The Profit-Purpose Paradox: Responsible Research in the Private Sector

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Jul 14, 2023

When I was first applying to TDL, one of the conversations I had with my doctoral advisor was about what joining the private sector may mean for my career. I was worried about how it would impact my ability to sustain the equity framework that I so valued. 

It’s a question many for-profit organizations ask themselves: how can we sustain a social purpose when we also need to profit? 

Perhaps your purpose is something adjacent to your organization’s main business (like societal leadership or corporate social responsibility). Or perhaps it is something that is deeply baked into your business mission (at TDL, we call social consciousness the S in our SPICE). 

Whatever we call it, businesses of all sizes struggle with the profit–purpose paradox, constantly needing to reconcile sales and service (to communities). 

As a research consultant, I have wondered what it means to create knowledge for social good in an otherwise profit-making, client-driven context. What I have come to realize is that when it comes to knowledge creation, the profit-purpose paradox stems (at least in part) from our limited understanding of the functionalities of social responsibility. Research about communities must be conducted responsibly, not only because responsible research is important in its own right but also because responsible research improves the quality of our outputs and the likelihood of its success in application. 

It is not a matter of reconciling sales through research with service to communities— because, if done well, social good can drive research and innovation. In other words, we can avoid walking the profit–purpose tightrope altogether by allowing our purpose to drive our profit.

So I would like to share more concretely what it has meant, for me, to find the profit in my purpose. The problem (or at least a significant part of it) is too often that of lacking reflexivity and the willingness to center communities in research. 

Behavioral Science, Democratized

We make 35,000 decisions each day, often in environments that aren’t conducive to making sound choices. 

At TDL, we work with organizations in the public and private sectors—from new startups, to governments, to established players like the Gates Foundation—to debias decision-making and create better outcomes for everyone.

More about our services

Some background on TDL

At TDL, we prioritize projects that center communities who are on the margins, working on more than 50 such initiatives since our founding in 2016. Through these projects, we’ve developed tools to debias decision-making, to equitably improve financial health among client users in Europe and North America, and to advance the mental and physical health of underserved communities in Canada, the UK, Nepal, Uganda, Italy, South Africa, and beyond. 

Reflecting on how we are able to reconcile purpose and profit at TDL, I found that centering community through embeddedness, co-construction, and trust-building are key necessary steps for a successful research process and result.1

Reflexivity and Community: The Keys to Responsible Research

We have long known that reflexivity and community ownership in research can allow researchers to center target communities in their work — but in the consulting sphere, we continue to practice both poorly. Reflexivity enables researchers to consider the power dynamics2 that stem from participant vulnerability and interactional dynamics between the researcher and participant (regardless of the topic being researched). These power dynamics can result in inaccurate data collection and/or skewed understandings of the participants' world. All of this is only complicated further when funder interests are not aligned with community priorities, as funders’ confirmation bias may lead them to double down on (inaccurate and biased) findings that dovetail with their existing efforts. 

Similarly, neglecting to confront the question of who owns the research (or who sets the agenda)3 can have devastating ramifications for communities. When research agendas are not set by those who are meant to benefit from the work, research ends up being conducted about communities instead of with communities. In other words, the voices of those who hold a critical perspective on the research at hand and have the most at stake in its outcomes are excluded from the process of conducting it. In research consulting, this can sometimes stem from the clients we choose to engage: perhaps the client does not prioritize social good in the same way, or, more likely still, the target community lacks representation within the client organization. Other times it stems from our own inability to support our clients in the problem framing process: consultancies may be reluctant to really advocate for target communities or to propose truly impactful projects, because they fear losing the client’s business. 

However, the dichotomy of community versus client is often false. For instance, centering target communities in research activities can improve the quality of our research outputs by addressing issues of bias in our research process. And bias, globally, is only growing to manifest in more complicated ways within our increasingly virtual world. 

Consequences of Not Centering Community: Examples from AI

Earlier this year, the Wall Street Journal published an article4 on generative artificial intelligence (AI) technology and the new challenges it is now posing for businesses looking to remove algorithmic bias from their own technologies. This has had profound implications for the health sector5, housing markets6, employment selection procedures7, the financial sector, and even criminal justice systems8 — and many of these issues are not new. For instance, a ProPublica article9 from 2016 reported that an analysis of Northpointe’s AI tool COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) found that Black defendants were more likely to be incorrectly judged by the algorithm as having a higher risk of recidivism than white defendants. 

AI is not innately biased; however, when tools are created by entities that are not inclusive of all the populations they intend to serve and do not center those populations from the onset, biases become baked into the system and end up being further perpetuated by the very tools we create. The question of who owns the research narrative is not just about who should tell the story (i.e., a cry for equity frameworks) but also about who can tell it. If we want our interventions/tools to work, then it stands to reason that they must be designed with a representative sample of our users in mind.

Opportunities to Center Community

Being purpose driven means being community driven. It is caring about impact — more specifically impact that is valued by the communities who are meant to benefit from the work that we are doing. 

Ultimately, what this means is that target communities should be at the center of the entire research process, from agenda-setting to reporting insights. Embedding ourselves in the target communities, co-constructing the entire research process, and building trust with target communities to lift their stories are merely ways our work can be better guided by target communities.

Embeddedness

Embeddedness refers to the researcher actually integrating themselves into the community context. At TDL, we most often do this through stakeholder and user interviews. These conversations give communities the opportunity to frame the problem context through their own perspective, giving us a new lens to work through as researchers. 

Embeddedness also allows us to get connected to members of the wider community (e.g., peripheral users of a particular product or learning communities in school districts), allowing us to embark on an exploratory process with a greater diversity of stakeholders. Sometimes this takes the form of a traditional journey mapping process; other times, it may be something like a usability test. In any case, these are the key opportunities for researchers to explore the world of the target communities, and we leverage them as such. 

Physical presence is not always a requirement to embed ourselves well into target communities, but occasionally, it is important for us to do so. In South Africa, TDL worked with local partners to reduce the harmful effects of substance abuse by leveraging behavioral science for the prevention of problem drinking. This was a project we could have technically conducted virtually (through collaboration with community partners), but we went to the field so that we ourselves could better understand the local context and culture. Through this process, we were able to understand the significant yet nuanced role of social networks, and consequently develop and pilot a digital support program that leveraged local ambassadors. 

Co-construction

Co-construction refers to the process of community authorship via shared decision-making in the research process, so that community members inform “where we look” in our inquiry. A key process through which TDL is able to accomplish this is through leaning on ethnographic interview techniques.10 Be it during usability tests or through semi-structured interviews, we look for opportunities for participants to help guide the interaction, allowing them to take us in any directions they feel are important to their lived realities. 

One example of how we do this is in the structure of our interview questions: we use grand tour questions to allow the participant to stage their environment/circumstances before we follow with any mini tour or probing questions to understand their experiences. 

Additionally, through our analysis process, we allow space for inductive findings that may not have been anticipated by us or our clients. By creating opportunities for target community input (co-construction) at every stage of the research process, communities can shape the story our findings ultimately tell. A great example would be TDL’s recent work with Wellness Together Canada, a free government platform providing phone counseling, peer support, and educational resources about mental health and substance use. We led the design of a companion app for the platform, guided by an iterative co-design process. All of our decisions regarding the app’s content and design were grounded not just in behavioral science, but also in the lived experiences of target communities across Canada, informed by extensive surveying, focus groups, and ongoing collection of user feedback. 

Trust-building

The most obvious but arguably the most difficult task we are learning to do at TDL is to build trust with target communities. Currently, we are working with the Bill & Melinda Gates Foundation to improve U.S. school district leaders’ use of evidence in the learning materials purchasing process. Through interviews with school leaders, it quickly became apparent to us that one of the key drivers and barriers guiding the decision-making of school leaders was trust: (mis)trust of evidence and the organizations who create/curate it or (mis)trust of vendors that sell learning materials/services. 

We began to dig into the literature on trust and were able to understand this target community (and ourselves) better through a typology crafted by McKnight & Chervany (2000).11 Their conceptual analysis identified four trusting beliefs that we believe capture the importance of how we approach building trust with target communities: benevolence (acting in the community interest), integrity (fulfilling our promises), predictability (being consistent in our actions), and competence (building our capacity to do what needs to be done). Two related key processes through which TDL has been working to address these trusting beliefs are 1) increasing the transparency of our processes and our outputs, and 2) prioritizing public benefit whenever possible. In our work with U.S. school districts, the Bill & Melinda Gates Foundation encouraged us to openly share our research process and findings in the education space with all interested parties. In true TDL fashion, we did not just share our work, we reprocessed, reorganized, redesigned, and repackaged it for easier use with the new (broader audience), and we are currently creating an open-access online tool so everyone can benefit from our findings. Transparency about the work that we do and broader public benefit are outcomes we strive for whenever our clients are open and willing.

Putting It Into Practice

I am not here to simply implore you to be more intentional about equity (although: please be more intentional about equity). The added point I am making is that you do not necessarily have to compromise your profit in order to do so. Centering target communities in research can also drive profit because it leads to more rigorous (contextualized) and quality research that then translates to quality (relevant evidence-based) interventions, which in turn will ultimately lead to higher degrees of impact and happier clients.

So, how can you go about concretely implementing this new approach to research? To recap, I would suggest two key strategies: 

1. Incorporate embeddedness and co-construction into the research process

Involve communities from the start and engage them in the planning, goal-setting, designing, interpretation, and analysis process, including being creative about how to ensure they can meaningfully participate (e.g., leveraging local ambassadors). 

  • When working with a client that is based within the target community (or has some insider perspective on the target community), leverage stakeholder interviews as an opportunity to deeply understand the community context and realities. 
  • Take an ethnographic approach to user research by beginning with a more holistic understanding of the user before delving into the target behaviors being explored.
  • Make space for member-checking within the research timeline to validate that the generated insights and the subsequent interventions make sense for (and are beneficial to) target communities.

2. Work at building trust

The status quo has eroded the trust of target communities. Gaining it back will require investing time and effort in building trusting and mutually beneficial relationships, fostering open and transparent communication, actively listening to community perspectives, and demonstrating respect for community knowledge and expertise.

  •  Make research accessible to communities and foster long-term collaborations with them beyond the project. 
  • Be open to feedback from communities, and be ready to reflect on and adapt processes continuously.
  • Highlight your commitment to social good as a mutually beneficial endeavor in order to “call in” your clients and foster trust with target communities.
  • Consistently and proactively work across projects to identify opportunities where insights from research can be widely disseminated and/or used to inform/create public goods, or goods to support target communities. 

Conclusion

Responsible research is often (and very rightfully) discussed as an effort to uphold ethical principles and safeguard the well-being of communities. A secondary but no less salient value is that it enhances the validity and reliability of research results, promotes transparency and accountability, and maintains the integrity of scientific inquiry. In so doing, responsible research can allow us to better serve our clients in the consulting sphere: letting our purpose drive our profit. 

References

1. Internal Note: Hallsworth (2023) recently identified three categories of efforts for applied behavioral science–scope, methods, and values–which align well with these suggestions (i.e., scope = embeddedness, methods = co-construction, values = trust-building).

2. https://www.nature.com/articles/s43586-022-00150-6#ref-CR13

3. https://chicagobeyond.org/researchequity/

4. https://www.wsj.com/articles/rise-of-ai-puts-spotlight-on-bias-in-algorithms-26ee6cc9

5. https://pubmed.ncbi.nlm.nih.gov/37266959/

6. https://themarkup.org/denied/2021/08/25/the-secret-bias-hidden-in-mortgage-approval-algorithms

7. https://www.eeoc.gov/select-issues-assessing-adverse-impact-software-algorithms-and-artificial-intelligence-used

8. https://www.npr.org/2022/02/13/1080464162/lack-of-diversity-in-ai-development-causes-serious-real-life-harm-for-people-of-

9. https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm

10. https://www.google.ca/books/edition/The_Ethnographic_Interview/KZ3lCwAAQBAJ?hl=en&gbpv=0

11. https://www.researchgate.net/publication/254579694_What_is_Trust_A_Conceptual_Analysis_and_an_Interdisciplinary_Model

About the Author

Dr. Maraki Kebede

Maraki is an Education Consultant at The Decision Lab. Her research focuses on social and spatial equity in education globally, and has been featured in peer-reviewed journals, edited volumes, and international conferences. Maraki has worked with several international organizations to craft pathways to empower underserved school-aged children and youth in Africa, including UNESCO, the World Bank, the Institute of International Education, and Geneva Global Inc.

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