Information regarding our 2023/24 admissions cycle

Our 2023/24 postgraduate taught admissions cycle will open on Monday, 10 October. For most programmes, the application form will not open until this date.

MSc Innovation Management and Entrepreneurship

Year of entry: 2023

Course unit details:
Tools and Methods for Innovation Analysis

Course unit fact file
Unit code BMAN71751
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 1
Offered by Alliance Manchester Business School
Available as a free choice unit? No


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 products and services, or innovative ventures.


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


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 analyse 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 project in terms of overall project development and planning, research design and implementation, data analyses, and 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.

Second, the unit introduces quantitative methods: including research hypothesis development, sampling, the nature of quantitative data, the use of secondary data, primary collection techniques, 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.

Learning outcomes

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.


Assessment methods

Coursework (100%):

Formative mini report (20%)

Summative report (80%)


Feedback methods

Informal advice and discussion during lectures and seminars.

Lecture time will also include specific coursework-related feedback.

Written comments on formative and summative coursework.


Recommended reading

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)


Study hours

Scheduled activity hours
Lectures 20
Supervised time in studio/wksp 10
Independent study hours
Independent study 120

Teaching staff

Staff member Role
Adrien Querbes Unit coordinator

Additional notes

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

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