战略管理研究中的机器学习：研究述评与展望 -- 上海财经大学期刊社

1 陈冬梅, 王俐珍, 陈安霓. 数字化与战略管理理论——回顾、挑战与展望[J]. 管理世界, 2020, 36(5): 220-236. DOI:10.3969/j.issn.1002-5502.2020.05.016

2 洪永淼, 汪寿阳. 大数据、机器学习与统计学: 挑战与机遇[J]. 计量经济学报, 2021, 1(1): 17-35. DOI:10.12012/T03-19

3 刘景江, 郑畅然, 洪永淼. 机器学习如何赋能管理学研究?——国内外前沿综述和未来展望[J]. 管理世界, 2023, 39(9): 191-216.

4 Aceves, P, Evans, J A. Mobilizing Conceptual Spaces: How Word Embedding Models Can Inform Measurement and Theory Within Organization Science[J]. Organization Science, 2023, https://doi.org/10.1287/orsc.2023.1686.

5 Alley, M, Biggs, M, Hariss, R, et al. Pricing for Heterogeneous Products: Analytics for Ticket Reselling[J]. Manufacturing & Service Operations Management, 2022, 25(2): 409-426.

6 Athey, S. The Impact of Machine Learning on Economics[M]. The Economics of Artificial Intelligence. University of Chicago Press, 2019.

7 Athey, S, Imbens, G W. Machine Learning Methods That Economists Should Know About[J]. Annual Review of Economics, 2019, 11(1): 685-725. DOI:10.1146/annurev-economics-080217-053433

8 Athey, S, Tibshirani, J, Wager, S. Generalized random forests[J]. The Annals of Statistics, 2019, 47(2): 1148-1178.

9 Belloni, A, Chernozhukov, V, Hansen, C. Inference on Treatment Effects after Selection among High-Dimensional Controls[J]. The Review of Economic Studies, 2014, 81(2): 608-650. DOI:10.1093/restud/rdt044

10 Bellstam, G, Bhagat, S, Cookson, J A. A Text-Based Analysis of Corporate Innovation[J]. Management Science, 2021, 67(7): 4004-4031. DOI:10.1287/mnsc.2020.3682

11 Bhatia, S, Olivola, C Y, Bhatia, N, et al. Predicting leadership perception with large-scale natural language data[J]. The Leadership Quarterly, 2022, 33(5): 101535. DOI:10.1016/j.leaqua.2021.101535

12 Blei, D, NG, A, Jordan, M. Latent Dirichlet Allocation[J]. Journal of Machine Learning Research, 2003, 14: 601-608.

13 Boudt, K, Thewissen, J. Jockeying for Position in CEO Letters: Impression Management and Senti ment Analytics[J]. Financial Management, 2019, 48(1): 77-115. DOI:10.1111/fima.12219

14 Breiman, L, Friedman, J H, Olshen, R A. et al. Classification and Regression Trees[J]. Biometrics, 1984, https://doi.org/10.1201/9781315139470.

15 Chen, Y, Iyengar, R, Iyengar, G. Modeling Multimodal Continuous Heterogeneity in Conjoint Analysis - A Sparse Learning Approach[J]. Marketing Science, 2017, 36(1): 140-156. DOI:10.1287/mksc.2016.0992

16 Chernozhukov, V, Chetverikov, D, Demirer, M, et al. Double machine learning for treatment and causal parameters, 2016, https://doi.org/10.48550/arXiv.1608.00060.

17 Chernozhukov, V, Demirer, M, Duflo, E, et al. Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, with an Application to Immunization in India[R]. Working Paper, 2018.

18 Choi, J, Menon, A, Tabakovic, H. Using machine learning to revisit the diversification–performance relationship[J]. Strategic Management Journal, 2021, 42(9): 1632-1661. DOI:10.1002/smj.3317

19 Chou, Y-C, Chuang, H H C, Chou, P-F, et al. Supervised machine learning for theory building and testing: Opportunities in operations management[J]. Journal of Operations Management, 2023, 69(4): 643-675. DOI:10.1002/joom.1228

20 Choudhury, P, Allen, R, Endres, M. Machine learning for pattern discovery in management research[J]. Strategic Management Journal, 2021, a,42(1): 30-57.

21 Choudhury, P, Foroughi, C, Larson, B. Work-from-anywhere: The productivity effects of geographic flexibility[J]. Strategic Management Journal, 2021, b,42(4): 655-683.

22 Choudhury, P, Kim, D Y. The ethnic migrant inventor effect: Codification and recombination of knowledge across borders[J]. Strategic Management Journal, 2019, 40(2): 203-229. DOI:10.1002/smj.2977

23 Choudhury, P, Wang, D, Carlson, N, et al. Machine Learning Approaches to Facial and Text Analysis: Discovering CEO Oral Communication Styles[J]. Strategic Management Journal, 2019, 40(11): 1705-1732. DOI:10.1002/smj.3067

24 Ciocan, D F & Misic, V V. Interpretable Optimal Stopping[J]. Management Science, 2022, 68(3): 1616-1638. DOI:10.1287/mnsc.2020.3592

25 Cortes, C, Vapnik, V. Support-vector networks[J]. Machine Learning, 1995, 20: 273-297.

26 Cover, T, Hart, P. Nearest neighbor pattern classification[J]. IEEE Transactions on Information Theory, 1967, 13(1): 21-27. DOI:10.1109/TIT.1967.1053964

27 Ding, K, Lev, B, Peng, X, et al. Machine learning improves accounting estimates: evidence from insurance payments[J]. Review of Accounting Studies, 2020, 25(3): 1098-1134. DOI:10.1007/s11142-020-09546-9

28 Domingos, P, Pazzani, M. On the Optimality of the Simple Bayesian Classifier under Zero-One Loss[J]. Machine Learning, 1997, 29(2): 103-130.

29 Dutta, H A Consensus Algorithm for Linear Support Vector Machines[J]. Management Science, 2022, 68 (5): 3703-3725.

30 Eklund, J C, Mannor, M J. Keep Your Eye on the Ball or on the Field? Exploring the Performance Implications of Executive Strategic Attention[J]. Academy of Management Journal, 2021, 64(6): 1685-1713. DOI:10.5465/amj.2019.0156

31 Feng, G, Giglio, S, Xiu, D. Taming the Factor Zoo: A Test of New Factors[J]. The Journal of Finance, 2020, 75(3): 1327-1370. DOI:10.1111/jofi.12883

32 Gaber, M M, Zaslavsky, A, Krishnaswamy, S. A Survey of Classification Methods in Data Streams[M]. Data Streams. Advances in Database Systems, Springer, Boston, MA, 2007.

33 Gamache, D, Mcnamara, G, Mannor, M J, et al. Motivated to acquire? The impact of ceo regulatory focus on firm acquisitions[J]. Academy of Management Journal, 2014, 58(4): 1261-1282.

34 Ghoddusi, H, Creamer, G G, Rafizadeh, N. Machine learning in energy economics and finance: A review[J]. Energy Economics, 2019, 81: 709-727. DOI:10.1016/j.eneco.2019.05.006

35 Giardili, S, Ramdas, K, Williams, J W Leadership and Productivity: A Study of US Automobile Assembly Plants[J]. Management Science, 2022, 69 (3): 1500-1517.

36 Guzman, J, Li, A. Measuring Founding Strategy[J]. Management Science, 2022, 69(1): 101-118.

37 Guzman, J, Stern, S. The State of American Entrepreneurship: New Estimates of the Quality and Quantity of Entrepreneurship for 32 US States, 1988-2014. Working Paper, 2020.

38 Haans, R. What's the value of being different when everyone is? The effects of distinctiveness on performance in homogeneous versus heterogeneous categories[J]. Strategic Management Journal, 2019, 40(1): 3-27. DOI:10.1002/smj.2978

39 Hambrick, D C Upper echelons theory: An update[J]. Academy of Management Review, 2007, 32 (2): 334-343.

40 Harrison, J, Josefy, M, Kalm, M, et al. Using supervised machine learning to scale human-coded data: A method and dataset in the board leadership context[J]. Strategic Management Journal, 2022, 44(7): 1780-1802.

41 Harrison, J S, Thurgood, G R, Boivie, S, et al. Measuring CEO personality: Developing, validating, and testing a linguistic tool[J]. Strategic Management Journal, 2019, 40(8): 1316-1330. DOI:10.1002/smj.3023

42 Hasan, M M, Taylor, G, Richardson, G. Brand Capital and Stock Price Crash Risk[J]. Management Science, 2022, 68(10): 7221-7247. DOI:10.1287/mnsc.2021.4197

43 He, V, Puranam, P, Shrestha, Y, et al. Resolving governance disputes in communities: A study of software license decisions[J]. Strategic Management Journal, 2020, 41(10): 1837-1868. DOI:10.1002/smj.3181

44 Hoehn, W, Koenig, F G, Moehring, R H, et al. Integrated Sequencing and Scheduling in Coil Coating[J]. Management Science, 2011, 57(4): 647-666. DOI:10.1287/mnsc.1100.1302

45 Hofman, J M, Sharma, A, Watts, D J. Prediction and explanation in social systems[J]. Science, 2017, 355(6324): 486-488. DOI:10.1126/science.aal3856

46 Joachims, T. Text categorization with Support Vector Machines: Learning with many relevant features. In Proceedings of the ECML. Chemnitz, Germany, 1998, 137-142.

47 Jones, G, Khanna, T. Bringing history (back) into international business[J]. Journal of International Business Studies, 2006, 37(4): 453-468. DOI:10.1057/palgrave.jibs.8400198

48 Jones, S. Corporate bankruptcy prediction: a high dimensional analysis[J]. Review of Accounting Studies, 2017, 22(3): 1366-1422. DOI:10.1007/s11142-017-9407-1

49 Joseph, J, Borland, H, Orlitzky, M, et al. Seeing Versus Doing: How Businesses Manage Tensions in Pursuit of Sustainability[J]. Journal of Business Ethics, 2020, 164(2): 349-370. DOI:10.1007/s10551-018-4065-1

50 Kaplan, S, Vakili, K. The double-edged sword of recombination in breakthrough innovation[J]. Strategic Management Journal, 2015, 36(10): 1435-1457. DOI:10.1002/smj.2294

51 Kotsiantis, S B. Decision trees: a recent overview[J]. Artificial Intelligence Review, 2013, 39(4): 261-283. DOI:10.1007/s10462-011-9272-4

52 Krogh, G, Roberson, Q, Gruber, M. Recognizing and Utilizing Novel Research Opportunities with Artificial Intelligence[J]. Academy of Management Journal, 2023, 66(2): 367-373. DOI:10.5465/amj.2023.4002

53 Lee, A L, Inceoglu, I, Hauser, O, et al. Determining causal relationships in leadership research using Machine Learning: The powerful synergy of experiments and data science[J]. Leadership Quarterly, 2022, 33(5): 101426. DOI:10.1016/j.leaqua.2020.101426

54 Maron, M E, Kuhns, J L. On Relevance, Probabilistic Indexing and Information Retrieval[J]. Journal of the Acm, 1960, 7(3): 216-244. DOI:10.1145/321033.321035

55 Menon, A, Nave, G, bhatia, S. Emotional Expressions Predict Risky Decisions by S&P 500 Executives. SSRN Electronic Journal, 2018, 10.2139/ssrn. 3303000.

56 Miric, M, Boudreau, K J, Jeppesen, L, B. Protecting their digital assets: The use of formal & informal appropriability strategies by App developers[J]. Research Policy, 2019, 48(8): 103738. DOI:10.1016/j.respol.2019.01.012

57 Miric, M, Jeppesen, L B. Does piracy lead to product abandonment or stimulate new product development?: Evidence from mobileplatform-baseddeveloper firms[J]. Strategic Management Journal, 2020, 41(12): 2155-2184. DOI:10.1002/smj.3208

58 Miric, M, Jia, N, Huang, K. Using Supervised Machine Learning for Large-scale Classification in Management Research: The Case of Identifying Artificial Intelligence Patents[J]. Strategic Management Journal, 2023, 44(2): 491-519. DOI:10.1002/smj.3441

59 Miric, M, Pagani, M, Sawy, O. When and Who Do Platform Companies Acquire? Understanding the Role of Acquisitions in the Growth of Platform Companies[J]. MIS Quarterly, 2021, 45(4): 2159-2174. DOI:10.25300/MISQ/2022/16374

60 Mitchell, T M. Machine Learning, McGraw-Hill, Inc, 1997.

61 Mullainathan, S, Spiess, J. Machine Learning: An Applied Econometric Approach[J]. Journal of Economic Perspectives, 2017, 31(2): 87-106. DOI:10.1257/jep.31.2.87

62 Nauhaus, S, Luger, J, Raisch, S. Strategic Decision Making in the Digital Age: Expert Sentiment and Corporate Capital Allocation[J]. Journal of Management Studies, 2021, 58(7): 1933-1961. DOI:10.1111/joms.12742

63 Ng, A, Jordan, M I. On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes[J]. Neural Information Processing Systems, 2001, 2: 841-848.

64 Ng, W, Sherman, E L. In Search of Inspiration: External Mobility and the Emergence of Technology Intrapreneurs[J]. Organization Science, 2022, 33(6): 2300-2321. DOI:10.1287/orsc.2021.1530

65 Ngai, E W T, Wu, Y Y. Machine learning in marketing: A literature review, conceptual framework, and research agenda[J]. Journal of Business Research, 2022, 145(2): 35-48.

66 Rathje, J M, Katila, R. Enabling Technologies and the Role of Private Firms: A Machine Learning Matching Analysis[J]. Strategy Science, 2021, 6(1): 5-21. DOI:10.1287/stsc.2020.0112

67 Sen, P, Puranam, P. Do Alliance Portfolios Encourage or Impede New Business Practice Adoption? Theory and Evidence from the Private Equity Industry[J]. Strategic Management Journal, 2022, 43(11): 2279-2312. DOI:10.1002/smj.3399

68 Tidhar, R, Eisenhardt, K. Get Rich or Die Trying… Finding Revenue Model fit Using Machine Learning and Multiple Cases[J]. Strategic Management Journal, 2020, 41(7): 1245-1273. DOI:10.1002/smj.3142

69 Wager, S, Athey, S. Estimation and Inference of Heterogeneous Treatment Effects using Random Forests[J]. Journal of the American Statistical Association, 2018, 113(523): 1228-1242. DOI:10.1080/01621459.2017.1319839

70 Yang, J-C, Chuang, H-C, Kuan, C-M. Double machine learning with gradient boosting and its application to the Big N audit quality effect[J]. Journal of Econometrics, 2020, 216(1): 268-283. DOI:10.1016/j.jeconom.2020.01.018