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Partial least squares structural equation modeling in HRM research

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References

  • Abdi, H., Chin, W. W., Esposito Vinzi, V., Russolillo, G., & Trinchera, L. (2016). New perspectives in partial least squares and related methods. In Springer proceedings in mathematics & statistics. New York, NY: Springer.
  • Aguirre-Urreta, M. I., & Rönkkö, M. (in press). Statistical inference with PLSc using bootstrap confidence intervals. MIS Quarterly.
  • 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 (Vol. II, pp. 409–425). Heidelberg: Springer.10.1007/978-3-540-32827-8
  • Ali, F., Rasoolimanesh, S. M., Sarstedt, M., Ringle, C. M., & Ryu, K. (in press). An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research. International Journal of Contemporary Hospitality Management.
  • Almond, P., & Gonzalez Menendez, M. C. (2012). Cross-national comparative human resource management and the ideational sphere: A critical review. The International Journal of Human Resource Management, 25, 2591–2607.
  • Avkiran, N. K., & Ringle, C. M. (2018). Partial least squares structural equation modeling: Recent advances in banking and finance. Heidelberg: Springer.
  • Baluch, A. M., Salge, T. O., & Piening, E. P. (2013). Untangling the relationship between HRM and hospital performance: The mediating role of attitudinal and behavioural HR outcomes. The International Journal of Human Resource Management, 24, 3038–3061.10.1080/09585192.2013.775027
  • Banks, G. C., & Kepes, S. (2015). The influence of internal HRM activity fit on the dynamics within the “black box”. Human Resource Management Review, 25, 352–367.10.1016/j.hrmr.2015.02.002
  • Beatson, A., Lings, I., & Gudergan, S. P. (2008). Service staff attitudes, organisational practices and performance drivers. Journal of Management & Organization, 14, 168–179.
  • Becker, J.-M., Rai, A., & Rigdon, E. E. (2013). Predictive validity and formative measurement in structural equation modeling: Embracing practical relevance. In 2013 Proceedings of the International Conference on Information Systems, Milan.
  • 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, 665–694.10.25300/MISQ
  • Bello-Pintado, A. (2015). Bundles of HRM practices and performance: Empirical evidence from a Latin American context. Human Resource Management Journal, 25, 311–330.10.1111/hrmj.2015.25.issue-3
  • Bollen, K. A., & Diamantopoulos, A. (2017). In defense of causal-formative indicators: A minority report. Psychological Methods, 22, 581–596.10.1037/met0000056
  • Boyd, B. K., Haynes, K. T., Hitt, M. A., Bergh, D. D., & Ketchen, D. J. (2012). Contingency hypotheses in strategic management research. Journal of Management, 38, 278–313.10.1177/0149206311418662
  • Brunetto, Y., Teo, S. T. T., Shacklock, K., & Farr-Wharton, R. (2012). Emotional intelligence, job satisfaction, well-being and engagement: Explaining organisational commitment and turnover intentions in policing. Human Resource Management Journal, 22, 428–441.10.1111/hrmj.2012.22.issue-4
  • Buonocore, F., & Russo, M. (2013). Reducing the effects of work–family conflict on job satisfaction: The kind of commitment matters. Human Resource Management Journal, 23, 91–108.10.1111/hrmj.2013.23.issue-1
  • Cassel, C., Hackl, P., & Westlund, A. H. (1999). Robustness of partial least-squares method for estimating latent variable quality structures. Journal of Applied Statistics, 26, 435–446.10.1080/02664769922322
  • Cenfetelli, R. T., & Bassellier, G. (2009). Interpretation of formative measurement in information systems research. MIS Quarterly, 33, 689–708.10.2307/20650323
  • Cepeda Carrión, G., Henseler, J., Ringle, C. M., & Roldán, J. L. (2016). Prediction-oriented modeling in business research by means of PLS path modeling: Introduction to a JBR special section. Journal of Business Research, 69, 4545–4551.10.1016/j.jbusres.2016.03.048
  • Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–358). Mahwah: Erlbaum.
  • Chin, W. W. (2003). PLS-Graph 3.0. Houston: Soft Modeling.
  • Chin, W. W. (2010). How to write up and report PLS analyses. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares (Vol. II, pp. 655–690). Heidelberg: Springer.10.1007/978-3-540-32827-8
  • Chin, W. W., & Dibbern, J. (2010). A permutation based procedure for multi-group PLS analysis: Results of tests of differences on simulated data and a cross cultural analysis of the sourcing of information system services between Germany and the USA. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares (Vol. II, pp. 171–193). Heidelberg: Springer.10.1007/978-3-540-32827-8
  • Chowhan, J. (2016). Unpacking the black box: Understanding the relationship between strategy, HRM practices, innovation and organizational performance. Human Resource Management Journal, 26, 112–133.10.1111/hrmj.v26.2
  • Cliff, N. (1983). Some cautions concerning the application of causal modeling methods. Multivariate Behavioral Research, 18, 115–126.10.1207/s15327906mbr1801_7
  • Delery, J. E., & Doty, D. H. (1996). Modes of theorizing in strategic human resource management: Tests of universalistic, contingency, and configurations. performance predictions. Academy of Management Journal, 39, 802–835.10.2307/256713
  • Diamantopoulos, A., Sarstedt, M., Fuchs, C., Wilczynski, P., & Kaiser, S. (2012). Guidelines for choosing between multi-item and single-item scales for construct measurement: A predictive validity perspective. Journal of the Academy of Marketing Science, 40, 434–449.10.1007/s11747-011-0300-3
  • Diamantopoulos, A., & Siguaw, J. A. (2006). Formative versus reflective indicators in organizational measure development: A comparison and empirical illustration British Journal of Management, 17, 263–282.10.1111/bjom.2006.17.issue-4
  • Diamantopoulos, A., & Winklhofer, H. M. (2001). Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research, 38, 269–277.10.1509/jmkr.38.2.269.18845
  • Dijkstra, T. K., & Henseler, J. (2015). Consistent and asymptotically normal PLS estimators for linear structural equations. Computational Statistics & Data Analysis, 81, 10–23.10.1016/j.csda.2014.07.008
  • Doty, D. H., & Glick, W. H. (1994). Typologies as a unique form of theory building: Toward improved understanding and modeling. Academy of Management Review, 19, 230–251.
  • Edwards, J. R. (2001). Multidimensional constructs in organizational behavior research: An integrative analytical framework. Organizational Research Methods, 4, 144–192.10.1177/109442810142004
  • Esposito Vinzi, V., Chin, W. W., Henseler, J., & Wang, H. (2010). Handbook of partial least squares. Springer Handbooks of Computational Statistics Series. Vol. II. Heidelberg: Springer.10.1007/978-3-540-32827-8
  • Fleetwood, S., & Hesketh, A. (2008). Theorising under‐theorisation in research on the HRM‐performance link. Personnel Review, 37, 126–144.10.1108/00483480810850506
  • Garson, G. D. (2016). Partial least squares regression and structural equation models. Asheboro: Statistical Associates.
  • Gefen, D., Rigdon, E. E., & Straub, D. W. (2011). Editor’s comments: An update and extension to SEM guidelines for administrative and social science research. MIS Quarterly, 35, iii–xiv.10.2307/23044042
  • Gefen, D., & Straub, D. (2005). A practical guide to factorial validity using PLS-graph: Tutorial and annotated example. Communications of the AIS, 16, 91–109.
  • Goodhue, D. L., Lewis, W., & Thompson, R. (2012). Does PLS have advantages for small sample size or non-normal data? MIS Quarterly, 36, 981–1001.
  • Gudergan, S. P., Ringle, C. M., Wende, S., & Will, A. (2008). Confirmatory tetrad analysis in PLS path modeling. Journal of Business Research, 61, 1238–1249.10.1016/j.jbusres.2008.01.012
  • Haenlein, M., & Kaplan, A. M. (2004). A beginner’s guide to partial least squares analysis. Understanding Statistics, 3, 283–297.10.1207/s15328031us0304_4
  • Hahn, C., Johnson, M. D., Herrmann, A., & Huber, F. (2002). Capturing customer heterogeneity using a finite mixture PLS approach. Schmalenbach Business Review, 54, 243–269.10.1007/BF03396655
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis. Harlow: Pearson.
  • Hair, J. F., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117, 442–458.10.1108/IMDS-04-2016-0130
  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks, CA: Sage.
  • Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017). Mirror, mirror on the wall: A comparative evaluation of composite-based structural equation modeling methods. Journal of the Academy of Marketing Science, 45, 616–632.10.1007/s11747-017-0517-x
  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. The Journal of Marketing Theory and Practice, 19, 139–152.10.2753/MTP1069-6679190202
  • Hair, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM). European Business Review, 26, 106–121.10.1108/EBR-10-2013-0128
  • 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, 320–340.10.1016/j.lrp.2012.09.008
  • 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.
  • Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40, 414–433.10.1007/s11747-011-0261-6
  • Harvey, C., Kelly, A., Morris, H., & Rowlinson, M. (2010). Academic journal quality guide. London: The Association of Business Schools.
  • Henseler, J. (2017). Bridging design and behavioral research with variance-based structural equation modeling. Journal of Advertising, 46, 178–192.10.1080/00913367.2017.1281780
  • Henseler, J., & Chin, W. W. (2010). A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Structural Equation Modeling: A Multidisciplinary Journal, 17, 82–109.10.1080/10705510903439003
  • 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. Organizational Research Methods, 17, 182–209.10.1177/1094428114526928
  • Henseler, J., & Fassott, G. (2010). Testing moderating effects in PLS path models: An illustration of available procedures. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares (Vol. II, pp. 713–735). Heidelberg: Springer.10.1007/978-3-540-32827-8
  • Henseler, J., Hubona, G. S., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116, 1–19.
  • 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, 115–135.10.1007/s11747-014-0403-8
  • Henseler, J., Ringle, C. M., & Sarstedt, M. (2016). Testing measurement invariance of composites using partial least squares. International Marketing Review, 33, 405–431.10.1108/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 & P. N. Ghauri (Eds.), Advances in international marketing (pp. 277–320). Bingley: Emerald.10.1108/S1474-7979(2009)0000020014
  • Henseler, J., & Sarstedt, M. (2013). Goodness-of-fit indices for partial least squares path modeling. Computational Statistics, 28, 565–580.10.1007/s00180-012-0317-1
  • Innocenti, L., Pilati, M., & Peluso, A. M. (2011). Trust as moderator in the relationship between HRM practices and employee attitudes. Human Resource Management Journal, 21, 303–317.10.1111/hrmj.2011.21.issue-3
  • Jöreskog, K. G. (1978). Structural analysis of covariance and correlation matrices. Psychometrika, 43, 443–477.10.1007/BF02293808
  • Jöreskog, K. G., & Wold, H. O. A. (1982). The ML and PLS techniques for modeling with latent variables: Historical and comparative aspects. In H. O. A. Wold & K. G. Jöreskog (Eds.), Systems under indirect observation, part I (pp. 263–270). Amsterdam: North-Holland.
  • Kaufmann, L., & Gaeckler, J. (2015). A structured review of partial least squares in supply chain management research. Journal of Purchasing and Supply Management, 21, 259–272.10.1016/j.pursup.2015.04.005
  • Kock, N., & Hadaya, P. (2018).  Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, 28 (1), 227–261.
  • Konradt, U., Warszta, T., & Ellwart, T. (2013). Fairness perceptions in web-based selection: Impact on applicants’ pursuit intentions, recommendation intentions, and intentions to reapply. International Journal of Selection and Assessment, 21, 155–169.10.1111/ijsa.2013.21.issue-2
  • Latan, H., & Noonan, R. (2017). Partial least squares path modeling. Heidelberg: Springer.10.1007/978-3-319-64069-3
  • Lings, I., Beatson, A., Gudergan, S. (2008). The impact of implicit and explicit communications on frontline service delivery staff. The Service Industries Journal, 28(10), 1431–1443.
  • Lohmöller, J.-B. (1989). Latent variable path modeling with partial least squares. Heidelberg: Physica.10.1007/978-3-642-52512-4
  • Lowry, P. B., & Gaskin, J. E. (2014). Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it. IEEE Transactions on Professional Communication, 57, 123–146.10.1109/TPC.2014.2312452
  • Marcoulides, G. A., & Chin, W. W. (2013). You write, but others read: Common methodological misunderstandings in PLS and related methods. In H. Abdi, W. W. Chin, V. Esposito Vinzi, G. Russolillo, & L. Trinchera (Eds.), New perspectives in partial least squares and related methods (pp. 31–64). New York, NY: Springer.10.1007/978-1-4614-8283-3
  • Marcoulides, G. A., Chin, W. W., & Saunders, C. (2012). When imprecise statistical statements become problematic: A response to Goodhue, Lewis, and Thompson. MIS Quarterly, 36, 717–728.
  • Marcoulides, G. A., & Saunders, C. (2006). PLS: A silver bullet? MIS Quarterly, 30, III–IX.10.2307/25148727
  • Marler, J. H. (2012). Strategic human resource management in context: A historical and global perspective. Academy of Management Perspectives, 26, 6–11.10.5465/amp.2012.0063
  • Martín-Alcázar, F., Romero-Fernández, P. M., & Sánchez-Gardey, G. (2005). Strategic human resource management: Integrating the universalistic, contingent, configurational and contextual perspectives. The International Journal of Human Resource Management, 16, 633–659.10.1080/09585190500082519
  • Martín-Alcázar, F., Romero-Fernández, P. M., & Sánchez-Gardey, G. (2008). Human resource management as a field of research. British Journal of Management, 19, 103–119.10.1111/j.1467-8551.2007.00540.x
  • Mason, C. H., & Perreault, W. D. (1991). Collinearity, power, and interpretation of multiple regression analysis. Journal of Marketing Research, 28, 268–280.10.2307/3172863
  • Mateos-Aparicio, G. (2011). Partial least squares (PLS) methods: Origins, evolution, and application to social sciences. Communications in Statistics – Theory and Methods, 40, 2305–2317.10.1080/03610921003778225
  • Matthews, L. (2018). Applying multi-group analysis in PLS-SEM: A step-by-step process. In H. Latan & R. Noonan (Eds.), Partial least squares structural equation modeling: Basic concepts, methodological issues and applications (pp. 219–243). Heidelberg: Springer.
  • 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, 19–35.10.1016/j.acclit.2016.09.003
  • Nitzl, C., & Chin, W. W. (2017). The case of partial least squares (PLS) path modeling in managerial accounting research. Journal of Management Control, 28, 137–156.10.1007/s00187-017-0249-6
  • Nitzl, C., Roldan, J. L., & Cepeda, G. (2016). Mediation analysis in partial least squares path modeling. Industrial Management & Data Systems, 116, 1849–1864.10.1108/IMDS-07-2015-0302
  • 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, 467–480.10.1016/j.jom.2012.06.002
  • Ramayah, T., Cheah, J., Chuah, F., Ting, H., & Memon, M. A. (2016). Partial least squares structural equation modeling (PLS-SEM) using SmartPLS 3.0: An updated and practical guide to statistical analysis. Singapore: Pearson.
  • Ratzmann, M., Gudergan, S. P., & Bouncken, R. (2016). Capturing heterogeneity and PLS-SEM prediction ability: Alliance governance and innovation. Journal of Business Research, 69, 4593–4603.10.1016/j.jbusres.2016.03.051
  • Reinartz, W. J., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of Research in Marketing, 26, 332–344.10.1016/j.ijresmar.2009.08.001
  • 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, 589–597.10.1016/j.emj.2016.08.001
  • Rigdon, E. E. (2012). Rethinking partial least squares path modeling. In praise of simple methods. Long Range Planning, 45, 341–358.10.1016/j.lrp.2012.09.010
  • Rigdon, E. E. (2014). Rethinking Partial Least Squares Path Modeling: Breaking Chains and Forging Ahead. Long Range Planning, 47, 161–167.
  • Rigdon, E. E. (2016). Choosing PLS path modeling as analytical method in European management research: A realist perspective. European Management Journal, 34, 598–605.10.1016/j.emj.2016.05.006
  • Rigdon, E. E., Becker, J.-M.,& Sarstedt, M. (2017). Equating unobserved conceptual variables and common factors in structural equation models. Working Paper.
  • Rigdon, E. E., Sarstedt, M., & Ringle, C. M. (2017). On comparing results from CB-SEM and PLS-SEM. Five perspectives and five recommendations. Marketing ZFP, 39, 4–16.10.15358/0344-1369-2017-3
  • Ringle, C. M., Sarstedt, M., & Schlittgen, R. (2014). Genetic algorithm segmentation in partial least squares structural equation modeling. OR Spectrum, 36, 251–276.10.1007/s00291-013-0320-0
  • Ringle, C. M., Sarstedt, M., Schlittgen, R., & Taylor, C. R. (2013). PLS path modeling and evolutionary segmentation. Journal of Business Research, 66, 1318–1324.10.1016/j.jbusres.2012.02.031
  • Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). A critical look at the use of PLS-SEM in MIS quarterly. MIS Quarterly, 36, iii–xiv.
  • Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. Bönningstedt: SmartPLS.
  • Ringle, C. M., Wende, S., & Will, A. (2005). SmartPLS 2. Hamburg: SmartPLS.
  • Rönkkö, M., & Evermann, J. (2013). A critical examination of common beliefs about partial least squares path modeling. Organizational Research Methods, 16, 425–448.10.1177/1094428112474693
  • Rönkkö, M., McIntosh, C. N., & Antonakis, J. (2015). On the adoption of partial least squares in psychological research: Caveat emptor. Personality and Individual Differences, 87, 76–84.10.1016/j.paid.2015.07.019
  • Rönkkö, M., McIntosh, C. N., Antonakis, J., & Edwards, J. R. (2016). Partial least squares path modeling: Time for some serious second thoughts. Journal of Operations Management, 47–48, 9–27.10.1016/j.jom.2016.05.002
  • Rosopa, P. J., & Kim, B. (2017). Robustness of statistical inferences using linear models with meta-analytic correlation matrices. Human Resource Management Review, 27, 216–236.10.1016/j.hrmr.2016.09.012
  • Sanders, K., Cogin, J. A., & Bainbridge, H. T. J. (2014). Research methods for human resource management. New York: Routledge.
  • Saridakis, G., Lai, Y., & Cooper, C. L. (2017). Exploring the relationship between HRM and firm performance: A meta-analysis of longitudinal studies. Human Resource Management Review, 27, 87–96.10.1016/j.hrmr.2016.09.005
  • Sarstedt, M., Bengart, P., Shaltoni, A. M., & Lehmann, S. (in press). The use of sampling methods in advertising research: A gap between theory and practice. International Journal of Advertising.
  • 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, 3998–4010.10.1016/j.jbusres.2016.06.007
  • Sarstedt, M., Henseler, J., & Ringle, C. M. (2011). Multi-group analysis in partial least squares (PLS) path modeling: Alternative methods and empirical results. In M. Sarstedt, M. Schwaiger, & C. R. Taylor (Eds.), Advances in international marketing (Vol. 22, pp. 195–218). Bingley: Emerald.
  • Sarstedt, M., & Mooi, E. A. (2014). A concise guide to market research: The process, data, and methods using IBM SPSS statistics. Heidelberg: Springer.10.1007/978-3-642-53965-7
  • Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017). Partial least squares structural equation modeling. In C. Homburg, M. Klarmann, & A. Vomberg (Eds.), Handbook of market research. Heidelberg: Springer. Retrieved from: https://link.springer.com/referenceworkentry/10.1007/978-3-319-05542-8_15-1
  • Sarstedt, M., Ringle, C. M., Henseler, J., & Hair, J. F. (2014). On the Emancipation of PLS-SEM: A Commentary on Rigdon (2012). Long Range Planning, 47, 154–160.
  • Sarstedt, M., Ringle, C. M., Smith, D., Reams, R., & Hair, J. F. (2014). Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers. Journal of Family Business Strategy, 5(1), 105–115.
  • Schlägel, C., & Sarstedt, M. (2016). Assessing the measurement invariance of the four-dimensional cultural intelligence scale across countries: A composite model approach. European Management Journal, 34, 633–649.10.1016/j.emj.2016.06.002
  • 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, 4604–4612.10.1016/j.jbusres.2016.03.052
  • Shah, R., & Goldstein, S. M. (2006). Use of structural equation modeling in operations management research: Looking back and forward. Journal of Operations Management, 24, 148–169.10.1016/j.jom.2005.05.001
  • Sharma, P. N., Sarstedt, M., Shmueli, G., Thiele, K. O., & Kim, K. H. (2017). Model selection in MIS research using PLS-SEM. Working Paper.
  • Shaw, J. D., Dineen, B. R., Fang, R., & Vellella, R. F. (2009). Employee-organization exchange relationships, HRM practices, and quit rates of good and poor performers. Academy of Management Journal, 52, 1016–1033.10.5465/AMJ.2009.44635525
  • Shen, J. (2016). Principles and applications of multilevel modeling in human resource management research. Human Resource Management, 55, 951–965.10.1002/hrm.2016.55.issue-6
  • 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, 4552–4564.10.1016/j.jbusres.2016.03.049
  • Sosik, J. J., Kahai, S. S., & Piovoso, M. J. (2009). Silver bullet or voodoo statistics? Group & Organization Management, 34, 5–36.10.1177/1059601108329198
  • 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, 618–632.10.1016/j.emj.2016.06.003
  • Subramony, M. (2009). A meta-analytic investigation of the relationship between HRM bundles and firm performance. Human Resource Management, 48, 745–768.10.1002/hrm.v48:5
  • Tenenhaus, M., Vinzi, V., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48, 159–205.10.1016/j.csda.2004.03.005
  • Teo, S. T. T., Le Clerc, M., & Galang, M. C. (2011). Human capital enhancing HRM systems and frontline employees in Australian manufacturing SMEs. The International Journal of Human Resource Management, 22, 2522–2538.10.1080/09585192.2011.588034
  • Thommes, K., & Weiland, K. (2010). Explanatory factors for firms’ use of temporary agency work in Germany. European Management Journal, 28, 55–67.10.1016/j.emj.2009.04.003
  • Triguero-Sánchez, R., Peña-Vinces, J. C., & Sánchez-Apellániz, M. (2013). Hierarchical distance as a moderator of HRM practices on organizational performance. International Journal of Manpower, 34, 794–812.10.1108/IJM-03-2012-0046
  • Van de Ven, A. H., & Drazin, R. (1985). The concept of fit in contingency theory. Research in Organisational Behaviour, 7, 333–365.
  • van Riel, A. C. R., Henseler, J., Kemény, I., & Sasovova, Z. (2017). Estimating hierarchical constructs using consistent partial least squares. Industrial Management & Data Systems, 117, 459–477.10.1108/IMDS-07-2016-0286
  • Vermeeren, B., Steijn, B., Tummers, L., Lankhaar, M., Poerstamper, R.-J., & van Beek, S. (2014). HRM and its effect on employee, organizational and financial outcomes in health care organizations. Human Resources for Health, 12, 635.10.1186/1478-4491-12-35
  • Wold, H. O. A. (1985). Partial least squares. In S. Kotz & N. L. Johnson (Eds.), Encyclopedia of statistical sciences (pp. 581–591). New York, NY: Wiley.
  • Wright, P. M., Gardner, T. M., Moynihan, L. M., & Allen, M. R. (2005). The relationship between hr practices and firm performance: Examining causal order. Personnel Psychology, 58, 409–446.10.1111/peps.2005.58.issue-2

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