Using partial least squares in archival accounting researchan application to earnings quality measuring

  1. Ana Licerán-Gutiérrez 1
  2. Manuel Cano-Rodríguez 1
  1. 1 Universidad de Jaén

    Universidad de Jaén

    Jaén, España


Revista española de financiación y contabilidad

ISSN: 0210-2412

Any de publicació: 2020

Volum: 49

Número: 2

Pàgines: 143-170

Tipus: Article

DOI: 10.1080/02102412.2019.1608705 DIALNET GOOGLE SCHOLAR

Altres publicacions en: Revista española de financiación y contabilidad


Cites rebudes

  • Cites en Scopus: 4 (22-01-2024)
  • Citas en Web of Science: 5 (31-10-2023)
  • Cites en Dimensions: 6 (24-01-2024)

JCR (Journal Impact Factor)

  • Any 2020
  • Factor d'impacte de la revista: 1.244
  • Factor d'impacte sense autocites: 0.976
  • Article influence score: 0.161
  • Quartil major: Q4
  • Àrea: BUSINESS, FINANCE Quartil: Q4 Posició en l'àrea: 87/110 (Edició: SSCI)

SCImago Journal Rank

  • Any 2020
  • Impacte SJR de la revista: 0.268
  • Quartil major: Q3
  • Àrea: Accounting Quartil: Q3 Posició en l'àrea: 110/177
  • Àrea: Economics and Econometrics Quartil: Q3 Posició en l'àrea: 453/743
  • Àrea: Finance Quartil: Q3 Posició en l'àrea: 185/315

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  • Impacte de la revista: 0,800
  • Àmbit: ECONOMÍA Quartil: C1 Posició en l'àmbit: 20/170


  • Ciències Socials: B

Scopus CiteScore

  • Any 2020
  • CiteScore de la revista: 1.8
  • Àrea: Finance Percentil: 54
  • Àrea: Economics and Econometrics Percentil: 50
  • Àrea: Accounting Percentil: 42

Journal Citation Indicator (JCI)

  • Any 2020
  • JCI de la revista: 0.64
  • Quartil major: Q3
  • Àrea: BUSINESS, FINANCE Quartil: Q3 Posició en l'àrea: 112/221


(Dades actualitzats a data de 24-01-2024)
  • Cites totals: 6
  • Cites recents (2 anys): 1
  • Field Citation Ratio (FCR): 3.09


Despite the advantages of Structural Equation Modelling (SEM) over regression models that have contributed to its popularisation in several fields of research in social sciences, it has not been broadly applied in archival accounting research. In this paper, we present a guidance for the application of SEM – and, particularly, the Partial Least Squares (PLS) method – to the (arguably) most recurrent topic on empirical archival accounting research: earnings quality. We highlight several problems that arise in earnings quality measuring, indicating how PLS can help to overcome them. We also run a simulation process whose results show that PLS method outperforms the other approaches even in situations of limited information.

Informació de finançament


Referències bibliogràfiques

  • Aguirre-Urreta, M. I, Marakas, G. M. (2012). Revisiting bias due to construct misspecification: different results from considering coefficients in standardized form. Mis Quarterly, 36(1), 123-138.
  • Babbie, E. (2017). The basics of social research (7th ed.). Boston, United States, Boston: Wadsworth Publishing. (Cengage, Ed.)
  • Ball, R., Shivakumar, L. (2005). Earnings quality in UK private firms: Comparative loss recognition timeliness. Journal of Accounting & Economics, 39(1), 83–128. Retrieved from
  • Barclay, D. W., Higgins, C. A., Thompson, R. (1995). The partial least squares approach to causal modeling: Personal computer adoption and use as illustration. Technology Studies, 2(2), 285–309.
  • Barth, M. E., Landsman, W. R., Lang, M. H. (2008). International accounting standards and accounting quality. Journal of Accounting Research, 46(3), 467–498. Retrieved from
  • Becker, J. M., Rai, A., Rigdon, E. E. (2013). Predictive validity and formative measurement in structural equation modeling: Embracing practical relevance. Thirty Fourth International Conference on Information Systems, Association for Information Systems, Milan, 1–19.
  • Bhattacharya, N., Ecker, F., Olsson, P., Schipper, K. (2012). Direct and mediated associations among earnings quality, information asymmetry, and the cost of equity. The Accounting Review, 87(2), 449–482.
  • Bhattacharya, U., Daouk, H., Welker, M. (2003). The world price of earnings opacity. The Accounting Review, 78(3), 641–678.
  • Biddle, G. C., Hilary, G. (2006). Accounting quality and firm-level capital investment. The Accounting Review, 81(5), 963–982.
  • Bisbe, J., Batista-Foguet, J.-M., Chenhall, R. (2007). Defining management accounting constructs: A methodological note on the risks of conceptual misspecification. Accounting, Organizations and Society, 32(7–8), 789–820.
  • Blanthorne, C., Jones-Farmer, L. A., Almer, E. D. (2006). Why you should consider SEM: A guide to getting started. In V. Arnold, B. D. Clinton, P. Luckett, R. Roberts, C. Wolfe, & S. Wright (Eds.), Advances in accounting behavioral research (Vol. 9, pp. 179–207). Emerald Group Publishing, Limited. Bingley, United Kingdom.
  • Bollen, K. A. (2002). Latent variables in psychology and the social sciences. Annual Review of Psychology, 53, 605–634.
  • Bollen, K. A., Bauldry, S. (2011). Three Cs in measurement models: Causal indicators, composite indicators, and covariates. Psychological Methods, 16(3), 265–284.
  • Boulton, T. J., Smart, S. B., Zutter, C. J. (2011). Earnings quality and international IPO underpricing. The Accounting Review, 86(2), 483–505.
  • Burgstahler, D., Hail, L., Leuz, C. (2006). The importance of reporting incentives: Earnings management in European private and public firms. The Accounting Review, 81(5), 983–1016.
  • Carmines, E. G., Zeller, R. A. (1979). Reliability and validity assessment. SAGE University papers on quantitative applications in the social sciences 07-17. Beverly Hills, CA: SAGE.
  • Chaney, P. K., Cooil, B., Jeter, D. (2008). A latent class model of earnings attributes.(June 12, 2008). Available at SSRN:
  • Chang, W., Franke, G. R., Lee, N. (2016). Comparing reflective and formative measures: New insights from relevant simulations. Journal of Business Research, 69(8), 3177–3185.
  • Chin, W. (1998). The partial least squares approach for estructural equation modeling. Modern methods for business researchs (pp. 295–336). Psychology Press. Hove, United Kingdom.
  • 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: Concepts, methods and applications (pp. 655–690). Berlin, Germany: Springer-Verlag. Retrieved from
  • Christodoulou, D., Ma, L., Vasnev, A. (2018). Inference-in-residuals as an estimation method for earnings management. Abacus, 54(2), 154–180.
  • Davick, N. S. (2014). The use and misuse of structural equation modeling in management research: A review and critique. Journal of Advances in Management Research, 35(2008), 441–458.
  • Dechow, P., Dichev, I. (2002). The quality of accruals and earnings: The role of accrual estimation errors. The Accounting Review, 77(Supplement), 35–59.
  • Dechow, P., Ge, W., Schrand, C. (2010). Understanding earnings quality: A review of the proxies, their determinants and their consequences. Journal of Accounting & Economics, 50(2/3), 344–401.
  • Dechow, P., Schrand, C. (2004). Earnings quality. The research foundation of CFA institute. Charlottesville,VA., United States.
  • Dechow, P., Sloan, R. G., Sweeney, A. P. (1995). Detecting earnings management. The Accounting Review, 70(2), 193–225.
  • DeFond, M. L. (2010). Earnings quality research: Advances, challenges and future research. Journal of Accounting and Economics, 50(2–3), 402–409.
  • Demerjian, P., Lewis, M., Lev, B., McVay, S. (2013). Managerial ability and earnings quality. The Accounting Review, 88(2), 463–498.
  • Dichev, I., Tang, V. W. (2009). Earnings volatility and earnings predictability. Journal of Accounting and Economics, 47(1–2), 160–181.
  • Dijkstra, T. K. (2015). Consistent partial least squares path modeling. MIS Quarterly, 39(2), 297–316.
  • Dijkstra, T. K., Henseler, J. (2015). Consistent and asymptotically normal PLS estimators for linear structural equations. Computational Statistics and Data Analysis, 81, 10–23.
  • Doupnik, T. S. (2008). Influence of culture on earnings management: A note. Abacus, 44(3), 317–340.
  • Edwards, J. R. (2001). Multidimensional constructs in organizational behavior research: An integrative analytical framework. Organizational Research Methods, 4(2), 144–192.
  • Edwards, J. R., Bagozzi, R. P. (2000). On the nature and direction of relationships between constructs and measures. Psychological Methods, 5(2), 155–174.
  • Ewert, R., Wagenhofer, A. (2011). Earnings quality metrics and what they measure (Working Paper). University of Graz.
  • Ferrer, C., Lainez, J. (2013). Detecting differences on the earnings quality measurement: Empirical evidence on Spanish firms. Revista de Métodos Cuantitativos Para La Economía y La Empresa, 16, 5–28.
  • Fornell, C., Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
  • Francis, J., LaFond, R., Olsson, P., Schipper, K. (2004). Costs of equity and earnings attributes. The Accounting Review, 79(4), 967–1010. Retrieved from
  • Francis, J., Nanda, D., Olsson, P. (2008). Voluntary disclosure, earnings quality and cost of capital. Journal of Accounting Research, 46(1), 53–99.
  • Gaio, C., Raposo, C. (2011). Earnings quality and firm valuation: International evidence. Accounting and Finance, 51(2), 467–499.
  • Gefen, D., Rigdon, E. E., Straub, D. W. (2011). An update and extension to SEM guidelines for administrative and social science research. MIS Quarterly, 35(2), iii–A7.
  • Gefen, D., Straub, D. W., Boudreau, M.-C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(1), 7.
  • Givoly, D., Hayn, C. (2000). The changing time-series properties of earnings, cash flows and accruals: Has financial reporting become more conservative? Journal of Accounting & Economics, 29(3), 287–320. Retrieved from accountid=14555
  • Goodhue, D., Lewis, W., Thompson, R. (2012). Does PLS have advantages for small sample size or non-normal data? MIS Quarterly, 36(3), 981–1001.
  • Gow, I. D., Larcker, D. F., Reiss, P. C. (2016). Causal inference in accounting research. Journal of Accounting Research, 54(2), 477–523.
  • Hair, J. F., Ringle, C. M., Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. The Journal of Marketing Theory and Practice, 19(2), 139–152.
  • Hair, J. F., Ringle, C. M., Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1–2), 1–12.
  • 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(5–6), 320–340.
  • Hair, J. F., Sarstedt, M., Ringle, C. M., Gudergan, S. P. (2017). Advanced issues in partial least squares structural equation modeling (2nd ed.). Thousand Oaks, CA: Sage.
  • Hair, J. F., Tomas, G., Hult, M., Ringle, C. M., Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Thousand Oaks, CA: Sage.
  • Hampton, C. (2015). Estimating and reporting structural equation models with behavioral accounting data. Behavioral Research in Accounting, 27(2), 1–34.
  • Henri, J.-F. (2007). A quantitative assessment of the reporting of structural equation modeling information. The case of management accounting research. Journal of Accounting Literature, 26, 76–115. Retrieved from
  • Henseler, J., Ringle, C. M., Sarstedt, M. (2012). Using partial least squares path modeling in advertising research: Basic concepts and recent issues. In S. Okazaki (Ed.), Handbook of research on international advertising (pp. 252–276). Cheltenham, UK: Edward Elgar.
  • Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., Calantone, R. (2014). Common beliefs and reality about PLS: comments on Ronkko and Evermann (2013). Organizational Research Methods, 17(2), 182–209.
  • 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.
  • Henseler, J., Ringle, C. M., Sinkovics, R. R. (2009). The use of partial least squares structural equation modeling in marketing research. Advances in International Marketing, 20, 277–320.
  • Henseler, J., Sarstedt, M. (2013). Goodness-of-fit indices for partial least squares path modeling. Computational Statistics, 28(2), 565–580.
  • Hermanns, S. (2006). Financial information and earnings quality: A literature review. (April 2006). Available at SSRN:
  • Hinson, L. A., Utke, S. (2018). Structural equation modeling in archival capital markets research. (July 2, 2018). Available at SSRN:
  • IASB. (2010). The conceptual framework for financial reporting. International Accounting Standards Board, London.
  • Jackson, A. B. (2018). Discretionary accruals: Earnings management . . . or Not? Abacus, 54(2), 136–153.
  • 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.
  • Jones, K. L., Krishnan, G. V., Melendrez, K. D. (2008). Do models of discretionary accruals detect actual cases of fraudulent and restated earnings? An empirical analysis. Contemporary Accounting Research, 25(2), 6. Retrieved from
  • Laksmana, I., Yang, Y. (2009). Corporate citizenship and earnings attributes. Advances in Accounting, 25(1), 40–48.
  • Larcker, D. F., Rusticus, T. O. (2010). On the use of instrumental variables in accounting research. Journal of Accounting and Economics, 49(3), 186–205.
  • Lee, L., Petter, S., Fayard, D., Robinson, S. (2011). On the use of partial least squares path modeling in accounting research. International Journal of Accounting Information Systems, 12 (4), 305–328.
  • Leuz, C., Nanda, D., Wysocki, P. D. (2003). Earnings management and investor protection: An international comparison. Journal of Financial Economics, 69(3), 505–527.
  • Leuz, C., Wysocki, P. D. (2016). The economics of disclosure and financial reporting regulation: Evidence and suggestions for future research. Journal of Accounting Research, 54(2), 525–622.
  • Libby, R., Bloomfield, R., Nelson, M. W. (2002). Experimental research in financial accounting. Accounting, Organizations and Society, 27(8), 775–810.
  • Licerán-Gutiérrez, A., Cano-Rodríguez, M. (2019). A review on the multidimensional analysis of earnings qualilty. Revista de Contabilidad, 22(1), 41-60.
  • MacKenzie, S. B., Podsakoff, P. M., Jarvis, C. B. (2005). The problem of measurement model misspecification in behavioral and organizational research and some recommended solutions. Journal of Applied Psychology, 90(4), 710–730.
  • Mackenzie, S. B., Podsakoff, P. M., Podakoff, N. P. (2011). Construct measurement and validation procedures in mis and behavioral research: Integrating new and existing techniques. MIS Quarterly, 35(2), 293–334.
  • Mateos-Aparicio, G. (2011). Partial least squares (PLS) methods: Origins, evolution, and application to social sciences. Communications in Statistics - Theory and Methods, 40(13), 2305–2317.
  • McNichols, M. F., Stubben, S. R. (2018). Research design issues in studies using discretionary accruals. Abacus, 54(2), 227–246.
  • 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.
  • Numally, J. C., Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York, NY: Mc Graw Hill.
  • Penman, S. H., Zhang, X.-J. (2002). Accounting conservatism, the quality of earnings, and stock returns. The Accounting Review, 77(2), 237–264. Retrieved from
  • Perotti, P., Wagenhofer, A. (2014). Earnings quality measures and excess returns. Journal of Business Finance & Accounting, 41(5–6), 545–571.
  • Reinartz, W., 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 (4), 332–344.
  • Rigdon, E. E. (2012). Rethinking partial least squares path modeling: In praise of simple methods. Long Range Planning, 45(5–6), 341–358.
  • Rigdon, E. E. (2014). Rethinking partial least squares path modeling: Breaking chains and forging ahead. Long Range Planning, 47(3), 161–167.
  • Rigdon, E. E. (2016). Choosing PLS path modeling as analytical method in European management research: A realist perspective. European Management Journal, 34, 1–8.
  • Ringle, C. M., Sarstedt, M., Schlittgen, R. (2014). Genetic algorithm segmentation in partial least squares structural equation modeling. OR Spectrum, 36(1), 251–276.
  • Ringle, C. M., Sarstedt, M., Straub, D. W. (2012). A critical look at the use of PLS-SEM in MIS Quarterly. MIS Quarterly, 36(1), 3–14.
  • Rodgers, W., Guiral, A. (2011). Potential model misspecification bias: Formative indicators enhancing theory for accounting researchers. The International Journal of Accounting, 46(1), 25–50.
  • Roldán, J. L., Sánchez-Franco, M. J. (2012). Variance-based structural equation modeling: Guideliness for using partial least squares in information systems research. In M. Mora, O. Gelman, A. Steenkamp, & M. S. Raisinghani (Eds.), Research methodologies, innovations and philosophies in software systems engineering and information systems (pp. 193–211). Hershey, PA: IGI Global.
  • Ronkko, M., Evermann, J. (2013). A critical examination of common beliefs about partial least squares path modeling. Organizational Research Methods, 16(3), 425–448.
  • 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.
  • Sarstedt, M., Ringle, C. M., Hair, J. F. (2014). PLS-SEM: Looking back and moving forward. Long Range Planning, 47(3), 132–137.
  • Schipper, K., Vincent, L. (2003). Earnings quality. Accounting Horizons, 17, 97–110.
  • Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., Lauro, C. (2005). PLS path modeling. Computational Statistics and Data Analysis, 48(1), 159–205.
  • Ullman, J. B. (2006). Structural equation modeling : Reviewing the basics and moving forward. Journal of Personality Assessment, 87(1), 35–50.
  • VanTendeloo, B., Vanstraelen, A. (2008). Earnings management and audit quality in Europe: Evidence from the private client segment market. European Accounting Review, 17(3), 447–469.
  • Wold, H. (1980). Model construction and evaluation when theoretical knowledge is scarce (pp. 47–74). Bureau of Economic Research. Cambridge, United Kingdom.
  • Wold, H. (1985, September). Systems analysis by partial least squares. Measuring the unmeasurable, Llumina Press. NYC, United States.