Call for submissions - IJMR special edition on AI in market research
09 July 2024

The International Journal of Market Research (IJMR), which is available to MRS Certified members, has issued a call for articles for a special edition on the topic of 'Artificial Intelligence in Market Research & Insight', to be published mid 2025.

More information about the IJMR is available here.

The growing power and availability of Artificial Intelligence tools has the potential to revolutionise consumer insight (Ma & Sun, 2020). For example, the use of tools to analyse and/or summarise datasets to speed up the analysis process and remove inconsistencies in human interpretation. Another example is the generation of ‘synthetic’ datasets to address the problems of data quality such as addressing survey non-response and other forms of bias (Marr, 2023).

Seen through this lens AI has the potential to solve many of the inefficiencies and structural difficulties impacting market research. AI powered tools have the potential to not only speed up the analysis of data, but make it more practical to systematically analyse new forms of unstructured data such as video or audio. In fact, AI may potentially democratise market research and consumer insights by increasing their accessibility and adoption by a wider range of people including small businesses, startups, and individual entrepreneurs (Kopalle et al., 2022).

Whilst these tools may be already available, recent advances in AI reduce the barriers for entry in applying these technologies. Improvements in ‘feeling AI’ that are able to integrate the emotional context in which research is taking place (Huang & Rust, 2023) suggest that AI has the potential to generate entirely new research techniques. For instance, new conversational survey interfaces are becoming available in the market, effectively altering participants’ behaviour in terms of breadth of writing and speed of survey completion (Zehnle & Hildebrand, 2021)

And yet, with so much potential there are also pitfalls (Hunkenschroer and Luetge, 2022). The hype around AI has grown concerns over “AI washing” where the label of AI is being applied to existing analytical or predictive techniques. Given the opacity over many current generative AI technologies there is a lack of agency over how they work and how the quality of the outputs can be evaluated (Rai, 2020).

Developing bespoke AI technologies requires access to high quality datasets which is challenging given the widespread issues around data quality impacting consumer research. More fundamentally, the ways in which AI is being adopted are challenging many of the core ethical, legal and regulatory principals that underpin research and important concerns arise over fairness and privacy (Wirtz et al., 2023).

Scope: This special issue seeks to explore key applications and implication of Artificial Intelligence specifically in the context of the practice of market research and insight.

Conceptual and/ or methodological contributions that offer insight into this area are welcomed by the Special Issue Editors. The following is a non-exhaustive list of potential themes for this Special Issue: 

• Techniques for use of AI tools in analysis of data
• Advances and use of AI in market research methodologies
• Use and generation of synthetic data sets
• Use of AI in unstructured data sets
• The role of machine learning in predictive consumer behavior models
• The use of natural language processing on qualitative and sentiment analysis in market research
• Limitations of AI in market research
• Ethical and regulatory challenges with AI in market research
• AI and Fairness in market research
• Consumer privacy and data security in AI-enabled market research
• Consumer/research participant perceptions of AI-enabled market research
• Impact of AI on research participants behaviours in market research 

Submission Details:

Authors wishing to propose an article for the special issues should initially send an abstract (no more than 500 words) to billy.sung@curtin.edu.au and leo.paas@auckland.ac.nz by 31th October 2024. Authors should include one or more bullet points identifying which theme(s) they are addressing, whether or not they are covered by the above list. Authors will be informed in late November 2024 if their abstract has been selected to be invited to progress further.

Full papers will then be subjected to a double-blind review. Papers submitted must not have been published, accepted for publication, or presently be under consideration for publication elsewhere. Those full manuscripts that are successful after the review process will then be included in the Special Issue. Abstracts for both full papers and research notes are welcome. Details of word length and article formats are available at https://journals.sagepub.com/author-instructions/MRE

This Special Issue will be published in mid 2025.

 Special Issue Editors:

Leo Paas, University of Auckland, New Zealand
Valentina Pitardi, University of Surrey, UK
Billy Sung, Curtin University Australia,
Trixie Cartwright, Ipsos, UK

General queries about the journal, submission systems and formats can be sent to ijmr.queries@sagepub.com

References

Huang, M. & Rust, R. (2023). A Framework for Collaborative Artificial Intelligence in Marketing. Journal of Retailing 98(2), 209-223.

Hunkenschroer, A. L., and Luetge, C. (2022). Ethics of AI-enabled recruiting and selection: A review and research agenda. Journal of Business Ethics, 178(4), 977-1007.

Kopalle, P. K., Gangwar, M., Kaplan, A., Ramachandran, D., Reinartz, W., & Rindfleisch, A. (2022). Examining artificial intelligence (AI) technologies in marketing via a global lens: Current trends and future research opportunities. International Journal of Research in Marketing, 39(2), 522-540.

Ma, L., & Sun, B. (2020). Machine learning and AI in marketing–Connecting computing power to human insights. International Journal of Research in Marketing, 37(3), 481-504.

Marr (2023). The Amazing Ways Snowflake Uses Generative AI For Synthetic Data And Natural Language Queries. https://www.forbes.com/sites/bernardmarr/2023/09/12/the-amazing-ways-snowflake-uses-generative-ai-for-synthetic-data-and-natural-language-queries/?sh=453d71ec236c

Rai, A. (2020). Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science, 48, 137-141.

Wirtz, J., Kunz, W. H., Hartley, N., & Tarbit, J. (2023). Corporate digital responsibility in service firms and their ecosystems. Journal of Service Research, 26(2), 173-190.

Zehnle, M., & Hildebrand, C. (2021, November). Less is more? How Conversational Interfaces Alter Survey Outcomes. In TMS Proceedings 2021. PubPub.