Protocol: How to deal with Partial Least Squares (PLS) research in Operations Management. A guide for sending papers to academic journals
DOI:
https://doi.org/10.4995/wpom.v10i1.10802Keywords:
PLS, partial least squares, operations management, systematic literature review, protocolAbstract
This work protocol form part of a three-phase publication (Marin-Garcia, 2019). Its objective is to establish a work procedure to answer these questions: 1) in which journals have articles about Operations Management with Partial Least Squares (PLS) been published?; 2) Do the results of previous reviews on this topic still prevail based on the very limited set of journals that it have been conducted (and before substantial modifications were made to report methods of PLS-based studies)?; 3) Do recent articles fulfil report recommendations; 4) What kind of measurement model has been considered for the constructs most frequently used in the selected articles?; 5) What are the usual R2 values in the cross-sectional studies represented in the selected articles?; 6) Within what statistical power range do the relations analysed with PLS fall?
The article summarises current recommendations to select the analysis procedures and to report the research works that have used structural equations with PLS. We believe that this is an excellent contribution for researchers because it helps to improve the analyses and reports that derive from using PLS to, thus, increase the probabilities of them being accepted in relevant journals.
Another contribution made by the present work, apart from establishing the aforementioned protocol, is to provide a list of the recent articles about operations management that have used PLS and the coding procedure to conduct our systematic review (to be subsequently published).
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References
Alfalla-Luque, R., Machuca, J. A. D., & Marin-Garcia, J. A. (2018). Triple-a and competitive advantage in supply chains: Empirical research in developed countries. International Journal of Production Economics, 203(September), 48-61. doi:https://doi.org/10.1016/j.ijpe.2018.05.020
Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An r-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. doi:https://doi.org/10.1016/j.joi.2017.08.007
Ato, M., & Vallejo, G. (2011). Los efectos de terceras variables en la investigación psicológica. Anales de Psicología, 27(2), 550-561.
Avkiran, N. K. (2018). An in-depth discussion and illustration of partial least squares structural equation modeling in health care. Health Care Management Science, 21(3), 401-408. doi:10.1007/s10729-017-9393-7
Becker, J. M., Rai, A., & Rigdon, E. E. (2013a). Predictive validity and formative measurement in structural equation modeling: Embracing practical relevance. Paper presented at the Proceedings of the International Conference on Information Systems, Milan, Italy.
Becker, J. M., Rai, A., Ringle, C. M., & Völckner, F. (2013b). Discovering unobserved heterogeneity in structural equation models to avert validity threats. MIS Quarterly: Management Information Systems, 37(3), 665-694. doi:10.25300/MISQ/2013/37.3.01
Benet-Zepf, A., Marin-Garcia, J. A., & Küster, I. (2018). Clustering the mediators between the sales control systems and the sales performance using the amo model: A narrative systematic literature review. Intangible Capital, 14(3), 387-408. doi:http://dx.doi.org/10.3926/ic.1222
Blome, C., Hollos, D., & Paulraj, A. (2014). Green procurement and green supplier development: Antecedents and effects on supplier performance. International Journal of Production Research, 52(1), 32-49. doi:10.1080/00207543.2013.825748
Bodoff, D., & Ho, S. Y. (2016). Partial least squares structural equation modeling approach for analyzing a model with a binary indicator as an endogenous variable. Communications of the Association for Information Systems, 28(23). doi:10.17705/1CAIS.03823
Bollen, K. A. (2011). Evaluating effect, composite, and causal indicators in structural equation models. Mis Quarterly, 35(2), 359-372. doi:10.2307/23044047
Bollen, K. A., & Bauldry, S. (2011). Three cs in measurement models: Causal indicators, composite indicators, and covariates. Psychological Methods, 16(3), 265-284. doi:10.1037/a0024448
Bond, T. (2008). Tuneable goodness-of-fit statistics. Rasch Measurement Transactions, 22(1), 1156-1157.
Boronat-Soler, T. (2018). Which are the leading journals in human resources management and operations management in the web of science and scopus databases? Design and application of a classification method. WPOM-Working Papers on Operations Management, 9(2), 127-181. doi:10.4995/wpom.v9i2.10763
Cepeda-Carrion, G., Cegarra-Navarro, J.-G., & Cillo, V. (2019). Tips to use partial least squares structural equation modelling (pls-sem) in knowledge management. Journal of Knowledge Management, 23(1), 67-89. doi:doi:10.1108/JKM-05-2018-0322
Champely, S. (2018). Pwr: Basic functions for power analysis. R package version 1.2-2.: https://CRAN.R-project.org/package=pwr
Chin, W. W. (1998). Commentary: Issues and opinion on structural equation modeling. Mis Quarterly, vii-xvi.
Diamantopoulos, A. (2006). The error term in formative measurement models: Interpretation and modeling implications. Journal of modelling in management, 1(1), 7-17. doi:10.1108/17465660610667775
Diamantopoulos, A., & Siguaw, J. A. (2006). Formative versus reflective indicators in organizational measure development: A comparison and empirical illustration. British Journal of Management, 17(4), 263-282. doi:10.1111/j.1467-8551.2006.00500.x
Dijkstra, T. K., & Henseler, J. (2015a). Consistent and asymptotically normal pls estimators for linear structural equations. Computational Statistics & Data Analysis, 81, 10-23. doi:10.1016/j.csda.2014.07.008
Dijkstra, T. K., & Henseler, J. (2015b). Consitent partial least squares path modeling. Mis Quarterly, 39(2), 297-316. https://doi.org/10.25300/MISQ/2015/39.2.02
do Valle, P. O., & Assaker, G. (2016). Using partial least squares structural equation modeling in tourism research: A review of past research and recommendations for future applications. Journal of Travel Research, 55(6), 695–708. doi:https://doi.org/10.1177/0047287515569779
Duarte, P., & Amaro, S. (2018). Methods for modelling reflective-formative second order constructs in pls: An application to online travel shopping. Journal of Hospitality and Tourism Technology, 9(3), 295-313. doi:doi:10.1108/JHTT-09-2017-0092
Eck, N. J. V., & Waltman, L. (2014). Visualizing bibliometric networks (pp. 285-320). https://doi.org/10.1007/978-3-319-10377-8_13
Esposito Vinzi, V., Chin, W. W., Henseler, J., & Wang, H. (2010). Handbook of partial least squares. Concepts, methods and applications. London: Springer. https://doi.org/10.1007/978-3-540-32827-8
Fassott, G., Henseler, J., & P.S, C. (2016). Testing moderating effects in pls path models with composite variables. Industrial Management & Data Systems, 116(9), 1887-1900. doi:10.1108/IMDS-06-2016-0248
Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using g*power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149-1160. doi:10.3758/BRM.41.4.1149
Felipe, C., Roldán, J., & Leal-Rodríguez, A. (2017). Impact of organizational culture values on organizational agility. Sustainability, 9(12), 2354. https://doi.org/10.3390/su9122354
Garfield, E. (2004). Historiographic mapping of knowledge domains literature. Journal of Information Science, 30(2), 119-145. doi:10.1177/0165551504042802
Grace, J. B., & Bollen, K. A. (2008). Representing general theoretical concepts in structural equation models: The role of composite variables. Environmental and Ecological Statistics, 15(2), 191-213. doi:10.1007/s10651-007-0047-7
Gudergan, S. P., Ringle, C. M., Wende, S., & Will, A. (2008). Confirmatory tetrad analysis in pls path modeling. Journal of Business Research, 61(12), 1238-1249. https://doi.org/10.1016/j.jbusres.2008.01.012
Hair, J. F., Hult, G. T., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (pls-sem). 2nd edition. Thousand Oaks: Sage. https://doi.org/10.15358/9783800653614
Hair, J. F., Hult, G. T., Ringle, C. M., Sarstedt, M., Castillo Apraiz, J., Cepeda Carrión, G., & Roldan, J. L. (2019a). Manual de partial least squares structural equation modeling (pls-sem) (2nd ed.). Terrassa, Spain: OmniaScience:.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2012a). Partial least squares: The better approach to structural equation modeling? Long Range Planning, 45(5), 312-319. doi:https://doi.org/10.1016/j.lrp.2012.09.011
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019b). When to use and how to report the results of pls-sem. European Business Review, 31(1), 2-24. doi:doi:10.1108/EBR-11-2018-0203
Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. (2018). Advanced issues in partial least squares structural equation modeling. Los Angeles: SAGE. https://doi.org/10.1007/978-3-319-05542-8_15-1
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. (2012b). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414-433. https://doi.org/10.1007/s11747-011-0261-6
Henseler, J. (2015). Is the whole more than the sum of its parts? On the interplay of marketing and design research: Initial lecture: Universiteit Twente
Henseler, J. (2017a). Bridging design and behavioral research with variance-based structural equation modeling. Journal of Advertising, 46(1), 178-192. doi:10.1080/00913367.2017.1281780
Henseler, J. (2017b). User manual adanco 2.0.1 (1st ed.). Kleve: Composite Modeling GmbH & Co.
Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., . . . Calantone, R. J. (2014). Common beliefs and reality about pls: Comments on ronkko and evermann (2013). Organizational Research Methods, 17(2), 182-209. doi:10.1177/1094428114526928
Henseler, J., Hubona, G., & Ray, P. A. (2016a). Using pls path modeling in new technology research : Updated guidelines. Industrial Management & Data Systems, 116(1), 2-20. doi:10.1108/IMDS-09-2015-0382
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. doi:10.1007/s11747-014-0403-8
Henseler, J., Ringle, C. M., & Sarstedt, M. (2016b). Testing measurement invariance of composites using partial least squares. International Marketing Review, 33(3), 405-431. doi:10.1108/IMR-09-2014-0304
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In R. R. Sinkovics & N. G. Pervez (Eds.), Advances in international marketing (pp. 277-319). Bingley: Emerald. https://doi.org/10.1108/S1474-7979(2009)0000020014
Henseler, J., & Roldan, J. L. (2017). Coping with endogeneity in composite modeling. Paper presented at the Paper presented at 8th Annual Conference of the European Decision Sciences Institute, Granada, Spain.
Hock, C., Ringle, C. M., & Sarstedt, M. (2010). Management of multi-purpose stadiums: Importance and performance measurement of service interfaces. International Journal of Services Technology and Management, 14(2-3), 188-207. doi:10.1504/IJSTM.2010.034327
Hult, G. T. M., Hair, J. F., Proksch, D., Sarstedt, M., Pinkwart, A., & Ringle, C. M. (2018). Addressing endogeneity in international marketing applications of partial least squares structural equation modeling. Journal of International Marketing, 26(3), 1-21. doi:10.1509/jim.17.0151
Illia, L., Sonpar, K., & Bauer, M. W. (2014). Applying co-occurrence text analysis with alceste to studies of impression management. British Journal of Management, 25(2), 352-372. doi:10.1111/j.1467-8551.2012.00842.x
Image. (2015). Alceste 2015. A software for textual data analysis. Windows version. Toulouse: Image.
Jarvis, C.-B., MacKenzie, S.-B., & Podsakoff, P.-M. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30(2), 199-218. https://doi.org/10.1086/376806
Kareem, J. A. H., & Amin, O. (2017). Ethical and psychological factors in 5s and total productive maintenance. Journal of Industrial Engineering and Management-Jiem, 10(3), 444-475. doi:10.3926/jiem.2313
Kaufmann, L., & Gaeckler, J. (2015). A structured review of partial least squares in supply chain management research. Journal of Purchasing and Supply Management, 21(4), 259-272. doi:https://doi.org/10.1016/j.pursup.2015.04.005
Khan, G. F., Sarstedt, M., Shiau, W.-L., Hair, J. F., Ringle, C. M., & Fritze, M. P. (2019). Methodological research on partial least squares structural equation modeling (pls-sem): An analysis based on social network approaches. Internet Research. doi:10.1108/IntR-12-2017-0509
Kumar, D. S., & Purani, K. (2018). Model specification issues in pls-sem: Illustrating linear and non-linear models in hospitality services context. Journal of Hospitality and Tourism Technology, 9(3), 338-353. doi:doi:10.1108/JHTT-09-2017-0105
Latan, H. (2018). Pls path modeling in hospitality and tourism research: The golden age and days of future past Applying partial least squares in tourism and hospitality researchedition: 1chapter: 4. Bingley: Emerald Publishing Limited. https://doi.org/10.1108/978-1-78756-699-620181004
Losilla, J.-M., Oliveras, I., Marin-Garcia, J. A., & Vives, J. (2018). Three risk of bias tools lead to opposite conclusions in observational research synthesis. Journal of Clinical Epidemiology(101), 61-72. doi:10.1016/j.jclinepi.2018.05.021
Marin-Garcia, J. A. (2015). Publishing in two phases for focused research by means of "research collaborations". WPOM-Working Papers on Operations Management, 6(2), 76-80. doi:http://dx.doi.org/10.4995/wpom.v6i2.4459
Marin-Garcia, J. A. (2018). Development and validation of spanish version of fincoda: An instrument for self-assessment of innovation competence of workers or candidates for jobs. WPOM-Working Papers on Operations Management, 9(2), 182-215. doi:10.4995/wpom.v9i2.10800
Marin-Garcia, J. A. (2019). Publishing in three stages to support evidence based practice in om, hrm and teaching&learning innovation. WPOM-Working Papers on Operations Management, 10(2), in press.
Marin-Garcia, J. A., & Alfalla-Luque, R. (2018). Protocol: Is there agreement or disagreement between the absolute and relative impact indices obtained from the web of science and scopus data? WPOM-Working Papers on Operations Management, Vol 9(1), 53-80. doi:10.4995/wpom.v9i1.8989
Marin-Garcia, J. A., Alfalla-Luque, R., & Machuca, J. A. D. (2018). A triple-a supply chain measurement model: Validation and analysis. International Journal of Physical Distribution & Logistics Management, 48(10), 976-994. doi:10.1108/IJPDLM-06-2018-0233
Marin-Garcia, J. A., & Martinez Tomas, J. (2016). Deconstructing amo framework: A systematic review. Intangible Capital, 12(4), 1040-1087. doi:http://dx.doi.org/10.3926/ic.838
Marin-Garcia, J. A., & Mateo Martínez, R. (2013). Barreras y facilitadores de la implantación del tpm. Intangible Capital, 9(3), 823-853. doi:http://dx.doi.org/10.3926/ic.360
Matthews, L. M., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2016). Identifying and treating unobserved heterogeneity with fimix-pls: Part ii – a case study. European Business Review, 28(2), 208-224. doi:doi:10.1108/EBR-09-2015-0095
Medina-López, C., Marin-Garcia, J. A., & Alfalla-Luque, R. (2010). Una propuesta metodológica para la realización de búsquedas sistemáticas de bibliografía (a methodological proposal for the systematic literature review). WPOM-Working Papers on Operations Management, 1(2), 13-30. doi:http://dx.doi.org/10.4995/wpom.v1i2.786
Muller, T., Schuberth, F., & Henseler, J. (2018). Pls path modeling - a confirmatory approach to study tourism technology and tourist behavior. Journal of Hospitality and Tourism Technology, 9(3), 249-266. doi:10.1108/jhtt-09-2017-0106
Nitzl, C. (2016). The use of partial least squares structural equation modelling (pls-sem) in management accounting research: Directions for future theory development. Journal of Accounting Literature, 37, 19-35. doi:https://doi.org/10.1016/j.acclit.2016.09.003
Peng, D. X., & Lai, F. (2012). Using partial least squares in operations management research: A practical guideline and summary of past research. Journal of Operations Management, 30(6), 467-480. https://doi.org/10.1016/j.jom.2012.06.002
Petter, S. (2018). "Haters gonna hate": Pls and information systems research. SIGMIS Database, 49(2), 10-13. doi:10.1145/3229335.3229337
Reinert, M. (1990). Alceste une méthodologie d'analyse des données textuelles et une application: Aurelia de gerard de nerval. Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique, 26(1), 24-54. doi:10.1177/075910639002600103
Reyes, J., Alvarez, K., Martinez, A., & Guaman, J. (2018). Total productive maintenance for the sewing process in footwear. Journal of Industrial Engineering and Management-Jiem, 11(4), 814-822. doi:10.3926/jiem.2644
Richter, N. F., Cepeda, G., Roldán, J. L., & Ringle, C. M. (2016). European management research using partial least squares structural equation modeling (pls-sem). European Management Journal, 34(6), 589-597. doi:https://doi.org/10.1016/j.emj.2016.08.001
Rigdon, E., Sarstedt, M., & Ringle, C. (2017). On comparing results from cb-sem and pls-sem: Five perspectives and five recommendations. ZFP-Journal of Research and Management, 39(3), 4-16. doi:10.15358/0344-1369-2017-3-4
Rigdon, E. E. (2012). Rethinking partial least squares path modeling: In praise of simple methods. Long Range Planning, 45(5–6), 341-358. doi:http://dx.doi.org/10.1016/j.lrp.2012.09.010
Rigdon, E. E. (2014). Rethinking partial least squares path modeling: Breaking chains and forging ahead. Long Range Planning, 47(3), 161-167. doi:http://dx.doi.org/10.1016/j.lrp.2014.02.003
Rigdon, E. E. (2016). Choosing pls path modeling as analytical method in european management research: A realist perspective. European Management Journal, 34(6), 598-605. doi:http://dx.doi.org/10.1016/j.emj.2016.05.006
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. doi:10.1108/IMDS-10-2015-0449
Ringle, C. M., Sarstedt, M., Mitchell, R., & Gudergan, S. P. (2018). Partial least squares structural equation modeling in hrm research. The International Journal of Human Resource Management, 1-27. doi:10.1080/09585192.2017.1416655
Ringle, C. M., Sarstedt, M., & Straub, D. (2012). Editor's comments: A critical look at the use of pls-sem in "mis quarterly". Mis Quarterly, 36(1), ii-xiv. doi:10.2307/41410402
Ringle, C. M., Wende, S., & Becker, J. M. (2015). Smartpls 3. Boenningstedt: SmartPLS GmbH. Available at http://www.smartpls.com
Roberts, N., & Thatcher, J. B. (2009). Conceptualizing and testing formative constructs: Tutorial and annotated example. The DATA BASE for Advances in Information Systems, 4(3), 9-39. https://doi.org/10.1145/1592401.1592405
Roldán, J. L., & Sánchez-Franco, M. J. (2012). Variance-based structural equation modeling: Guidelines for using partial least squares in information systems research. In M. Mora, O. Gelman, A. Steenkamp, & M. Raisinghani (Eds.), Research methodologies, innovations and philosophies in software systems engineering and information systems (pp. 193–221). Hershey PA: IGI Global. https://doi.org/10.4018/978-1-4666-0179-6.ch010
Sanchez-Franco, M. J., Cepeda-Carrion, G., & Roldán, J. L. (2019). Understanding relationship quality in hospitality services: A study based on text analytics and partial least squares. Internet Research, 0(0), in press. doi:doi:10.1108/IntR-12-2017-0531
Sarstedt, M., Hair, J. F., Ringle, C. M., Thiele, K. O., & Gudergan, S. P. (2016). Estimation issues with pls and cbsem: Where the bias lies! Journal of Business Research, 69(10), 3998-4010. doi:http://dx.doi.org/10.1016/j.jbusres.2016.06.007
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017). Treating unobserved heterogeneity in pls-sem: A multi-method approach. In H. Latan & R. Noonan (Eds.), Partial least squares path modeling: Basic concepts, methodological issues and applications (pp. 197-217). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-64069-3_9
Schuberth, F., Henseler, J., & Dijkstra, T. K. (2018a). Confirmatory composite analysis. Frontiers in Psychology, 9(2541), 1-14. doi:10.3389/fpsyg.2018.02541
Schuberth, F., Henseler, J., & Dijkstra, T. K. (2018b). Partial least squares path modeling using ordinal categorical indicators. Quality & Quantity, 52(1), 9-35. doi:10.1007/s11135-016-0401-7
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, in press. https://doi.org/10.17705/1jais.00538
Shmueli, G., Ray, S., Velasquez Estrada, J. M., & Chatla, S. B. (2016). The elephant in the room: Predictive performance of pls models. Journal of Business Research, 69(10), 4552-4564. doi:https://doi.org/10.1016/j.jbusres.2016.03.049
Streukens, S., & Leroi-Werelds, S. (2016). Bootstrapping and pls-sem: A step-by-step guide to get more out of your bootstrap results. European Management Journal, 34(6), 618-632. doi:https://doi.org/10.1016/j.emj.2016.06.003
Tabet, S. M., Lambie, G. W., Jahani, S., & Rasoolimanesh, S. M. (2019a). An analysis of the world health organization disability assessment schedule 2.0 measurement model using partial least squares–structural equation modeling. Assessment, 1073191119834653. doi:10.1177/1073191119834653
Tabet, S. M., Lambie, G. W., Jahani, S., & Rasoolimanesh, S. M. (2019b). The factor structure of outcome questionnaire–45.2 scores using confirmatory tetrad analysis–partial least squares. Journal of Psychoeducational Assessment, 0734282919842035. doi:10.1177/0734282919842035
Urbach, N., & Ahleman, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application, 11(2), 1-39.
Usakli, A., & Kucukergin, K. G. (2018). Using partial least squares structural equation modeling in hospitality and tourism: Do researchers follow practical guidelines? International Journal of Contemporary Hospitality Management, 30(11), 3462-3512. doi:doi:10.1108/IJCHM-11-2017-0753
Voorhees, C., Brady, M., Calantone, R., & Ramirez, E. (2016). Discriminant validity testing in marketing: An analysis, causes for concern, and proposed remedies. Journal of the Academy of Marketing Science, 44(1), 119-134. doi:10.1007/s11747-015-0455-4
Whitt, H. P. (1986). The sheaf coefficient: A simplified and expanded approach. Social Science Research, 15(2), 174-189. doi:https://doi.org/10.1016/0049-089X(86)90014-1
Wulff Barreiro, E. (2007). El uso del software histcite para identificar artículos significativos en búsquedas por materias en la web of science. Documentación de las Ciencias de la Información, 30, 45-64.