營建產業相關企業在執行專案的任何階段都面臨著一長串的道德挑戰，當營建企業採取非法、錯誤的商業行為恐導致經濟損失，甚至對工人和居民造成生命安全上的威脅。因此，對於營建企業和其利益相關者而言，可否識別公司是否存有不當行為與其可能影響的後果至關重要。
以往對營建企業不當行為的研究多著重在組織中的董事會組成與非法商業行為的關係，甚少有研究針對營建企業不當行為、財務資訊及公司年報文字情緒的關係進行釐清。公司年報係指涵蓋公司一整年商業活動的綜合報告，內容包括四大財務報表以及大量文字敘述，故可透過閱覽年報內的資訊或數值，獲取與財務表現、營運描述及不當行為等相關的重要訊息。因此本研究目的是針對臺灣上市營建企業不當行為、財務績效與年報文字情緒三者間的關聯性進行釐清與探究，其基礎假設是建立在當公司處於某些(財務)環境條件或組織結構下，恐會誘使公司進行非法或不當行為，並反應在年報文字敘述；亦或是當公司因有不當行為時，其公司營運績效會受到影響而有所變化。
本研究以我國上市的48家營建企業為研究對象，研究期間由2012至2020年總計387筆公司年度的觀察樣本進行多元迴歸分析。結果顯示營建企業的不當行為會造成當年度與下一年度財務績效下滑，同時也發現當公司財務績效不佳時，同樣會影響當年度與下一年度發生不當行為的次數，顯示不當行為與財務績效下滑兩者息息相關。此外，本研究也發現當財務績效表現亮眼，以及公司不當行為次數增加時，會影響「致股東報告書」正面或負面情緒詞彙之引用。最後是「致股東報告書」段落文字情緒與下一年度財務績效具有關聯性，以及「致股東報告書」負面文字情緒與下一年度公司發生不當行為的次數也顯示有所關聯。

Construction companies face many moral challenges at any stage of business operations. When construction companies conduct illegal and wrongful business practices, that may possibly lead to economic losses and even threaten the lives and safety of workers. Therefore, it is critical for construction companies and their stakeholders to identify whether their companies have misconduct behaivors and the extent of the possible consequences.
Previous researches on corporate misconduct mainly focuse on the relationship between board composition and illegal business practices within the organizations. Less attention has been paid on clarifing the relationship between corporate misconduct, financial performance and company annual report text sentiment, escpecailly for construction related companies. As the annual report is a comprehensive report summarizing a company's business activities throughout the fiscal year, including financial statements and extensive text descriptions, Hence, annual reports are used in this study as the main source to reveal the information of financial performance, text sentiment and misconduct.
The purpose of this research is to clarify and explore the relationship between the misconduct, financial performance and sentiment of publicly listed construction corporations via the published annual reports in Taiwan. The basic hypotheses are (i) when a company is under specific environmental condition or financial pressure, it may conduct illegal behaivor or misconduct, which may reflect on text description, (ii) the misconduct of a company may affect its operating performance and lead to unsatisfactory financial performance.
This study collects 48 listed construction companies in Taiwan. The study period is from 2012 to 2020 with 387 annual samples. Multiple regression analysis is conducted for investigating the relationship between corporate misconduct, financial performance and written text sentiment. The results show that the number and the amount of misconducts would impact on the financial performance of the current year and the following year. On the other hand, when a company facing unsatisfactory financial performance, the number of misconducts for that year and the following year would increase, showing there is a negative correlation between misconduct and financial performance. In addition, the outcomes reveal that when financial performance is considered to be adequate, and the numbers of misconducts increase, both would affect the positive and negative descriptive words, respectively. Finally, the sentiment in annual reports is found to be correlated with the following fiscal year financial performance, and the negative sentiment is positively correlated with the number of misconducts in the following year.

Abrahamson, E., & Amir, E. (1996). The information content of the president's letter to shareholders. Journal of Business Finance and Accounting, 23, 1157-1182.
Akaichi, J., Dhouioui, Z., & Pérez, M. J. L. H. (2013, October). Text mining facebook status updates for sentiment classification. In 2013 17th International conference on system theory, control and computing (ICSTCC) (pp. 640-645). IEEE.
Albrecht, W. S., Albrecht, C. C., & Albrecht, C. O. (2006). Fraud Examination, Thomson South-Western.
Al-Natour, S., & Turetken, O. (2020). A comparative assessment of sentiment analysis and star ratings for consumer reviews. International Journal of Information Management, 54, 102132.
Ameyaw, E. E., Pärn, E., Chan, A. P., Owusu-Manu, D. G., Edwards, D. J., & Darko, A. (2017). Corrupt practices in the construction industry: Survey of Ghanaian experience. Journal of Management in Engineering, 33(6), 05017006.
Baucus, M. S., & Near, J. P. (1991). Can illegal corporate behavior be predicted? An event history analysis. Academy of management Journal, 34(1), 9-36.
Bhana, N. (2009). The chairman's statements and annual reports: Are they reporting the same company performance to investors?. Investment Analysts Journal, 38(70), 32-46.
Cambria, E., Das, D., Bandyopadhyay, S., & Feraco, A. (2017). Affective computing and sentiment analysis. In A practical guide to sentiment analysis (pp. 1-10). Springer, Cham.
Clatworthy, M. A., & Jones, M. J. (2006). Differential patterns of textual characteristics and company performance in the chairman's statement. Accounting, Auditing & Accountability Journal.
Dikolli, S. S., Mayew, W. J., & Nanda, D. (2014). CEO tenure and the performance-turnover relation. Review of accounting studies, 19(1), 281-327.
Dorée, A. G. (2004). Collusion in the Dutch construction industry: an industrial organization perspective. Building research & information, 32(2), 146-156.
Eccles, R. G., & Mavrinac, S. C. (1995). Improving the corporate disclosure process. MIT Sloan Management Review, 36(4), 11.
Fanning, K. M., & Cogger, K. O. (1998). Neural network detection of management fraud using published financial data. Intelligent Systems in Accounting, Finance & Management, 7(1), 21-41.
Gabbioneta, C., Greenwood, R., Mazzola, P., & Minoja, M. (2013). The influence of the institutional context on corporate illegality. Accounting, Organizations and Society, 38(6-7), 484-504.
Goel, S., & Uzuner, O. (2016). Do sentiments matter in fraud detection? Estimating semantic orientation of annual reports. Intelligent Systems in Accounting, Finance and Management, 23(3), 215-239.
Greve, H. R., Palmer, D., & Pozner, J. E. (2010). Organizations gone wild: The causes, processes, and consequences of organizational misconduct. Academy of Management annals, 4(1), 53-107.
Gupta, A., Dengre, V., Kheruwala, H. A., & Shah, M. (2020). Comprehensive review of text-mining applications in finance. Financial Innovation, 6(1), 1-25.
Hajek, P., & Henriques, R. (2017). Mining corporate annual reports for intelligent detection of financial statement fraud–A comparative study of machine learning methods. Knowledge-Based Systems, 128, 139-152.
Harris, J., & Bromiley, P. (2007). Incentives to cheat: The influence of executive compensation and firm performance on financial misrepresentation. Organization Science, 18(3), 350-367.
Ho, C. M. F. (2011). Ethics management for the construction industry: A review of ethical decision‐making literature. Engineering, construction and architectural management, 18(5), 516-537.
Huang, S. Y., Lin, C. C., Chiu, A. A., & Yen, D. C. (2017). Fraud detection using fraud triangle risk factors. Information Systems Frontiers, 19(6), 1343-1356.
Humpherys, S. L., Moffitt, K. C., Burns, M. B., Burgoon, J. K., & Felix, W. F. (2011). Identification of fraudulent financial statements using linguistic credibility analysis. Decision Support Systems, 50(3), 585-594.
Kangari, R., Farid, F., & Elgharib, H. M. (1992). Financial performance analysis for construction industry. Journal of Construction Engineering and Management, 118(2), 349-361.
Kloptchenko, A., Eklund, T., Karlsson, J., Back, B., Vanharanta, H., & Visa, A. (2004). Combining data and text mining techniques for analysing financial reports. Intelligent Systems in Accounting, Finance & Management: International Journal, 12(1), 29-41.
Chia-Jung, L., Ran, W., Cheng-Yu, L., Hung Cathy, C. W., & Shu-Chien, H. (2018). Board Structure and Directors’ Role in Preventing Corporate Misconduct in the Construction Industry. American Society of Civil Engineers.
Liu, J., Zhao, X., & Li, Y. (2017). Exploring the factors inducing contractors’ unethical behavior: case of China. Journal of Professional Issues in Engineering Education and Practice, 143(3), 04016023.
Locatelli, G., Mariani, G., Sainati, T., & Greco, M. (2017). Corruption in public projects and megaprojects: There is an elephant in the room!. International Journal of Project Management, 35(3), 252-268.
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.
Luhn, H. P. (1957). A statistical approach to mechanized encoding and searching of literary information. IBM Journal of research and development, 1(4), 309-317.
Magnusson, C., Arppe, A., Eklund, T., Back, B., Vanharanta, H., & Visa, A. (2005). The language of quarterly reports as an indicator of change in the company’s financial status. Information & Management, 42(4), 561-574.
May, D., Wilson, O., & Skitmore, M. (2001). Bid cutting: an empirical study of practice in South‐East Queensland. Engineering, Construction and Architectural Management.
Minhas, S., & Hussain, A. (2016). From spin to swindle: Identifying falsification in financial text. Cognitive computation, 8(4), 729-745.
Mishina, Y., Dykes, B. J., Block, E. S., & Pollock, T. G. (2010). Why “good” firms do bad things: The effects of high aspirations, high expectations, and prominence on the incidence of corporate illegality. Academy of Management Journal, 53(4), 701-722.
Pejić Bach, M., Krstić, Ž., Seljan, S., & Turulja, L. (2019). Text mining for big data analysis in financial sector: A literature review. Sustainability, 11(5), 1277.
Ravisankar, P., Ravi, V., Rao, G. R., & Bose, I. (2011). Detection of financial statement fraud and feature selection using data mining techniques. Decision support systems, 50(2), 491-500.
Rebeiz, K. S., & Salameh, Z. (2006). Relationship between governance structure and financial performance in construction. Journal of Management in Engineering, 22(1), 20-26.
Simpson, S. S. (2002). Corporate crime, law, and social control.
Smith, M., & Taffler, R. J. (2000). The chairman’s statement‐A content analysis of discretionary narrative disclosures. Accounting, Auditing & Accountability Journal.
Spathis, C., Doumpos, M., & Zopounidis, C. (2003). Using client performance measures to identify pre-engagement factors associated with qualified audit reports in Greece. The International Journal of Accounting, 38(3), 267-284.
Suyanto, S. (2009). Fraudulent financial statement: evidence from statement on auditing standard no. 99. Gadjah Mada International Journal of Business, 11(1), 117-144.
Upadhyay, A. D., Bhargava, R., & Faircloth, S. D. (2014). Board structure and role of monitoring committees. Journal of Business Research, 67(7), 1486-1492.
Wang, R., Asghari, V., Hsu, S. C., Lee, C. J., & Chen, J. H. (2020). Detecting corporate misconduct through random forest in China’s construction industry. Journal of cleaner production, 268, 122266.
Yao, J., Pan, Y., Yang, S., Chen, Y., & Li, Y. (2019). Detecting fraudulent financial statements for the sustainable development of the socio-economy in China: a multi-analytic approach. Sustainability, 11(6), 1579.
石慧妤（2009）。應用企業年報中之文字資訊於盈餘預測之研究。國立臺灣大學會計學研究所碩士論文，台北市。
林宜萱（2013）。財經領域情緒辭典之建置與其有效性之驗證-以財經新聞為元件。國立臺灣大學會計學研究所碩士論文，台北市。
馬儷砡（2012）。舞弊風險評量之財務危機預警模式。國立中正大學國際經濟研究所碩士論文，嘉義縣。
曾政興（2018）。企業財務預警模型 – 以羅吉斯迴歸分析。淡江大學企業管理學系碩士在職專班碩士論文，新北市。
黃娟娟（2012）。公司年報文字探勘與財務預警資訊內涵。逢甲大學商學博士學位學程博士論文，台中市。
黃振豊, & 呂紹強. (2000). 企業財務危機預警模式之研究-以財務及非財務因素構建. 當代會計, 1(1), 27-65.
黃慶華（2011）。訴訟宣告與判決對台灣上市公司績效的影響。國立交通大學財務金融研究所碩士論文，新竹市。
楊文賓（2017）。以資料探勘方法建立財務報表舞弊預測模型。中國文化大學會計學系碩士論文，台北市。
賴佳筠（2012）。年報中語調之改變對市場與未來績效之影響。國立臺灣大學會計學研究所碩士論文，台北市。
蘇柏翰（2016）。運用資料探勘技術偵測財務報表舞弊－以台灣上市(櫃)公司為例。國立成功大學會計學系碩士論文，台南市。