Is negative internet posting a threat to management? Impression management and firm values: Evidence from China.

Is negative internet posting a threat to management? Impression management and firm values: Evidence from China. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Is negative internet posting a threat to management? Impression management and firm values: Evidence from China. Kwok Yip Cheung, Chung Yee Lai, Kuok Kei Law, Tai Wai David Lai, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8697348/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose - This study examines the effects of negative internet postings on impression management as well as the impacts of impression management on firm values in China before and during the COVID-19 pandemic. Design/methodology/approach - We collect data on all A-shares listed in Mainland China between 2018 and 2021 from China Stock Market & Accounting Research Database (CSMAR). A total of 3021 firm-year observations were obtained. Findings - We find that management uses more positive tones under the pressure of negative internet postings. Management uses more positive tones and longer MD&A reports during the COVID-19. This study extends the literature by identifying an additional determinant, negative internet postings, of impression management in MD&A disclosures. Originality/value - The findings support the view that management is more likely to use MD&A to mislead investors about firm performance during financial difficulties and under external pressure rather than convey true economic realities of companies. Negative internet posting Impression management Firm value China G34 G38 M42 1. Introduction We conduct the study to examine the relationships between negative internet postings and degree of impression management using Management Discussion and Analysis reports (MD&A hereafter) and in turn the impacts on firm values. Quantitative and qualitative information in corporate financial disclosures are useful for communicating firm performance and situation with its stakeholders (Luo & Zhou 2020 ). While quantitative information refers to financial information explicitly disclosed, qualitative disclosures include textual information conveyed to stakeholders in notes, conference calls, press releases, earnings announcements as well as MD&A (Pouryousof et al. 2022 ) for providing supplementary data to reflect the complete situations of firms (Cho, Roberts & Pattern 2010). Previous studies suggest that MD&A communicates the firms’ comprehensive situations by providing incremental information to reduce information asymmetry between investors and managers (Lee & Park 2019 ). As financial data and analysts’ estimates can be biased or incomplete, managers may adopt disclosure tone in qualitative descriptions to honestly deliver a signal of private information about the firm’s unobservable qualities such as risks, prospects and operational performance (Luo & Zhou 2020 ). Rather than honestly deliver a signal, Managers may adjust their tone through optimistic or pessimistic language in qualitative narratives to exploit investors’ expectations on firm performance (Luo & Zhou 2020 ). As a communication vehicle for managers, qualitative disclosures help to convey biased information through framing the tone, hereby affecting how the readers process and react to the information (Kiattikulwattana 2019 ). Managers may opportunistically use the market signaling function of tone disclosure to conceal bad news and inflate investors’ perceptions for impression management (Loughran and McDonald 2011 ). Since quantitative information is subject to stringent requirements and governance in accordance to accounting standards and rules of stock exchange (Lo, 2008 ), it is less difficult and risky for firms to embellish their bad performance through manipulating qualitative information (Jiang and Kim 2015 ). China provides a unique context for studying the impacts of impression management through MD&As for multiple reasons. First, managers of Chinese firms are experiencing weaker external regulations, compared with those in the mature markets (Jiang & Kim 2015 ). The weak judicial and regulatory institutional environment may provide a platform which preferentially favors and advocates firm managers to engage in impression management (Froese et al. 2019 ). Thus, it is meaningful to study impression management in the emerging Chinese market. Second, majority of investors are retail investors in China. Retail investors are inclined to spend more time on online stock message boards because of their low latency and cost. As for gathering financial information, over 80% of retail investors prefer to use sources from official web portals, social media platforms and online forums (Feng, Li & Su 2020 ). Breaking news and heated discussions online may probably draw investors’ attention, creating external pressure to management for using impression management. Our study makes the following two contributions. First, our findings show that the tone of qualitative information in MD&As are misleading as positive tones lead to lower firm values in next year, thereby improving the understanding of investors, board of directors and policymakers about the likelihood that management will engage in impression management during crises. Second, we extend the investigation of determinants of impression management to internet negative postings. To our best knowledge, this is the first research to examine the impacts of internet postings from investors’ perspective on impression management. As majority of investors are retail investors in China, we postulate that internet negative postings serve as the external pressure to drive management to engage in impression management. The remaining part of this paper is organized as follows. Section 2 presents the theoretical framework that supports the relationships of the dependent and independent variables. Section 3 reviews literature and develops hypotheses to be tested. Section 4 discusses research design, method and data collection. Section 5 presents and discusses the results. Finally, Section 6 contains conclusions. 2. Theoretical frameworks 2.1 Impression management theory Impression management refers to the goal-directed act by individuals or organizations to control others’ perception for creating a desired image to target audiences (Bolino et al. 2008 ). In the organizational context, it is a management behavior through which managers intentionally or unintentionally attempt to control the impressions of stakeholders by emphasizing positive performance or obfuscating negative performance of a firm (Merkl-Davies et al. 2011). In the context of corporate disclosure, impression management is effective in affecting investors and other stakeholders’ interpretation and attitudes towards the firms through selecting the content and the form of the information (Pouryousof et al. 2022 ). The disclosure tones can be manipulated as an instrument of impression management for creating a designated public impression by word choice (Hart, Childers & Lind 2013 ). On the positive side, tones in qualitative disclosures can be deployed to facilitate the communication of incrementally practical information between organizations and their stakeholders. On the negative side, tones can also be exploited to distort the information by exaggerating, incorrectly reporting the performance of a firm or showing overly optimistic forecasts (Kang & Chen 2021 ). 2.2 Agency theory Agency theory suggests two possible problems associated with the complicated agency relationship between managers (agent) and shareholders (principal) of a business (Moloi & Marwala 2020 ). First, conflicts of interest arise between the agent and the principal when they have different desires and goals. While managers are supposed to act and make decisions for sufficient stockholders’ interests, they may make use of their power for satisfying self-interest (Cotter, Lokman & Najah 2011 ). Second, it can be costly or difficult for the principal to monitor the action taken by the agent because of information asymmetry (Yong, 2014 ). It is difficult for stockholders to fully understand and predict the performance of a firm. In the process of corporate disclosure, there are always differences between the information the firm provides and the information stockholders require for decision making (Luo, Zhang & Zhang 2021 ). 2.3 Stewardship theory In contrast with agency theory, stewardship theory suggests that managers are likely to use MD&A to convey their expectation about the performance of firms rather than use it to mislead investors. For instance, managers may want to inform stakeholders if they think shares are overvalued so as to prevent future disappointment (Skinner 1994 ). Stewardship theory posits that a manager acts as the “model of man” with steward behavior (Donaldson & Davis 1994 ) who prioritizes pro-organizational, collectivistic behaviors over individualistic, self-serving behaviors as proposed by agency theory (Davis et al. 1997 ). The theory portrays managers as stewards with strong sense of belonging to organizations who are intrinsically motivated to pursue organizational interests (Corbetta & Salvato 2004 ; Zahra et al. 2008 ). In line with the stewardship theory, when firms face negative internet postings, managers are more likely to use MD&A to convey their believed information of the prospect of firms to investors rather than to mislead them. As such, according to stewardship theory, we expect that managers are more likely to use positive tones in MD&A when managers believe that the firms will perform well regardless of the negative internet postings. 3. Literature review and hypothesis developments 3.1 Impression management and firm values In line with the agency theory and impression management theory, some studies conclude that management strategically uses disclosure tone to obfuscate poor firm outcomes. When management engages in self-serving behavior, information asymmetries may be created, which make the monitoring of management’s behavior difficult. By carefully choosing the content, tone, and manner of accounting disclosures, managers aim to influence stakeholders' interpretations and create favorable impressions aligned with their intended narratives, ultimately facilitating desired social and economic outcomes (Neu, 1991 ). For instance, Schleicher and Walker ( 2010 ) review how managers bias the tone of forward-looking narratives and observe that firms with significant loss or risk demonstrate a more positive tone in disclosures. Melloni, Stacchezzini and Lai ( 2016 ) affirm that managers incline to use positive tones in reports to conceal poor firm performance. Jiang et al. ( 2019 ) provide evidence that manager sentiment is an important negative determinant of stock market returns. Abou-El-Sood and El-Sayed ( 2022 ) provide evidence that firms with lower levels of abnormal disclosure tone demonstrate higher earning persistence. This shows that discretionary tone in qualitative disclosures can be opportunistically manipulated for impression management and signifying future firm performance, which is representative of firm values. In line with stewardship theory, some studies find that managers are likely to use MD&A to convey their expectation about the firm performance rather than mislead investors. This suggests that the opinions of managers and the linguistic tone of the MD&A are reliable indicators of a company's going concern status. Consequently, this challenges the prediction of agency theory, as it indicates that managers may not always prioritize stakeholder interests over their own self-interests. For example, Loughran and McDonald ( 2011 ) observe that textual tone has explanatory power to earnings forecast which signals the values of a business in terms of firms’ profitability. In the same vein, Davis, Piger and Sedor ( 2012 ) find that the use of net optimistic tone in earnings press releases positively correlates with future firm performance. Li ( 2010b ) reports that the use of optimistic language in discussions of future events and forward-looking statements in MD&A has positive association with future earnings and liquidity. In this study, we examine the effects of impression management on one-year leading firm values. Although there are tensions between agency theory, impression management theory and stewardship theory, we still expect that impression management is likely to be negatively associated with one-year leading firm values regardless of whether the periods are before or during COVID-19. As such, we have formulated the following hypothesis. H1: The degree of impression management is significantly related to leading firm value. 3.2 Negative internet postings on social media and impression management Internet stock message boards can be an effective part of the external governance mechanism which serves the monitoring function (Zheng et al. 2020). As there is no social media platform like Twitter and Seeking Alpha in China, the stock message boards in China have become a popular platform for investors to share and obtain firm information (Ang et al. 2021 ). Through improving the information environment, online stock message boards can help to lower the costs for external parties to monitor organizational performance and managerial behaviors (Amiram et al. 2018 ). As the biggest emerging financial market in the world, the China stock market is dominated by immature individual investors (Lee, Lee & Wu 2021 ). They are more likely to demonstrate irrational herd behaviors because of opaque market information and limited professional knowledge (Chong, Liu & Zhu 2017 ). From the discussion above, we expect that similar to the media coverage, the number of negative postings on internet about the firms to be an external pressure, so management is under tremendous pressure to manage investors’ impression if many investors post negative messages on internet. Particularly, we expect that the pressure is intensified during COVID-19, so management becomes more likely to engage in impression management during COVID-19. As such, we have formulated the following hypotheses. H2a: Negative internet postings are positively related to the degree of impression management. H2b: Negative internet postings are more positively related to the degree of impression management during COVID-19 than before COVID-19. 4. Research design 4.1 Sample and data collection We collect data on all A-shares listed in Mainland China between 2018 and 2021 for examining the hypotheses before and during COVID-19. All data are gathered from China Stock Market & Accounting Research Database (CSMAR). Firms with incomplete disclosure of financial information and audit committee information are removed. We also eliminate firms with missing data of internet postings. As a result, we obtain 3021 firm-year observations. 4.2 Regression models To test the hypotheses in this study, we have formulated the model for the study as follows: Where: $$\:TOB{\:}_{it+1}\:\:=\:{\beta\:}_{0\:}+\:{{\beta\:}_{1}TONE}_{it}+\:{\beta\:}_{2\:}{LNBSIZE\:}_{it}+\:{\beta\:}_{3}LNBREN{\:}_{it}\:+\:{\beta\:}_{4}\:LNBMEET{\:}_{it}+\:{\beta\:}_{5}LNACSIZE{\:}_{it}+\:\:{\beta\:}_{6}LEV{\:}_{i,t}+\:{\beta\:}_{7\:}CHOLD{\:}_{it}+\:{\beta\:}_{8}BGENDER{\:}_{it}+\:{\beta\:}_{9}BIND{\:}_{it}\:+\:{\beta\:}_{10}ROA{\:}_{it}+\:{e}_{it}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(1\right)$$ $$\:TONE{\:}_{it}$$ $$\:=\:{\beta\:}_{0\:}+\:{{\beta\:}_{1}NEGPOST}_{it}+{\beta\:}_{2\:}{BGENDER\:}_{it}+{\beta\:}_{3\:}{{LNBREN\:}_{it}\:}_{}+\:{\beta\:}_{4\:}{LNBMEET\:}_{it}$$ $$\:+\:{\beta\:}_{5}LNBSIZE{\:}_{it}\:+\:{\beta\:}_{6}\:LNACSIZE{\:}_{it}+\:{\beta\:}_{7}ROA{\:}_{it}+{\beta\:}_{8}\:LNATEN{\:}_{it}+\:\:{\beta\:}_{9}LEV{\:}_{it}$$ $$\:+\:{\beta\:}_{10\:}LNTA{\:}_{it}\:+\:{e}_{it}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(2\right)$$ Where: TOB Tobin’s Q TONE (Positive words – negative words)/(positive words + negative words) NEGPOST Natural log value of total number of negative internet posting of firms in the last month of a year. LNBSIZE Natural log value of total number of board directors LNBREN Natural log value of total directors’ remuneration LNBMEET Natural log value of total number of board meetings LNACSIZE Natural log value of total number of directors on the audit committee LNTA Natural log value of total assets LEV Total liabilities divided by total assets CHOLD Proportion of shares held by the board chairperson to total outstanding shares BGENDER Proportion of female directors on the board BIND Proportion of independent directors on the board ROA Net income divided by total assets LNATEN Natural log value of auditor tenure 4.3 Dependent variables Our first dependent variable is Tobin’s Q (TOB). The higher the ratio, the higher the firm value. We expect that positive tones negatively correlate with one-year leading Tobin’s Q as management has higher tendency to use positive tones to hide poor performance reflected in next year. Our second dependent variable is the levels of impression management measured as the tones of MD&A (TONE). Aligned with previous studies (Zhang et al. 2022 ), we measure the tones by the ratio of the difference of number of positive and negative words to the sum of positive and negative words. The ratios are extracted from CSMAR which measures positive and negative vocabulary based on the list provided by Loughran and McDonald ( 2011 ). The list provides positive words and negative words for textual analysis. It uses Longhran and McDonald (2011) as a reference for emotional vocabulary to count the optimistic and pessimistic words. The ratio ranges between + 1 and − 1 for perfectly positive tone and perfectly negative tone, respectively. The classification into positive and negative classification is used by many previous studies (Bassyouny, Abdelfattach & Tao 2022). We expect that external pressure from investors encourages management to engage in impression management, so tones are positively related to negative internet postings of the firms. 4.4 Independent variables Following the research by Tumarkin and Whitelaw ( 2001 ) which defines the internet forum information variable as the log of the number of postings, we have negative internet postings, measured as natural log values of total number of negative internet postings of firms in the last month of a year (NEGPOST), as the key independent variable in this study. The number of daily negative postings in the last month is summed to count as the number of negative postings in the last month of a year. The number of postings is extracted from CSMAR which obtains the data from Eastmoney Stock Forum (Huang et al. 2022 ). The forum is a crucial section of the Oriental Fortune website, one of the financial and economic websites with heaviest traffic in China. Eastmoney Stock Forum, as an open forum available to retail investors, is being considered as a trustworthy online source of textual data in terms of frequency, activity and user influence (Zhou & Liu 2022 ). The emotion is analyzed and classified as negative using natural language processing (NLP) by CSMAR (Tan et al. 2021 ). The last month of a year is used because we conjecture that management should be more sensitive to negative postings near the year end during which management is likely to report the financial performance of a company. Duz and Tas (2021) state that the predictive power of social media postings on stock returns is short-sighted and the trading strategies that rely on social media postings might be formed only for short-term investment. 5. Results and discussion 5.1 Descriptive statistics, correlation matrix and variance inflation factors Table 1 shows the descriptive statistics. The mean of TOB is 1.97 with a minimum of 0.67 and maximum of 20.17. The mean of NEGPOST is 5.56 with a minimum of 0.69 and maximum of 9.34. On average, the natural log value of negative posting is 9.34 (11384.4), indicating a total of 11384.4 negative postings in the month. The mean of LSUM is 9.35 (11498 words), which suggests that Chinese firms use 11498 words in MD&A on average. Table 1 Descriptive statistics Mean SD Min Max TOB 1.97 1.77 0.67 20.17 LNSUM 9.35 0.44 5.95 11.23 TONE 0.26 0.13 -0.61 0.69 NEGPOST 5.56 1.11 0.69 9.34 BGENDER 0.16 0.13 0 0.78 LNBREN 14.73 0.88 9.95 18.22 BIND 0.26 0.09 0 0.71 LNBMEET 2.23 0.41 0.69 4.06 LNBSIZE 2.13 0.20 1.39 2.83 LNACSIZE 1.18 0.19 0 2.08 CHOLD 5.89 12.06 0 70.42 ROA 0.03 0.19 -1.96 7.96 LNATEN 1.73 0.96 0 3.40 LEV 0.38 0.31 0.00 10.79 LNTA 22.40 1.30 17.5 28.23 Table 2 Correlation matrix TOB TONE NEGPOST LNBSIZE LNBREN LNBMEET LNACSIZE TOB 1 TONE -0.0210 1 NEGPOST -0.0324 0.0253 1 LNBSIZE -0.0552** 0.0564** 0.0264 1 LNBREN -0.0193 0.161*** 0.116*** 0.213*** 1 LNBMEET -0.0524** 0.0541** 0.135*** 0.0220 0.157*** 1 LNACSIZE -0.0634*** 0.0183 0.0751*** 0.271*** 0.0221 0.0573** 1 LEV -0.0209 -0.0496** 0.0309 0.000918 -0.0218 0.177*** 0.0370* CHOLD 0.0800*** 0.0517** -0.118*** -0.187*** 0.0983*** -0.0407* -0.153*** BGENDER 0.0206 0.00746 -0.0303 -0.0724*** -0.0235 -0.0203 -0.0519** BIND -0.0368* -0.0137 0.0525** -0.483*** -0.114*** 0.0705*** 0.294*** ROA 0.0259 0.125*** -0.0326 0.00339 0.127*** -0.0627*** 0.0204 LNATEN -0.0296 0.0225 -0.000124 0.0453* 0.0743*** -0.0691*** 0.00357 LNTA -0.182*** 0.136*** 0.277*** 0.270*** 0.330*** 0.281*** 0.188*** LNSUM -0.0288 0.339*** 0.147*** 0.0409* 0.257*** 0.204*** -0.00966 LEV CHOLD BGENDER BIND ROA LNATEN LNTA LNSUM LEV 1 CHOLD -0.0510** 1 BGENDER -0.0151 0.0967*** 1 BIND 0.0405* -0.0190 0.000235 1 ROA -0.252*** 0.0570** -0.0176 0.0101 1 LNATEN -0.0344 -0.0266 0.0335 -0.0411* 0.0349 1 LNTA 0.136*** -0.198*** -0.166*** 0.0367* 0.0490** 0.0237 1 LNSUM 0.00151 0.0623*** -0.00202 -0.00367 0.0332 0.0271 0.212*** 1 Note: * p < 0.10; ** p < 0.05; *** p < 0.01. Standard errors are in the parenthesis. The TOB has a mean of 1.97, with a minimum value of 0.67 and maximum value of 20.17. Wang, Wu & Yan ( 2021 ) find that the mean of Tobin’s Q is 2.208 using the data from 2008 to 2017. Tobin’ Q is lower in our sample because firms in our sample are at the times of COVID-19 pandemic. The mean of tones is 0.26 with a minimum of -0.61 and a maximum of 0.69. On average, firms use positive tones in MD&A. The result is consistent with Wang, Wu & Yan ( 2021 ) which find that managers of Chinese firms are more inclined to use positive tones. 5.2 Main results Table 3 shows the relationships between one-year leading TOB and TONE. We identify a negative association between one-year leading TOB and TONE (β = − 1.781; p < 0.01), implying that the more positive the tones, the lower the firm values in one year later. The results align with our expectation that management is likely to use more positive tones if they expect that the firms do not perform well in the future. Our results suggest that agency theory prevails over stewardship theory in China. The results align with the studies by Mai et al. (2019), Wei et al. (2019) and Jiang et al. ( 2019 ) which claim that management is likely to engage in impression management to hide bad news. Thus H1 is supported. Table 3 Results of fixed panel data regression: Leading TOB and TONE 2018–2021 TOB TONE -1.781*** (0.57) LNBSIZE -1.391** (0.68) LNBREN -0.0166 (0.11) LNBMEET -0.291* (0.18) LACSIZE 0.540 (0.633) LEV -1.890*** (0.314) CHOLD 0.0006 (0.001) BGENDER 0.961 (0.715) BIND -3.596*** (1.08) ROA -0.457 (0.48) CON 7.154*** (2.23) YEAR DUMMY YES INDUSTRY DUMMY YES N 3021 Prob > F 0.0000*** Adj r-square 0.0042 Note: * p < 0.10; ** p < 0.05; *** p < 0.01. Standard errors are in the parenthesis. Table 4 shows the associations between TONE and NEGPOST. We confirm that NEGPOST positively correlates with TONE between 2018 and 2021 (β = 0.006; p < 0.05), suggesting that the more the negative internet postings the more positive the tones. Thereby, H2a is supported. The results are consistent with our expectation that if investors become pessimistic, management is more likely to adopt positive tones to convince investors that firms will perform better in the future. The result is consistent with that of Luo et al. (2019), which show that high media attention to environmental performance increases environmental legitimate risks, prompting management to disclose information that meets stakeholders’ expectation. Table 4 Results of fixed panel data regression results: TONE and NEGPOST 2018–2021 2020–2021 2018–2019 TONE TONE TONE NEGPOST 0.006** 0.028** 0.005 (0.00) (0.01) (0.01) BGENDER 0.064 0.179 -0.139 (0.04) (0.16) (0.17) LNBREN 0.020*** 0.053** 0.008 (0.01) (0.03) (0.03) LNBMEET 0.006 -0.060* 0.041 (0.01) (0.04) (0.03) LNBSIZE 0.019 0.075 -0.014 (0.03) (0.12) (0.11) LNACSIZE 0.024 -0.094 0.019 (0.04) (0.10) (0.11) ROA 0.102*** -0.024 0.203*** (0.03) (0.12) (0.06) LNATEN 0.007 0.011 0.008 (0.00) (0.02) (0.01) LEV -0.024 -0.021 -0.046 (0.02) (0.16) (0.10) LNTA 0.003 0.101 0.040 (0.01) (0.07) (0.05) CON -0.242 -2.885* -0.860 (0.29) (1.63) (1.13) YEAR DUMMY YES YES YES INDUSTRY DUMMY YES YES YES N 3021 2252 769 Prob > F 0.0000*** 0.0000*** 0.0000*** Adj r-square 0.039 0.021 0.077 Note: * p < 0.10; ** p < 0.05; *** p < 0.01. Standard errors are in the parenthesis. Our further analysis indicates that the positive association between negative internet postings and tone is significant between 2020 and 2021 (β = 0.028; p 0.10). The findings suggest that management is more sensitive and active to engage in impression management in the periods of COVID-19 pandemic (2020–2021) due to external pressure from investors. Thus H2b is supported. This is in contrast with the finding of Moreno & Jones ( 2021 ) which suggest managers are less motivated to engage in impression management during external global crisis as the negative performance aligns with the extreme economic context. However, our result is consistent with that of Hossain, Alam and Mazumder ( 2022 ), which agrees that managers strategically use language tactics in annual reports to manage investors’ impression during COVID-19 pandemic 5.3 Robust tests We use the natural log value of total word counts as an alternative measure of impression management. Prior studies show that management is more likely to use lengthy and complex disclosures to mislead investors. Table 5 shows that the results are largely consistent with those of the main results. NEGPOST positively correlates with LNSUM between 2018 and 2021 (β = 0.035; p < 0.01), suggesting that if investors are pessimistic, management is under pressure to manage investors’ impression. The positive association is also significant between 2020 and 2021 (β = 0.117; p 0.10). Table 5 Results of fixed panel data regressions: LNSUM and NEGPOST 2018–2021 2020–2021 2018–2019 LNSUM LNSUM LNSUM NEGPOST 0.035*** 0.117*** 0.005 (0.01) (0.04) (0.02) BGENDER 0.125 0.246 -0.212 (0.12) (0.47) (0.31) LNBREN 0.024 0.088 -0.050 (0.02) (0.07) (0.05) LNBMEET 0.089*** -0.197* 0.091 (0.03) (0.11) (0.06) LNBSIZE -0.037 -0.298 0.458** (0.10) (0.35) (0.21) LNACIZE 0.086 0.065 -0.212 (0.11) (0.29) (0.20) ROA 0.015 -0.349 -0.168 (0.08) (0.35) (0.11) LNATEN 0.012 0.051 -0.010 (0.01) (0.05) (0.02) LEV -0.063 -0.717 -0.343* (0.05) (0.48) (0.19) LNTA 0.187*** 1.373*** 0.193** (0.04) (0.22) (0.09) CON 4.381*** -22.092*** 4.898** (0.82) (4.83) (2.11) YEAR DUMMY YES YES YES INDUSTRY DUMMY YES YES YES N 3021 2252 769 Prob > F 0.0000*** 0.0000*** 0.0000*** Adj R-square 0.0648 0.0453 0.0441 Note: * p < 0.10; ** p < 0.05; *** p < 0.01. Standard errors are in the parenthesis. 5.4 Endogeneity Endogeneity is always a major problem in corporate governance studies. The endogeneity problem is present when an observed or unobserved variable, which is not integrated in the model, is connected to a variable incorporated in the research model (Dodoo, Appiah & Donkor 2020 ). The issue of endogeneity arises when an explanatory variable correlates to the error term, leading to biased estimators (Adkins & Hill 2008 ). It may also exist because of measurement error, unobservable in form of omitted variable “bias” (Wooldridge 2012 ) and reverse causality (Roberts & Whited 2011 ). We expect that reverse causality is not a significant issue as we use leading Tobin’s Q in this study. Additionally, it is unlikely that the number of negative internet postings is determined by the tones and length of MD&A. Following the study by Arellano and Bond ( 1991 ), this study employs Dynamic Panel Difference GMM regression. The results of dynamic panel data regression are comparable to those in the main study using fixed effect panel data regression, except that the relationships between LNSUM and NEGPOST become less significant while the sign is consistent and positive. The findings are summarized in Table 6 . Table 6 Results of dynamic panel data regression TONE LNSUM LAGTONE -0.345* (0.21) LAGLNSUM 3.479*** (1.34) NEGPOST 0.012* 0.039 (0.01) (0.06) BGENDER 0.107 -1.239 (0.11) (1.07) LNBREN 0.035** -0.081 (0.02) (0.14) LNBMEET -0.003 0.204 (0.03) (0.22) LNBSIZE 0.036 0.619 (0.08) (0.75) LNACSIZE -0.171** -1.045 (0.08) (0.71) ROA -0.010 0.394 (0.06) (0.55) LNATEN 0.009 0.148 (0.01) (0.10) LEV 0.041 -0.102 (0.09) (0.75) LNTA 0.043 0.035 (0.04) (0.42) CON -1.166 -23.337** (0.95) (10.81) YEAR DUMMY YES YES INDUSTRY DUMMY YES YES Note: * p < 0.10; ** p < 0.05; *** p < 0.01. Standard errors are in the parenthesi 6. Conclusion This study examines the impacts of impression management on firm values and how negative internet postings drive the management to engage in impression management using a sample of listed companies in China between 2018 and 2021. Our findings show that the management is more likely to use more positive tones when firm values are lower in the next year, indicating that management tends to use more positive tones to create the image that the firms will perform well while the firms will not. Additionally, our findings provide evidence that management is sensitive to negative internet postings in the last month of the year-end. The negative internet postings will prompt management to engage in impression management as management may fear that negative internet postings lower the firm values. Therefore, management tends to disclose more positive tones to neutralize the effects of negative internet postings. In our additional analysis, the positive relationships between negative postings and positive tone are more significant during COVID-19, suggesting that management becomes more sensitive to negative internet posting during COVID-19. The findings are important as they provide additional mechanisms for investors to identify the impacts of tones and length of MD&A on firm values and how management engages in impression management. More importantly, the findings provide additional evidence that management is sensitive to negative internet postings, particularly during the crisis period. The study is subject to caveats. First, the study only uses tones and length of MD&A as proxies for impression management. However, management may do so using clarity and grammatical structure of MD&A. Future studies may examine the relationships using grammatical structure and clarify as proxies for impression management. Second, this study is conducted in Mainland China, so the results cannot be generalized to other countries. The scope could be expanded to other Asian countries with different institutional and judicial environment. For example, future studies can be conducted in a country where institutional investors account for the majority of investors. Declarations Conflict of interest The authors declare no conflicts of interest Funding This work was not supported by any funds. Author Contribution Conceptualization, K.Y.C., C.Y.L., and H.Y.H.N; methodology, K.Y.C., K.K.L ; validation, C.Y.L. and T.W.D.L, formal analysis, K.Y.C., C.Y.L., K.K.L; investigation, K.Y.C.; writing—original draft preparation, K.Y.C., K.K.L, H.Y.H.N; writing—Review and Editing, C.Y.L., H.Y.H.N., T.W.D.L; All authors have read and agreed to the published version of the manuscript. Data Availability The data that support the findings of this study are available upon reasonable request from the authors. References Abou-El-Sood, H., El-Sayed, D.: Abnormal disclosure tone, earnings management and earnings quality. J. Appl. Acc. Res. 23 (2), 402–433 (2022) Adkins, L.C., Hill, R.C.: Using Stata for principles of econometrics, 3rd edn. John Willey (2008) Amiram, D., Bozanic, Z., Cox, J.D., Dupont, Q., Karpoff, J.M., Sloan, R.: Financial reporting fraud and other forms of misconduct: a multidisciplinary review of the literature. Rev. Acc. Stud. 23 (2), 732–783 (2018) An, Y., Su, F.: Do internet stock message boards influence firm value? Evidence from China. Asia-pacific J. Acc. Econ. 30 (2), 327–353 (2023) Ang, J.S., Hsu, C., Tang, D., Wu, C.: The Role of Social Media in Corporate Governance. Acc. Rev. 96 (2), 1–32 (2021) Arellano, M., Bond, S.: Some tests of specification for panel date: Monte Carlo evidence and application to employment equations. Rev. Econ. Stud. 58 (2), 277–297 (1991) Bassyouny, H., Abdelfattah, T., Tao, L.: Narrative disclosure tone: A review and areas for future research. J. Int. Acc. Auditing Taxation, 110511. (2022) Bolino, M.C., Kacmar, K.M., Turnley, W.H., Gilstrap, J.B.: A multi-level review of impression management motives and behaviors. J. Manag. 34 (6), 1080–1109 (2008) Cheung, C., Chan, A.C.: Benefits of Hong Kong Chinese CEOs’ Confucian and Daoist leadership styles. Leadersh. Organ. Dev. J. 29 , 474–503 (2008) Cho, C.H., Roberts, R.W., Patten, D.M.: The language of US corporate environmental disclosure. Acc. Organ. Soc. 35 (4), 431–443 (2010) Chong, T.T.L., Liu, X., Zhu, C.: What explains herd behavior in the Chinese stock market? J. Behav. Finance. 18 (4), 448–456 (2017) Corbetta, G., Salvato, C.: Self-serving or self‐actualizing? Models of man and agency costs in different types of family firms: A commentary on comparing the agency costs of family and non‐family firms: Conceptual issues and exploratory evidence. Entrepreneurship Theory Pract. 28 (4), 355–362 (2004) Cotter, J., Lokman, N., Najah, M.M.: Voluntary disclosure research: Which theory is relevant? J. Theoretical Acc. Res. 6 (2), 77–95 (2011) Davis, A.K., Piger, J.M., Sedor, L.M.: Beyond the numbers: Measuring the information content of earnings press release language. Contemp. Acc. Res. 29 (3), 845–868 (2012) Davis, J.H., Schoorman, F.D., Donaldson, L.: Toward a stewardship theory of management. Acad. Manage. Rev. 22 (1), 20–47 (1997) Dodoo, R.N.A., Appiah, M., Donkor, D.T.: Examining the factors that influence firm performance in Chana: a GMM and OLS approach. Natl. Acc. Rev. 2 (3), 309–323 (2020) Donaldson, L., Davis, J.H.: Boards and company performance: Research challenges the conventional wisdom. Corp. Governance: Int. Rev. 2 (3), 151–160 (1994) Duz.,T, S., Tas, O.: Social media sentiment in international stock returns and trading activity. J. Behav. Finance. 22 (2), 221–234 (2021) Feng, X., Li, X., Su, F.: Investor attention and stock return comovement: Evidence from China’s A-share Bolino stock market. Unpublished Working Paper. (2020) Froese, F.J., Sutherland, D., Lee, J.Y., Liu, Y., Pan, Y.: Challenges for foreign companies in China: Implications for research and practice. Asian Bus. Manage. 18 (4), 249–262 (2019) Hart, R.P., Childers, J.P., Lind, C.J.: Political tone: How leaders talk & why. University of Chicago Press (2013) Hossain, D.M., Alam, M.S., Mazumder, M.M.M.: Impression management tactics in Covid-19 related disclosures: a study on the annual reports of Bangladeshi listed insurance companies. Asian Journal of Economics and Banking . (2022). https://doi.org/10.1108/AJEB-04-2022-0042 Huang, C., Cao, Y., Lu, M., Shan, Y., Zhang, Y.: Messages in online stock forums and stock price synchronicity: evidence from China. Acc. Finance. 00 , 1–31 (2022) Jiang, F., Kim, K.: Corporate governance in China: A modern perspective. J. Corp. Finance. 32 , 190–216 (2015) Jiang, F., Lee, J., Martin, X., Zhou, G.: Manager sentiment and stock returns. J. Financ. Econ. 131 (1), 126–149 (2019) Jugnandan, S., Willows, G.D.: It’s a long story… – impression management in South African corporate reporting. Acc. Res. J. 35 (5), 581–597 (2022) Kang, F., Chen, H.: Analyst Optimistic Forecasting and Impression Management of CSR Disclosure: Empirical evidence from China. 2021 International Conference on Tourism, Economy and Environmental Sustainability , 275, 03070. (2021). https://doi.org/10.1051/e3sconf/202125103094 Kiattikulwattana, P.: Do letters to shareholders have information content? Asian Rev. Acc. 27 (1), 137–159 (2019) Lee, C.C., Lee, C.C., Wu, Y.: The impact of COVID-19 pandemic on hospitality stock returns in China. Int. J. Finance Econ. (2021). https://doi.org/10.1002/ijfe.2508 Lee, J., Park, J.: The impact of audit committee financial expertise on management discussion and analysis (MD&A) tone. Eur. Acc. Rev. 28 , 129–150 (2019) Li, F.: Survey of the literature. J. Acc. Literature. 29 , 143–165 (2010b) Lo, K.: Earnings management and earnings quality. J. Account. Econ. 45 (2), 350–357 (2008) Loughran, T., McDonald, B.: When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. J. Finance. 66 (1), 35–65 (2011) Luo, X., Zhang, Q., Zhang, S.: External financing demands, media attention and the impression management of carbon information disclosure. Carbon Manag. 12 (3), 235–247 (2021) Luo, Y., Zhou, L.: Textual tone in corporate financial disclosures: A survey of the literature. Int. J. Disclosure Gov. 17 , 101–110 (2020) Melloni, G., Stacchezzini, R., Lai, A.: The tone of business model disclosure: an impression management analysis of the integrated reports. J. Manage. Governance. 20 , 295–320 (2016) Merkl-Davies, Brennan, D.M., Niamh, M., McLeay, S.J.: Impression management and retrospective sense-making in corporate narratives: A social psychology perspective. Acc. Auditing Account. 24 (3), 315–344 (2011) Modigliani, F., Miller, M.: Some estimates of the cost of capital to the electric utility industry 1954–1957. Am. Econ. Rev. 56 , 333–391 (1966) Moloi, T., Marwala, T.: The Agency Theory. In: Artificial Intelligence in Economics and Finance Theories. In: Advanced Information and Knowledge Processing. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-42962-1_11 Moreno, A., Jones, M.J.: Impression management in corporate annual reports during the global financial crisis. European Management Journal , 40(4), 503–517. (2021) Neu, D.: Trust, impression management and the public accounting profession. Crit. Perspect. Acc. 2 (3), 295–313 (1991) Pouryousof, A., Farzaneh, N., Reza, H., Davood, A.: The Relationship between Managers’ Disclosure Tone and the Trading Volume of Investors. J. Risk Financial Manage., 15 (618). (2022) Roberts, M., Whited, T.: Endogeneity in corporate finance, Working Paper, Wharton. (2011) Schleicher, T., Walker, M.: Bias in the tone of forward-looking narratives. Acc. Bus. 40 (4), 371–390 (2010) Skinner, D.J.: Why firms voluntarily disclose bad news. J. Acc. Res. 32 (1), 38–60 (1994) Tan, H., Peng, S., Zhu, C., You, Z., Miao, M., Kuai, S.: Long-term Effects of the COVID-19 Pandemic on Public Sentiments in Mainland China: Sentiment Analysis of Social Media Posts. J. Med. Internet. Res., 23 (8), e29150 (2021) Tumarkin, R., Whitelaw, R.F.: News or noise? Internet postings and stock prices. Financial Anal. J. 57 (3), 41–51 (2001) Wang, J., Ye, K.: Media coverage and firm valuation: Evidence from China. J. Bus. Ethics. 127 (3), 501–511 (2015) Wang, Q., Wu, D., Yan, L.: Effect of positive tone in MD&A disclosure on capital structure adjustment speed: evidence from China. Acc. Finance. 61 (4), 5001–5890 (2021) Wooldridge, J.M.: Introductory econometrics: A modern approach, 5th edn. South-Western Cengage Learning (2012) Yong, T.: Corporate governance in the banking sector. Performance. Risk Competition Chin. Bank. Industry: Chandos Asian Stud. Ser., 39–64. (2014) Zahra, S.A., Hayton, J.C., Neubaum, D.O., Dibrell, C., Craig, J.: (2008). Culture of family commitment and strategic flexibility: The moderating effect of stewardship Entrepreneurship: Theory Pract., 32 (6), 1035–1054 Zhang, Z., Luo, M., Hu, Z., Niu, H.: Textual Emotional Tone and Financial Crisis Identification in Chinese Companies: A Multi-Source Data Analysis Based on Machine Learning. Applied Sciences, 12, 6662. (2022) Zhou, S., Liu, X.: Internet postings and investor herd behavior: evidence from China’s open-end fund market. Humanities and Social Sciences Communications , 9(441). (2022) Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8697348","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":585305758,"identity":"6a7451b7-822a-45ce-a0a8-f02df0ef26fd","order_by":0,"name":"Kwok Yip Cheung","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYDACCQgpx8befAzMZGMnTouNMT/PsTQGhgSgFmbitKQlzpzhYwbWwkBIC//s5mPSBb8OGxvc4Pn24OOPbfJ8zAyMHz7m4LHkzrE06Zl9h+UMbvduN5yRcNuwjZmBWXLmNtxaDCRyzKR5e4C23Dm7TZon4TYjUAsbMy9eLfnfQFoSN9zIeQbSYk+Elhw2aZ4fIO+DGAm3EwlqkbiRZmzN2wAOZDPJGWm3k9uYGZvx+oV/RvLD2zx/wFH5TOKDzW3b+e3NBz98xKMFDBjbULkNBNSDwB8i1IyCUTAKRsHIBQCjqU4V5oMPAgAAAABJRU5ErkJggg==","orcid":"","institution":"Hong Kong University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Kwok","middleName":"Yip","lastName":"Cheung","suffix":""},{"id":585305761,"identity":"01759d0e-79a2-43da-a4e6-fcc6086f33b2","order_by":1,"name":"Chung Yee Lai","email":"","orcid":"","institution":"Brittany University","correspondingAuthor":false,"prefix":"","firstName":"Chung","middleName":"Yee","lastName":"Lai","suffix":""},{"id":585305767,"identity":"aacc529e-2640-48d4-bfa4-6d09a3e72922","order_by":2,"name":"Kuok Kei Law","email":"","orcid":"","institution":"Nagoya University of Commerce \u0026 Business","correspondingAuthor":false,"prefix":"","firstName":"Kuok","middleName":"Kei","lastName":"Law","suffix":""},{"id":585305768,"identity":"dc86c972-e4b5-457a-8af8-f6404a7b89c4","order_by":3,"name":"Tai Wai David Lai","email":"","orcid":"","institution":"Hong Kong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Tai","middleName":"Wai David","lastName":"Lai","suffix":""},{"id":585305770,"identity":"7b097a89-c64e-4a8a-ac66-5b67381a78d8","order_by":4,"name":"Hiu Yue Hugo Ngaw","email":"","orcid":"","institution":"H.F.Yip \u0026 Co Solicitors","correspondingAuthor":false,"prefix":"","firstName":"Hiu","middleName":"Yue Hugo","lastName":"Ngaw","suffix":""}],"badges":[],"createdAt":"2026-01-26 07:08:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8697348/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8697348/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101854128,"identity":"8fd7c467-8991-465e-99fd-ed74da2b93cc","added_by":"auto","created_at":"2026-02-04 10:28:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1158427,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8697348/v1/e1018d0a-860c-426f-b780-35eca69cb801.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Is negative internet posting a threat to management? Impression management and firm values: Evidence from China.","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWe conduct the study to examine the relationships between negative internet postings and degree of impression management using Management Discussion and Analysis reports (MD\u0026amp;A hereafter) and in turn the impacts on firm values. Quantitative and qualitative information in corporate financial disclosures are useful for communicating firm performance and situation with its stakeholders (Luo \u0026amp; Zhou \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While quantitative information refers to financial information explicitly disclosed, qualitative disclosures include textual information conveyed to stakeholders in notes, conference calls, press releases, earnings announcements as well as MD\u0026amp;A (Pouryousof et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) for providing supplementary data to reflect the complete situations of firms (Cho, Roberts \u0026amp; Pattern 2010).\u003c/p\u003e \u003cp\u003ePrevious studies suggest that MD\u0026amp;A communicates the firms\u0026rsquo; comprehensive situations by providing incremental information to reduce information asymmetry between investors and managers (Lee \u0026amp; Park \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). As financial data and analysts\u0026rsquo; estimates can be biased or incomplete, managers may adopt disclosure tone in qualitative descriptions to honestly deliver a signal of private information about the firm\u0026rsquo;s unobservable qualities such as risks, prospects and operational performance (Luo \u0026amp; Zhou \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Rather than honestly deliver a signal, Managers may adjust their tone through optimistic or pessimistic language in qualitative narratives to exploit investors\u0026rsquo; expectations on firm performance (Luo \u0026amp; Zhou \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As a communication vehicle for managers, qualitative disclosures help to convey biased information through framing the tone, hereby affecting how the readers process and react to the information (Kiattikulwattana \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Managers may opportunistically use the market signaling function of tone disclosure to conceal bad news and inflate investors\u0026rsquo; perceptions for impression management (Loughran and McDonald \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Since quantitative information is subject to stringent requirements and governance in accordance to accounting standards and rules of stock exchange (Lo, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), it is less difficult and risky for firms to embellish their bad performance through manipulating qualitative information (Jiang and Kim \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChina provides a unique context for studying the impacts of impression management through MD\u0026amp;As for multiple reasons. First, managers of Chinese firms are experiencing weaker external regulations, compared with those in the mature markets (Jiang \u0026amp; Kim \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The weak judicial and regulatory institutional environment may provide a platform which preferentially favors and advocates firm managers to engage in impression management (Froese et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Thus, it is meaningful to study impression management in the emerging Chinese market. Second, majority of investors are retail investors in China. Retail investors are inclined to spend more time on online stock message boards because of their low latency and cost. As for gathering financial information, over 80% of retail investors prefer to use sources from official web portals, social media platforms and online forums (Feng, Li \u0026amp; Su \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Breaking news and heated discussions online may probably draw investors\u0026rsquo; attention, creating external pressure to management for using impression management.\u003c/p\u003e \u003cp\u003eOur study makes the following two contributions. First, our findings show that the tone of qualitative information in MD\u0026amp;As are misleading as positive tones lead to lower firm values in next year, thereby improving the understanding of investors, board of directors and policymakers about the likelihood that management will engage in impression management during crises. Second, we extend the investigation of determinants of impression management to internet negative postings. To our best knowledge, this is the first research to examine the impacts of internet postings from investors\u0026rsquo; perspective on impression management. As majority of investors are retail investors in China, we postulate that internet negative postings serve as the external pressure to drive management to engage in impression management.\u003c/p\u003e \u003cp\u003eThe remaining part of this paper is organized as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the theoretical framework that supports the relationships of the dependent and independent variables. Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e3\u003c/span\u003e reviews literature and develops hypotheses to be tested. Section \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e4\u003c/span\u003e discusses research design, method and data collection. Section \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents and discusses the results. Finally, Section \u003cspan refid=\"Sec20\" class=\"InternalRef\"\u003e6\u003c/span\u003e contains conclusions.\u003c/p\u003e"},{"header":"2. Theoretical frameworks","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Impression management theory\u003c/h2\u003e \u003cp\u003eImpression management refers to the goal-directed act by individuals or organizations to control others\u0026rsquo; perception for creating a desired image to target audiences (Bolino et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In the organizational context, it is a management behavior through which managers intentionally or unintentionally attempt to control the impressions of stakeholders by emphasizing positive performance or obfuscating negative performance of a firm (Merkl-Davies et al. 2011). In the context of corporate disclosure, impression management is effective in affecting investors and other stakeholders\u0026rsquo; interpretation and attitudes towards the firms through selecting the content and the form of the information (Pouryousof et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The disclosure tones can be manipulated as an instrument of impression management for creating a designated public impression by word choice (Hart, Childers \u0026amp; Lind \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). On the positive side, tones in qualitative disclosures can be deployed to facilitate the communication of incrementally practical information between organizations and their stakeholders. On the negative side, tones can also be exploited to distort the information by exaggerating, incorrectly reporting the performance of a firm or showing overly optimistic forecasts (Kang \u0026amp; Chen \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Agency theory\u003c/h2\u003e \u003cp\u003eAgency theory suggests two possible problems associated with the complicated agency relationship between managers (agent) and shareholders (principal) of a business (Moloi \u0026amp; Marwala \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). First, conflicts of interest arise between the agent and the principal when they have different desires and goals. While managers are supposed to act and make decisions for sufficient stockholders\u0026rsquo; interests, they may make use of their power for satisfying self-interest (Cotter, Lokman \u0026amp; Najah \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Second, it can be costly or difficult for the principal to monitor the action taken by the agent because of information asymmetry (Yong, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). It is difficult for stockholders to fully understand and predict the performance of a firm. In the process of corporate disclosure, there are always differences between the information the firm provides and the information stockholders require for decision making (Luo, Zhang \u0026amp; Zhang \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Stewardship theory\u003c/h2\u003e \u003cp\u003eIn contrast with agency theory, stewardship theory suggests that managers are likely to use MD\u0026amp;A to convey their expectation about the performance of firms rather than use it to mislead investors. For instance, managers may want to inform stakeholders if they think shares are overvalued so as to prevent future disappointment (Skinner \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Stewardship theory posits that a manager acts as the \u0026ldquo;model of man\u0026rdquo; with steward behavior (Donaldson \u0026amp; Davis \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) who prioritizes pro-organizational, collectivistic behaviors over individualistic, self-serving behaviors as proposed by agency theory (Davis et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). The theory portrays managers as stewards with strong sense of belonging to organizations who are intrinsically motivated to pursue organizational interests (Corbetta \u0026amp; Salvato \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Zahra et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn line with the stewardship theory, when firms face negative internet postings, managers are more likely to use MD\u0026amp;A to convey their believed information of the prospect of firms to investors rather than to mislead them. As such, according to stewardship theory, we expect that managers are more likely to use positive tones in MD\u0026amp;A when managers believe that the firms will perform well regardless of the negative internet postings.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Literature review and hypothesis developments","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Impression management and firm values\u003c/h2\u003e \u003cp\u003eIn line with the agency theory and impression management theory, some studies conclude that management strategically uses disclosure tone to obfuscate poor firm outcomes. When management engages in self-serving behavior, information asymmetries may be created, which make the monitoring of management\u0026rsquo;s behavior difficult. By carefully choosing the content, tone, and manner of accounting disclosures, managers aim to influence stakeholders' interpretations and create favorable impressions aligned with their intended narratives, ultimately facilitating desired social and economic outcomes (Neu, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). For instance, Schleicher and Walker (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) review how managers bias the tone of forward-looking narratives and observe that firms with significant loss or risk demonstrate a more positive tone in disclosures. Melloni, Stacchezzini and Lai (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) affirm that managers incline to use positive tones in reports to conceal poor firm performance. Jiang et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) provide evidence that manager sentiment is an important negative determinant of stock market returns. Abou-El-Sood and El-Sayed (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) provide evidence that firms with lower levels of abnormal disclosure tone demonstrate higher earning persistence. This shows that discretionary tone in qualitative disclosures can be opportunistically manipulated for impression management and signifying future firm performance, which is representative of firm values.\u003c/p\u003e \u003cp\u003eIn line with stewardship theory, some studies find that managers are likely to use MD\u0026amp;A to convey their expectation about the firm performance rather than mislead investors. This suggests that the opinions of managers and the linguistic tone of the MD\u0026amp;A are reliable indicators of a company's going concern status. Consequently, this challenges the prediction of agency theory, as it indicates that managers may not always prioritize stakeholder interests over their own self-interests. For example, Loughran and McDonald (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) observe that textual tone has explanatory power to earnings forecast which signals the values of a business in terms of firms\u0026rsquo; profitability. In the same vein, Davis, Piger and Sedor (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) find that the use of net optimistic tone in earnings press releases positively correlates with future firm performance. Li (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2010b\u003c/span\u003e) reports that the use of optimistic language in discussions of future events and forward-looking statements in MD\u0026amp;A has positive association with future earnings and liquidity.\u003c/p\u003e \u003cp\u003eIn this study, we examine the effects of impression management on one-year leading firm values. Although there are tensions between agency theory, impression management theory and stewardship theory, we still expect that impression management is likely to be negatively associated with one-year leading firm values regardless of whether the periods are before or during COVID-19. As such, we have formulated the following hypothesis.\u003c/p\u003e \u003cp\u003eH1: The degree of impression management is significantly related to leading firm value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Negative internet postings on social media and impression management\u003c/h2\u003e \u003cp\u003eInternet stock message boards can be an effective part of the external governance mechanism which serves the monitoring function (Zheng et al. 2020). As there is no social media platform like Twitter and Seeking Alpha in China, the stock message boards in China have become a popular platform for investors to share and obtain firm information (Ang et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Through improving the information environment, online stock message boards can help to lower the costs for external parties to monitor organizational performance and managerial behaviors (Amiram et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). As the biggest emerging financial market in the world, the China stock market is dominated by immature individual investors (Lee, Lee \u0026amp; Wu \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). They are more likely to demonstrate irrational herd behaviors because of opaque market information and limited professional knowledge (Chong, Liu \u0026amp; Zhu \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom the discussion above, we expect that similar to the media coverage, the number of negative postings on internet about the firms to be an external pressure, so management is under tremendous pressure to manage investors\u0026rsquo; impression if many investors post negative messages on internet. Particularly, we expect that the pressure is intensified during COVID-19, so management becomes more likely to engage in impression management during COVID-19. As such, we have formulated the following hypotheses.\u003c/p\u003e \u003cp\u003eH2a: Negative internet postings are positively related to the degree of impression management.\u003c/p\u003e \u003cp\u003eH2b: Negative internet postings are more positively related to the degree of impression management during COVID-19 than before COVID-19.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Research design","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Sample and data collection\u003c/h2\u003e \u003cp\u003eWe collect data on all A-shares listed in Mainland China between 2018 and 2021 for examining the hypotheses before and during COVID-19. All data are gathered from China Stock Market \u0026amp; Accounting Research Database (CSMAR). Firms with incomplete disclosure of financial information and audit committee information are removed. We also eliminate firms with missing data of internet postings. As a result, we obtain 3021 firm-year observations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Regression models\u003c/h2\u003e \u003cp\u003eTo test the hypotheses in this study, we have formulated the model for the study as follows:\u003c/p\u003e \u003cp\u003eWhere:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:TOB{\\:}_{it+1}\\:\\:=\\:{\\beta\\:}_{0\\:}+\\:{{\\beta\\:}_{1}TONE}_{it}+\\:{\\beta\\:}_{2\\:}{LNBSIZE\\:}_{it}+\\:{\\beta\\:}_{3}LNBREN{\\:}_{it}\\:+\\:{\\beta\\:}_{4}\\:LNBMEET{\\:}_{it}+\\:{\\beta\\:}_{5}LNACSIZE{\\:}_{it}+\\:\\:{\\beta\\:}_{6}LEV{\\:}_{i,t}+\\:{\\beta\\:}_{7\\:}CHOLD{\\:}_{it}+\\:{\\beta\\:}_{8}BGENDER{\\:}_{it}+\\:{\\beta\\:}_{9}BIND{\\:}_{it}\\:+\\:{\\beta\\:}_{10}ROA{\\:}_{it}+\\:{e}_{it}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:TONE{\\:}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:=\\:{\\beta\\:}_{0\\:}+\\:{{\\beta\\:}_{1}NEGPOST}_{it}+{\\beta\\:}_{2\\:}{BGENDER\\:}_{it}+{\\beta\\:}_{3\\:}{{LNBREN\\:}_{it}\\:}_{}+\\:{\\beta\\:}_{4\\:}{LNBMEET\\:}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:+\\:{\\beta\\:}_{5}LNBSIZE{\\:}_{it}\\:+\\:{\\beta\\:}_{6}\\:LNACSIZE{\\:}_{it}+\\:{\\beta\\:}_{7}ROA{\\:}_{it}+{\\beta\\:}_{8}\\:LNATEN{\\:}_{it}+\\:\\:{\\beta\\:}_{9}LEV{\\:}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:+\\:{\\beta\\:}_{10\\:}LNTA{\\:}_{it}\\:+\\:{e}_{it}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTOB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTobin\u0026rsquo;s Q\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTONE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Positive words \u0026ndash; negative words)/(positive words\u0026thinsp;+\u0026thinsp;negative words)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEGPOST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNatural log value of total number of negative internet posting of firms in the last month of a year.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNBSIZE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNatural log value of total number of board directors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNBREN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNatural log value of total directors\u0026rsquo; remuneration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNBMEET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNatural log value of total number of board meetings\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNACSIZE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNatural log value of total number of directors on the audit committee\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNatural log value of total assets\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal liabilities divided by total assets\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHOLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProportion of shares held by the board chairperson to total outstanding shares\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBGENDER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProportion of female directors on the board\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProportion of independent directors on the board\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNet income divided by total assets\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNATEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNatural log value of auditor tenure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Dependent variables\u003c/h2\u003e \u003cp\u003eOur first dependent variable is Tobin\u0026rsquo;s Q (TOB). The higher the ratio, the higher the firm value. We expect that positive tones negatively correlate with one-year leading Tobin\u0026rsquo;s Q as management has higher tendency to use positive tones to hide poor performance reflected in next year. Our second dependent variable is the levels of impression management measured as the tones of MD\u0026amp;A (TONE). Aligned with previous studies (Zhang et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), we measure the tones by the ratio of the difference of number of positive and negative words to the sum of positive and negative words. The ratios are extracted from CSMAR which measures positive and negative vocabulary based on the list provided by Loughran and McDonald (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The list provides positive words and negative words for textual analysis. It uses Longhran and McDonald (2011) as a reference for emotional vocabulary to count the optimistic and pessimistic words. The ratio ranges between +\u0026thinsp;1 and \u0026minus;\u0026thinsp;1 for perfectly positive tone and perfectly negative tone, respectively. The classification into positive and negative classification is used by many previous studies (Bassyouny, Abdelfattach \u0026amp; Tao 2022). We expect that external pressure from investors encourages management to engage in impression management, so tones are positively related to negative internet postings of the firms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Independent variables\u003c/h2\u003e \u003cp\u003eFollowing the research by Tumarkin and Whitelaw (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) which defines the internet forum information variable as the log of the number of postings, we have negative internet postings, measured as natural log values of total number of negative internet postings of firms in the last month of a year (NEGPOST), as the key independent variable in this study. The number of daily negative postings in the last month is summed to count as the number of negative postings in the last month of a year.\u003c/p\u003e \u003cp\u003eThe number of postings is extracted from CSMAR which obtains the data from Eastmoney Stock Forum (Huang et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The forum is a crucial section of the Oriental Fortune website, one of the financial and economic websites with heaviest traffic in China. Eastmoney Stock Forum, as an open forum available to retail investors, is being considered as a trustworthy online source of textual data in terms of frequency, activity and user influence (Zhou \u0026amp; Liu \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The emotion is analyzed and classified as negative using natural language processing (NLP) by CSMAR (Tan et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The last month of a year is used because we conjecture that management should be more sensitive to negative postings near the year end during which management is likely to report the financial performance of a company. Duz and Tas (2021) state that the predictive power of social media postings on stock returns is short-sighted and the trading strategies that rely on social media postings might be formed only for short-term investment.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Results and discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Descriptive statistics, correlation matrix and variance inflation factors\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the descriptive statistics. The mean of TOB is 1.97 with a minimum of 0.67 and maximum of 20.17. The mean of NEGPOST is 5.56 with a minimum of 0.69 and maximum of 9.34. On average, the natural log value of negative posting is 9.34 (11384.4), indicating a total of 11384.4 negative postings in the month. The mean of LSUM is 9.35 (11498 words), which suggests that Chinese firms use 11498 words in MD\u0026amp;A on average.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTOB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNSUM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTONE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEGPOST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBGENDER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNBREN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNBMEET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNBSIZE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNACSIZE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHOLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNATEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation matrix\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eTOB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eTONE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNEGPOST\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLNBSIZE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLNBREN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLNBMEET\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eLNACSIZE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTOB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTONE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-0.0210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEGPOST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-0.0324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.0253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNBSIZE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-0.0552**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.0564**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNBREN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-0.0193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.161***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.116***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.213***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNBMEET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-0.0524**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.0541**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.135***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.157***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNACSIZE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-0.0634***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.0183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0751***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.271***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0573**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-0.0209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.0496**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.177***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0370*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHOLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.0800***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.0517**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.118***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.187***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0983***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.0407*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.153***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBGENDER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.0206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.00746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0724***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.0203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.0519**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-0.0368*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.0137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0525**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.483***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.114***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0705***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.294***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.0259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.125***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.127***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.0627***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNATEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-0.0296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.0225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.000124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0453*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0743***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.0691***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.00357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-0.182***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.136***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.277***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.270***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.330***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.281***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.188***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNSUM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-0.0288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.339***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.147***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0409*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.257***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.204***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.00966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eLEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eCHOLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBGENDER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBIND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLNATEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eLNTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eLNSUM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHOLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-0.0510**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBGENDER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-0.0151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.0967***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.0405*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.0190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-0.252***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.0570**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNATEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-0.0344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.0266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0411*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.136***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0.198***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.166***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0367*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0490**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNSUM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.00151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.0623***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.00202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.00367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.212***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eNote: * p\u0026thinsp;\u0026lt;\u0026thinsp;0.10; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01. Standard errors are in the parenthesis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe TOB has a mean of 1.97, with a minimum value of 0.67 and maximum value of 20.17. Wang, Wu \u0026amp; Yan (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) find that the mean of Tobin\u0026rsquo;s Q is 2.208 using the data from 2008 to 2017. Tobin\u0026rsquo; Q is lower in our sample because firms in our sample are at the times of COVID-19 pandemic. The mean of tones is 0.26 with a minimum of -0.61 and a maximum of 0.69. On average, firms use positive tones in MD\u0026amp;A. The result is consistent with Wang, Wu \u0026amp; Yan (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) which find that managers of Chinese firms are more inclined to use positive tones.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Main results\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the relationships between one-year leading TOB and TONE. We identify a negative association between one-year leading TOB and TONE (β\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.781; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), implying that the more positive the tones, the lower the firm values in one year later. The results align with our expectation that management is likely to use more positive tones if they expect that the firms do not perform well in the future. Our results suggest that agency theory prevails over stewardship theory in China. The results align with the studies by Mai et al. (2019), Wei et al. (2019) and Jiang et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) which claim that management is likely to engage in impression management to hide bad news. Thus H1 is supported.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of fixed panel data regression: Leading TOB and TONE\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018\u0026ndash;2021\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTOB\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTONE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.781***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNBSIZE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.391**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNBREN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNBMEET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.291*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLACSIZE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.633)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.890***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.314)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHOLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBGENDER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.715)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.596***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCON\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.154***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYEAR DUMMY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINDUSTRY DUMMY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj r-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eNote: * p\u0026thinsp;\u0026lt;\u0026thinsp;0.10; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01. Standard errors are in the parenthesis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the associations between TONE and NEGPOST. We confirm that NEGPOST positively correlates with TONE between 2018 and 2021 (β\u0026thinsp;=\u0026thinsp;0.006; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that the more the negative internet postings the more positive the tones. Thereby, H2a is supported. The results are consistent with our expectation that if investors become pessimistic, management is more likely to adopt positive tones to convince investors that firms will perform better in the future. The result is consistent with that of Luo et al. (2019), which show that high media attention to environmental performance increases environmental legitimate risks, prompting management to disclose information that meets stakeholders\u0026rsquo; expectation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of fixed panel data regression results: TONE and NEGPOST\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018\u0026ndash;2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2020\u0026ndash;2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2018\u0026ndash;2019\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTONE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTONE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTONE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEGPOST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.006**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.028**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBGENDER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNBREN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.020***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.053**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNBMEET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.060*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNBSIZE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNACSIZE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.102***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.203***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNATEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCON\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.885*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.860\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYEAR DUMMY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINDUSTRY DUMMY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e769\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj r-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: * p\u0026thinsp;\u0026lt;\u0026thinsp;0.10; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01. Standard errors are in the parenthesis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOur further analysis indicates that the positive association between negative internet postings and tone is significant between 2020 and 2021 (β\u0026thinsp;=\u0026thinsp;0.028; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but insignificant between 2018 and 2019 (β\u0026thinsp;=\u0026thinsp;0.005; p\u0026thinsp;\u0026gt;\u0026thinsp;0.10). The findings suggest that management is more sensitive and active to engage in impression management in the periods of COVID-19 pandemic (2020\u0026ndash;2021) due to external pressure from investors. Thus H2b is supported. This is in contrast with the finding of Moreno \u0026amp; Jones (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) which suggest managers are less motivated to engage in impression management during external global crisis as the negative performance aligns with the extreme economic context. However, our result is consistent with that of Hossain, Alam and Mazumder (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which agrees that managers strategically use language tactics in annual reports to manage investors\u0026rsquo; impression during COVID-19 pandemic\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Robust tests\u003c/h2\u003e \u003cp\u003eWe use the natural log value of total word counts as an alternative measure of impression management. Prior studies show that management is more likely to use lengthy and complex disclosures to mislead investors. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that the results are largely consistent with those of the main results. NEGPOST positively correlates with LNSUM between 2018 and 2021 (β\u0026thinsp;=\u0026thinsp;0.035; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting that if investors are pessimistic, management is under pressure to manage investors\u0026rsquo; impression. The positive association is also significant between 2020 and 2021 (β\u0026thinsp;=\u0026thinsp;0.117; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), but insignificant between 2018 and 2019 (β\u0026thinsp;=\u0026thinsp;0.005; p\u0026thinsp;\u0026gt;\u0026thinsp;0.10).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of fixed panel data regressions: LNSUM and NEGPOST\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018\u0026ndash;2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2020\u0026ndash;2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2018\u0026ndash;2019\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLNSUM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLNSUM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLNSUM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEGPOST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.035***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.117***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBGENDER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNBREN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNBMEET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.089***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.197*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNBSIZE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.458**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNACIZE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNATEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.343*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.187***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.373***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.193**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCON\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.381***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-22.092***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.898**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYEAR DUMMY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINDUSTRY DUMMY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e769\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj R-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0441\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: * p\u0026thinsp;\u0026lt;\u0026thinsp;0.10; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01. Standard errors are in the parenthesis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Endogeneity\u003c/h2\u003e \u003cp\u003eEndogeneity is always a major problem in corporate governance studies. The endogeneity problem is present when an observed or unobserved variable, which is not integrated in the model, is connected to a variable incorporated in the research model (Dodoo, Appiah \u0026amp; Donkor \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The issue of endogeneity arises when an explanatory variable correlates to the error term, leading to biased estimators (Adkins \u0026amp; Hill \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). It may also exist because of measurement error, unobservable in form of omitted variable \u0026ldquo;bias\u0026rdquo; (Wooldridge \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and reverse causality (Roberts \u0026amp; Whited \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). We expect that reverse causality is not a significant issue as we use leading Tobin\u0026rsquo;s Q in this study. Additionally, it is unlikely that the number of negative internet postings is determined by the tones and length of MD\u0026amp;A.\u003c/p\u003e \u003cp\u003eFollowing the study by Arellano and Bond (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), this study employs Dynamic Panel Difference GMM regression. The results of dynamic panel data regression are comparable to those in the main study using fixed effect panel data regression, except that the relationships between LNSUM and NEGPOST become less significant while the sign is consistent and positive. The findings are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of dynamic panel data regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTONE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLNSUM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAGTONE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.345*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAGLNSUM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.479***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEGPOST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.012*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBGENDER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNBREN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.035**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNBMEET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNBSIZE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNACSIZE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.171**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNATEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCON\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-23.337**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(10.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYEAR DUMMY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINDUSTRY DUMMY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: * p\u0026thinsp;\u0026lt;\u0026thinsp;0.10; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01. Standard errors are in the parenthesi\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study examines the impacts of impression management on firm values and how negative internet postings drive the management to engage in impression management using a sample of listed companies in China between 2018 and 2021.\u003c/p\u003e \u003cp\u003eOur findings show that the management is more likely to use more positive tones when firm values are lower in the next year, indicating that management tends to use more positive tones to create the image that the firms will perform well while the firms will not. Additionally, our findings provide evidence that management is sensitive to negative internet postings in the last month of the year-end. The negative internet postings will prompt management to engage in impression management as management may fear that negative internet postings lower the firm values. Therefore, management tends to disclose more positive tones to neutralize the effects of negative internet postings. In our additional analysis, the positive relationships between negative postings and positive tone are more significant during COVID-19, suggesting that management becomes more sensitive to negative internet posting during COVID-19. The findings are important as they provide additional mechanisms for investors to identify the impacts of tones and length of MD\u0026amp;A on firm values and how management engages in impression management. More importantly, the findings provide additional evidence that management is sensitive to negative internet postings, particularly during the crisis period.\u003c/p\u003e \u003cp\u003eThe study is subject to caveats. First, the study only uses tones and length of MD\u0026amp;A as proxies for impression management. However, management may do so using clarity and grammatical structure of MD\u0026amp;A. Future studies may examine the relationships using grammatical structure and clarify as proxies for impression management. Second, this study is conducted in Mainland China, so the results cannot be generalized to other countries. The scope could be expanded to other Asian countries with different institutional and judicial environment. For example, future studies can be conducted in a country where institutional investors account for the majority of investors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eConflict of interest\u003c/strong\u003e \u003cp\u003eThe authors declare no conflicts of interest\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was not supported by any funds.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, K.Y.C., C.Y.L., and H.Y.H.N; methodology, K.Y.C., K.K.L ; validation, C.Y.L. and T.W.D.L, formal analysis, K.Y.C., C.Y.L., K.K.L; investigation, K.Y.C.; writing\u0026mdash;original draft preparation, K.Y.C., K.K.L, H.Y.H.N; writing\u0026mdash;Review and Editing, C.Y.L., H.Y.H.N., T.W.D.L; All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available upon reasonable request from the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbou-El-Sood, H., El-Sayed, D.: Abnormal disclosure tone, earnings management and earnings quality. J. Appl. Acc. Res. \u003cb\u003e23\u003c/b\u003e(2), 402\u0026ndash;433 (2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdkins, L.C., Hill, R.C.: Using Stata for principles of econometrics, 3rd edn. John Willey (2008)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmiram, D., Bozanic, Z., Cox, J.D., Dupont, Q., Karpoff, J.M., Sloan, R.: Financial reporting fraud and other forms of misconduct: a multidisciplinary review of the literature. Rev. Acc. Stud. \u003cb\u003e23\u003c/b\u003e(2), 732\u0026ndash;783 (2018)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAn, Y., Su, F.: Do internet stock message boards influence firm value? Evidence from China. Asia-pacific J. Acc. Econ. \u003cb\u003e30\u003c/b\u003e(2), 327\u0026ndash;353 (2023)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAng, J.S., Hsu, C., Tang, D., Wu, C.: The Role of Social Media in Corporate Governance. Acc. Rev. \u003cb\u003e96\u003c/b\u003e(2), 1\u0026ndash;32 (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArellano, M., Bond, S.: Some tests of specification for panel date: Monte Carlo evidence and application to employment equations. Rev. Econ. Stud. \u003cb\u003e58\u003c/b\u003e(2), 277\u0026ndash;297 (1991)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBassyouny, H., Abdelfattah, T., Tao, L.: Narrative disclosure tone: A review and areas for future research. J. Int. Acc. Auditing Taxation, 110511. (2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBolino, M.C., Kacmar, K.M., Turnley, W.H., Gilstrap, J.B.: A multi-level review of impression management motives and behaviors. J. Manag. \u003cb\u003e34\u003c/b\u003e(6), 1080\u0026ndash;1109 (2008)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheung, C., Chan, A.C.: Benefits of Hong Kong Chinese CEOs\u0026rsquo; Confucian and Daoist leadership styles. Leadersh. Organ. Dev. J. \u003cb\u003e29\u003c/b\u003e, 474\u0026ndash;503 (2008)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCho, C.H., Roberts, R.W., Patten, D.M.: The language of US corporate environmental disclosure. Acc. Organ. Soc. \u003cb\u003e35\u003c/b\u003e(4), 431\u0026ndash;443 (2010)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChong, T.T.L., Liu, X., Zhu, C.: What explains herd behavior in the Chinese stock market? J. Behav. Finance. \u003cb\u003e18\u003c/b\u003e(4), 448\u0026ndash;456 (2017)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorbetta, G., Salvato, C.: Self-serving or self‐actualizing? Models of man and agency costs in different types of family firms: A commentary on comparing the agency costs of family and non‐family firms: Conceptual issues and exploratory evidence. Entrepreneurship Theory Pract. \u003cb\u003e28\u003c/b\u003e(4), 355\u0026ndash;362 (2004)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCotter, J., Lokman, N., Najah, M.M.: Voluntary disclosure research: Which theory is relevant? J. Theoretical Acc. Res. \u003cb\u003e6\u003c/b\u003e(2), 77\u0026ndash;95 (2011)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis, A.K., Piger, J.M., Sedor, L.M.: Beyond the numbers: Measuring the information content of earnings press release language. Contemp. Acc. Res. \u003cb\u003e29\u003c/b\u003e(3), 845\u0026ndash;868 (2012)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis, J.H., Schoorman, F.D., Donaldson, L.: Toward a stewardship theory of management. Acad. Manage. Rev. \u003cb\u003e22\u003c/b\u003e(1), 20\u0026ndash;47 (1997)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDodoo, R.N.A., Appiah, M., Donkor, D.T.: Examining the factors that influence firm performance in Chana: a GMM and OLS approach. Natl. Acc. Rev. \u003cb\u003e2\u003c/b\u003e(3), 309\u0026ndash;323 (2020)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDonaldson, L., Davis, J.H.: Boards and company performance: Research challenges the conventional wisdom. Corp. Governance: Int. Rev. \u003cb\u003e2\u003c/b\u003e(3), 151\u0026ndash;160 (1994)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuz.,T, S., Tas, O.: Social media sentiment in international stock returns and trading activity. J. Behav. Finance. \u003cb\u003e22\u003c/b\u003e(2), 221\u0026ndash;234 (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng, X., Li, X., Su, F.: Investor attention and stock return comovement: Evidence from China\u0026rsquo;s A-share Bolino stock market. Unpublished Working Paper. (2020)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFroese, F.J., Sutherland, D., Lee, J.Y., Liu, Y., Pan, Y.: Challenges for foreign companies in China: Implications for research and practice. Asian Bus. Manage. \u003cb\u003e18\u003c/b\u003e(4), 249\u0026ndash;262 (2019)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHart, R.P., Childers, J.P., Lind, C.J.: Political tone: How leaders talk \u0026amp; why. University of Chicago Press (2013)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHossain, D.M., Alam, M.S., Mazumder, M.M.M.: Impression management tactics in Covid-19 related disclosures: a study on the annual reports of Bangladeshi listed insurance companies. \u003cem\u003eAsian Journal of Economics and Banking\u003c/em\u003e. (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/AJEB-04-2022-0042\u003c/span\u003e\u003cspan address=\"10.1108/AJEB-04-2022-0042\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang, C., Cao, Y., Lu, M., Shan, Y., Zhang, Y.: Messages in online stock forums and stock price synchronicity: evidence from China. Acc. Finance. \u003cb\u003e00\u003c/b\u003e, 1\u0026ndash;31 (2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang, F., Kim, K.: Corporate governance in China: A modern perspective. J. Corp. Finance. \u003cb\u003e32\u003c/b\u003e, 190\u0026ndash;216 (2015)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang, F., Lee, J., Martin, X., Zhou, G.: Manager sentiment and stock returns. J. Financ. Econ. \u003cb\u003e131\u003c/b\u003e(1), 126\u0026ndash;149 (2019)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJugnandan, S., Willows, G.D.: It\u0026rsquo;s a long story\u0026hellip; \u0026ndash; impression management in South African corporate reporting. Acc. Res. J. \u003cb\u003e35\u003c/b\u003e(5), 581\u0026ndash;597 (2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang, F., Chen, H.: Analyst Optimistic Forecasting and Impression Management of CSR Disclosure: Empirical evidence from China. \u003cem\u003e2021 International Conference on Tourism, Economy and Environmental Sustainability\u003c/em\u003e, 275, 03070. (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1051/e3sconf/202125103094\u003c/span\u003e\u003cspan address=\"10.1051/e3sconf/202125103094\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKiattikulwattana, P.: Do letters to shareholders have information content? Asian Rev. Acc. \u003cb\u003e27\u003c/b\u003e(1), 137\u0026ndash;159 (2019)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, C.C., Lee, C.C., Wu, Y.: The impact of COVID-19 pandemic on hospitality stock returns in China. Int. J. Finance Econ. (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ijfe.2508\u003c/span\u003e\u003cspan address=\"10.1002/ijfe.2508\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, J., Park, J.: The impact of audit committee financial expertise on management discussion and analysis (MD\u0026amp;A) tone. Eur. Acc. Rev. \u003cb\u003e28\u003c/b\u003e, 129\u0026ndash;150 (2019)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, F.: Survey of the literature. J. Acc. Literature. \u003cb\u003e29\u003c/b\u003e, 143\u0026ndash;165 (2010b)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLo, K.: Earnings management and earnings quality. J. Account. Econ. \u003cb\u003e45\u003c/b\u003e(2), 350\u0026ndash;357 (2008)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoughran, T., McDonald, B.: When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. J. Finance. \u003cb\u003e66\u003c/b\u003e(1), 35\u0026ndash;65 (2011)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo, X., Zhang, Q., Zhang, S.: External financing demands, media attention and the impression management of carbon information disclosure. Carbon Manag. \u003cb\u003e12\u003c/b\u003e(3), 235\u0026ndash;247 (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo, Y., Zhou, L.: Textual tone in corporate financial disclosures: A survey of the literature. Int. J. Disclosure Gov. \u003cb\u003e17\u003c/b\u003e, 101\u0026ndash;110 (2020)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMelloni, G., Stacchezzini, R., Lai, A.: The tone of business model disclosure: an impression management analysis of the integrated reports. J. Manage. Governance. \u003cb\u003e20\u003c/b\u003e, 295\u0026ndash;320 (2016)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMerkl-Davies, Brennan, D.M., Niamh, M., McLeay, S.J.: Impression management and retrospective sense-making in corporate narratives: A social psychology perspective. Acc. Auditing Account. \u003cb\u003e24\u003c/b\u003e(3), 315\u0026ndash;344 (2011)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eModigliani, F., Miller, M.: Some estimates of the cost of capital to the electric utility industry 1954\u0026ndash;1957. Am. Econ. Rev. \u003cb\u003e56\u003c/b\u003e, 333\u0026ndash;391 (1966)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoloi, T., Marwala, T.: The Agency Theory. In: Artificial Intelligence in Economics and Finance Theories. In: Advanced Information and Knowledge Processing. Springer, Cham (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-030-42962-1_11\u003c/span\u003e\u003cspan address=\"10.1007/978-3-030-42962-1_11\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoreno, A., Jones, M.J.: Impression management in corporate annual reports during the global financial crisis. \u003cem\u003eEuropean Management Journal\u003c/em\u003e, 40(4), 503\u0026ndash;517. (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeu, D.: Trust, impression management and the public accounting profession. Crit. Perspect. Acc. \u003cb\u003e2\u003c/b\u003e(3), 295\u0026ndash;313 (1991)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePouryousof, A., Farzaneh, N., Reza, H., Davood, A.: The Relationship between Managers\u0026rsquo; Disclosure Tone and the Trading Volume of Investors. J. Risk Financial Manage., \u003cb\u003e15\u003c/b\u003e(618). (2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoberts, M., Whited, T.: Endogeneity in corporate finance, Working Paper, Wharton. (2011)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchleicher, T., Walker, M.: Bias in the tone of forward-looking narratives. Acc. Bus. \u003cb\u003e40\u003c/b\u003e(4), 371\u0026ndash;390 (2010)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkinner, D.J.: Why firms voluntarily disclose bad news. J. Acc. Res. \u003cb\u003e32\u003c/b\u003e(1), 38\u0026ndash;60 (1994)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan, H., Peng, S., Zhu, C., You, Z., Miao, M., Kuai, S.: Long-term Effects of the COVID-19 Pandemic on Public Sentiments in Mainland China: Sentiment Analysis of Social Media Posts. J. Med. Internet. Res., \u003cb\u003e23\u003c/b\u003e (8), e29150 (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTumarkin, R., Whitelaw, R.F.: News or noise? Internet postings and stock prices. Financial Anal. J. \u003cb\u003e57\u003c/b\u003e(3), 41\u0026ndash;51 (2001)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, J., Ye, K.: Media coverage and firm valuation: Evidence from China. J. Bus. Ethics. \u003cb\u003e127\u003c/b\u003e(3), 501\u0026ndash;511 (2015)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, Q., Wu, D., Yan, L.: Effect of positive tone in MD\u0026amp;A disclosure on capital structure adjustment speed: evidence from China. Acc. Finance. \u003cb\u003e61\u003c/b\u003e(4), 5001\u0026ndash;5890 (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWooldridge, J.M.: Introductory econometrics: A modern approach, 5th edn. South-Western Cengage Learning (2012)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYong, T.: Corporate governance in the banking sector. Performance. Risk Competition Chin. Bank. Industry: Chandos Asian Stud. Ser., 39\u0026ndash;64. (2014)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZahra, S.A., Hayton, J.C., Neubaum, D.O., Dibrell, C., Craig, J.: (2008). Culture of family\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ecommitment and strategic flexibility: The moderating effect of stewardship\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEntrepreneurship: Theory Pract., \u003cb\u003e32\u003c/b\u003e(6), 1035\u0026ndash;1054\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Z., Luo, M., Hu, Z., Niu, H.: Textual Emotional Tone and Financial Crisis Identification in Chinese Companies: A Multi-Source Data Analysis Based on Machine Learning. Applied Sciences, 12, 6662. (2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, S., Liu, X.: Internet postings and investor herd behavior: evidence from China\u0026rsquo;s open-end fund market. \u003cem\u003eHumanities and Social Sciences Communications\u003c/em\u003e, 9(441). (2022)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Negative internet posting, Impression management, Firm value, China G34; G38; M42","lastPublishedDoi":"10.21203/rs.3.rs-8697348/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8697348/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePurpose - This study examines the effects of negative internet postings on impression management as well as the impacts of impression management on firm values in China before and during the COVID-19 pandemic.\u003c/p\u003e \u003cp\u003eDesign/methodology/approach - We collect data on all A-shares listed in Mainland China between 2018 and 2021 from China Stock Market \u0026amp; Accounting Research Database (CSMAR). A total of 3021 firm-year observations were obtained.\u003c/p\u003e \u003cp\u003eFindings - We find that management uses more positive tones under the pressure of negative internet postings. Management uses more positive tones and longer MD\u0026amp;A reports during the COVID-19. This study extends the literature by identifying an additional determinant, negative internet postings, of impression management in MD\u0026amp;A disclosures.\u003c/p\u003e \u003cp\u003eOriginality/value - The findings support the view that management is more likely to use MD\u0026amp;A to mislead investors about firm performance during financial difficulties and under external pressure rather than convey true economic realities of companies.\u003c/p\u003e","manuscriptTitle":"Is negative internet posting a threat to management? Impression management and firm values: Evidence from China.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-04 10:26:08","doi":"10.21203/rs.3.rs-8697348/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"11b65a6f-0f31-4765-8d54-b679e1b7c03e","owner":[],"postedDate":"February 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-04T10:26:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-04 10:26:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8697348","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8697348","identity":"rs-8697348","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}