowledge to commercial projects, driving value and making an impact where possible.
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Practical interpersonal skills to establish good working relationships with colleagues, stakeholders, and industrial partners.
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Demonstrated ability to take initiative and manage time and tasks effectively, with potential for leadership.
Desirable Criteria:
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A good understanding of Artificial Intelligence / Machine Learning modelling and implementation.
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Experience of working with industry stakeholders to develop commercial applications.
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Experience of measuring impact of digital transformation in organisations.
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Willingness to learn new technologies and paradigms to facilitate development of new knowledge.
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Experience of systems thinking and process modelling.
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Understanding of social, legal and ethical issues of digital transformation.
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Academic acumen to enable successful reporting through research publications in academic journals.
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Experience in writing or contributing to research proposals, technical reports, or peer-reviewed publications.
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Practical experience of working with senior managers in a Small to Medium Enterprise (SME) environments.
Overseas applicants
International applicants must also provide a valid English language qualification, such as International English Language Test System (IELTS) or equivalent with an overall score of 6.5 with no band below 6.0.
Contact:
If you have any questions or need further information, please use the contact details below:
- For enquiries about the project, please contact: Sharon.cox@bcu.ac.uk
- For enquiries about the application process, please contact: research.admissions@bcu.ac.uk