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Measuring Mutuality and Cultural Diversity

March 15, 2022
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In this week’s blog, we look at measuring mutuality. Based on a research paper written by Cultural Infusion’s Chief Technology Officer, Rezza Moieni, it reflects on and compares an organisation’s diversity to their communities’ diversity to ultimately enhance their business performance. The paper contributes to the practice of embracing cultural diversity and unlocking its benefits.  

A wide range of thoughts, perspectives and new approaches to problem solving are prominent in organisations that are attempting to be culturally diverse. To make the most of the benefits of diversity, an organisation needs to be competent across three dimensions: diversity, inclusion and mutuality.

 Diversity refers to the variety of attributes possessed by those in the workplace, including their spoken language, ethnicity, worldviews, education level, and so on. Inclusion is the extent to which fair and equitable opportunities are received. Mutuality, on the other hand, is the degree to which the diversity of an organisation reflects the diversity of its customer base.  

The success of a business is based on its ability to serve the needs of its customer base. Recent research indicates that clients are more likely to trust staff from their own communities, where staff member’s language skills, networks and understanding of global cultural environments are leveraged to ensure greater appreciation and better responses to what their community needs (Healthwest, 2020). 

A substantial gap in knowledge about how diversity is measured has resulted in analytical aspects of diversity being significantly neglected. According to the 2016 Census by the Australian Bureau of Statistics, Australia is growing increasingly diverse, where 33.4 percent of the population were found to be born in countries other than Australia, and 27.3 percent were non-English speakers. Optimising a two-way relationship between an organisation and its customer base has immense potential to radically improve their business performance.

Exploring the healthcare and retail industries provides justification for the need to enhance diversity in the workplace. 

Healthcare organisations can be examined in relation to how care to their patients is provided, and how clients trust organisations in return. A drop in patient satisfaction has occurred over the last decade due to cultural barriers, issues of ethical distrust, social injustice, and marginalisation in nurse-patient interactions (Morey, 2018). 

Customers and clients are more likely to trust staff from their own cultural communities and feel comfortable coming to the organisation for care, leading to improvements in all kinds of health outcomes (Healthwest, 2020). Understanding the demographics of the community that an organisation serves and accordingly diversifying its workforce is an essential strategy to minimise health disparities and achieve equity through an enhanced access to quality health care for all populations (Williams et al., 2014). 

In a retail setting, a workforce with diversity in key areas that reflects its customer base will significantly increase sales and improve customer loyalty. For instance, Americans with disabilities have an estimated $544 billion in disposable income, and organisations that employ workers with disabilities will have greater insight into the services and products that fit the needs of the specific customer base. 

However, these aspects have been analytically neglected due to a substantial gap in the knowledge about how diversity is measured within organisations, and little is known about the relative merits of various diversity initiatives.

Insightful data into diversity allows the organisation to ensure the early identification of issues and accordingly help to efficiently leverage scarce resources to areas that will benefit most.  Costly outcomes may be avoided and the organisation can make accurate data-driven decisions such as tracking progress, exploring business opportunities and meeting targets. The goal of this research is to design an empirical formula for the Mutuality Index to measure the mutuality of an organisation by benchmarking its diversity directly against that of its community. 

We now understand that mutuality is the degree of diversity matching between two objects. The extent of diversity matching can be estimated by measuring the similarity between them. 

To discover the overall mutuality index of any organisation, the degree of similarity is to be determined across all the four pillars of diversity being country of birth, ethnicity, worldview and language of the employees in an organisation against the customers in their client community. Usually, in the machine learning world, the similarity score is measured in the range of [0, 1]. 

Therefore, there can be two main components of similarity, represented by the following formula:

If x = organisation and y = client community, 

Similarity = 1 if x = y     

Similarity = 0 if x ≠ y 

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I examined various methods, the most suitable determined to be the cosine methodology to determine mutuality. Using the formula above, cosθ represents mutuality, x represents one of the four pillars of the organisation while y represents that of the client community.

Country of Birth Organisation Client Community Case 1  Client Community Case 2 Client Community Case 3
Australia  50 500 70 
India   20 200 100 
China   10 100 300 250 
Mutuality (0-1)
Comment Perfect mutualityVery low mutualityModerate mutuality

The table above provides a sample set of statistics to validate the feasibility of the Cosine Similarity, focusing on one specific pillar of diversity, the country of birth. The ‘Index of Mutuality’ is determined by implementing the relevant values for each case study into the formula above.

For example, to obtain the perfect mutuality score of 1, the formula is solved:

With Diversity Atlas, when the following details and the respective Mutuality Index score are exported to our comprehensive data visualisation tool, intuitive and aesthetically pleasing dashboards are generated automatically. Therefore, this data-driven approach can then help organisations become better at measuring, understanding, tracking, and delivering more informed diversity strategies which eventually enhances their business performance and leads to a stronger bottom line.

Our data-driven approach puts forward that business performance can be improved using the cosine similarity formula to determine the Mutuality Index score and level of synchronicity between an organisation and its community. This analytical score helps your organisation to track their current position in terms of workforce mutuality and understanding how much they can improve by adjusting the recruitment strategy accordingly. 

The focus of this research was on the four main pillars:  country of birth, worldviews, ethnicity and language. In future work, we would want to apply the concept to other demographic fields like disability, gender, level of education, age, among others to ensure the concept of mutuality is continuingly broadened.

I would like to extend a thank you to Jane Felstead and Tahlia Jankovski for editing my research in to this blog post. For more information on the Mutuality Index, please read my full research paper, ‘An Analytical Approach to Measure the Cultural Diversity Mutuality between two Communities’.

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