Advances in Necessary Condition Analysis

Jan Dul

First published October 10, 2021
Version 0.1 (First published October 10, 2021)
Version 1.0 (First published March 20, 2022)
Version 1.1 (First published August 2, 2022)
Version 1.2 (First published October 28, 2022; Last update: November 15, 2022)

Suggested reference: Dul, J. (2021). Advances in Necessary Condition Analysis. https://bookdown.org/ncabook/advanced_nca2/.

Welcome

This book addresses a variety of advanced topics in Necessary Condition Analysis (NCA) to support users, readers and reviewers of the method. The book supplements existing methodological publications that describe NCA at the basic level (Dul, 2016b, 2020; Dul, Van der Laan, et al., 2020). Also summaries of the method have appeared in different substantive fields including Human Resource Management (Hauff et al., 2021), Marketing (Dul, Hauff, et al., 2021), Tourism Management (Tóth et al., 2019), Education (Tynan et al., 2020), Creativity (Dul, Karwowski, et al., 2020), Supply Chain Management (Bokrantz & Dul, 2022), Entrepreneurship (Linder et al., 2022), Public Health (Greco et al., 2022), and International Business (Richter & Hauff, 2022).

NCA is an emerging research method that is rapidly entering a variety of research fields in the social, medical and technical sciences. The publication in 2016 of NCA’s core paper (Dul, 2016b), which Bergh et al. (2022) consider a ‘gold standard’ of a methodological contribution, marks the start of NCA. Since then, the number of publications that apply NCA in Web of Science (Clarivate) ranked journals has increased to a total of 62 articles by the end of 2021 (see Figure 0.1).

Figure 0.1: Cumulative number of publications in Web of Science (Clarivate) ranked journals that apply NCA. ‘Q’ stands for the journal quality in terms of category journal rank by impact factor. Q1 = first quartile, etc. (Situation of 31 December 2021).

About a quarter (26%) of the articles use NCA as the sole or primary research method. Another about a quarter (23%) combine NCA with Qualitative Comparative Analysis (QCA). About half (51%) of the articles are multi-method articles that apply NCA in combination with regression based methods like Multiple Linear Regression (MLR), variance-based Structural Equation Modeling (SEM), or Partial-Least-Squares Structural Equation Modeling (PLS-SEM).

Recent examples of articles with NCA as the main method are Bokhorst et al. (2022), Yan et al. (2022), Glück et al. (2022), Frommeyer et al. (2022), Camitan IV & Bajin (2021), Kumar (2021), Tuuli & Rhee (2021), Chaurasia et al. (2020) and Lee & Jeong (2020).

Recent examples of articles that combine NCA with QCA are Luo et al. (2022), Ding (2022), Zheng et al. (2022), Gu et al. (2022), Peng et al. (2022), Huang et al. (2022), Liang et al. (2022), Fredrich et al. (2022), L. Wang et al. (2022), G. Wang et al. (2022), Deng et al. (2022), Eggers et al. (2022), Gantert et al. (2022), Du & Kim (2021), Kopplin & Rösch (2021), Mazumder & Garg (2021), Torres & Godinho (2022), Bouncken, Fredrich, et al. (2020), Bouncken, Ratzmann, et al. (2020) and Delgosha et al. (2021).

Recent examples of articles that combine NCA with regression-based methods are for MLR Yang & Hurmelinna-Laukkanen (2022), Jankowska & Karwowski (2022), Sharma et al. (2022), Costa et al. (2022), Bakır et al. (2022), Jain et al. (2022), Jaiswal & Zane (2022), Klimas et al. (2022), Korman et al. (2022), Kobarg et al. (2020), Karwowski et al. (2020) and Richter, Schlaegel, et al. (2020), for SEM Lee et al. (2022), Lee & Jeong (2021) and Franke & Foerstl (2021), and for PLS-SEM Bolı́var et al. (2022), Afrin & Prybutok (2022), Frezatti et al. (2022), Alhassan & Adam (2022), Yu et al. (2022), Saraf et al. (2022), Prakash et al. (2022), Hayat et al. (2022), Loh et al. (2022), Pinochet et al. (2022), Sukhov et al. (2022), Pangarso et al. (2022), Ortigueira-Sánchez et al. (2022), Cheung et al. (2022), Ortigueira-Sánchez et al. (2022), Pangarso et al. (2022), Koay et al. (2022), Zahrai et al. (2022), Della Corte et al. (2021), Richter et al. (2021), Kopplin & Rausch (2022), Kopplin et al. (2022), Liu et al. (2022) and Lyu et al. (2021). A recent example of combining NCA with both a regression-based method (in this case PLS-SEM) and fsQCA can be found in Ruiz-Equihua et al. (2022).

Several articles that apply NCA did not find a necessary condition (Batey et al., 2021; Golini et al., 2016; Gu et al., 2022; Luo et al., 2022; Peng et al., 2022) but the majority identified at least one. Not finding a necessary condition might be a valuable result, because such result tells that a supposed essential factor for an outcome does not need to be present and its absence can be compensated (e.g., Arenius et al., 2017). On the other hand, finding a large number of potential necessary conditions in an explorative study may not be informative when several conditions do not have a theoretical meaning or are trivial (e.g., Gantert et al., 2022; Klimas et al., 2022; Stek & Schiele, 2021).

NCA can be used in any type of research that aims to find causal relationships between causes and effects. With conventional regression based research methods (e.g, multiple linear regression, structural equation modeling), research questions about cause-effect relationships are often formulated like ‘what is the effect of \(X\) on \(Y\)?’ and related hypotheses similar to ‘\(X\) has a positive (or negative) effect on \(Y\)’. Such causal relationship can be graphically shown as an arrow between \(X\) and \(Y\) indicating a temporal direction: first \(X\), then \(Y\), with a + or - sign shown above the arrow indicating that \(X\) increases or decreases \(Y\). However, the type of causality is usually not specified. The regression equation consisting of terms that are added to produce the effect including an ‘error term’, implicitly assumes additive causal logic and average effects. Consequently, cause \(X\) is assumed to contribute to producing effect \(Y\) on average, and the absence of \(X\)can be compensated by other causes. In other words: \(X\) is a sufficient but not necessary contributor to \(Y\).

However, in NCA, cause \(X\) is assumed to be necessary but not sufficient for effect \(Y\). The research question is formulated as ‘is \(X\) necessary for \(Y\)?’ and the corresponding hypothesis as ‘\(X\) is necessary for \(Y\)’. If \(X\) is necessary for \(Y\), single cause \(X\) can stop the outcome when it is absent or has a low value. NCA does not make a claim about the average contributing effect of necessary causes.

This means that any conventional research question or hypothesis about causal relationships can be revisited from the perspective of necessity. The cause could be necessary (essential) for the effect. It is important to know whether causes are necessary or not, because if any necessary cause is absent, the effect cannot be achieved. In practice this means that acting on other causes than the bottleneck cause has no effect (no compensation possible) and would be a waste of effort.

The entire NCA method consists of three parts:

• Using necessity logic for developing theoretical causal statements (theory, hypotheses).

• Data analysis for calculating necessity parameters.

• Statistical testing for evaluating necessity parameters.

Because NCA is an emerging method, new insights are gained, and extensions are being developed. This book intends to provide the latest insights and developments of NCA. The topics are selected from interactions with the research community at conferences, webinars, workshops and in email exchanges. Some topics are extensively covered, others await elaboration.

This book was first published online in 2021. As the NCA approach is developing, the content of the book is developing as well. The book will therefore remain an online book with a version number. Please, contact the author if you want to react on the book or wish to have new topics covered in it. Your contribution will be acknowledged.

The suggested reference to the book is Dul, J. (2021) Advances in Necessary Condition Analysis. The book is hosted on https://bookdown.org/ncabook/advanced_nca2/.

Version history:

Version 0.1 (October 10, 2021). First published (draft) version. Several sections under construction.

Version 1.0 (March 20, 2022). First full version. All sections available.

Version 1.1 (August 02, 2022). Extended with basic guidelines for good NCA practice.

Version 1.2 (October 28, 2022). Several extensions about necessity theories, combining NCA and regression, interpretation of the bottleneck table, etc.

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Linder, C., Moulick, A. G., & Lechner, C. (2022). Necessary conditions and theory-method compatibility in quantitative entrepreneurship research. Entrepreneurship Theory and Practice, 10422587221102103. https://journals.sagepub.com/doi/10.1177/10422587221102103

Liu, Y., Yu, C., & Damberg, S. (2022). Exploring the drivers and consequences of the “awe” emotion in outdoor sports–a study using the latest partial least squares structural equation modeling technique and necessary condition analysis. International Journal of Sports Marketing and Sponsorship, 23(2), 278–294. https://doi.org/10.1108/IJSMS-12-2020-0232

Loh, X.-M., Lee, V.-H., & Leong, L.-Y. (2022). Mobile-lizing continuance intention with the mobile expectation-confirmation model: An SEM-ANN-NCA approach. Expert Systems with Applications, 117659. https://doi.org/10.1016/j.eswa.2022.117659

Luo, L., Wang, Y., Liu, Y., Zhang, X., & Fang, X. (2022). Where is the pathway to sustainable urban development? Coupling coordination evaluation and configuration analysis between low-carbon development and eco-environment: A case study of the yellow river basin, china. Ecological Indicators, 144, 109473. https://doi.org/10.1016/j.ecolind.2022.109473

Lyu, V. C., Chin, W., Zhang, H., & Liu, V. (2021). Value added or overload? A study of the countervailing effects of non-core features on mobile banking apps. Journal of Consumer Behaviour. https://doi.org/10.1002/cb.2003

Mazumder, S., & Garg, S. (2021). Decoding digital transformational outsourcing: The role of service providers’ capabilities. International Journal of Information Management, 58, 102295. https://doi.org/10.1016/j.ijinfomgt.2020.102295

Ortigueira-Sánchez, L. C., Welsh, D. H., & Stein, W. C. (2022). Innovation drivers for export performance. Sustainable Technology and Entrepreneurship, 1(2), 100013. https://doi.org/10.1016/j.stae.2022.100013

Pangarso, A., Winarno, A., Aulia, P., & Ritonga, D. A. (2022). Exploring the predictor and the consequence of digital organisational culture: A quantitative investigation using sufficient and necessity approach. Leadership & Organization Development Journal. https://doi-org.eur.idm.oclc.org/10.1108/LODJ-11-2021-0516

Peng, B., Zhao, Y., Elahi, E., & Wan, A. (2022). Pathway and key factor identification of third-party market cooperation of china’s overseas energy investment projects. Technological Forecasting and Social Change, 183, 121931. https://doi.org/10.1016/j.techfore.2022.121931

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Ruiz-Equihua, D., Romero, J., Loureiro, S. M. C., & Ali, M. (2022). Human–robot interactions in the restaurant setting: The role of social cognition, psychological ownership and anthropomorphism. International Journal of Contemporary Hospitality Management, ahead-of-print. https://doi.org/10.1108/IJCHM-05-2022-0597

Saraf, P. K., Saha, S., & Anand, A. (2022). Supportive culture and job involvement in public sector: The mediating role of participation in decision making and organizational learning. International Journal of Public Sector Management. https://doi.org/10.1108/IJPSM-09-2021-0218

Sharma, A., Dwivedi, R., Mariani, M. M., & Islam, T. (2022). Investigating the effect of advertising irritation on digital advertising effectiveness: A moderated mediation model. Technological Forecasting and Social Change, 180, 121731. https://doi.org/10.1016/j.techfore.2022.121731

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