隨著資訊科技的快速進展，研究可取得的資料在複雜度與粒度上大幅提升，常以多重、重疊且彼此高度相關的訊號與指標形式呈現，並來自多元情境與樣本。面對這類資料結構，單靠傳統迴歸往往難以兼顧穩健性、可解釋性與可用於決策的預測能力，因而需要結合計量方法與統計機器學習，以學習出透明且可解釋的稀疏結構。本論文聚焦於兩個在實證研究中最常見、也最影響推論品質的問題：高度共線性導致估計不穩定與解釋困難，以及觀察性資料中的異質性使得多指標結構衡量與效果推論容易混淆公司間差異與公司內變動。為回應上述挑戰，本論文由兩篇研究構成，並以稀疏主成分分析（Sparse PCA）作為資料導向的因子萃取核心，形成一個可遷移的分析框架，分別用於研發（R&D）資料中的可解釋預測，以及 ESG 指標中的結構衡量與異質性效果分解。

在第一篇研究中，探討了一家領先的高科技 IC 設計公司在兩階段 PCB 研發過程中的預測建模，該過程利用早期階段產生的大量相關模擬輸出，來預測後期成本較高的實體測試指標（vMargin 和 tMargin）。本研究使用稀疏主成分分析（PMD/EESPCA）進行可解釋的降維，隨後進行 LASSO 回歸，評估了「先降維後預測」策略，並與包括同步「降維並預測」方法（SPCR）在內的替代方案進行比較。透過對七個專案（樣本數 n=972，18 個預測因子）進行基於專案的交叉驗證，SPCA-LASSO 模型的樣本外表現（out-of-sample performance）顯著優於直接將 LASSO 應用於原始相關輸入的情況；其將 vMargin的均方誤差（MSE）降低了約 22%，tMargin 降低了 40%，同時產出了稀疏且工程師可解釋的因子。

在第二篇研究中，透過提出一個兩階段、以數據為中心的框架，解決 ESG 研究中的測量與異質性挑戰。首先，它透過 EESPCA 直接從細緻的 Refinitiv 指標中學習稀疏且可解釋的 ESG 因子，避免了由上而下、以功能為中心的聚合方式。其次，它使用混合模型（Hybrid Model）將這些因子與企業財務績效（ROA 和 Tobin’s Q）連結，該模型將效應分解為「公司間差異」與「公司隨時間變動」。與傳統的綜合評分（整體、支柱和類別聚合）相比，此以數據為中心的十組件模型設定（ten-component specification），在訓練期與測試期皆實現了更強的解釋力與預測表現。此外，「組內—組間」分解（within–between decomposition）釐清了 ESG 與績效之間的連結是反映了持續的公司間差異、隨時間變化的公司內調整，抑或兩者兼具，從而強化了解釋力與管理實務的相關性。

Advances in information technology have substantially increased the complexity and granularity of data available for empirical research. Such data are often recorded as multiple, overlapping measurements that are highly correlated and drawn from diverse settings, creating practical tension between statistical reliability, interpretability, and decision-relevant prediction. Addressing this tension requires combining econometric reasoning with adaptable statistical machine-learning tools that learn transparent and interpretable sparse structure from correlated measurements. This dissertation focuses on two pervasive challenges that arise in these contexts: severe collinearity, which inflates estimation variance and compromises both inference and predictive stability, and heterogeneity in observational data, where multi-indicator constructs and temporal variation can blur stable between-unit differences with within-unit change over time. To respond to these challenges, the dissertation is organized into two essays unified by a data-driven factor-extraction framework centered on Sparse Principal Component Analysis (Sparse PCA), which supports interpretable prediction in R&D analytics and improves construct measurement and heterogeneous-effect interpretation in ESG research.

Essay I investigates predictive modeling in a two-stage PCB R&D process at a leading high-tech IC design firm, where numerous correlated simulation outputs from the early stage are used to forecast later, costlier physical-test metrics (vMargin and tMargin). Using Sparse PCA (PMD/EESPCA) for interpretable dimension reduction followed by LASSO regression, the study evaluates a reduce-then-predict strategy against alternatives, including a simultaneous reduce-and-predict approach (SPCR). With project-based cross-validation on seven projects (n=972, 18 predictors), the SPCA–LASSO specification materially improves out-of-sample performance relative to applying LASSO directly to the raw correlated inputs, reducing MSE by approximately 22% for vMargin and 40% for tMargin while yielding sparse, engineer-interpretable factors.

Essay II addresses measurement and heterogeneity challenges in ESG research by proposing a two-stage, data-centric framework. First, it learns sparse and interpretable ESG factors directly from granular Refinitiv indicators via EESPCA, avoiding top-down, function-centric aggregation. Second, it links these factors to corporate financial performance (ROA and Tobin’s Q) using a Hybrid Model that decomposes effects into between-firm differences and within-firm changes. Benchmarking against conventional composite scores (overall, pillar, and category aggregates), the data-centric ten-component specification achieves stronger explanatory and predictive performance in both training and testing periods. Moreover, the within–between decomposition clarifies whether ESG–performance links reflect persistent cross-firm differences, time-varying within-firm adjustments, or both, thereby strengthening interpretation and managerial relevance.

Agnese, P., Cerciello, M., Giacomini, E., & Taddeo, S. (2023). Environmental, social and governance controversies: the role of European bank boards. Management Decision, 61(12), 3739–3754. https://doi.org/10.1108/md-01-2023-0082
Allee, K. D., Do, C., & Raymundo, F. G. (2022). Principal component analysis and factor analysis in accounting research. Journal of Financial Reporting, 7(2), 1–39.
Allison, P. D. (2005). Fixed Effects Regression Methods for Longitudinal Data Using SAS. SAS Institute.
Amershi, S., Begel, A., Bird, C., DeLine, R., Gall, H., Kamar, E., Nagappan, N., Nushi, B., & Zimmermann, T. (2019, 25–31 May 2019). Software Engineering for Machine Learning: A Case Study. 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP),
Assembly, R. P. (2024, 2024, October 15). Mitigating risk factors for PCB manufacturing lead times. Rayming PCB & Assembly. https://www.linkedin.com/pulse/mitigating-risk-factors-pcb-manufacturing-lead-times-m31pc/
Avramov, D., Cheng, S., Lioui, A., & Tarelli, A. (2022). Sustainable investing with ESG rating uncertainty. Journal of Financial Economics, 145(2, Part B), 642–664. https://doi.org/https://doi.org/10.1016/j.jfineco.2021.09.009
Awaysheh, A., Heron, R. A., Perry, T., & Wilson, J. I. (2020). On the relation between corporate social responsibility and financial performance. Strategic Management Journal, 41(6), 965–987. https://doi.org/https://doi.org/10.1002/smj.3122
Berchicci, L., & King, A. A. (2022). Building knowledge by mapping model uncertainty in six studies of social and financial performance. Strategic Management Journal, 43(7), 1319–1346. https://doi.org/https://doi.org/10.1002/smj.3374
Berg, F., Kölbel, J. F., & Rigobon, R. (2022). Aggregate Confusion: The Divergence of ESG Ratings*. Review of Finance, 26(6), 1315–1344. https://doi.org/10.1093/rof/rfac033
Billio, M., Costola, M., Hristova, I., Latino, C., & Pelizzon, L. (2021). Inside the ESG ratings: (Dis)agreement and performance. Corporate Social Responsibility and Environmental Management, 28(5), 1426–1445. https://doi.org/https://doi.org/10.1002/csr.2177
Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning (Vol. 4). Springer.
Bliese, P. D., Schepker, D. J., Essman, S. M., & Ployhart, R. E. (2020). Bridging Methodological Divides Between Macro- and Microresearch: Endogeneity and Methods for Panel Data. Journal of Management, 46(1), 70–99. https://doi.org/10.1177/0149206319868016
Browning, T. R., & Eppinger, S. D. (2002). Modeling impacts of process architecture on cost and schedule risk in product development. IEEE Transactions on Engineering Management, 49(4), 428–442. https://doi.org/10.1109/TEM.2002.806709
Brunton, S. L., & Kutz, J. N. (2022). Data-driven science and engineering: Machine learning, dynamical systems, and control. Cambridge University Press.
Certo, S. T., Withers, M. C., & Semadeni, M. (2017). A tale of two effects: Using longitudinal data to compare within- and between-firm effects. Strategic Management Journal, 38(7), 1536–1556. https://doi.org/https://doi.org/10.1002/smj.2586
Chatterji, A. K., Durand, R., Levine, D. I., & Touboul, S. (2016). Do ratings of firms converge? Implications for managers, investors and strategy researchers. Strategic Management Journal, 37(8), 1597–1614. https://doi.org/https://doi.org/10.1002/smj.2407
Choi, J., Menon, A., & Tabakovic, H. (2021). Using machine learning to revisit the diversification–performance relationship. Strategic Management Journal, 42(9), 1632–1661. https://doi.org/https://doi.org/10.1002/smj.3317
Chou, P., Chuang, H. H.-C., Chou, Y.-C., & Liang, T.-P. (2022). Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning. European Journal of Operational Research, 296(2), 635–651. https://doi.org/https://doi.org/10.1016/j.ejor.2021.04.021
Chou, Y.-C., Chuang, H. H.-C., Chou, P., & Oliva, R. (2023). Supervised machine learning for theory building and testing: Opportunities in operations management. Journal of Operations Management, 69(4), 643–675. https://doi.org/https://doi.org/10.1002/joom.1228
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/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/https://doi.org/10.1002/smj.3067
Christensen, D. M., Serafeim, G., & Sikochi, A. (2022). Why is Corporate Virtue in the Eye of The Beholder? The Case of ESG Ratings. The Accounting Review, 97(1), 147–175. https://doi.org/10.2308/tar-2019-0506
Chuang, H. H.-C., Chou, Y.-C., & Oliva, R. (2021). Cross-item learning for volatile demand forecasting: An intervention with predictive analytics. Journal of Operations Management, 67(7), 828–852. https://doi.org/https://doi.org/10.1002/joom.1152
Demers, E., Hendrikse, J., Joos, P., & Lev, B. (2021). ESG did not immunize stocks during the COVID-19 crisis, but investments in intangible assets did. Journal of Business Finance & Accounting, 48(3-4), 433–462. https://doi.org/https://doi.org/10.1111/jbfa.12523
Denton, P., Parke, S., Tao, T., & Zhang, X. (2022). Eigenvectors from eigenvalues: A survey of a basic identity in linear algebra. Bulletin of the American Mathematical Society, 59(1), 31–58. https://doi.org/https://doi.org/10.1090/bull/1722
Durand, R., Paugam, L., & Stolowy, H. (2019). Do investors actually value sustainability indices? Replication, development, and new evidence on CSR visibility. Strategic Management Journal, 40(9), 1471–1490. https://doi.org/https://doi.org/10.1002/smj.3035
El Gibari, S., Gómez, T., & Ruiz, F. (2019). Building composite indicators using multicriteria methods: a review. Journal of Business Economics, 89(1), 1–24. https://doi.org/10.1007/s11573-018-0902-z
Fan, J., Wang, K., Zhong, Y., & Zhu, Z. (2021). Robust high dimensional factor models with applications to statistical machine learning. Statistical science: a review journal of the Institute of Mathematical Statistics, 36(2), 303.
Fool, T. M. (2017). 1 Way Intel Corporation Wants to Improve R&D Efficiency. Retrieved 2017 April 4 from https://www.nasdaq.com/articles/1-way-intel-corporation-wants-improve-rd-efficiency-2017-04-04
Friedman, J. H., Hastie, T., & Tibshirani, R. (2010). Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 33(1), 1 – 22. https://doi.org/10.18637/jss.v033.i01
Frost, H. R. (2022). Eigenvectors from Eigenvalues Sparse Principal Component Analysis. Journal of Computational and Graphical Statistics, 31(2), 486–501. https://doi.org/10.1080/10618600.2021.1987254
Gibson Brandon, R., Philipp, K., & and Schmidt, P. S. (2021). ESG Rating Disagreement and Stock Returns. Financial Analysts Journal, 77(4), 104–127. https://doi.org/10.1080/0015198X.2021.1963186
Gillan, S. L., Koch, A., & Starks, L. T. (2021). Firms and social responsibility: A review of ESG and CSR research in corporate finance. Journal of Corporate Finance, 66, 101889. https://doi.org/https://doi.org/10.1016/j.jcorpfin.2021.101889
Goay, C. H., Aziz, A. A., Ahmad, N. S., & Goh, P. (2019). Eye Diagram Contour Modeling Using Multilayer Perceptron Neural Networks With Adaptive Sampling and Feature Selection. IEEE Transactions on Components, Packaging and Manufacturing Technology, 9(12), 2427–2441. https://doi.org/10.1109/TCPMT.2019.2938583
Greco, S., Ishizaka, A., Tasiou, M., & Torrisi, G. (2019). On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness. Social Indicators Research, 141(1), 61–94. https://doi.org/10.1007/s11205-017-1832-9
Guerra-Urzola, R., Van Deun, K., Vera, J. C., & Sijtsma, K. (2021). A Guide for Sparse PCA: Model Comparison and Applications. Psychometrika, 86(4), 893–919. https://doi.org/10.1007/s11336-021-09773-2
Halbritter, G., & Dorfleitner, G. (2015). The wages of social responsibility — where are they? A critical review of ESG investing. Review of Financial Economics, 26, 25–35. https://doi.org/https://doi.org/10.1016/j.rfe.2015.03.004
Hale, P. D., Jargon, J., Wang, C. M. J., Grossman, B., Claudius, M., Torres, J. L., Dienstfrey, A., & Williams, D. F. (2012). A Statistical Study of De-Embedding Applied to Eye Diagram Analysis. IEEE Transactions on Instrumentation and Measurement, 61(2), 475–488. https://doi.org/10.1109/TIM.2011.2164853
Harrison, J. S., Josefy, M. A., Kalm, M., & Krause, R. (2023). Using supervised machine learning to scale human-coded data: A method and dataset in the board leadership context. Strategic Management Journal, 44(7), 1780–1802. https://doi.org/https://doi.org/10.1002/smj.3480
Hastie, T., Tibshirani, R., Friedman, J., & Franklin, J. (2005). The elements of statistical learning: data mining, inference and prediction. The Mathematical Intelligencer, 27(2), 83–85.
Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2). Springer.
Hawn, O., Chatterji, A. K., & Mitchell, W. (2018). Do investors actually value sustainability? New evidence from investor reactions to the Dow Jones Sustainability Index (DJSI). Strategic Management Journal, 39(4), 949–976. https://doi.org/https://doi.org/10.1002/smj.2752
Ho, B., Ho, C. Y., Hsu, J., & Wu, K. T. (2023, 25–27 Oct. 2023). High-speed Channel Design and Correlation Between Signal Integrity and Electrical Validation of Datacenter Platforms. 2023 18th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT),
Hoda, R., Salleh, N., & Grundy, J. (2018). The Rise and Evolution of Agile Software Development. IEEE Software, 35(5), 58–63. https://doi.org/10.1109/MS.2018.290111318
Hofman, J. M., Sharma, A., & Watts, D. J. (2017). Prediction and explanation in social systems. Science, 355(6324), 486–488. https://doi.org/doi:10.1126/science.aal3856
Hsiao, P. C., Chou, Y. C., & Chuang, H. H. C. (2025). Reduce-Then-Predict or Simultaneous Reduce-and-Predict? Data-Driven Sparse Modeling for Improving R&D Efficiency. IEEE Transactions on Engineering Management, 72, 2646–2660. https://doi.org/10.1109/TEM.2025.3577580
Hsu, J., Lin, L., Ho, B., Hsiao, N., Chen, D., Su, T., & Zhang, X. (2021, 21–23 Dec. 2021). Robotic System with Intel® Automatic In-Board Characterization for Customer Board Design Quality Check. 2021 16th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT),
Huang, D., Jiang, F., Li, K., Tong, G., & Zhou, G. (2022). Scaled PCA: A New Approach to Dimension Reduction. Management Science, 68(3), 1678–1695. https://doi.org/10.1287/mnsc.2021.4020
Huang, L., Wu, J., & Yan, L. (2015). Defining and measuring urban sustainability: a review of indicators. Landscape Ecology, 30(7), 1175–1193. https://doi.org/10.1007/s10980-015-0208-2
Isaksson, L., & Kiessling, T. (2023). Corporate Social Responsibility (CSR) and Engineering Management: Performance Implications. IEEE Transactions on Engineering Management, 70(11), 4021–4031. https://doi.org/10.1109/TEM.2021.3091702
Kalnins, A. (2018). Multicollinearity: How common factors cause Type 1 errors in multivariate regression. Strategic Management Journal, 39(8), 2362–2385. https://doi.org/https://doi.org/10.1002/smj.2783
Kalnins, A. (2022). When does multicollinearity bias coefficients and cause type 1 errors? A reconciliation of Lindner, Puck, and Verbeke (2020) with Kalnins (2018). Journal of International Business Studies, 53(7), 1536–1548. https://doi.org/10.1057/s41267-022-00531-9
Kawano, S., Fujisawa, H., Takada, T., & Shiroishi, T. (2015). Sparse principal component regression with adaptive loading. Computational Statistics & Data Analysis, 89, 192–203. https://doi.org/https://doi.org/10.1016/j.csda.2015.03.016
Kawano, S., Fujisawa, H., Takada, T., & Shiroishi, T. (2018). Sparse principal component regression for generalized linear models. Computational Statistics & Data Analysis, 124, 180–196. https://doi.org/https://doi.org/10.1016/j.csda.2018.03.008
Kim, S., Lee, G., & Kang, H.-G. (2021). Risk management and corporate social responsibility. Strategic Management Journal, 42(1), 202–230. https://doi.org/https://doi.org/10.1002/smj.3224
Kimbrough, M. D., Xu, W., Sijing, W., & and Zhang, J. (2024). Does Voluntary ESG Reporting Resolve Disagreement among ESG Rating Agencies? European Accounting Review, 33(1), 15–47. https://doi.org/10.1080/09638180.2022.2088588
Liang, D., & Xue, F. (2023). Integrating automated machine learning and interpretability analysis in architecture, engineering and construction industry: A case of identifying failure modes of reinforced concrete shear walls. Computers in Industry, 147, 103883. https://doi.org/https://doi.org/10.1016/j.compind.2023.103883
Lindner, T., Puck, J., & Verbeke, A. (2020). Misconceptions about multicollinearity in international business research: Identification, consequences, and remedies. Journal of International Business Studies, 51(3), 283–298. https://doi.org/10.1057/s41267-019-00257-1
Lindner, T., Puck, J., & Verbeke, A. (2022). Beyond addressing multicollinearity: Robust quantitative analysis and machine learning in international business research. Journal of International Business Studies, 53(7), 1307–1314. https://doi.org/10.1057/s41267-022-00549-z
LSEG. (2024). LSEG ESG Scores Methodology. London Stock Exchange Group. Retrieved February 28, 2025 from https://www.lseg.com/content/dam/data-analytics/en_us/documents/methodology/lseg-esg-scores-methodology.pdf
Majzoubi, M., & Zhao, E. Y. (2023). Going beyond optimal distinctiveness: Strategic positioning for gaining an audience composition premium. Strategic Management Journal, 44(3), 737–777. https://doi.org/https://doi.org/10.1002/smj.3460
Mikkola, J. H. (2007). Management of Product Architecture Modularity for Mass Customization: Modeling and Theoretical Considerations. IEEE Transactions on Engineering Management, 54(1), 57–69. https://doi.org/10.1109/TEM.2006.889067
Miric, M., Jia, N., & Huang, K. G. (2023). Using supervised machine learning for large-scale classification in management research: The case for identifying artificial intelligence patents. Strategic Management Journal, 44(2), 491–519. https://doi.org/https://doi.org/10.1002/smj.3441
Morningstar Sustainalytics. (2024). ESG Risk Ratings – Version 3.1: Methodology Abstract. https://www.sustainalytics.com/docs/knowledgehublibraries/default-document-library/sustainalytics_-esg-risk-ratings_-version-3-1_-methodology-abstract_-june-2024.pdf
Muck, M., & Schmidl, T. (2024). Comparing ESG score weighting approaches and stock performance differentiation. Finance Research Letters, 67, 105924. https://doi.org/https://doi.org/10.1016/j.frl.2024.105924
Nasdaq Index Research Team. (2023). When performance matters: nasdaq-100 vs. S&p 500 q4, 2023 | nasdaq. https://www.nasdaq.com/articles/when-performance-matters-nasdaq-100-vs-sp-500-q4-2023
OECD. (2008). Handbook on constructing composite indicators: methodology and user guide. OECD publishing.
Park, J., Choi, S., Kim, J. J., Kim, Y., Lee, M., Kim, H., Bae, B., Song, H., Cho, K., Lee, S., Lee, H., & Kim, J. (2018). A Novel Stochastic Model-Based Eye-Diagram Estimation Method for 8B/10B and TMDS-Encoded High-Speed Channels. IEEE Transactions on Electromagnetic Compatibility, 60(5), 1510–1519. https://doi.org/10.1109/TEMC.2017.2766295
Park, J., Jung, D. H., Kim, B., Choi, S., Kim, Y., Park, S., Park, G., Cho, K., Lee, S., & Kim, J. (2019). A Novel Eye-Diagram Estimation Method for Pulse Amplitude Modulation With N-Level (PAM-N) on Stacked Through-Silicon Vias. IEEE Transactions on Electromagnetic Compatibility, 61(4), 1198–1206. https://doi.org/10.1109/TEMC.2018.2843813
Park, J., & Kim, D. (2024). Statistical Eye Diagrams for High-Speed Interconnects of Packages: A Review. IEEE Access, 12, 22880–22891. https://doi.org/10.1109/ACCESS.2024.3359037
Paruolo, P., Saisana, M., & Saltelli, A. (2012). Ratings and Rankings: Voodoo or Science? Journal of the Royal Statistical Society Series A: Statistics in Society, 176(3), 609–634. https://doi.org/10.1111/j.1467-985X.2012.01059.x
Pelger, M., & Xiong, R. (2022). Interpretable Sparse Proximate Factors for Large Dimensions. Journal of Business & Economic Statistics, 40(4), 1642–1664. https://doi.org/10.1080/07350015.2021.1961786
Pissourios, I. A. (2013). An interdisciplinary study on indicators: A comparative review of quality-of-life, macroeconomic, environmental, welfare and sustainability indicators. Ecological Indicators, 34, 420–427. https://doi.org/https://doi.org/10.1016/j.ecolind.2013.06.008
Plyakha, Y., Uppal, R., & Vilkov, G. (2021). Equal or value weighting? Implications for asset-pricing tests. In Financial risk management and modeling (pp. 295–347). Springer.
Rathje, J., Katila, R., & Reineke, P. (2024). Making the most of AI and machine learning in organizations and strategy research: Supervised machine learning, causal inference, and matching models. Strategic Management Journal, 45(10), 1926–1953. https://doi.org/https://doi.org/10.1002/smj.3604
Rytchkov, O., & Zhong, X. (2020). Information Aggregation and P-Hacking. Management Science, 66(4), 1605–1626. https://doi.org/10.1287/mnsc.2018.3259
S&P Global Switzerland SA. (2025). CSA Methodology Handbook. https://portal.s1.spglobal.com/survey/documents/CSA_Methodology%20Handbook_COS.pdf
Salles Melo Lima, M., Eryarsoy, E., & Delen, D. (2021). Predicting and Explaining Pig Iron Production on Charcoal Blast Furnaces: A Machine Learning Approach. INFORMS Journal on Applied Analytics, 51(3), 213–235. https://doi.org/10.1287/inte.2020.1058
Schluse, M., Priggemeyer, M., Atorf, L., & Rossmann, J. (2018). Experimentable Digital Twins—Streamlining Simulation-Based Systems Engineering for Industry 4.0. IEEE Transactions on Industrial Informatics, 14(4), 1722–1731. https://doi.org/10.1109/TII.2018.2804917
Schunck, R. (2013). Within and between Estimates in Random-Effects Models: Advantages and Drawbacks of Correlated Random Effects and Hybrid Models. The Stata Journal, 13(1), 65–76. https://doi.org/10.1177/1536867x1301300105
Senoner, J., Netland, T., & Feuerriegel, S. (2022). Using Explainable Artificial Intelligence to Improve Process Quality: Evidence from Semiconductor Manufacturing. Management Science, 68(8), 5704–5723. https://doi.org/10.1287/mnsc.2021.4190
Setiarini, A., Gani, L., Diyanty, V., & Adhariani, D. (2023). Strategic orientation, risk-taking, corporate life cycle and environmental, social and governance (ESG) practices: Evidence from ASEAN countries. Business Strategy & Development, 6(3), 491–502. https://doi.org/https://doi.org/10.1002/bsd2.257
Shen, H., & Huang, J. Z. (2008). Sparse principal component analysis via regularized low rank matrix approximation. Journal of Multivariate Analysis, 99(6), 1015–1034. https://doi.org/https://doi.org/10.1016/j.jmva.2007.06.007
Shen, Y., Wang, H., Blaabjerg, F., Zhao, H., & Long, T. (2020). Thermal Modeling and Design Optimization of PCB Vias and Pads. IEEE Transactions on Power Electronics, 35(1), 882–900. https://doi.org/10.1109/TPEL.2019.2915029
Shi, B., Zhou, Y., Sun, H., Nguyen, T., & Schutt-Ainé, J. E. (2024). Statistical Method for Eye Diagram Simulation in High-Speed Link Nonlinear System Applications. IEEE Transactions on Components, Packaging and Manufacturing Technology, 1–1. https://doi.org/10.1109/TCPMT.2024.3471661
Shimao, H., Kim, S. J., Khern-Am-Nuai, W., & Cohen, M. C. (2025). Revisiting the CEO Effect Through a Machine Learning Lens. Management Science, 71(6), 5396–5408. https://doi.org/10.1287/mnsc.2023.03625
Shinn, M. (2023). Phantom oscillations in principal component analysis. Proceedings of the National Academy of Sciences, 120(48), e2311420120. https://doi.org/doi:10.1073/pnas.2311420120
Shmueli, G. (2010). To Explain or to Predict? Statistical Science, 25(3), 289–310, 222.
Shmueli, G., & Koppius, O. R. (2011). Predictive Analytics in Information Systems Research. MIS Quarterly, 35(3), 553–572. https://doi.org/10.2307/23042796
Shrestha, Y. R., He, V. F., Puranam, P., & Krogh, G. v. (2021). Algorithm Supported Induction for Building Theory: How Can We Use Prediction Models to Theorize? Organization Science, 32(3), 856–880. https://doi.org/10.1287/orsc.2020.1382
Shrestha, Y. R., He, V. F., Puranam, P., & von Krogh, G. (2021). Algorithm Supported Induction for Building Theory: How Can We Use Prediction Models to Theorize? Organization Science, 32(3), 856–880. https://doi.org/10.1287/orsc.2020.1382
Soo-Haeng, C., & Eppinger, S. D. (2005). A simulation-based process model for managing complex design projects. IEEE Transactions on Engineering Management, 52(3), 316–328. https://doi.org/10.1109/TEM.2005.850722
Taljaard, B. H., & Maré, E. (2021). Why has the equal weight portfolio underperformed and what can we do about it? Quantitative Finance, 21(11), 1855–1868. https://doi.org/10.1080/14697688.2021.1889020
Tay, J. K., Friedman, J., & Tibshirani, R. (2021). Principal component-guided sparse regression. Canadian Journal of Statistics, 49(4), 1222–1257. https://doi.org/https://doi.org/10.1002/cjs.11617
Teng, T., Wang, M., Hsu, J., Lee, W., Su, T., Kan, J., & Chen, B. (2022, 26–28 Oct. 2022). PCIe-Express Channel Design Optimization for Out-of-Guideline Three Connector Topology. 2022 17th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT),
Terwiesch, C. (2019). OM Forum—Empirical Research in Operations Management: From Field Studies to Analyzing Digital Exhaust. Manufacturing & Service Operations Management, 21(4), 713–722. https://doi.org/10.1287/msom.2018.0723
Terwiesch, C., & Ulrich, K. (2009). Innovation tournaments: Creating and selecting exceptional opportunities. Harvard business press.
Thota, M. K., Shajin, F. H., & Rajesh, P. (2020). Survey on software defect prediction techniques. International Journal of Applied Science and Engineering, 17(4), 331–344. https://doi.org/10.6703/IJASE.202012_17(4).331
Tibshirani, R. (1996a). Regression Shrinkage and Selection Via the Lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
Tibshirani, R. (1996b). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B: Statistical Methodology, 58(1), 267–288.
Trappey, A. J. C., Trappey, C. V., Govindarajan, U. H., & Sun, J. J. H. (2021). Patent Value Analysis Using Deep Learning Models—The Case of IoT Technology Mining for the Manufacturing Industry. IEEE Transactions on Engineering Management, 68(5), 1334–1346. https://doi.org/10.1109/TEM.2019.2957842
Trumpp, C., Endrikat, J., Zopf, C., & Guenther, E. (2015). Definition, Conceptualization, and Measurement of Corporate Environmental Performance: A Critical Examination of a Multidimensional Construct. Journal of Business Ethics, 126(2), 185–204. https://doi.org/10.1007/s10551-013-1931-8
Vasileska, E., Argilovski, A., Tomov, M., Jovanoski, B., & Gecevska, V. (2024). Implementation of Machine Learning for Enhancing Lean Manufacturing Practices for Metal Additive Manufacturing. IEEE Transactions on Engineering Management, 71, 14836–14845. https://doi.org/10.1109/TEM.2024.3459645
von Krogh, G., Roberson, Q., & Gruber, M. (2023). Recognizing and Utilizing Novel Research Opportunities with Artificial Intelligence. Academy of Management Journal, 66(2), 367–373. https://doi.org/10.5465/amj.2023.4002
Wilms, I., & Croux, C. (2018). An algorithm for the multivariate group lasso with covariance estimation. Journal of Applied Statistics, 45(4), 668–681. https://doi.org/10.1080/02664763.2017.1289503
Wiréhn, L., Danielsson, Å., & Neset, T.-S. S. (2015). Assessment of composite index methods for agricultural vulnerability to climate change. Journal of Environmental Management, 156, 70–80. https://doi.org/https://doi.org/10.1016/j.jenvman.2015.03.020
Witten, D. M., Tibshirani, R., & Hastie, T. (2009). A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics, 10(3), 515–534. https://doi.org/10.1093/biostatistics/kxp008
Yu, W., Liu, Q., Zhao, G., & Song, Y. (2023). Exploring the Effects of Data-Driven Hospital Operations on Operational Performance From the Resource Orchestration Theory Perspective. IEEE Transactions on Engineering Management, 70(8), 2747–2759. https://doi.org/10.1109/TEM.2021.3098541
Zhu, X., Ninh, A., Zhao, H., & Liu, Z. (2021). Demand Forecasting with Supply‐Chain Information and Machine Learning: Evidence in the Pharmaceutical Industry. Production and Operations Management, 30(9), 3231–3252. https://doi.org/10.1111/poms.13426
Zou, H. (2006). The Adaptive Lasso and Its Oracle Properties. Journal of the American Statistical Association, 101(476), 1418–1429. https://doi.org/10.1198/016214506000000735
Zou, H., & Hastie, T. (2005). Regularization and Variable Selection Via the Elastic Net. Journal of the Royal Statistical Society Series B: Statistical Methodology, 67(2), 301–320. https://doi.org/10.1111/j.1467-9868.2005.00503.x
Zou, H., Hastie, T., & Tibshirani, R. (2006). Sparse Principal Component Analysis. Journal of Computational and Graphical Statistics, 15(2), 265–286. https://doi.org/10.1198/106186006X113430
Zou, H., & Xue, L. (2018). A Selective Overview of Sparse Principal Component Analysis. Proceedings of the IEEE, 106(8), 1311–1320. https://doi.org/10.1109/JPROC.2018.2846588