CYGNA: Necessary Condition Analysis: What, Why and How?
Reports on our 58th CYGNA meeting where we were enthralled with an emerging powerful methodology that both qualitative and quantitative researchers can use
After a lovely Christmas meeting (CYGNA: Our 4th Christmas meeting - failure & fun) in December and a wonderful on-site meeting on sustainable academic careers organised by the CYGNA North team in January, we decided it was time for a meeting focusing on developing new skills, and more in particular necessary condition analysis (NCA).
We have done quite a few of these skills meetings over the years (e.g. MBTI, big data, social media, social network analysis, negotiating better, secondary data, managing research networks) and it is always great to refresh one's methodological and personal skills toolkit. It is even better to be able to do this in a safe space where there is no such thing as stupid questions.
We were very pleased that 36 CYGNA members joined us to learn about this topic, 32 of which are included in the group picture above; some unfortunately had to leave before this was taken. The meeting included quite a lot of first-timers: Edume Iñigo, Mitaali Katoch, Nada Elnahla, Olivia Tomlinson, Pinkie Mthembu, Ruth Christina Roy, and Siyi Chen. Welcome to CYGNA and we hope it will be an "academic home away from home" for you.
Nicole Franziska Richter (University of Southern Denmark) and Tatiana Andreeva (Maynooth University, Ireland) did an absolutely brilliant job in a flawless duo-presentation to initiate us into the logic of NCA. You can download their full presentation here. It received uniformly positive feedback and the chat was overflowing with ideas and questions.
In this blogpost Tatiana provides you with an overview of the key topics we discussed and resources that may help you if you want to apply NCA to your own study.
What is Necessary Condition Analysis?
You most likely have seen in the literature statements about necessary conditions – e.g., such as “critical success factors”, “prerequisites” or “bottlenecks”. Until recently, we had no method to evaluate these claims. But now we do: It is called Necessary Condition Analysis (NCA). It is applicable to any discipline and can be used by both qualitative and quantitative researchers.
Theorizing first, technicalities follow
One of the questions asked during the session was about the challenges we faced in learning and applying the method. Both Nicole and I agreed that the technicalities of the necessary conditions analysis are quick and easy to learn. For me, the most challenging part was to shift the mindset in theorizing from the sufficiency logic we are so familiar with (e.g., “the more, the better”), to necessity logic.
Indeed, NCA is not just another method of analysing the data, it is – first and foremost – a different way of thinking about your research problem. Unlike the more common, sufficiency way of thinking, necessity logic focuses on predicting the absence of the outcome.
Therefore, at times I found it challenging to integrate existing literature into my theorizing – until I realized that if it uses a different logic to the necessity one, I should not even try to integrate it. To this end, I personally find Table 1 from Richter & Hauff (2022) particularly useful to support developing arguments about necessary conditions.
To illustrate that NCA is first and foremost about theorizing - it can be used as a lens for conceptual or literature review papers. Yes, you do not necessarily have to work with empirical data to use NCA! Liehr & Hauff (2022) provide an interesting example of the NCA-focused literature review.
When to use Necessary Condition analysis?
NCA answers a different type of research questions compared to other methods of data analysis so it can open new avenues in your research and complements rather than substitutes other methods. To illustrate this point, Nicole shared an example of her research that combines NCA with regression-based methods (for details, check Richter & Hauff, 2022 and Richter et al., 2020).
I find Table 1 from Richter et al. (2020) particularly useful to demonstrate how to interpret different scenarios that may emerge from using NCA and regression-based methods in conjunction. NCA can be also used as a stand-alone method – it all depends on the research questions you’d like to explore.
How large should my sample be?
The question about the sample size is one of the frequently asked questions about NCA, especially by qualitative researchers, who typically think that statistical methods of analysis cannot apply to their data due to sample size limitations. The short answer to it – at least one, but the more the better. Yes, it’s not a typo – depending on your research question and context, your sample size can start from one! (see Allard-Poesi & Dul (2023) for an illustration).
The longer answer is that while the method itself does not put any specific limits on the sample size, there are important aspects to consider in your research design, in particular, in your approach to sampling, to ensure that your NCA findings are meaningful and valid. Dul (2024) provides clear guidelines on how to approach these, for both qualitative and quantitative studies.
NCA is not for academia only
What I really appreciate about NCA, is that – because it identifies “bottlenecks” – its results are usually of high relevance and interest for practitioners. Hence, using NCA as a lens helps researchers to speak to practice and deliver meaningful and actionable insights for managers.
Moreover, as NCA is an easy-to-learn and apply tool, it is very accessible for practitioners themselves. For example, NCA website offers a basic NCA calculator that you can do a quick first analysis of your data in seconds, without the need to install or master any specific software and identify where your main roadblocks are.
Resources
Here are several resources on Necessary Condition Analysis that you may find useful:
Core article that describes the method
- Dul, J. (2016). Necessary Condition Analysis (NCA): Logic and methodology of “necessary but not sufficient” causality. Organizational Research Methods, 19(1), 10-52.
Top journal papers on application of NCA in different management fields
- Richter, N. F., & Hauff, S. (2022). Necessary conditions in international business research–advancing the field with a new perspective on causality and data analysis. Journal of World Business, 57(5), 101310.
- Hauff, S., Guerci, M., Dul, J., van Rhee, H. (2020). Exploring Necessary Conditions in HRM Research: Fundamental Issues and Methodological Implications. Human Resource Management Journal, 31(1), 18-36
Step-by-step guidelines / tutorials on how to apply the method
- A guideline that helps to combine NCA with regression based methods, illustrated with a PLS-SEM model: Richter, N. F., Schubring, S., Hauff, S., Ringle, C. M., & Sarstedt, M. (2020). When predictors of outcomes are necessary: Guidelines for the combined use of PLS-SEM and NCA. Industrial Management & Data Systems, 120, 2243-2267.
- A tutorial to perform a NCA either as a stand-alone or in combination with regression-based methods in the software SmartPLS: Richter, N. F., Hauff, S., Ringle, C. M., Sarstedt, M., Kolev, A. E., & Schubring, S. (2023). How to apply necessary condition analysis in PLS-SEM. In H. Latan, J. F. Hair, & R. Noonan (Eds.), Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications: Springer.
- Dul, J. (2024, in-press): How to sample in Necessary Condition Analysis (NCA). European Journal of International Management.
Examples of using NCA in a less common way
- For a single case study: Allard-Poesi, F. & Dul, J. (2023) A method for unraveling the complexity of single disaster cases: Lessons for “normal” functioning. European Management Review, 1–18.
- For a literature review: Liehr, J., Hauff, S. (2022). Must have or nice to have? Necessary leadership competencies to enable employees’ innovative behaviour, International Journal of Innovation Management, 26:10
Related video
Related pages
- About Cygna - Background on the CYGNA network
- Quick overview - Overview of presenations in our meetings with linked slidedecks
- Meetings - Information about forthcoming CYGNA meetings, and links to prior years
- Membership - Information for and about the Cygna network membership
- Readings and inspirations - Inspirational readings and resources for female academics
- The Cygna charter - Documents our CYGNA charter
- Cygna videos - Repository of introduction videos of our CYGNA coordination team
- Cygna history - Tracing the history of our network since 2014, includes links to all of our meetings
- Frequently asked questions - Everything you may want to know about the CYGNA network and more
- The SWAN project - Initiated by Christa Sathish and Clarice Santos and implemented by Jacqueline Leon Ribas, this project created two swans reflecting CYGNA’s equal, inclusive, collective identity and the diversity of the network and its members
- Conference meet-ups - Provides brief write-ups of CYGNA conference meet-ups
- 10-year Anniversary event - Programme page for our 10-year anniversary event
- International Women's Day - Our collection of posts for international women's day
Copyright © 2024 Tatiana Andreeva. All rights reserved. Page last modified on Thu 5 Sep 2024 06:57
Tatiana Andreeva is Associate Professor in Management and Organizational Behavior at the School of Business at the Maynooth University, Ireland. She served as a Research Director for the School 2018-2023. Her research addresses the challenges of managing knowledge in organizations. For example, Tatiana seeks to understand why people share or hide knowledge (and why they don’t), and what managers can do to facilitate (or prevent) these behaviours. Her ongoing research projects examine the effects of the shift to hybrid work on knowledge sharing and collaboration in organisations – what challenges companies face and how to address them. Tatiana is also interested in gender aspects of knowledge behaviours. Tatiana teaches a range of organisational behaviour, knowledge management, evidence-based management, and research methods topics, including a PhD course on “Research problems, literature reviews and theory building in business and management research”.
