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Measuring Organisational Culture for Positive Interventions: A Machine Learning Based Tool

Primary supervisor

Waqar Hussain


This project involves development of a machine learning based tool to ‘passively’ learn and measure organisational culture in real time using existing organisational data and propose positive cultural interventions.


The COVID 19 pandemic has significantly impacted our work and brought many disruptive changes to organisational culture e.g. forced digital transformation. The impact of such changes on organisational culture is difficult to understand and measure [1], for example how work from home disproportionately affects low-wage workers, women, or underprivileged minorities etc. [2].

Traditional instruments to assess and measure organisational culture e.g. through employee surveys or questionnaires are increasingly becoming impractical because of the effort required to capture and analyse data and the undue delay in gaining necessary insights to plan empirically informed cultural interventions [3].

This is especially true for corporates with a large workforce, complex culture and the huge amounts of data they produce that renders many of the once useful traditional mechanisms and instruments for measuring organisational culture obsolete [4].

The Opportunity

AI and M/L based technology turns this problem into an opportunity using computerised textual analysis that thrives on manipulating and sense making of large amounts of data [5, 6] and measuring organisational culture [7].

Many information sources of data exist in large and medium sized organisations e.g. hiring/training records, health and wellbeing programs, incentive and reward packages etc. However, most of them are usually disparate and siloed therefore remain unused for various insights and potential culture-related intervention opportunities. From a big data analysis perspective, even informal sources of information such as the Internal Social Media platform (FB workplace) with publicly available data (within the organisation) hold immense potential to understand and measure existing organisational culture and its evolution [8] [9, 10]. Data on such platforms offer opportunities to evaluate or measure for example, the impact of the already introduced cultural interventions [11] such as new policies, incentives programs, diversity campaigns, internal innovation hackathons etc.



Student cohort

Single Semester
Double Semester


Project Aim

This project aims to produce an initial prototype of a machine learning and Natural Language Processing (NLP) based tool to ‘passively’ learn and measure organisational culture in real time using publicly available organisational data and propose positive cultural interventions. The tool will offer previously untapped insights about the organisational culture.


The tool will utilise huge amounts of internally available public data on social platforms such as Facebook Workplace, Yammer etc. The tool would analyze data such as ‘likes’, emojis, comments used to show appreciation, support/opposition of ideas to measure, assess employee engagement efficiently and in a timely manner.

Our tool will be based on the 6 – factor approach [12] to understand various aspects of organisational culture from the existing data. These 6 factors have been identified as common to many definitions of culture that can reveal themes covering the most important aspects of the organisational culture [12]. 

The ideology behind our tool is inspired by the ‘Fail Fast’ approach in Agile development [13]. That is, rather than relying on and waiting for the unnecessarily long and slow manual analysis of surveys we could apply a much faster AI based approach to get the results. This near ‘run-time’ analysis of data allows identification of cultural traits and the impact of any cultural intervention introduced. The insights about positive and negative trends allow decision makers to take necessary actions to either reinforce the interventions that work or timely-pivot to introduce alternative approaches where required.


This project goes beyond the mere understanding of the organisational culture, rather it focuses on introducing intervention (e.g. help employees take ownership in choosing their own career paths). It allows near run-time data driven analysis and assessment of the impact of cultural interventions and allows readjustment or pivoting for the organisational leaders and decision makers. 

The proposed tool improves the state of current practice in four ways:

1) our machine learning –based tool utilises NLP approaches to automate the organisational cultural assessment process and significantly reduces the effort /time to administer assessment tools and manually analyse their results

2) makes use of large amounts of non-sensitive, publicly available corporate data, organisational material such as policy documents, incentive programs and/or data on the internal social media platforms e.g. FB workplace, Yammer etc.)

3) makes recommendations for cultural interventions for positive cultural change

4) unlike existing approaches, our tool allows combining and comparing the findings from the traditional approaches to assess organisational culture and interventions to gain deeper insights


1.               Garg, V., Managing Organizational Culture and Shaping Human Resources Priorities During COVID 19. The Future of Service Post-COVID-19 Pandemic, Volume 2: Transformation of Services Marketing, 2021: p. 1-25.

2.               Tai, D.B.G., et al., The disproportionate impact of COVID-19 on racial and ethnic minorities in the United States. Clinical Infectious Diseases, 2021. 72(4): p. 703-706.

3.               Taras, V., J. Rowney, and P. Steel, Half a century of measuring culture: Review of approaches, challenges, and limitations based on the analysis of 121 instruments for quantifying culture. Journal of International Management, 2009. 15(4): p. 357-373.

4.               Glendon, A.I. and N.A. Stanton, Perspectives on safety culture. Safety science, 2000. 34(1-3): p. 193-214.

5.               L’heureux, A., et al., Machine learning with big data: Challenges and approaches. Ieee Access, 2017. 5: p. 7776-7797.

6.               Zhou, L., et al., Machine learning on big data: Opportunities and challenges. Neurocomputing, 2017. 237: p. 350-361.

7.               Pandey, S. and S.K. Pandey, Applying natural language processing capabilities in computerized textual analysis to measure organizational culture. Organizational Research Methods, 2019. 22(3): p. 765-797.

8.               Grimmer, J., We are all social scientists now: How big data, machine learning, and causal inference work together. PS: Political Science & Politics, 2015. 48(1): p. 80-83.

9.               Ashri, R., The AI-Powered Workplace: How Artificial Intelligence, Data, and Messaging Platforms Are Defining the Future of Work. 2020: Springer.

10.             Sivarajah, U., et al., Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 2017. 70: p. 263-286.

11.             Bail, C.A., The cultural environment: Measuring culture with big data. Theory and Society, 2014. 43(3-4): p. 465-482.

12.             Coleman, J., Six components of a great corporate culture. Harvard business review, 2013. 5(6): p. 2013.

13.             Belling, S., Design Thinking with Agile, in Succeeding with Agile Hybrids. 2020, Springer. p. 109-117.

Required knowledge

Solid Programming skills in ML

Basic knowledge of Deep Learning,  Natural Language Processing (NLP) and Neural Networks.

Able to work with, critically review and analyse machine learning and ethical AI literature