Using Partial Least Squares Structural Equation Modeling (PLS-SEM) in Quality Management

Call for papers for: The TQM Journal

This special issue will open for submissions: 1st October 2021
The deadline for submission to this special issue is: 30th November 2021
 

GUEST EDITORS

Francesca Magno, University of Bergamo, Italy, [email protected]

Fabio Cassia, University of Verona, Italy, [email protected]

Christian M. Ringle, Hamburg University of Technology (TUHH), Germany, and University of Waikato, New Zealand, [email protected]

 

MOTIVATION AND AIM OF THE SPECIAL ISSUE

The partial least squares structural equation modeling (PLS-SEM; Hair et al. 2017; Hair et al. 2018) is well established across business research disciplines. PLS-SEM offers multiple benefits to researchers such as managing small samples, estimating complex models, and balancing prediction and explanations (Hair et al. 2019; Tenenhaus 2008). In business research, the method is particularly suitable for success factor research or for exploring the sources of competitive advantages (Albers 2010; Hair et al. 2012).

In quality management studies, few researchers have also begun to successfully exploit the potentials of PLS-SEM for their analyses to obtain relevant results (e.g., Turkyilmaz et al. 2010). In addition, a number of important methodological PLS-SEM developments have recently been introduced that further expand the method’s usefulness for quality management. These advances include, for example, the combination of PLS-SEM and the necessary condition analysis (Richter et al. 2020), prediction oriented model assessment (Shmueli et al. 2016; Shmueli et al. 2019), predictive model comparison and selection (Liengaard et al. 2020; Sharma et al. 2019), PLS-SEM in agent-based simulation (Schubring et al. 2016), uncovering unobserved heterogeneity by latent class segmentation (Becker et al. 2013; Sarstedt et al. 2017), moderation and multigroup analysis (Hair et al. 2018), importance-performance map analysis (Ringle and Sarstedt 2016), mediation (Nitzl et al. 2016), and higher-order constructs (Sarstedt et al. 2019). This broad analysis portfolio makes PLS-SEM a valuable method to address research questions which are fundamental to quality management, such as, identifying the antecedents and outcomes of customer perceived quality.

However, a comprehensive exposition of PLS-SEM’s application in quality management research has not yet been carried out. This is a significant research gap compared to other disciplines where such a discussion has already begun to make the most of the PLS-SEM’s capabilities. The goal of this special issue is to present the use of advanced PLS-SEM methods in quality management and to introduce them to a wider audience. It is also intended to contribute to expanding the methodological toolbox of quality management research and thus making it more effective. Overall, this special issue aims to shed new light on how the application of advanced PLS-SEM methods can enrich both existing theories and business practices in the quality management discipline.

 

POTENTIAL TOPICS

Potential topics include, but are not limited to:

- Applications of (advanced) PLS-SEM methods to predict key outcome constructs in the context of total quality management and related topics (e.g. lean management, six sigma, new technology adoption) and predictive results assessment

- Model comparison and selection for decision making in quality management (e.g., to predict quality outcomes such as satisfaction, loyalty and word-of-mouth)

- Use of nonlinear effects, (multiple) mediation, moderation or moderated mediation to uncover complex relationships between quality and other constructs

- Applications of PLS-SEM to understand customers’ and employees’ quality perceptions and wellbeing

- Multi-group analysis and quality perceptions across countries, customer segments, etc.

- Uncovering unobserved heterogeneity by applying latent class procedures (e.g., FIMIX-PLS and PLS-POS)

- Measurement issues in quality management

- Importance-performance map analysis in PLS-SEM to develop more comprehensive management recommendations

- PLS-SEM applications to understand the effects of the costs of quality on the performance of the organization

- Other applications of PLS-SEM in quality management in different industries or countries

 

MANUSCRIPT SUBMISSION AND REVIEW PROCESS

In preparing manuscripts, authors are expected to follow the TQM author guidelines that can be found here. All submissions to be made via the TQM Journal ScholarOne manuscript submission portal: https://mc.manuscriptcentral.com/tqm. Special Issue submission folder on ScholarOne will be open to submissions starting from 1 October 2021.

All submissions will be screened by the guest editors and, if deemed suitable, they will be sent out to a team of reviewers to undergo the usual double-blind peer-review process. The deadline for submission is 30 November 2021.

The publication of this special issue is anticipated in 2022.

 

REFERENCES

Albers, S. (2010). PLS and Success Factor Studies in Marketing. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of Partial Least Squares: Concepts, Methods and Applications  (Springer Handbooks of Computational Statistics Series, vol. II) (pp. 409-425). Heidelberg, Dordrecht, London, New York: Springer.

Becker, J.-M., Rai, A., Ringle, C. M., & Völckner, F. (2013). Discovering Unobserved Heterogeneity in Structural Equation Models to Avert Validity Threats. MIS Quarterly, 37(3), 665-694.

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2 ed.). Thousand Oaks, CA: Sage.

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to Use and How to Report the Results of PLS-SEM. European Business Review, 31(1), 2-24.

Hair, J. F., Sarstedt, M., Pieper, T. M., & Ringle, C. M. (2012). The Use of Partial Least Squares Structural Equation Modeling in Strategic Management Research: A Review of Past Practices and Recommendations for Future Applications. Long Range Planning, 45(5-6), 320-340.

Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2018). Advanced Issues in Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks, CA: Sage.

Liengaard, B., Sharma, P. N., Hult, G. T. M., Jensen, M. B., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2020). Prediction: Coveted, Yet Forsaken? Introducing a Cross-validated Predictive Ability Test in Partial Least Squares Path Modeling. Decision Sciences, forthcoming.

Nitzl, C., Roldán, J. L., & Cepeda Carrión, G. (2016). Mediation Analysis in Partial Least Squares Path Modeling: Helping Researchers Discuss More Sophisticated Models. Industrial Management & Data Systems, 119(9), 1849-1864.

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(12), 2243-2267.

Ringle, C. M., & Sarstedt, M. (2016). Gain More Insight from Your PLS-SEM Results: The Importance-Performance Map Analysis. Industrial Management & Data Systems, 116(9), 1865-1886.

Sarstedt, M., Hair, J. F., Cheah, J.-H., Becker, J.-M., & Ringle, C. M. (2019). How to Specify, Estimate, and Validate Higher-order Constructs in PLS-SEM. Australasian Marketing Journal, 27(3), 197-211.

Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017). Treating Unobserved Heterogeneity in PLS-SEM: A Multi-Method Approach. In R. Noonan & H. Latan (Eds.), Partial Least Squares Structural Equation Modeling: Basic Concepts, Methodological Issues and Applications (pp. 197-217). Heidelberg: Springer.

Schubring, S., Lorscheid, I., Meyer, M., & Ringle, C. M. (2016). The PLS Agent: Predictive Modeling with PLS-SEM and Agent-based Simulation. Journal of Business Research, 69(10), 4604-4612.

Sharma, P. N., Sarstedt, M., Shmueli, G., Kim, K. H., & Thiele, K. O. (2019). PLS-Based Model Selection: The Role of Alternative Explanations in Information Systems Research. Journal of the Association for Information Systems, 20(4).

Shmueli, G., Ray, S., Velasquez Estrada, J. M., & Chatla, S. B. (2016). The Elephant in the Room: Evaluating the Predictive Performance of PLS Models. Journal of Business Research, 69(10), 4552-4564.

Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive Model Assessment in PLS-SEM: Guidelines for Using PLSpredict. European Journal of Marketing, 53(11), 2322-2347.

Tenenhaus, M. (2008). Component-based Structural Equation Modelling. Total Quality Management & Business Excellence, 19(7-8), 871-886.

Turkyilmaz, A., Tatoglu, E., Zaim, S., & Ozkan, C. (2010). Use of Partial Least Squares (PLS) in TQM Research: TQM Practices and Business Performance in SMEs. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of Partial Least Squares: Concepts, Methods and Applications (pp. 605-620). Berlin, Heidelberg: Springer Berlin Heidelberg.