Abstract
The Statistical analysis tool is considered an essential tool for the research study in the social science domain. Of which partial least squares structural equation modeling (PLS-SEM) is a second-generation statistical modeling technique to be used for developing theories in exploratory research. That’s why PLS-SEM is considered to be used in the research field of education and ICT to validate a conceptual model of virtual teamwork in the context of online higher education. However, there should have a clear justification for choosing PLS-SEM for particular research. Also, a systematic procedure is needed to apply it to report the analysis and evaluation appropriately. Therefore, this paper mainly aims to present the reasons for choosing PLS-SEM statistical method for evaluating a virtual teamwork model in online higher education, and how to apply this method to evaluate or assess the validity of the model. Though the intent of this paper is to provide a comprehensive guideline about PLS-SEM for evaluating a virtual teamwork model in online higher education it can also be useful for the other research fields. So, the outcome of this paper will help researchers in any field for designing their research who considered applying PLS-SEM in their research study. Also, a new researcher will find it as a comprehensive overview of PLS-SEM, and why and how to apply PLS-SEM in research work.
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References
J.F. Hair et al., Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods. J. Acad. Mark. Sci. 45(5), 616–632 (2017)
C.M. Ringle et al., “Partial least squares structural equation modeling in HRM research.” Int. J. Hum. Res. Manage., (2018)
J.F. Hair et al., An assessment of the use of partial least squares structural equation modeling in marketing research. J. Acad. Mark. Sci. 40, 414–433 (2012)
J.J. Sosik et al., Silver bullet or voodoo statistics? a primer for using the partial least squares data analytic technique in group and organization research. Group Org. Manage. 34(1), 5–36 (2009)
D.X. Peng, F. Lai, “Using partial least squares in operations management research: a practical guideline and summary of past research.” J. Oper. Manage. 30(6), pp. 467–480 (2012)
J.F. Hair et al., 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 Plan. 45, 320–340 (2012)
C. Nitzl, The use of partial least squares structural equation modelling (pls-sem) in management accounting research: directions for future theory development. J. Acc. Lit. 37, 19–35 (2016)
Ringle, C. M., et al. “A critical look at the use of pls-sem in mis quarterly.” MIS Quarterly, 36(1) (2012)
N.F. Richter et al., “European management research using partial least squares structural equation modeling (pls-sem): editorial.” Eur. Manage. J. 34(6), 589–597
N.K. Avkiran, C.M. Ringle (eds.), Partial Least Squares Structural Equation Modeling: Recent Advances in Banking and Finance (Springer, 2018)
W.L. Shiau et al., Editorial: internet research using partial least squares structural equation modeling (pls-sem). Internet Research 29(3), 398–406 (2019)
G.D. Garson, Partial Least Squares Regression and Structural Equation Models (Statistical Associates, Asheboro, 2016)
T. Ramayah et al., Partial least squares structural equation modeling (pls-sem) using smartpls 3.0: an updated and practical guide to statistical analysis, (Pearson, Singapore, 2016)
A.I. Jony, E. Serradell-Lopez, “Key factors that positively affects the effectiveness of virtual teamwork in online higher education.” Rii forum proceedings of research & innovation forum (rii forum) annual conference, Athens, Greece, Springer, 2020
D. Gefen et al., Structural equation modeling and regression: guidelines for research and practice. Commun. Assoc. Inf. Syst. 4(7), 1–70 (2000)
T.F. Golob, Structural equation modeling for travel behavior research. Transp. Res. Part B: Methodol. 37(1), 1–25 (2003)
W.W. Chin, “The partial least squares approach to structural equation modeling.” Modern Methods for Business Research, ed. by G.A. Marcoulides (Mahwah, NJ: Lawrence Erlbaum, 1998), pp. 295–358
J.F. Hair et al., Multivariate data analysis (Prentice Hall, Englewood Cliffs, NJ, 2010)
J.F. Hair et al., Pls-sem: indeed a silver bullet. J. Mark. Theory Pract. 19, 139–151 (2011)
K.G. Jöreskog, “A general method for estimating a linear structural equation system.” Structural Equation Models in the Social Sciences, ed. by A.S. Goldberger, O.D. Duncan (Seminar Press, New York, NY, 1973), pp. 255–284
H.O.A. Wold, “Partial least squares.” Encyclopedia of statistical sciences, ed. by S. Kotz, N.L. Johnson (Wiley, New York, NY, 1985), pp. 581–591
H.O.A. Wold, “Path models with latent variables: the nipals approach.” Quantitative sociology: international perspectives on mathematical and statistical modeling, ed. by H.W. Blalock, A. Aganbegian, F.M. Borodkin, 1975, pp. 307–357
H.O.A. Wold, “Soft modeling: the basic design and some extensions.” Systems under indirect observations: Part ii, ed. by K.G. Jöeskog, H.O.A. Wold (Amsterdam, North-Holland, 1982), pp. 1–54
M. Tenenhaus, Component-based structural equation modeling. Total Qual. Manage. Bus. Excellence 19(7), 871–886 (2008)
G. Mateos-Aparicio, Partial least squares (pls) methods: Origins, evolution, and application to social sciences. Commun. Stat. 40(13), 2305–2317 (2011)
J.F. Hair et al., A Primer on Partial Least Squares Structural Equation Modeling (pls-sem), 2nd edn. (Sage, Los Angeles, 2017)
J. Henseler et al., Common beliefs and reality about partial least squares: comments on rönkkö & evermann (2013). Organ. Res. Methods 17, 182–209 (2014)
M. Sarstedt et al., Estimation issues with PLS and CB-SEM: where the bias lies! J. Bus. Res. 69, 3998–4010 (2016)
E.E. Rigdon, Rethinking partial least squares path modeling: in praise of simple methods. Long Range Plan. 45, 341–358 (2012)
W.W. Chin, P.R. Newsted, Structural Equation Modeling Analysis with Small Samples Using Partial Least Squares, Thousand Oaks (Sage Publications, CA, 1999)
A. Diamantopoulos, The error term in formative measurement models: Interpretation and modeling implications. J. Model. Manage. 1, 7–17 (2006)
M. Tenenhaus et al., Pls path modeling. Comput. Stat. Data Anal. 48(1), 159–202 (2005)
Y.M. Chatelin et al., “State-of-art on pls path modeling through the available software.” HEC research papers series 764, HEC Paris, 2002
J.R. Edwards, R.P. Bagozzi, On the nature and direction of relationships between con structs and measures. Psychol. Methods 5, 155–174 (2000)
A. Diamantopoulos, Incorporating formative measures into covariance-based structural equation models. MIS Q. 35, 335–358 (2011)
A. Powell et al., Virtual teams: a review of current literature and directions for future research. Data Base for Advances in Information Systems 35(1), 6–35 (2004)
R. Bennett, Employers’ demands for personal transferable skills in graduates: a content analysis of 1000 job advertisements and associated empirical study. J. Vocat. Educ. Training 54(4), 457–476 (2002)
M. Sarstedt et al., “Partial least squares structural equation modeling.” Handbook of Market Research, ed. by C. Homburg, M. Klarmann, A. Vomberg (Springer, Heidelberg, 2017)
E.E. Rigdon, Choosing pls path modeling as analytical method in European management research: a realist perspective. Eur. Manage. J. 34(6), 598–605 (2016)
J.F. Hair et al., “When to use and how to report the results of pls-sem.” Eur. Bus. Rev. 3(1), (2018) https://doi.org/10.1108/ebr-11-2018-0203
P. Andreev et al., “Validating formative partial least squares (PLS) models: methodological review and empirical illustration.” ICIS 2009 proceedings, 2009
M.E. Olobatuyi, A User’s Guide to Path Analysis. UPA, 2006
S. Wright, The method of path coefficients. Annal. Math. Stat. 5, 151–215 (1934)
B.S. Everitt, A. Skrondal, The Cambridge Dictionary of Statistics. 4th edn. Cambridge University Press, 2010
Wikipedia. “Coefficient of determination.” 29 April 2020, https://en.wikipedia.org/wiki/Coefficient_of_determination
M. Sarstedt et al., Measuring reputation in global markets: a comparison of reputation measures’ convergent and criterion validities. J. World Bus. 48, 329–339 (2013)
D. Straub et al., Validating guidelines for is positivist research. Commun. Assoc. Inf. Systems 13, 380–427 (2004)
G. James et al., An Introduction to Statistical Learning, 8th edn. (Springer, New York, 2017)
Wikipedia. “Variance Inflation factor.” 8 June 2020, https://en.wikipedia.org/wiki/Variance_inflation_factor
H. Latan, R. Noonan, Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications. Springer, 2017
B. Efron, R. Tibshirani, Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat. Sci. 1, 54–75 (1986)
R.E. Walpole, Probability & Statistics for Engineers & scientists, 7th edn. (Pearson, 2006)
Wikipedia. “T-statistic.” 11 May 2020, https://en.wikipedia.org/wiki/T-statistic
Fisz, M. “Significance Testing.” Probability theory and mathematical statistics. 3rd ed. (John Wiley and Sons, New York, 1963)
Wikipedia. “p-value.” 19 May 2020, https://en.wikipedia.org/wiki/P-value
J. Henseler et al., The use of partial least squares path modeling in international marketing. Adv. Int. Mark. 20, 277–320 (2009)
P.D. Ellis, The Essential Guide to Effect Sizes. Cambridge University Press, 2010
Wikipedia. “Effect size.” 6 June 2020, https://en.wikipedia.org/wiki/Effect_size
S. Cohen, “Statistical Power Analysis for the Behavioral Sciences (Cambridge University Press, Bootstrap methods and their application, Cambridge, UK, 1988)
S. Geisser, A predictive approach to the random effects model. Biometrika 61, 101–107 (1974)
M. Stone, Cross-validatory choice and assessment of statistical predictions. J. Royal Stat. Soc. 36, 111–147 (1974)
H. Apel, H.O.A. Wold, “Soft modeling with latent variables in two or more dimensions: Pls estimation and testing for predictive relevance.” Systems Under Indirect Observation, ed. by K.G. Jöreskog, H.O.A. Wold (Amsterdam, 1982), pp. 209–247
J. Henseler, M. Sarstedt, Goodness-of-fit indices for partial least squares path modeling. Comput. Stat. 28, 565–580 (2013). https://doi.org/10.1007/s00180-012-0317-1
J. Lohmöller, Latent Variable Path Modeling with Partial Least Squares (Physica, Heidelberg, Germany, 1989)
SmartPLS-Documentation. “Model Fit”, SmartPLS, 2020, www.smartpls.com/documentation/algorithms-and-techniques/model-fit
P.M. Bentler, D.G. Bonett, Significance tests and goodness-of-fit in the analysis of covariance structures. Psychol. Bull. 88, 588–600 (1980)
L.T. Hu, P.M. Bentler, Fit indices in covariance structure modeling: sensitivity to under parameterized model misspecification. Psychol. Methods 3, 424–453 (1998)
T.K. Dijkstra, J. Henseler, Consistent and asymptotically normal pls estimators for linear structural equations. Comput. Stat. Data Anal. 81(1), 10–23 (2015)
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This work is part of a Doctoral Thesis funded by and conducted at Universitat Oberta de Catalunya (Barcelona, Spain).
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Jony, A.I., Serradell-López, E. (2021). A PLS-SEM Approach in Evaluating a Virtual Teamwork Model in Online Higher Education: Why and How?. In: Visvizi, A., Lytras, M.D., Aljohani, N.R. (eds) Research and Innovation Forum 2020. RIIFORUM 2020. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-62066-0_17
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