nach oben

Quality & Quantity

Erschienen in:

22.01.2018

Partial least squares path modeling: Quo vadis?

verfasst von: Jörg Henseler

Erschienen in: Quality & Quantity | Ausgabe 1/2018

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Excerpt

Partial least squares (PLS) path modeling is a multivariate statistical technique that relies on an alternating least squares algorithm as invented by Wold (1974). It is regarded as the “most fully developed and general system” (McDonald 1996, p. 240) among variance-based estimators for structural equation modeling, and it is applied across a wide range of disciplines, including information systems research (Marcoulides and Saunders 2006) and marketing (Hair et al. 2012). In its most modern appearance, it can be regarded as a structural equation modeling (SEM) technique that can handle various forms of construct operationalizations, including reflective measurement and composite models (for a distinction, see Rigdon 2012; Henseler 2017). …

Nächster Artikel Partial least squares path modeling using ordinal categorical indicators

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Antonakis, J., Bendahan, S., Jacquart, P., Lalive, R.: On making causal claims: a review and recommendations. Leadersh. Q. 21(6), 1086–1120 (2010). https://doi.org/10.1016/j.leaqua.2010.10.010 CrossRef

Benitez, J., Ray, G., Henseler, J.: Impact of information technology infrastructure flexibility on mergers and acquisitions. MIS Q. 42(1), 25–43 (2018)CrossRef

Cepeda Carrión, G., Henseler, J., Ringle, C.M., Roldán, J.L.: Prediction-oriented modeling in business research by means of PLS path modeling. J. Bus. Res. 69(10), 4545–4551 (2016). https://doi.org/10.1016/j.jbusres.2016.03.048 CrossRef

Dijkstra, T.K., Henseler, J.: Linear indices in nonlinear structural equation models: best fitting proper indices and other composites. Qual. Quant. 45(6), 1505–1518 (2011). https://doi.org/10.1007/s11135-010-9359-z CrossRef

Dijkstra, T.K., Henseler, J.: Consistent and asymptotically normal PLS estimators for linear structural equations. Comput. Stat. Data Anal. 81(1), 10–23 (2015a). https://doi.org/10.1016/j.csda.2014.07.008 CrossRef

Dijkstra, T.K., Henseler, J.: Consistent partial least squares path modeling. MIS Q. 39(2), 297–316 (2015b)CrossRef

Dijkstra, T.K., Schermelleh-Engel, K.: Consistent partial least squares for nonlinear structural equation models. Psychometrika 79(4), 585–604 (2014). https://doi.org/10.1007/S11336-013-9370-0 CrossRef

Fassott, G., Henseler, J., Coelho, P.S.: Testing moderating effects in PLS path models with composite variables. Ind. Manag. Data Syst. 116(9), 1887–1900 (2016). https://doi.org/10.1108/IMDS-06-2016-0248 CrossRef

Fornell, C.: A national customer satisfaction barometer: the swedish experience. J. Market. 56(1), 6–21 (1992). https://doi.org/10.2307/1252129 CrossRef

Fornell, C., Johnson, M.D., Anderson, E.W., Cha, J., Bryant, B.E.: The American customer satisfaction index: nature, purpose, and findings. J. Market. 60(4), 7–18 (1996). https://doi.org/10.2307/1251898 CrossRef

Fricker, S., Kreisler, C., Tan, L.: An exploration of the application of pls path modeling approach to creating a summary index of respondent burden. In: JSM Proceedings, pp 4141–4155 (2012)

Goodhue, D.L., Thompson, R.L.: Task-technology fit and individual performance. MIS Q. 19(2), 213–236 (1995)CrossRef

Hair, J.F., Ringle, C.M., Sarstedt, M.: PLS-SEM: indeed a silver bullet. J. Market. Theory Pract. 18(2), 139–152 (2011). https://doi.org/10.2753/MTP1069-6679190202 CrossRef

Hair, J.F., Sarstedt, M., Ringle, C.M., Mena, J.A.: An assessment of the use of partial least squares structural equation modeling in marketing research. J. Acad. Market. Sci. 40(3), 414–433 (2012). https://doi.org/10.1007/s11747-011-0261-6 CrossRef

Henseler, J.: Bridging design and behavioral research with variance-based structural equation modeling. J. Advert. 46(1), 178–192 (2017). https://doi.org/10.1080/00913367.2017.1281780 CrossRef

Henseler, J., Fassott, G.: Testing moderating effects in PLS path models: an illustration of available procedures. In: Esposito Vinzi, V., Chin, W.W., Henseler, J., Wang, H. (eds.) Handbook of Partial Least Squares: Concepts, Methods and Applications, chap 30, pp 713–735. Springer, Berlin (2010). https://doi.org/10.1007/978-3-540-32827-8_31

Henseler, J., Sarstedt, M.: Goodness-of-fit indices for partial least squares path modeling. Comput. Stat. 28(2), 565–580 (2013). https://doi.org/10.1007/s00180-012-0317-1 CrossRef

Henseler, J., Ringle, C.M., Sinkovics, R.R.: The use of partial least squares path modeling in international marketing. In: Sinkovics, R.R., Ghauri, P.N. (eds.) Advances in International Marketing, vol 20, pp. 277–320. Emerald, Bingley (2009). https://doi.org/10.1108/S1474-7979(2009)0000020014

Henseler, J., Fassott, G., Dijkstra, T.K., Wilson, B.: Analysing quadratic effects of formative constructs by means of variance-based structural equation modelling. Eur. J. Inf. Syst. 21(1), 99–112 (2012). https://doi.org/10.1057/ejis.2011.36 CrossRef

Henseler, J., Dijkstra, T.K., Sarstedt, M., Ringle, C.M., Diamantopoulos, A., Straub, D.W., David, Ketchen J., Hair, J.F., Hult, G.T.M., Calantone, R.J.: Common beliefs and reality about PLS: comments on Rönkkö & Evermann (2013). Org. Res. Methods 17(2), 182–209 (2014). https://doi.org/10.1177/1094428114526928 CrossRef

Henseler, J., Ringle, C.M., Sarstedt, M.: A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Market. Sci. 43(1), 115–135 (2015). https://doi.org/10.1007/s11747-014-0403-8 CrossRef

Henseler, J., Hubona, G., Ray, P.A.: Using PLS path modeling in new technology research: updated guidelines. Ind. Manag. Data Syst. 116(1), 2–20 (2016). https://doi.org/10.1108/IMDS-09-2015-0382 CrossRef

Lazarsfeld, P., Henry, N.: Latent Structure Analysis. Houghton Mifflin, Boston (1968)

Marcoulides, G., Saunders, C.: PLS: a silver bullet? MIS Q. 30(2), iii–ix (2006)

McDonald, R.P.: Path analysis with composite variables. Multivar. Behav. Res. 31(2), 239–270 (1996). https://doi.org/10.1207/s15327906mbr3102_5 CrossRef

Nitzl, C., Roldán, J.L., Cepeda, G.: Mediation analyses in partial least squares structural equation modeling: helping researchers discuss more sophisticated models. Ind. Manag. Data Syst. 116(9), 1849–1864 (2016). https://doi.org/10.1108/IMDS-07-2015-0302 CrossRef

Petrarca, F., Terzi, S.: The Global Competitiveness Index: an alternative measure with endogenously derived weights. Qual. Quant. (forthcoming). https://doi.org/10.1007/s11135-017-0655-8

Reinartz, W., Haenlein, M., Henseler, J.: An empirical comparison of the efficacy of covariance-based and variance-based SEM. Int. J. Res. Market. 26(4), 332–344 (2009). https://doi.org/10.1016/j.ijresmar.2009.08.001 CrossRef

Rigdon, E.E.: Rethinking partial least squares path modeling: in praise of simple methods. Long Range Plan. 45(5–6), 341–358 (2012). https://doi.org/10.1016/j.lrp.2012.09.010 CrossRef

Rigdon, E.E.: Choosing PLS path modeling as analytical method in European management research: a realist perspective. Eur. Manag. J. 34(6), 598–605 (2016). https://doi.org/10.1016/j.emj.2016.05.006 CrossRef

Rigdon, E.E., Sarstedt, M., Ringle, C.M.: On comparing results from CB-SEM and PLS-SEM: five perspectives and five recommendations. Market. ZFP 39(3), 4–16 (2017). https://doi.org/10.15358/0344-1369-2017-3-4 CrossRef

Rönkkö, M., Evermann, J.: A critical examination of common beliefs about partial least squares path modeling. Organ. Res. Methods 16(3), 425–448 (2013). https://doi.org/10.1177/1094428112474693 CrossRef

Sarstedt, M., Ringle, C.M.: Treating unobserved heterogeneity in PLS path modelling: a comparison of FIMIX-PLS with different data analysis strategies. J. Appl. Stat. 37(8), 1299–1318 (2010). https://doi.org/10.1080/02664760903030213 CrossRef

Shmueli, G., Ray, S., Estrada, J.M.V., Chatla, S.B.: The elephant in the room: predictive performance of PLS models. J. Bus. Res. 69(10), 4552–4564 (2016). https://doi.org/10.1016/j.jbusres.2016.03.049 CrossRef

Sohn, S.Y., Moon, T.H.: Structural equation model for predicting technology commercialization success index (TCSI). Technol. Forecast. Soc. Change 70(9), 885–899 (2003). https://doi.org/10.1016/S0040-1625(03)00004-0 CrossRef

Sohn, S.Y., Han, H.K., Jeon, H.J.: Development of an Air Force Warehouse Logistics Index to continuously improve logistics capabilities. Eur. J. Oper. Res. 183(1), 148–161 (2007). https://doi.org/10.1016/j.ejor.2006.09.073 CrossRef

Tenenhaus, M., Esposito Vinzi, V.: PLS regression, PLS path modeling and generalized Procrustean analysis: a combined approach for multiblock analysis. J. Chemom. 19(3), 145–153 (2005). https://doi.org/10.1002/cem.917 CrossRef

Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y.M., Lauro, C.: PLS path modeling. Comput. Stat. Data Anal. 48(1), 159–205 (2005). https://doi.org/10.1016/j.csda.2004.03.005 CrossRef

van Riel, A.C., Henseler, J., Kemény, I., Sasovova, Z.: Estimating hierarchical constructs using consistent partial least squares: the case of second-order composites of common factors. Ind. Manag. Data Syst. 117(3), 459–477 (2017). https://doi.org/10.1108/IMDS-07-2016-0286 CrossRef

Wold, H.O.A.: Nonlinear estimation by iterative least squares procedures. In: David, F.N. (ed.) Research Papers in Statistics: Festschrift for J. Neyman, pp. 411–444. Wiley, New York (1966)

Wold, H.O.A.: Causal flows with latent variables: partings of the ways in the light of NIPALS modelling. Eur. Econ. Rev. 5(1), 67–86 (1974). https://doi.org/10.1016/0014-2921(74)90008-7 CrossRef

Wold, H.O.A.: Model construction and evaluation when theoretical knowledge is scarce: theory and application of partial least squares. In: Kmenta, J., Ramsey, J.B. (eds.) Evaluation of Econometric Models, pp. 47–74. Academic Press, Cambridge (1980)CrossRef

Wold, H.O.A.: Soft modeling: the basic design and some extensions. In: Jöreskog, K.G., Wold, H.O. (eds.) Systems Under Indirect Observation, vol. 2, pp. 1–54. North-Holland, Amsterdam (1982)

Wold, H.O.A.: Systems analysis by partial least squares. In: Nijkamp, P., Leitner, H., Wrigley, N. (eds.) Measuring the Unmeasurable, pp. 221–251. Lancaster, Martinus Nijhoff, Dordrecht (1985)CrossRef

Wold, H.O.A.: Introduction to the second generation of multivariate analysis. In: Theoretical Empiricism: A General Rationale for Scientific Model-building, Paragon House, New York (1989)

Titel: Partial least squares path modeling: Quo vadis?
verfasst von: Jörg Henseler
Publikationsdatum: 22.01.2018
Verlag: Springer Netherlands
Erschienen in: Quality & Quantity / Ausgabe 1/2018
Print ISSN: 0033-5177
Elektronische ISSN: 1573-7845
DOI: https://doi.org/10.1007/s11135-018-0689-6

Springer Professional

Excerpt

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 1/2018

Spatially varying associations between creative worker concentrations and social diversity in Shenzhen, China

Turkish newspapers’ peace journalism exam: Gezi Park protests

The effects of employment promotion measures on labour market participation of disabled people: the case of Italy

Firm performance: taxonomy of European companies using self-organizing maps

Partial least squares path modeling using ordinal categorical indicators

What matters most: importance-performance matrix analysis of the factors influencing international postgraduate students’ psychological and sociocultural adaptations

Premium Partner