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Special Issue on “Innovative Data Sources in Management Accounting Research and Practice”

Machine Learning in Management Accounting Research: Literature Review and Pathways for the Future

ORCID Icon, ORCID Icon & ORCID Icon
Pages 607-636 | Received 28 Feb 2021, Accepted 23 Aug 2022, Published online: 04 Nov 2022

References

  • Ahmed, S., Ranta, M., & Vähämaa, S. (2020). Facial attractiveness and CEO compensation: Evidence from the banking industry (SSRN Scholarly Paper ID 3744808). Social Science Research Network. https://doi.org/10.2139/ssrn.3744808.
  • Allee, K. D., & Deangelis, M. D. (2015). The structure of voluntary disclosure narratives: Evidence from tone dispersion. Journal of Accounting Research, 53(2), 241–274. https://doi.org/10.1111/1475-679X.12072
  • Angelov, D. (2020). Top2Vec: Distributed representations of topics. ArXiv:2008.09470 [Cs, Stat]. http://arxiv.org/abs/2008.09470.
  • Appelbaum, D., Kogan, A., Vasarhelyi, M., & Yan, Z. (2017). Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems, 25, 29–44. https://doi.org/10.1016/j.accinf.2017.03.003
  • Athey, S., Bayati, M., Imbens, G., & Qu, Z. (2019). Ensemble methods for causal effects in panel data settings. AEA Papers and Proceedings, 109, 65–70. https://doi.org/10.1257/pandp.20191069
  • Auvinen, T. P., Sajasalo, P., Sintonen, T., Takala, T., & Järvenpää, M. (2018). Antenarratives in ongoing strategic change: Using the story index to capture daunting and optimistic futures. In H. Krämer, & M. Wenzel (Eds.), How organizations manage the future: Theoretical perspectives and empirical insights (pp. 133–151). Springer International Publishing. https://doi.org/10.1007/978-3-319-74506-0_7.
  • Bakhitov, E., & Singh, A. (2021). Causal gradient boosting: Boosted instrumental variable regression. ArXiv:2101.06078 [Econ, Stat]. http://arxiv.org/abs/2101.06078.
  • Ball, C., Hoberg, G., & Maksimovic, V. (2015). Disclosure, Business Change and Earnings Quality (SSRN Scholarly Paper ID 2260371). Social Science Research Network. https://doi.org/10.2139/ssrn.2260371.
  • Bao, Y., Ke, B., Li, B., Yu, Y. J., & Zhang, J. (2020). Detecting accounting fraud in publicly traded U.S. Firms using a machine learning approach. Journal of Accounting Research, 58(1), 199–235. https://doi.org/10.1111/1475-679X.12292
  • Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405–417. https://doi.org/10.1016/j.eswa.2017.04.006
  • Bartov, E., Faurel, L., & Mohanram, P. S. (2018). Can twitter help predict firm-level earnings and stock returns? The Accounting Review, 93(3), 25–57. https://doi.org/10.2308/accr-51865
  • Bellstam, G., Bhagat, S., & Cookson, J. A. (2021). A text-based analysis of corporate innovation. Management Science, 67(7), 4004–4031. https://doi.org/10.1287/mnsc.2020.3682
  • Bertomeu, J. (2020). Machine learning improves accounting: Discussion, implementation and research opportunities. Review of Accounting Studies, 25(3), 1135–1155. https://doi.org/10.1007/s11142-020-09554-9
  • Bertomeu, J., Cheynel, E., Floyd, E., & Pan, W. (2021). Using machine learning to detect misstatements. Review of Accounting Studies, 26(2), 468–519. https://doi.org/10.1007/s11142-020-09563-8
  • Bhatia, S., Olivola, C. Y., Bhatia, N., & Ameen, A. (2021). Predicting leadership perception with large-scale natural language data. The Leadership Quarterly, 101535, https://doi.org/10.1016/j.leaqua.2021.101535
  • Bhave, D. P. (2014). The invisible eye? Electronic performance monitoring and employee job performance. Personnel Psychology, 67(3), 605–635. https://doi.org/10.1111/peps.12046
  • Bingler, J. A., Kraus, M., & Leippold, M. (2021). Cheap talk and cherry-picking: What ClimateBert has to say on corporate climate risk disclosures (SSRN Scholarly Paper ID 3796152). Social Science Research Network. https://papers.ssrn.com/abstract=3796152.
  • Blei, D. M., & Lafferty, J. D. (2006). Dynamic topic models. Proceedings of the 23rd International conference on machine learning, 113–120. https://doi.org/10.1145/1143844.1143859.
  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. The Journal of Machine Learning Research, 3(null), 993–1022.
  • Brown, N. C., Crowley, R. M., & Elliott, W. B. (2020b). What are you saying? Using topic to detect financial misreporting. Journal of Accounting Research, 58(1), 237–291. https://doi.org/10.1111/1475-679X.12294
  • Brown, S. V., Hinson, L. A., & Tucker, J. W. (2021). Financial statement adequacy and firms’ MD&A disclosures (SSRN Scholarly Paper ID 3891572). Social Science Research Network. https://doi.org/10.2139/ssrn.3891572.
  • Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., & Agarwal, S. (2020a). Language models are few-shot learners. ArXiv:2005.14165 [Cs]. http://arxiv.org/abs/2005.14165.
  • Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company.
  • Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. ArXiv:1603.02754 [Cs]. https://doi.org/10.1145/2939672.2939785.
  • Choudhury, P., Allen, R. T., & Endres, M. G. (2021). Machine learning for pattern discovery in management research. Strategic Management Journal, 42(1), 30–57. https://doi.org/10.1002/smj.3215
  • Choudhury, P., Wang, D., Carlson, N. A., & Khanna, T. (2019). Machine learning approaches to facial and text analysis: Discovering CEO oral communication styles. Strategic Management Journal, 40(11), 1705–1732. https://doi.org/10.1002/smj.3067
  • Covert, I., Lundberg, S., & Lee, S.-I. (2020). Understanding global feature contributions with additive importance measures. ArXiv:2004.00668 [Cs, Stat]. http://arxiv.org/abs/2004.00668.
  • Davis, A. K., & Tama-Sweet, I. (2012). Managers’ Use of language across alternative disclosure outlets: Earnings press releases versus MD&A*. Contemporary Accounting Research, 29(3), 804–837. https://doi.org/10.1111/j.1911-3846.2011.01125.x
  • Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. ArXiv:1810.04805 [Cs]. http://arxiv.org/abs/1810.04805.
  • Ding, K., Lev, B., Peng, X., Sun, T., & Vasarhelyi, M. A. (2020). Machine learning improves accounting estimates: Evidence from insurance payments. Review of Accounting Studies, 25(3), 1098–1134. https://doi.org/10.1007/s11142-020-09546-9
  • Donaldson, D., & Storeygard, A. (2016). The view from above: Applications of satellite data in economics. Journal of Economic Perspectives, 30(4), 171–198. https://doi.org/10.1257/jep.30.4.171
  • Doornenbal, B. M., Spisak, B. R., & van der Laken, P. A. (2021). Opening the black box: Uncovering the leader trait paradigm through machine learning. The Leadership Quarterly, 101515), https://doi.org/10.1016/j.leaqua.2021.101515
  • Duan, Y., Hsieh, T.-S., Wang, R. R., & Wang, Z. (2020). Entrepreneurs’ facial trustworthiness, gender, and crowdfunding success. Journal of Corporate Finance, 64, 101693. https://doi.org/10.1016/j.jcorpfin.2020.101693
  • Dyer, T., Lang, M., & Stice-Lawrence, L. (2017). The evolution of 10-K textual disclosure: Evidence from latent dirichlet allocation. Journal of Accounting and Economics, 64(2), 221–245. https://doi.org/10.1016/j.jacceco.2017.07.002
  • Efron, B. (1979). Bootstrap methods: Another look at the jackknife. The Annals of Statistics, 7(1), 1–26. https://doi.org/10.1214/aos/1176344552
  • Eisenhardt, K. M. (1989). Building theories from case study research. The Academy of Management Review, 14(4), 532–550. https://doi.org/10.2307/258557
  • Elmsili, B., & Outtaj, B. (2018). Artificial neural networks applications in economics and management research: An exploratory literature review. 2018 4th International conference on optimization and applications (ICOA), 1–6. https://doi.org/10.1109/ICOA.2018.8370600.
  • El-Haj, M., Rayson, P., Walker, M., Young, S., & Simaki, V. (2019). In search of meaning: Lessons, resources and next steps for computational analysis of financial discourse. Journal of Business Finance & Accounting, 46(3–4), 265–306. https://doi.org/10.1111/jbfa.12378
  • Feldman, R., Govindaraj, S., Livnat, J., & Segal, B. (2010). Management’s tone change, post earnings announcement drift and accruals. Review of Accounting Studies, 15(4), 915–953. https://doi.org/10.1007/s11142-009-9111-x
  • Fisher, I. E., Garnsey, M. R., & Hughes, M. E. (2016). Natural language processing in accounting, auditing and finance: A synthesis of the literature with a roadmap for future research. Intelligent Systems in Accounting, Finance and Management, 23(3), 157–214. https://doi.org/10.1002/isaf.1386
  • Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280. https://doi.org/10.1016/j.techfore.2016.08.019
  • Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232. https://doi.org/10.1214/aos/1013203451
  • Futagami, K., Fukazawa, Y., Kapoor, N., & Kito, T. (2021). Pairwise acquisition prediction with SHAP value interpretation. The Journal of Finance and Data Science, 7, 22–44. https://doi.org/10.1016/j.jfds.2021.02.001
  • Garanina, T., Ranta, M., & Dumay, J. (2021). Blockchain in accounting research: Current trends and emerging topics. Accounting, Auditing & Accountability Journal, ahead-of-print(ahead-of-print). https://doi.org/10.1108/AAAJ-10-2020-4991.
  • Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing Research, 17(4), 364. https://doi.org/10.1097/00006199-196807000-00014
  • Green, T. C., Huang, R., Wen, Q., & Zhou, D. (2019). Crowdsourced employer reviews and stock returns. Journal of Financial Economics, 134(1), 236–251. https://doi.org/10.1016/j.jfineco.2019.03.012
  • Grennan, J. (2019). A corporate culture channel: How increased shareholder governance reduces firm value (SSRN Scholarly Paper ID 2345384). Social Science Research Network. https://doi.org/10.2139/ssrn.2345384.
  • Guiso, L., Sapienza, P., & Zingales, L. (2015). The value of corporate culture. Journal of Financial Economics, 117(1), 60–76. https://doi.org/10.1016/j.jfineco.2014.05.010
  • Gulbrandsen, I. T., Just, S. N., & Dahlman, S. E. M. (2019). Inside the responsibility machine: Exploring the algorithmic strategizing of a fintech start-up. In Paper presented at 14th Organization studies summer workshop, Mykonos, Greece.
  • Hales, J., Moon, J. R., & Swenson, L. A. (2018). A new era of voluntary disclosure? Empirical evidence on how employee postings on social media relate to future corporate disclosures. Accounting, Organizations and Society, 68-69, 88–108. https://doi.org/10.1016/j.aos.2018.04.004
  • Harrison, J. S., Thurgood, G. R., Boivie, S., & Pfarrer, M. D. (2019). Measuring CEO personality: Developing, validating, and testing a linguistic tool. Strategic Management Journal, 40(8), 1316–1330. https://doi.org/10.1002/smj.3023
  • Hart, R. P. (2001). Redeveloping DICTION: Theoretical considerations. Progress in Communication Sciences, 43–60.
  • Henry, E. (2008). Are investors influenced by how earnings press releases are written? Journal of Business Communication, 45(4), 363–407. https://doi.org/10.1177/0021943608319388
  • Hsieh, T.-S., Kim, J.-B., Wang, R. R., & Wang, Z. (2020). Seeing is believing? Executives’ facial trustworthiness, auditor tenure, and audit fees. Journal of Accounting and Economics, 69(1), 101260. https://doi.org/10.1016/j.jacceco.2019.101260
  • Huang, A. H., Lehavy, R., Zang, A. Y., & Zheng, R. (2018). Analyst information discovery and interpretation roles: A topic modeling approach. Management Science, 64(6), 2833–2855. https://doi.org/10.1287/mnsc.2017.2751
  • Huang, K., Li, Meng, & Markov, S. (2020). What do employees know? Evidence from a social media platform. Accounting Review, 95(2), 199–226. https://doi.org/10.2308/accr-52519
  • Huang, A. H., Wang, H., & Yang, Y. (2022). FinBERT: A large language model for extracting information from financial text. Contemporary Accounting Research, forthcoming. https://doi.org/10.1111/1911-3846.12832
  • Huang, M., Li, P., Meschke, F., & Guthrie, J. P. (2015). Family firms, employee satisfaction, and corporate performance. Journal of Corporate Finance, 34, 108–127. https://doi.org/10.1016/j.jcorpfin.2015.08.002
  • Iaria, A., Schwarz, C., & Waldinger, F. (2018). Frontier knowledge and scientific production: Evidence from the collapse of international science*. The Quarterly Journal of Economics, 133(2), 927–991. https://doi.org/10.1093/qje/qjx046
  • Jang, H. (2019). A decision support framework for robust R&D budget allocation using machine learning and optimization. Decision Support Systems, 121, 1–12. https://doi.org/10.1016/j.dss.2019.03.010
  • Jones, S. (2017). Corporate bankruptcy prediction: A high dimensional analysis. Review of Accounting Studies, 22(3), 1366–1422. https://doi.org/10.1007/s11142-017-9407-1
  • Kang, J. K., Stice-Lawrence, L., & Wong, Y. T. F. (2021). The firm next door: Using satellite images to study local information advantage. Journal of Accounting Research, 59(2), 713–750. https://doi.org/10.1111/1475-679X.12360
  • Katona, Z., Painter, M., Patatoukas, P. N., & Zeng, J. (2018). On the capital market consequences of alternative data: Evidence from outer space (SSRN Scholarly Paper ID 3222741). Social Science Research Network. https://doi.org/10.2139/ssrn.3222741.
  • Keating, P. J. (1995). A Framework for classifying and evaluating the theoretical contribution of case research in management accounting. Journal of Management Accounting Research, 7(1), 66–86.
  • Kosinski, M., Bachrach, Y., Kohli, P., Stillwell, D., & Graepel, T. (2014). Manifestations of user personality in website choice and behaviour on online social networks. Machine Learning, 95(3), 357–380. https://doi.org/10.1007/s10994-013-5415-y
  • Kozlowski, S. W. J., Chao, G. T., Chang, C.-H., & Fernandez, R. (2016). Using big data to advance the science of team effectiveness. In S. Tonidandel, E. B. King, & J. M. Cortina (Eds.), Big data at work: The data science revolution and organizational psychology (pp. 272–309). Routledge/Taylor & Francis Group.
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
  • Lee, A., Inceoglu, I., Hauser, O., & Greene, M. (2020). Determining causal relationships in leadership research using machine learning: The powerful synergy of experiments and data science. The Leadership Quarterly, 101426), https://doi.org/10.1016/j.leaqua.2020.101426
  • Lei, L. (Gillian), Li, Y., & Luo, Y. (2019). Production and dissemination of corporate information in social media: A review. Journal of Accounting Literature, 42(1), 29–43. https://doi.org/10.1016/j.acclit.2019.02.002
  • Li, F. (2010). Textual analysis of corporate disclosures: A survey of the literature. Journal of Accounting Literature, 29(1), 143–165.
  • Li, K., Mai, F., Shen, R., & Yan, X. (2020). Measuring corporate culture using machine learning. The Review of Financial Studies, hhaa079. https://doi.org/10.1093/rfs/hhaa079
  • Loughran, T., & Mcdonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. The Journal of Finance, 66(1), 35–65. https://doi.org/10.1111/j.1540-6261.2010.01625.x
  • Loughran, T., & Mcdonald, B. (2016). Textual analysis in accounting and finance: A survey. Journal of Accounting Research, 54(4), 1187–1230. https://doi.org/10.1111/1475-679X.12123
  • Lukka, K., & Modell, S. (2010). Validation in interpretive management accounting research. Accounting, Organizations and Society, 35(4), 462–477. https://doi.org/10.1016/j.aos.2009.10.004
  • Lukka, K., & Suomala, P. (2014). Relevant interventionist research: Balancing three intellectual virtues. Accounting and Business Research, 44(2), 204–220. https://doi.org/10.1080/00014788.2013.872554
  • Lukka, K., & Vinnari, E. (2014). Domain theory and method theory in management accounting research. Accounting, Auditing & Accountability Journal, 27(8), 1308–1338. https://doi.org/10.1108/AAAJ-03-2013-1265
  • Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 56–67. https://doi.org/10.1038/s42256-019-0138-9
  • Malmi, T., & Granlund, M. (2009). In search of management accounting theory. European Accounting Review, 18(3), 597–620. https://doi.org/10.1080/09638180902863779
  • Mantere, S., & Ketokivi, M. (2013). Reasoning in organization science. Academy of Management Review, 38(1), 70–89. https://doi.org/10.5465/amr.2011.0188
  • Mathur, M. B., & Reichling, D. B. (2016). Navigating a social world with robot partners: A quantitative cartography of the Uncanny Valley. Cognition, 146, 22–32. https://doi.org/10.1016/j.cognition.2015.09.008
  • Mauritz, C., Nienhaus, M., & Oehler, C. (2021). The role of individual audit partners for narrative disclosures. Review of Accounting Studies, https://doi.org/10.1007/s11142-021-09634-4
  • Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013a). Efficient estimation of word representations in vector space. ArXiv:1301.3781 [Cs]. http://arxiv.org/abs/1301.3781.
  • Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013b). Distributed representations of words and phrases and their compositionality. ArXiv:1310.4546 [Cs, Stat]. http://arxiv.org/abs/1310.4546.
  • Miller, G. S. (2017). Discussion of “the evolution of 10-K textual disclosure: Evidence from latent dirichlet allocation”. Journal of Accounting and Economics, 64(2), 246–252. https://doi.org/10.1016/j.jacceco.2017.07.004
  • Miller, G. S., & Skinner, D. J. (2015). The evolving disclosure landscape: How changes in technology, the media, and capital markets are affecting disclosure. Journal of Accounting Research, 53(2), 221–239. https://doi.org/10.1111/1475-679X.12075
  • Moll, J., & Yigitbasioglu, O. (2019). The role of internet-related technologies in shaping the work of accountants: New directions for accounting research. The British Accounting Review, 51(6), 100833. https://doi.org/10.1016/j.bar.2019.04.002
  • Molnar, C. (n.d.). Interpretable machine learning. Retrieved April 8, 2021, from https://christophm.github.io/interpretable-ml-book/.
  • Moro, S., Cortez, P., & Rita, P. (2015). Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent dirichlet allocation. Expert Systems with Applications, 42(3), 1314–1324. https://doi.org/10.1016/j.eswa.2014.09.024
  • Peters, J., Janzing, D., & Schölkopf, B. (2017). Elements of causal inference: Foundations and learning algorithms. The MIT Press.
  • Porter, M. F. (1980). An algorithm for suffix stripping. Program, 14(3), 130–137. https://doi.org/10.1108/eb046814
  • Ranta, M., & Ylinen, M. (2021). Board composition and workplace diversity: A machine learning approach (SSRN Scholarly Paper ID 3812296). Social Science Research Network. https://doi.org/10.2139/ssrn.3812296.
  • Ravid, D. M., Tomczak, D. L., White, J. C., & Behrend, T. S. (2020). Epm 20/20: A review, framework, and research agenda for electronic performance monitoring. Journal of Management, 46(1), 100–126. https://doi.org/10.1177/0149206319869435
  • Rehurek, R., & Sojka, P. (2010). Software framework for topic modelling with large corpora. In proceedings of the Lrec 2010 workshop on new challenges for Nlp frameworks, 45–50.
  • Rikhardsson, P., & Yigitbasioglu, O. (2018). Business intelligence & analytics in management accounting research: Status and future focus. International Journal of Accounting Information Systems, 29, 37–58. https://doi.org/10.1016/j.accinf.2018.03.001
  • Roberts, M. E., Stewart, B. M., & Tingley, D. (2019). Stm: An R package for structural topic models. Journal of Statistical Software, 91(2), 1–40. https://doi.org/10.18637/jss.v091.i02
  • Rouwelaar, H., Schaepkens, F., & Widener, S. K. (2020). Skills, influence, and effectiveness of management accountants. Journal of Management Accounting Research, 33(2), 211–235. https://doi.org/10.2308/jmar-18-048
  • Scheibenreif, L., Mommert, M., & Borth, D. (2021). Estimation of air pollution with remote sensing data: revealing greenhouse gas emissions from space. ArXiv:2108.13902 [Cs]. http://arxiv.org/abs/2108.13902.
  • Shmueli, G. (2010). To explain or to predict? Statistical Science, 25(3), 289–310. https://doi.org/10.1214/10-STS330
  • Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 61(4), 66–83. https://doi.org/10.1177/0008125619862257
  • Storm, H., Baylis, K., & Heckelei, T. (2020). Machine learning in agricultural and applied economics. European Review of Agricultural Economics, https://doi.org/10.1093/erae/jbz033
  • Tidhar, R., & Eisenhardt, K. M. (2020). Get rich or die trying … finding revenue model fit using machine learning and multiple cases. Strategic Management Journal, 41(7), 1245–1273. https://doi.org/10.1002/smj.3142
  • Todorov, A., Pakrashi, M., & Oosterhof, N. N. (2009). Evaluating faces on trustworthiness after minimal time exposure. Social Cognition, 27(6), 813–833. https://doi.org/10.1521/soco.2009.27.6.813
  • Varian, H. R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28(2), 3–28. https://doi.org/10.1257/jep.28.2.3
  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. ArXiv:1706.03762 [Cs]. http://arxiv.org/abs/1706.03762.
  • Wager, S., & Athey, S. (2018). Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association, 113(523), 1228–1242. https://doi.org/10.1080/01621459.2017.1319839
  • Wang, G., Li, J., & Hopp, W. J. (2021). An instrumental variable forest approach for detecting heterogeneous treatment effects in observational studies. Management Science, https://doi.org/10.1287/mnsc.2021.4084
  • Ylinen, M., & Ranta, M. (2021). Employee-friendly corporate culture and firm performance: evidence from a machine learning approach (SSRN Scholarly Paper ID 3813075). Social Science Research Network. https://doi.org/10.2139/ssrn.3813075.
  • Zengul, F. D., Oner, N., Byrd, J. D., & Savage, A. (2021). Revealing research themes and trends in 30 Top-ranking accounting journals: A text-mining approach. Abacus, 57(3), 468–501. https://doi.org/10.1111/abac.12214
  • Zimmerman, J. L. (2001). Conjectures regarding empirical managerial accounting research. Journal of Accounting and Economics, 32(1), 411–427. https://doi.org/10.1016/S0165-4101(01)00023-4