With Great Transparency Comes Great Discovery: Open Science for Marketing Research

Written by Carolina Cuervo-Robert, Ph.D. candidate at Toulouse School of Management (France)

In the ever-evolving world of marketing research, the quest for useful truths and transparency has never been more critical. As researchers, we strive to uncover insights that can drive business decisions, shape consumer behaviour, and make a meaningful impact. But this journey is fraught with challenges: data confidentiality, replication difficulties, and pressure to produce flawless results. The solution? Enter Open Science, the transformative force here to save the day and revolutionize how we conduct and share research.

source: https://acf.gov/opre/project/open-science-portfolio

Our Messy Research Landscape

Marketing research is messy. The real world doesn’t conform to neat experiments or provide clean, unambiguous data. Researchers face data complexity, ambiguity, proprietary information, and the challenge of ensuring both rigor and relevance in their work. One major hurdle is Data sharing. Confidential or sensitive datasets make replication difficult, which threatens the credibility and applicability of our findings into the “real world”. That’s where Open Science steps in.

Open Science to the Rescue!

Open Science isn’t just a buzzword; it’s a game-changer. At its core, Open Science is about transparency, collaboration, and making research accessible. It involves sharing not just data, but the entire research process, from hypotheses to results. This approach not only enhances research credibility, it also fosters a culture of collaboration and learning, thereby empowering researchers to tackle big, bolder questions.

IJRM has been a trailblazer in promoting Open Science in all marketing subfields. Even though this trend has been primarily adopted by quantitative researchers, qualitative researchers can implement it and benefit from it too, particularly through the documentation of research materials (Haven et al. 2020). This is especially valuable as the journal’s editorial team emphasizes the importance of transparency and encourages authors to be open about their choices, even if at the end of the process, perfection eludes us.

As Koen Pauwels, Editor-in-Chief, puts it,

“The real world and its problems are messy, so limiting the research scope runs the risk of getting perfect what does not matter much.”

Koen Pauwels

Open Science allows researchers to embrace this messiness while maintaining rigor and relevance.

But let’s be honest -adopting Open Science practices can feel like an uphill battle. Researchers often see it as extra work that isn’t always rewarded. It can feel time-consuming, and the academic system doesn’t always recognize these efforts. It’s no wonder some researchers hesitate to dive in. But here’s the thing: the benefits far outweigh the effort.

A Win-Win: The Benefits of Open Science for Researchers

Yes, Open Science requires some extra steps, but the payoff is huge. Imagine being able to revisit your work years later and understand not only what you did, but how and why you did it.

As Susanne shared,

“I need to understand what I did six months ago. It's much easier if I have everything commented and labelled.” 

Susanne Adler

Thorough documentation isn’t just a gift to your future self -it’s a gift to the community, making your work more accessible and reproducible.

Then there’s the magic of Registered Reports, a format that supports experimental and exploratory work. By submitting your study design and methodology for review, you can get feedback and even conditional acceptance based on the strength of your research question and methods. This means you can focus on asking bold, innovative questions without the pressure to produce “perfect” results. It’s a win-win for researchers and science alike.

Finally, let’s add the Open Science Elasticity Initiative, launched by IJRM. A project allowing researchers to explore how different analytical choices impact research outcomes using the same dataset.

Recommendations for Researchers

So, how can you embrace Open Science and become a research superhero? Here are some key recommendations:

1. Be Transparent About Your Choices: Document your research process, including how and when data was collected, which variables were considered, and any unexpected findings. Transparency isn’t just good science, it’s good practice.

2. Consider Pre-Registration: For empirical research, pre-registration can be a game-changer. It helps organize your thoughts, aligns your team, and ensures your research process is transparent.

3. Leverage Synthetic Data: For confidential datasets, synthetic versions can be a lifesaver, preserving statistical properties while protecting sensitive information (Arora et al. 2024; Sarstedt et al. 2023). To ensure transparency, don’t forget to explain how the data was generated, including models, algorithms and assumptions.

4. Engage with Registered Reports: If you’re working on a high-impact study, a Registered Report is a fantastic way to get feedback early in the process and shift the focus from the results to the strength of your research question and methods.

5. Iterate and Improve: As Lachlan puts it,

“We shouldn’t view it as an on-off switch, where today I flick the switch and I’m fully an open science researcher and I’m doing everything” 

Lachlan Deer

Open Science isn’t an all-or-nothing proposition. Start small and gradually incorporate more Open Science practices into your workflow. Every step counts.

6. Remember the big picture: our findings can influence managers and regulatory agencies. If our research isn’t robust or credible, how can it shape strategy or policy? Open science, at its core, is about building trust in scientific research.  

The Future of (Open Science in) Marketing Research

The future of marketing research is bright, and Open Science is leading the charge. Tools like generative AI are already making it easier to document research, create synthetic data, and disseminate findings (e.g., Hayes, 2025; Peres et al. 2023). These innovations aren’t just about saving time, they’re also about making research more accessible, transparent, and impactful.

The journey toward Open Science has its challenges. It requires a cultural shift, a willingness to embrace imperfection, and a commitment to transparency. But as Susanne, Lachlan and Koen have argued, the rewards are worth the effort. By embracing Open Science, we can enhance the credibility and the positive impact of research.

So, what are you waiting for? Whether it’s pre-registering a study, sharing your data, or simply documenting your code, every small action brings us closer to a more open, transparent, and collaborative future in marketing research. Open Science is here to save the day—let’s makes the most of it.

Read the paper

Interested in learning how to implement Open Science in your research? Read the full paper here.

Want to cite the paper?

L. Deer, S. J. Adler, H. Datta, N. Mizik and M. Sarstedt, Toward Open Science in Marketing Research, International Journal of Research in Marketing, 42(1).

References for a deeper dive into the topic

N. Arora, I. Chakraborty, Y. Nishimura, AI–Human Hybrids for Marketing Research: Leveraging Large Language Models (LLMs) as Collaborators, Journal of Marketing, DOI: 10.1177/00222429241276529

T. L. Haven, T. M. Errington, K. S. Gleditsch, L. van Grootel, A. M. Jacobs, F. G. Kern, R. Piñeiro, F. Rosenblatt, L. B. Mokkink, Preregistering Qualitative Research: A Delphi Study, International Journal of Qualitative Methods, DOI: 10.1177/1609406920976417

A. S. Hayes, “Conversing” With Qualitative Data: Enhancing Qualitative Research Through Large Language Models (LLMs), International Journal of Qualitative Methods, DOI: 10.1177/16094069251322346

R. Peres, M. Schreier, D. Schweidel, A. Sorescu, On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice, International Journal of Research in Marketing, https://doi.org/10.1016/j.ijresmar.2023.03.001

M. Sarstedt, S. J. Adler, L. Rau, & B. Schmitt (2024). Using large language models to generate silicon samples in consumer and marketing research: Challenges, opportunities, and guidelines. Psychology and Marketing, 41(6), 1254–1270. https://doi.org/10.1002/mar.21982

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Meet Susanne Adler and Lachlan Deer

Lachlan Deer, Assistant professor at Tilburg University

Susanne Adler, Postdoctoral fellow at Ludwig Maximilians University

If you were not a marketing researcher, what would you be?

Lachlan:

One option would be that I’d have my own bakeshop somewhere, and I’d bake and have coffee, and I would just be like some jolly baker with a store. The alternative version of that would be that I’d have a farm doing the same thing, and I would have cows, sheep, and crops. That feels like something that the more I think about it the more I wish I did.

Susanne:

To me it would also be the farm thing. I used to ride horses when I was a kid, so I would probably have some horses and cats and other animals. This is actually my emergency plan in case I don’t want to be a researcher anymore. I would like to live without an internet connection.

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This article was written by

Carolina Cuervo-Robert

Ph.D. candidate at Toulouse School of Management (France)