MSc Innovation Management and Entrepreneurship

For entry in the academic year beginning September 2027, the tuition fees are as follows:

• MSc (full-time)
  UK students (per annum): £17,900
  International, including EU, students (per annum): £33,600

The fees quoted above are fully inclusive of tuition, administration and computational costs.

Fees for entry are subject to yearly review. The University reserves the right to increase your tuition fee by up to 7% each year for courses lasting more than one year, including to reflect rising costs associated with delivering our educational and wider student experience. Postgraduate fees information .

Always contact the admissions team if you are unsure which fee applies to your qualification award and method of attendance.

All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).

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Innovation is difficult to measure. To take robust decisions about innovation, as entrepreneurs, business leaders, investors, regulators or policy makers, we need to understand the methods to describe and visualise technological innovation and innovative entrepreneurship. This course will emphasize the critical analysis of sources and empirical data. And it will apply descriptive methods for the analysis and evaluation of new technologies, or innovative ventures.

BMAN71751 Programme Req: BMAN71751 is only available as a core unit to students on MSc IME

Syllabus (indicative curriculum content):

Innovation is challenging to measure, requiring nuanced approaches to capture its driving factors and gauge its outcomes for businesses and society. Robust decision-making in innovation—whether by entrepreneurs, business leaders, investors, regulators, or policymakers—demands a thorough understanding of the methods used to describe, analyse, and visualize technological innovation and its broader implications. This course is designed to guide students in developing evidence-based research reports. It covers key aspects of research design, including formulating research questions, employing diverse data collection techniques, and data analysis and visualization skills using statistical software (i.e., R). Students will learn to present solid evidence to diverse public audiences effectively. A central emphasis of the course is on critically evaluating empirical data sources, ensuring the reliability and relevance of the insights derived. Moreover, the course explores the application of statistical descriptive methods to analyse and evaluate emerging technologies and innovative ventures, equipping students with the analytical tools needed for informed decision-making in innovation-driven contexts.

 

The purpose of this unit is to introduce methods and approaches commonly used in research and business analysis of innovation. Specifically the unit is designed to provide you with the professional skills to carry out your own research, to assess the results of your own or others research, and to apply that analysis to decision-making innovation policies, management or entrepreneurial ventures.

The course will also facilitate successful preparation and execution of a research project in terms of research question development, research design strategies and implementation, data analyses using ‘statistical software R’, and finally the interpretation of evidence.

 

First, the aim is to develop a practical experience in sourcing information, data and evidence. You will be introduced to various databases, and you will develop your skills to analyse secondary data critically. Here you will also be introduced to statistical software R.

 

Second, the unit introduces quantitative methods: including research hypothesis development, sampling, the nature of quantitative data, the use of secondary data, approaches to analysing and interpreting quantitative data. The aim is to apply descriptive and visualising tools to discuss technologies and innovation for business planning, research, consulting, or policy design.

 

Syllabus (indicative curriculum content):

 

Introduction: Why Research Matters, Data-Driven Decision Making and Ethics  

Problem formulation and literature review  

Databases, data quality and sampling  

Science and technology indicators

Business innovation indicators and data4good  

Introduction to R and data exploration for innovation analysis  

Data wrangling and transformation

Data visualization

Advanced methods for innovation analysis in R  

Making your analysis useful: making sure your research influences outcomes  

Assignment 2 Revision and virtual data demonstration   
 

The course will provide an introduction to a set of professional research and analytical skills that are essential in career paths in a large number of settings and situations, whether in enterprise, research or government and policy.

• Understand the main quantitative methodologies used to measure and understand innovation and be aware of the strengths and weaknesses associated with different techniques, data collection, use and meaning of data.
• Understand and be able to utilise quantitative methods that are used to gather and analyse data for research and analysis.
• Develop and justify a research question and select the appropriate quantitative methodology in order to analyse a phenomenon.
• Interpret and critically assess quantitative findings and draw analytical conclusions.

 

The delivery will be done via lectures and workshops. Lectures will provide the intellectual background and help navigate the broad field of innovation analysis. Workshops will be aimed more practically towards translating the content of the lecture into the components of the research proposal and research report.

Lecture Hours

22 hours: 11 lectures of 2 hours each (based on a blended approach of recorded material and in person / synchronous sessions)

Workshop Hours

11 hours: 11 workshops of 1 hour each  

Private Study  

117 hours (supervised using mandatory readings and research related to the completion of the assessment)

Total Study Hours

150 hours

 

During the lectures, students will be encouraged to discuss the topics and any difficulties they may have. The workshop involves constant feedback and interaction between students and staff. There is also a Padlet in place on blackboard for FAQs and raising new questions, which can be found via clicking Asking Questions in the left-side menu.   

The course will provide an introduction to a set of professional research and analytical skills that are essential in career paths in a large number of settings and situations, whether in enterprise, research or government and policy.

•  Understand the main quantitative methodologies used to measure and understand innovation and be aware of the strengths and weaknesses associated with different techniques, data collection, use and meaning of data.
•  Understand and be able to utilise quantitative methods that are used to gather and analyse data for research and analysis.
•  Develop and justify a research question and select the appropriate quantitative methodology in order to analyse a phenomenon.
•  Interpret and critically assess quantitative findings and draw analytical conclusions.
 

Individual analysis (40%)
Individual report  (60%)

 

Informal advice and discussion during lectures and seminars.

Lecture time will also include specific coursework-related feedback.

Written comments on formative and summative coursework.
 

 

The course contains relatively independent sub-modules with their own specific readings that will be made available on Blackboard prior to the lecture.

A general overview of concepts and tools related to innovation data can be found in: Gault, Fred. Handbook of Innovation Indicators and Measurement. Cheltenham: Edward Elgar, 2013. (available online through the University of Manchester Library) General (not technical) issues related to quantitative data analysis and decision making are summarized in:

Kenett, Ron, and Redman, Thomas C. The Real Work of Data Science¿: Turning Data into Information, Better Decisions, and Stronger Organizations. Hoboken, NJ, USA: Wiley, 2019. (also available online through the University of Manchester Library)
 

Examples of lectures:

• Collecting and evaluating external data sources
• Writing critical literature reviews
• Measuring and evaluating innovation, including patent data and innovation surveys
• Collecting and evaluating quantitative data, including Big Data, networks and web scraping
• Descriptive analysis of quantitative data and communicating data