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PLS-SEM for Software Engineering Research: An Introduction and Survey

Russo, Daniel, Stol, Klaas-Jan·2022·ACM Computing Surveys
DOI 10.1145/3447580
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Abstract

Software Engineering (SE) researchers are increasingly paying attention to organizational and human factors. Rather than focusing only on variables that can be directly measured, such as lines of code, SE research studies now also consider unobservable variables, such as organizational culture and trust. To measure such latent variables, SE scholars have adopted Partial Least Squares Structural Equation Modeling (PLS-SEM), which is one member of the larger SEM family of statistical analysis techniques. As the SE field is facing the introduction of new methods such as PLS-SEM, a key issue is that not much is known about how to evaluate such studies. To help SE researchers learn about PLS-SEM, we draw on the latest methodological literature on PLS-SEM to synthesize an introduction. Further, we conducted a survey of PLS-SEM studies in the SE literature and evaluated those based on recommended guidelines.

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Research works printed but not linked: 162

  1. M. I. Aguirre-Urreta and G. M. Marakas . 2008. The use of PLS when analyzing formative constructs: Theoretical analysis and results from simulations . In International Conference on Information Systems. M. I. Aguirre-Urreta and G. M. Marakas. 2008. The use of PLS when analyzing formative constructs: Theoretical analysis and results from simulations. In International Conference on Information Systems.
  2. M. Aguirre-Urreta and M. Rönkkö . 2015 . Sample size determination and statistical power analysis in PLS using R: An annotated tutorial . Commun. Assoc. Inf. Syst. 36 (2015), 3 . M. Aguirre-Urreta and M. Rönkkö. 2015. Sample size determination and statistical power analysis in PLS using R: An annotated tutorial. Commun. Assoc. Inf. Syst. 36 (2015), 3.
  3. 10.1016/j.infsof.2006.05.004 · DOI
  4. 10.1016/j.infsof.2007.10.013 · DOI
  5. 10.1016/j.jss.2006.09.010 · DOI
  6. 10.4103/0972-6748.62274 · DOI · Paper page
  7. A. Barcomb , K.-J. Stol , D. Riehle , and B. Fitzgerald . 2019. Why do episodic volunteers stay in FLOSS communities? In the 41th International Conference on Software Engineering. ACM/IEEE , New York, NY, 948--954. A. Barcomb, K.-J. Stol, D. Riehle, and B. Fitzgerald. 2019. Why do episodic volunteers stay in FLOSS communities? In the 41th International Conference on Software Engineering. ACM/IEEE, New York, NY, 948--954.
  8. 10.1016/j.jss.2018.09.081 · DOI
  9. 10.1016/j.emj.2016.06.009 · DOI
  10. J.-M. Becker A. Rai C. Ringle and F. Volckner. 2013. Discovering unobserved heterogeneity in structural equation models to avert validity threats. MIS Quart. 37 (09 2013) 665--694. J.-M. Becker A. Rai C. Ringle and F. Volckner. 2013. Discovering unobserved heterogeneity in structural equation models to avert validity threats. MIS Quart. 37 (09 2013) 665--694. · DOI · Paper page
  11. J.-M. Becker , C. M. Ringle , and M. Sarstedt . 2018 . Estimating moderating effects in PLS-SEM and PLSc-SEM: Interaction term generation* Data treatment . J. Appl. Struct. Eq. Model. 2 , 2 (2018), 1 -- 21 . J.-M. Becker, C. M. Ringle, and M. Sarstedt. 2018. Estimating moderating effects in PLS-SEM and PLSc-SEM: Interaction term generation* Data treatment. J. Appl. Struct. Eq. Model. 2, 2 (2018), 1--21.
  12. 10.1007/s11002-014-9299-9 · DOI · Paper page
  13. 10.1037/0033-2909.88.3.588 · DOI · Paper page
  14. 10.5555/2017507.2017512 · doi:10.5555/2017507.2017512
  15. P. Bourque et al . 2014. Guide to the Software Engineering Body of Knowledge (SWEBOK (R)): Version 3.0 . IEEE Computer Society , Washington, DC . P. Bourque et al. 2014. Guide to the Software Engineering Body of Knowledge (SWEBOK (R)): Version 3.0. IEEE Computer Society, Washington, DC.
  16. 10.1016/j.jss.2017.12.012 · DOI
  17. 10.1037/h0046016 · DOI · Paper page
  18. 10.1108/JKM-05-2018-0322 · DOI · Paper page
  19. 10.1016/j.infsof.2010.02.003 · DOI
  20. 10.1016/j.infsof.2011.01.007 · DOI
  21. Jun-Hwa Cheah José L. Roldán Enrico Ciavolino Hiram Ting and T. Ramayah. 2020. Sampling weight adjustments in partial least squares structural equation modeling: Guidelines and illustrations. Total Qual. Manag. Bus. Excell. (2020) 1--20. DOI:10.1080/14783363.2020.1754125 10.1080/14783363.2020.1754125 · DOI
  22. Jun-Hwa Cheah José L. Roldán Enrico Ciavolino Hiram Ting and T. Ramayah. 2020. Sampling weight adjustments in partial least squares structural equation modeling: Guidelines and illustrations. Total Qual. Manag. Bus. Excell. (2020) 1--20. DOI:10.1080/14783363.2020.1754125 · DOI
  23. 10.1108/IJCHM-10-2017-0649 · DOI · Paper page
  24. 10.1016/j.infsof.2010.10.001 · DOI
  25. 10.1016/j.jss.2008.09.032 · DOI
  26. W. W. Chin . 1998 . The partial least squares approach to structural equation modeling . Mod. Meth. Bus. Res. 295 , 2 (1998), 295 -- 336 . W. W. Chin. 1998. The partial least squares approach to structural equation modeling. Mod. Meth. Bus. Res. 295, 2 (1998), 295--336.
  27. 10.5555/290231.290235 · doi:10.5555/290231.290235 · Paper page
  28. W. W. Chin and J. Dibbern. 2010. An introduction to 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 Handbook of Partial Least Squares: Concepts Methods and Applications V. Esposito Vinzi W. W. Chin J. Henseler and H. Wang (Eds.). Springer Berlin 171--193. W. W. Chin and J. Dibbern. 2010. An introduction to 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 Handbook of Partial Least Squares: Concepts Methods and Applications V. Esposito Vinzi W. W. Chin J. Henseler and H. Wang (Eds.). Springer Berlin 171--193. · DOI · Paper page
  29. Chao-Min Chiu , Meng-Hsiang Hsu , and Eric T. G . Wang . 2006 . Understanding knowledge sharing in virtual communities: An integration of social capital and social cognitive theories. Dec. Supp. Syst . 42, 3 (2006), 1872--1888. Chao-Min Chiu, Meng-Hsiang Hsu, and Eric T. G. Wang. 2006. Understanding knowledge sharing in virtual communities: An integration of social capital and social cognitive theories. Dec. Supp. Syst. 42, 3 (2006), 1872--1888.
  30. 10.1037/0033-2909.112.1.155 · DOI · Paper page
  31. 10.1145/3383444 · DOI
  32. L. J. Cronbach . 1951. Coefficient alpha and the internal structure of tests. Psychometrika 16, 3 (01 Sep. 1951 ), 297--334. L. J. Cronbach. 1951. Coefficient alpha and the internal structure of tests. Psychometrika 16, 3 (01 Sep. 1951), 297--334.
  33. 10.1016/j.jss.2019.07.002 · DOI
  34. 10.1007/s11747-011-0300-3 · DOI · Paper page
  35. 10.1509/jmkr.38.2.269.18845 · DOI · Paper page
  36. 10.25300/MISQ/2015/39.2.02 · DOI · Paper page
  37. T. K. Dijkstra . 2010. Latent variables and indices: Herman Wold’s basic design and partial least squares . In Handbook of Partial Least Squares: Concepts, Methods and Applications , V. Esposito Vinzi, W. W. Chin, J. Henseler, and H. Wang (Eds.). Springer , 23--46. T. K. Dijkstra. 2010. Latent variables and indices: Herman Wold’s basic design and partial least squares. In Handbook of Partial Least Squares: Concepts, Methods and Applications, V. Esposito Vinzi, W. W. Chin, J. Henseler, and H. Wang (Eds.). Springer, 23--46.
  38. T. K. Dijkstra . 2017. A perfect match between a model and a mode . In Partial Least Squares Path Modeling . Springer , 55--80. T. K. Dijkstra. 2017. A perfect match between a model and a mode. In Partial Least Squares Path Modeling. Springer, 55--80.
  39. 10.1016/j.csda.2014.07.008 · DOI · Paper page
  40. T. K. Dijkstra and K. Schermelleh-Engel. 2014. Consistent partial least squares for nonlinear structural equation models. Psychometrika 79 4 (01 Oct. 2014) 585--604. T. K. Dijkstra and K. Schermelleh-Engel. 2014. Consistent partial least squares for nonlinear structural equation models. Psychometrika 79 4 (01 Oct. 2014) 585--604. · DOI
  41. D. A. Dillman J. D. Smyth and L. Christian. 2014. Internet Phone Mail and Mixed-mode Surveys: The Tailored Design Method. John Wiley & Sons Hoboken NJ. D. A. Dillman J. D. Smyth and L. Christian. 2014. Internet Phone Mail and Mixed-mode Surveys: The Tailored Design Method. John Wiley & Sons Hoboken NJ. · DOI · Paper page
  42. 10.1177/109467050133001 · DOI · Paper page
  43. 10.1093/jpepsy/jsp004 · DOI · Paper page
  44. 10.1016/j.infsof.2005.08.009 · DOI · Paper page
  45. 10.1037/1082-989X.5.2.155 · DOI · Paper page
  46. V. Esposito Vinzi , T. Fahmy , Y. M. Chatelin , and M. Tenenhaus . 2007. PLS path modeling: Some recent methodological developments, a software integrated in XLSTAT and its application to customer satisfaction studies . In the Academy of Marketing Science Conference —“Marketing Theory and Practice in an Inter-Functional World.”11--14 . V. Esposito Vinzi, T. Fahmy, Y. M. Chatelin, and M. Tenenhaus. 2007. PLS path modeling: Some recent methodological developments, a software integrated in XLSTAT and its application to customer satisfaction studies. In the Academy of Marketing Science Conference —“Marketing Theory and Practice in an Inter-Functional World.”11--14.
  47. 10.1016/j.jbusres.2016.03.050 · DOI
  48. R. A. Fisher . 1992. Statistical Methods for Research Workers . Springer, New York , NY. R. A. Fisher. 1992. Statistical Methods for Research Workers. Springer, New York, NY.
  49. 10.2307/1251898 · DOI · Paper page
  50. 10.1177/002224378101800104 · DOI · Paper page
  51. N. Forsgren , J. Humble , and G. Kim . 2018 . Accelerate: Building and Scaling High Performing Technology Organizations . IT Revolution Press , Portland, OR . N. Forsgren, J. Humble, and G. Kim. 2018. Accelerate: Building and Scaling High Performing Technology Organizations. IT Revolution Press, Portland, OR.
  52. S. Fosso Wamba and L. Trinchera . 2014. Assessing unobserved heterogeneity in SEM using REBUS-PLS: A case of the application of TAM to social media adoption . In the 20th Americas Conference on Information Systems. AIS, 1-11 . S. Fosso Wamba and L. Trinchera. 2014. Assessing unobserved heterogeneity in SEM using REBUS-PLS: A case of the application of TAM to social media adoption. In the 20th Americas Conference on Information Systems. AIS, 1-11.
  53. 10.1108/IntR-12-2017-0515 · DOI
  54. C. Fuchs and A. Diamantopoulos . 2009 . Using single-item measures for construct measurement in management research: Conceptual issues and application guidelines . Die Betriebswirtschaft 69 , 2 (2009), 195 . C. Fuchs and A. Diamantopoulos. 2009. Using single-item measures for construct measurement in management research: Conceptual issues and application guidelines. Die Betriebswirtschaft 69, 2 (2009), 195.
  55. 10.5555/2017507.2017508 · doi:10.5555/2017507.2017508
  56. 10.1093/biomet/61.1.101 · DOI · Paper page
  57. 10.5555/2481655.2481672 · doi:10.5555/2481655.2481672 · Paper page
  58. O. Götz K. Liehr-Gobbers and M. Krafft. 2010. Evaluation of structural equation models using the partial least squares (PLS) approach. In Handbook of Partial Least Squares: Concepts Methods and Applications. Springer Berlin 691--711. O. Götz K. Liehr-Gobbers and M. Krafft. 2010. Evaluation of structural equation models using the partial least squares (PLS) approach. In Handbook of Partial Least Squares: Concepts Methods and Applications. Springer Berlin 691--711. · DOI · Paper page
  59. 10.1016/j.infsof.2004.10.004 · DOI
  60. 10.1016/j.jbusres.2008.01.012 · DOI · Paper page
  61. 10.1504/IJMDA.2017.10008574 · DOI
  62. 10.2753/MTP1069-6679190202 · DOI · Paper page
  63. 10.1108/EBR-11-2018-0203 · DOI · Paper page
  64. 10.1177/8756972819882132 · DOI · Paper page
  65. 10.1080/00913367.2017.1281780 · DOI · Paper page
  66. 10.1080/00913367.2017.1281780 · DOI · Paper page
  67. 10.1007/s11135-018-0689-6 · DOI · Paper page
  68. J. Henseler G. Hubona and P. A. Ray. 2017. Partial least squares path modeling: Updated guidelines. In Partial Least Squares Path Modeling: Basic Concepts Methodological Issues and Applications. Springer. J. Henseler G. Hubona and P. A. Ray. 2017. Partial least squares path modeling: Updated guidelines. In Partial Least Squares Path Modeling: Basic Concepts Methodological Issues and Applications. Springer. · DOI
  69. 10.1108/IMR-09-2014-0304 · DOI · Paper page
  70. J. Henseler C. M. Ringle and R. R. Sinkovics. 2009. The use of partial least squares path modeling in international marketing. In New Challenges to International Marketing. Emerald Group Publishing Limited Bingley UK 277--319. J. Henseler C. M. Ringle and R. R. Sinkovics. 2009. The use of partial least squares path modeling in international marketing. In New Challenges to International Marketing. Emerald Group Publishing Limited Bingley UK 277--319. · doi:10.1108/s1474-7979(2009)0000020014 · Paper page
  71. 10.5555/2457826.2457859 · doi:10.5555/2457826.2457859
  72. 10.1016/j.jbusres.2020.07.026 · DOI
  73. 10.1080/10705519909540118 · DOI · Paper page
  74. 10.1002/(SICI)1097-0266(199902)20:2<195::AID-SMJ13>3.0.CO;2-7 · DOI · Paper page
  75. 10.1509/jim.17.0151 · DOI · Paper page
  76. 10.1007/s41237-019-00085-5 · DOI
  77. 10.1016/j.jss.2011.05.017 · DOI
  78. G. James D. Witten T. Hastie and R. Tibshirani. 2013. An Introduction to Statistical Learning. Springer New York NY. G. James D. Witten T. Hastie and R. Tibshirani. 2013. An Introduction to Statistical Learning. Springer New York NY. · DOI · Paper page
  79. 10.1086/376806 · DOI
  80. 10.5555/1519536.1519665 · doi:10.5555/1519536.1519665
  81. 10.1007/s10664-007-9049-1 · DOI
  82. 10.1016/j.jss.2010.07.009 · DOI
  83. 10.1177/00131640121971185 · DOI · Paper page
  84. 10.1007/s10664-016-9437-5 · DOI · Paper page
  85. 10.1111/isj.12228 · DOI
  86. 10.1016/j.infsof.2014.03.004 · DOI
  87. C. Larsen and K. Bong. 2016. A tool for addressing construct identity in literature reviews and meta-analyses.MIS Quart. 40 3 (2016) 529--551. C. Larsen and K. Bong. 2016. A tool for addressing construct identity in literature reviews and meta-analyses.MIS Quart. 40 3 (2016) 529--551. · DOI
  88. 10.1016/j.jss.2015.04.084 · DOI
  89. 10.1016/j.jss.2010.04.077 · DOI
  90. 10.1016/j.infsof.2008.01.002 · DOI
  91. J.-B. Lohmöller . 1989. Predictive vs. structural modeling: Pls vs. ml . In Latent Variable Path Modeling with Partial Least Squares . Springer , 199--226. J.-B. Lohmöller. 1989. Predictive vs. structural modeling: Pls vs. ml. In Latent Variable Path Modeling with Partial Least Squares. Springer, 199--226.
  92. G. A. Marcoulides and C. Saunders . 2006 . Editor’s comments: PLS: A silver bullet?MIS Quart. 30, 2 (Jun. , 2006), pp. iii-ix. G. A. Marcoulides and C. Saunders. 2006. Editor’s comments: PLS: A silver bullet?MIS Quart. 30, 2 (Jun., 2006), pp. iii-ix.
  93. 10.5555/2017410.2017421 · doi:10.5555/2017410.2017421
  94. 10.1177/002224297704100112 · DOI · Paper page
  95. 10.1016/j.jss.2016.05.025 · DOI · Paper page
  96. 10.1177/1094428114529165 · DOI
  97. 10.1016/j.jss.2004.11.002 · DOI
  98. 10.18637/jss.v018.i02 · DOI
  99. J. Miles . 2014. Tolerance and Variance Inflation Factor . American Cancer Society , Hoboken, NJ . J. Miles. 2014. Tolerance and Variance Inflation Factor. American Cancer Society, Hoboken, NJ.
  100. 10.18637/jss.v048.i03 · DOI · Paper page
  101. J. Nunnally . 1978. Psychometric Theory . McGraw-Hill , New York, NY . J. Nunnally. 1978. Psychometric Theory. McGraw-Hill, New York, NY.
  102. 10.1016/j.jss.2009.06.047 · DOI
  103. 10.1016/j.infsof.2015.05.001 · DOI
  104. 10.1016/j.jss.2013.07.029 · DOI
  105. 10.1037/0033-2909.102.1.159 · DOI
  106. 10.1007/s10664-020-09875-y · DOI · Paper page
  107. 10.1016/j.emj.2014.12.001 · DOI
  108. 10.1016/j.emj.2016.05.006 · DOI · Paper page
  109. 10.1080/00273171.2018.1535420 · DOI
  110. 10.15358/0344-1369-2017-3-4 · DOI · Paper page
  111. 10.1016/j.lrp.2012.09.010 · DOI · Paper page
  112. E. E. Rigdon C. M. Ringle and M. Sarstedt. 2010. Structural modeling of heterogeneous data with partial least squares. In Review of Marketing Research. Emerald Group Publishing Limited Bingley UK 255--296. E. E. Rigdon C. M. Ringle and M. Sarstedt. 2010. Structural modeling of heterogeneous data with partial least squares. In Review of Marketing Research. Emerald Group Publishing Limited Bingley UK 255--296. · doi:10.1108/s1548-6435(2010)0000007011 · Paper page
  113. 10.1108/IMDS-10-2015-0449 · DOI · Paper page
  114. C. M. Ringle M. Sarstedt and E. A. Mooi. 2010. Response-based segmentation using finite mixture partial least squares. In Data Mining. Springer Cham 19--49. C. M. Ringle M. Sarstedt and E. A. Mooi. 2010. Response-based segmentation using finite mixture partial least squares. In Data Mining. Springer Cham 19--49. · DOI
  115. 10.5555/2208955.2208956 · doi:10.5555/2208955.2208956
  116. 10.1016/j.infsof.2015.03.004 · DOI
  117. 10.1177/1094428112474693 · DOI · Paper page
  118. 10.1016/j.paid.2015.07.019 · DOI
  119. 10.1016/j.jom.2016.05.002 · DOI · Paper page
  120. 10.5555/3063155.3063217 · doi:10.5555/3063155.3063217
  121. D. Russo , P. H. P. Hanel , S. Altnickel , and N. van Berkel . 2021. Predictors of well-being and productivity of software professionals during the COVID-19 Pandemic--A longitudinal study. Empir. Softw. Eng. (2021) . In press . D. Russo, P. H. P. Hanel, S. Altnickel, and N. van Berkel. 2021. Predictors of well-being and productivity of software professionals during the COVID-19 Pandemic--A longitudinal study. Empir. Softw. Eng. (2021). In press.
  122. 10.1108/17465660810890126 · DOI
  123. 10.1177/1354816618823921 · DOI · Paper page
  124. 10.1080/02650487.2017.1348329 · DOI · Paper page
  125. 10.1016/j.jbusres.2016.02.040 · DOI
  126. 10.1016/j.ausmj.2019.05.003 · DOI · Paper page
  127. 10.1177/1470785320915686 · DOI · Paper page
  128. M. Sarstedt and E. Mooi . 2014 . A Concise Guide to Market Research: The Process, Data, and Methods Using IBM SPSS Statistics . Springer , Berlin . M. Sarstedt and E. Mooi. 2014. A Concise Guide to Market Research: The Process, Data, and Methods Using IBM SPSS Statistics. Springer, Berlin.
  129. 10.1177/1354816618823921 · DOI · Paper page
  130. 10.1080/02664760903030213 · DOI
  131. M. Sarstedt C. M. Ringle and J. F. Hair. 2017. Treating unobserved heterogeneity in PLS-SEM: A multi-method approach. In Partial Least Squares Path Modeling. Springer Cham 197--217. M. Sarstedt C. M. Ringle and J. F. Hair. 2017. Treating unobserved heterogeneity in PLS-SEM: A multi-method approach. In Partial Least Squares Path Modeling. Springer Cham 197--217. · DOI · Paper page
  132. 10.1007/s12087-009-0023-7 · DOI
  133. F. Schuberth . 2020. Confirmatory composite analysis using partial least squares: Setting the record straight. Rev. Manag. Sci. (2020), 1--35 . In Press . F. Schuberth. 2020. Confirmatory composite analysis using partial least squares: Setting the record straight. Rev. Manag. Sci. (2020), 1--35. In Press.
  134. 10.3389/fpsyg.2018.02541 · DOI
  135. P. N. Sharma G. Shmueli M. Sarstedt N. Danks and S. Ray. 2021. Prediction-oriented model selection in partial least squares path modeling. Decision Sciences. https://doi.org/10.1111/deci.12329 10.1111/deci.12329 · DOI · Paper page
  136. P. N. Sharma G. Shmueli M. Sarstedt N. Danks and S. Ray. 2021. Prediction-oriented model selection in partial least squares path modeling. Decision Sciences. https://doi.org/10.1111/deci.12329 · DOI · Paper page
  137. 10.1016/j.infsof.2010.05.006 · DOI
  138. G. Shmueli et al . 2010 . To explain or to predict ? Statist. Sci. 25 , 3 (2010), 289 -- 310 . G. Shmueli et al. 2010. To explain or to predict? Statist. Sci. 25, 3 (2010), 289--310.
  139. 10.1016/j.jbusres.2016.03.049 · DOI · Paper page
  140. 10.1007/s11336-008-9101-0 · DOI · Paper page
  141. 10.1023/A:1006985528729 · DOI · Paper page
  142. 10.1108/01443579410056803 · DOI · Paper page
  143. 10.1016/j.jss.2007.08.034 · DOI
  144. 10.1145/3241743 · DOI · Paper page
  145. M. Stone . 1974 . Cross-validatory choice and assessment of statistical predictions . J. Roy. Statist. Soc. Series B (Methodol.) 36 , 2 (1974), 111 -- 147 . M. Stone. 1974. Cross-validatory choice and assessment of statistical predictions. J. Roy. Statist. Soc. Series B (Methodol.) 36, 2 (1974), 111--147. · DOI · Paper page
  146. 10.1007/s10664-020-09858-z · DOI
  147. 10.17705/1CAIS.01324 · DOI · Paper page
  148. 10.1016/j.jss.2006.06.014 · DOI
  149. 10.1016/j.jclepro.2018.06.226 · DOI
  150. 10.1016/j.techfore.2019.119793 · DOI · Paper page
  151. M. Tenenhaus , S. Amato , and V. Esposito Vinzi . 2004. A global goodness-of-fit index for PLS structural equation modelling . In the XLII SIS Scientific Meeting , Vol. 1 . 739--742. M. Tenenhaus, S. Amato, and V. Esposito Vinzi. 2004. A global goodness-of-fit index for PLS structural equation modelling. In the XLII SIS Scientific Meeting, Vol. 1. 739--742.
  152. 10.1016/j.csda.2004.03.005 · DOI · Paper page
  153. 10.3102/0013189X031003025 · DOI · Paper page
  154. 10.1207/s15327752jpa8701_03 · DOI · Paper page
  155. N. Urbach and F. Ahlemann . 2010 . Structural equation modeling in information systems research using partial least squares . J. Inf. Technol. Theor. Applic. 11 , 2 (2010), 5 -- 40 . N. Urbach and F. Ahlemann. 2010. Structural equation modeling in information systems research using partial least squares. J. Inf. Technol. Theor. Applic. 11, 2 (2010), 5--40.
  156. 10.5555/2069779.2070150 · doi:10.5555/2069779.2070150
  157. 10.1016/j.infsof.2005.04.003 · DOI
  158. 10.1016/j.infsof.2005.04.006 · DOI
  159. R. Wehrens and B.-H. Mevik . 2007 . The PLS package: Principal component and partial least squares regression in R . Journal of Statistical Software 18 , 2 (2007), 1 -- 23 . DOI:10.18637/jss.v018.i02 10.18637/jss.v018.i02 R. Wehrens and B.-H. Mevik. 2007. The PLS package: Principal component and partial least squares regression in R. Journal of Statistical Software 18, 2 (2007), 1--23. DOI:10.18637/jss.v018.i02 · DOI
  160. 10.1177/001316447403400104 · DOI
  161. H. Wold . 1982. Soft modeling: The basic design and some extensions . In Systems under Indirect Observation , K. Jöreskog and H. Wold (Eds.). North-Holland , 1--54. H. Wold. 1982. Soft modeling: The basic design and some extensions. In Systems under Indirect Observation, K. Jöreskog and H. Wold (Eds.). North-Holland, 1--54.
  162. 10.1177/0013164413495237 · DOI · Paper page

Cited by

Metadata

Authors
Russo, D., & Stol, K.-J.
Affiliations
  • Department of Computer Science, Aalborg University, Denmark
  • Lero—The Irish Software Research Centre and University College Cork, Ireland
ORCID
Venue
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Published
2021-05-03
Volume
54
Issue
4
Pages
1-38
ISSN
  • 0360-0300
  • 1557-7341
DOI
10.1145/3447580
Cited by
217
References
24
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
Science Foundation Ireland (15/SIRG/3293, 13/RC/2094_P2)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.