OIM and firm sentiment

Organizational representatives often leverage annual reports, shareholder meetings/conference calls, press releases, and corporate disclosures/documents to affect internal and external firm stakeholders’ perceptions of their firms (Bolino, Kacmar, Turnley, & Gilstrap, Reference Bolino, Kacmar, Turnley and Gilstrap2008; Brown, Reference Brown1997; Ginzel, Kramer, & Sutton, Reference Ginzel, Kramer, Sutton, Cummings and Staw1992). Organizational-level impression management is any activity purposefully formulated and implemented to influence an audience’s view of the organization (Elsbach, Sutton, & Principe, Reference Elsbach, Sutton and Principe1998). Mohamed, Gardner, and Paolillo (Reference Mohamed, Gardner and Paolillo1999) seminal taxonomy of OIM includes direct or indirect and either assertive or defensive impression management tactics. While in-direct tactics include ways to manage the perceptions of audiences through the firm’s association with other organizations, direct tactics are concerned with how firms utilize various information (e.g., firm accomplishments, characteristics, abilities, etc.) to generate a positive sentiment about their actions (Mohamed, Gardner, & Paolillo, Reference Mohamed, Gardner and Paolillo1999). Additionally, while defensive tactics are used when a firm’s reputation and survivability are in jeopardy, assertive tactics tend to be proactive. They are used to influence firm stakeholders’ perceptions. It is important to note that this study’s focus aligns with the directive-assertive impression management tactics as we seek to explore whether (and how) performance feedback influences firm impression management tactics through firm sentiment.

Past research has used ‘sentiment’ in understanding various organizational outcomes (Clatworthy & Jones, Reference Clatworthy and Jones2001; Thomas, Reference Thomas1997). For example, strategic leaders can shape the expectations of firm stakeholders by deliberately including positive-only information or disclosures in their narratives to signal positive sentiment. Specifically, they can choose to highlight the firm data that support leaders’ narratives while withholding or minimizing the negative news information (Boudt & Thewissen, Reference Boudt and Thewissen2019). For example, Boudt and Thewissen (Reference Boudt and Thewissen2019) states that using positive and negative sentiments in CEO letters is another way of managing impressions. Huang, Zhao, Niu, Ashford, and Lee (Reference Huang, Zhao, Niu, Ashford and Lee2014) supported the argument that strategic leaders tend to influence how investors perceive a stock by promoting high value before important events. Furthermore, using sentiment analysis, the authors found a significant positive relationship between the use of abnormal accruals in earnings management and qualitative impression management. The use of firm sentiment as a proxy for OIM stems from the understanding that OIM encompasses various tactics employed by organizations to shape the perception of stakeholders positively. In this context, firm sentiment refers to the overall sentiment the company conveys in its communications and interactions with stakeholders, which may include direct and indirect methods. In this sense, the overall sentiment counts the frequency of mentions of positive words, deducts the frequency of negative words, and then divides by the length of the transcript. For our analysis, we multiply the resulting measure by 100,000. This is to capture the firm’s overall intended sentiment rather than focus on positive vs. negative sentiments. This helps indirectly capture the intended message the firm is trying to communicate to the external stakeholders and analysts, perhaps without compromising their honesty and integrity. Although OIM entails several tactics, firm sentiment is mainly a major one often sensed and analyzed by external stakeholders, including investors, customers, employees, and the public. The goal of impression management is to shape perceptions and influence behavior. Firm sentiment, reflecting the collective impression of the firm, provides a practical and observable outcome of these efforts. It allows researchers to assess whether OIM strategies translate into tangible changes in how the firm is perceived and evaluated.

Positive performance feedback and firm sentiment

The behavioral theory of the firm proposed by Cyert and March (Reference Cyert and March1963) provides a comprehensive framework for understanding organizations’ behavior and decision-making processes. This theory emphasizes the importance of bounded rationality, satisficing, individual behavior, and organizational learning in shaping the behavior and performance of organizations (Cyert & March, Reference Cyert and March1963). The core tenet of this theory is that strategic leaders adopt an aspiration level to evaluate firm performance (Argote & Greve, Reference Argote and Greve2007). Specifically, research suggests that strategic leaders assess firm performance feedback as part of their decision-making process (March & Shapira, Reference March and Shapira1992; Schimmer & Brauer, Reference Schimmer and Brauer2012). As suggested by Lim and McCann (Reference Lim and McCann2014), this ‘backward-looking’ approach or benchmarking performance against firm aspirations has been widely studied in various contexts. For example, the effect of positive and/or negative performance feedback (or attainment discrepancies, see Lant, Reference Lant1992) on learning, achievement, and performance management (Lucas & Klangboonkrong, Reference Lucas, Klangboonkrong, Kacanski, Kabderian Dreyer and Sund2023), organizational learning (Audia & Greve, Reference Audia and Greve2021), entering new markets (Ref & Shapira, Reference Ref and Shapira2017), mergers & acquisitions (e.g., Iyer & Miller, Reference Iyer and Miller2008), on research and development intensity (e.g., Chen, Reference Chen2008; Chen & Miller, Reference Chen and Miller2007), risk-taking (e.g., Chen, Reference Chen2008), and organizational change (e.g., Greve, Reference Greve1998) has generally been supported.

Although performance feedback has been examined in several contexts, its effect on OIM (proxied by firm sentiment) has been minimally explored (e.g., Clatworthy & Jones, Reference Clatworthy and Jones2001; Goel & Madadian, Reference Goel and Madadian2023). For example, Goel and Madadian (Reference Goel and Madadian2023) examined peer-based performance in firms’ decision-making processes by benchmarking earnings/cash flows against other firms. The authors further explored whether peer-based performance comparison (i.e., the comparison of earnings/cash flows of a firm with its peers) affects disclosure tone. They found that firms with performance below the peer-based comparison are involved in greater downward tone in their earnings-related narrative disclosures, and the extent of this negativity increases the further performance falls below the benchmark. Although this study provides invaluable insights into the literature, it differs from ours in several ways. First, we use historical aspiration levels to focus on positive and negative performance feedback. Second, our study examines whether and how a firm’s sentiment (i.e., positive firm tone) inflates or deflates based on the firm’s below or above-performance feedback (historical aspiration level). Finally, we explore the top management implications of this relationship by examining the moderating role of CEO overconfidence.

Past research shows that while profitable companies tend to discuss their positive results, less profitable companies are inclined to mainly discuss changes in their board of directors (Clatworthy & Jones, Reference Clatworthy and Jones2001). Additionally, it is common practice that corporate disclosures contain active and passive verb choices as part of OIM tactics. For example, Thomas (Reference Thomas1997), analyzing CEO letters, found that while writers distance themselves from the message using passive voices, active voices represent success. Similarly, scholars found that adopting passive constructions signals the text as more neutral in tone and can be used by writers to dissociate themselves from the text (Sydserff & Weetman, Reference Sydserff and Weetman2002). Finally, in their novel study, Zaharopoulos and Kwok (Reference Zaharopoulos and Kwok2017) sought to understand OIM tactics used by US law firms on Twitter. Utilizing textual analysis of about 10,000 tweets, scholars evaluated how law firms differ from each other in their use of sentiment. They found that law firms with higher revenues displayed higher levels of optimism and certainty. It is evident that firms tend to use substantive and symbolic responses to enhance social performance and manage stakeholder expectations. Given that we measure ‘firm sentiment’ as representing an increase in positive tone in corporate communications to shareholders, our study’s use of ‘firm sentiment’ as an OIM proxy is consistent with previous research. Kotiloglu, Chen, and Lechler (Reference Kotiloglu, Chen and Lechler2021), in their meta-analytic review involving 113 empirical studies, found that considering different feedback conditions is essential, and further research must develop a more comprehensive understanding of organizational responses to performance feedback.

Heeding the call for further research and building on and extending past research on performance feedback – OIM relationship, we propose that firms with positive performance feedback (proxied by historical aspiration) will engage in a strategic narrative using a positive tone in firm sentiment. Hence, we propose the following hypotheses:

Negative performance feedback and firm sentiment

Simon (Reference Simon1947) suggests that ‘[d]ecision making is the heart of administration … [t]he vocabulary of administrative theory must be derived from the logic and psychology of human choice’ (p. xlvi). Firm choices and actions (represented by decision-makers) can involve reactive and deliberate decision-making processes, often searching for a solution to a short-term or long-term problem. In line with this argument, the core tenet of the behavioral theory of the firm is that performance below aspiration requires a search for solutions (Cyert & March, Reference Cyert and March1963; Jordan & Audia, Reference Jordan and Audia2012). Past literature on performance feedback examines this inclination of low-profitable firms to expect less and strive more (e.g., Greve, Reference Greve2003). Hence, performance feedback and its influence on firm choices and outcomes play an important role in the behavioral theory of the firm research (Cyert & March, Reference Cyert and March1963; Lim & McCann, Reference Lim and McCann2014; Lucas & Klangboonkrong, Reference Lucas, Klangboonkrong, Kacanski, Kabderian Dreyer and Sund2023; March & Shapira, Reference March and Shapira1992; Ref & Shapira, Reference Ref and Shapira2017). Consistent with this theoretical foundation, we propose that firms adjust their narrative structures and outlets to influence firm stakeholders through sentiments in documents/disclosures based on their performance feedback.

In our Hypothesis 1 discussion section, we argued that positive performance feedback is positively related to firm sentiment. In contrast, we propose in this section that negative performance feedback will lead to firms adopting a more dissociative tone in their sentiments, leading to decreased or deflated firm sentiment (rather than a negative sentiment). It is essential to highlight that, in this study, we propose a deflated firm sentiment rather than a negative firm tone; hence, our arguments are consistent with the behavioral theory of firm research (Jordan & Audia, Reference Jordan and Audia2012) and OIM (Jaafar et al., Reference Jaafar, Halim, Ismail and Ahmad2018; Yang & Liu, Reference Yang and Liu2017). For example, past and recent research suggests that firms tend to use impression management tactics strategically in the event of negative performance feedback. For example, Jordan and Audia (Reference Jordan and Audia2012) propose that decision-makers tend to enhance their self-image by assessing performance as satisfactory, arguing that low performance leads to increased search, change, and risk-taking. The authors argue that by revising the satisfactory performance goal, firms can reduce the extent of search, change, and risk-taking triggered by low performance.

Thomas (Reference Thomas1997) finds that a firm’s use of the pronoun ‘we’ declines as it experiences performance declines. The following quotes are from Thomas’s (Reference Thomas1997) sentiment analysis of a sample firm (i.e., Cross & Trecker, Corp.): ‘Fiscal year was very disappointing, but we believe that our basic position as a supplier of advanced manufacturing technology has never been stronger.’ ‘Fiscal 1988 represented a setback in our timetable for returning Cross & Trecker to profitability’ (Thomas, Reference Thomas1997, p. 35). These quotes are revealing examples of how decision-makers behave diplomatically in selecting the narrative avenues to discuss financial performance (Davis & Tama-sweet, Reference Davis and Tama-sweet2012). Examining the sentiment and market return events, Yang, Liu, Chen, and Hawkes (Reference Yang, Liu, Chen and Hawkes2018) found a significant link between positive returns and positive sentiment, as well as negative returns and negative sentiment.

Based on the arguments above, we suggest that firms with negative performance feedback (proxied by historical aspiration gap (HAG) if HAG < 0) will engage in a strategic narrative using decreased firm sentiment. Hence, we propose the following hypotheses:

The moderating role of CEO overconfidence

The baseline hypotheses above shed essential light on the relationship between performance feedback and OIM. However, it is imperative that a richer understanding of performance feedback’s effect on organizational behaviors requires consideration of moderating contexts. Past work has examined theoretical explanations of the effects of performance feedback on firm behaviors by exploring contingency factors. For example, Davidson, Jiraporn, Kim, and Nemec (Reference Davidson, Jiraporn, Kim and Nemec2004) found that individuals holding dual roles as ‘CEO–Chairperson’ of firms are more likely to manage the impressions about corporate earnings than when they only hold CEO roles. These scholars also show that such impression management tactics are most likely to occur following periods of poor firm performance.

Similarly, Boudt and Thewissen (Reference Boudt and Thewissen2019) show that strategic leadership is inclined to disclose information in such a way that the audience of the CEO letter has a more positive perception of the underlying narrative. Specifically, the scholars anticipate that the CEOs accomplish such objectives by including the most negative tones at the beginning of the text. They further argue that according to the recency theory and peak-end rule, shareholders would recall such adverse sentiments less once they read the text (Boudt & Thewissen, Reference Boudt and Thewissen2019).

Given that CEOs are at the forefront of a firm and the face of the entire organization, they have an immense responsibility to convey the right message to internal and external stakeholders in their letters to investors and earnings conference calls. However, how can they strike the right balance between an optimistic and pessimistic sentiment and avoid losing the trust of firm stakeholders? In their past study, Pearson, Hansen, and Gordon (Reference Pearson, Hansen and Gordon1979) suggest that CEO letters must be consistent with prior knowledge to help the audience comprehend while being transparent and upfront about the negative news. Furthermore, Amernic, Craig, and Tourish (Reference Amernic, Craig and Tourish2007) propose that CEO letters can serve multiple purposes, from defining performance measures and articulating the business model, strategy, and vision to maintaining confidence in the firm and functioning as accountability reports. All the purposes can be opportunities for OIM tactics. Hence, it is plausible that CEOs craft and relay the information to firm stakeholders.

Since all CEOs are not created equal, we can anticipate that CEOs with varying characteristics may craft and discuss firm information differently. One such different characteristic (and the contingency in our study) is CEO overconfidence. Malmendier and Tate (Reference Malmendier and Tate2005) argue that CEO overconfidence is defined as top corporate leaders’ persistent overestimation of their capabilities and superior decision-making skills compared to others. In a different context, Schumacher et al. (Reference Schumacher, Keck and Tang2020) found that managerial biases, in the form of overconfidence, adjust the interpretation of performance feedback and, in turn, shape a firm’s risk-taking in response to it. Chen, Crossland, and Luo (Reference Chen, Crossland and Luo2015) show that firms run by overconfident CEOs tend to be less responsive to corrective feedback in improving management forecast accuracy. Behavioral theorists study cognitive biases, such as confirmation bias, overconfidence, and anchoring, to understand how these biases affect decision-making processes and lead to suboptimal outcomes (Ahmad, Shah, & Abbass, Reference Ahmad, Shah and Abbass2021). For example, analyzing data from 224 publicly traded high-tech firms in the United States, Zhang, Yang, Yuan, and Fan (Reference Zhang, Yang, Yuan and Fan2024) find that below-aspiration performance and slack both contribute to emphasizing exploration over exploitation in alliance portfolios. Furthermore, CEO overconfidence intensifies the positive roles of below-aspiration levels and ‘slack’ in the preference for exploration alliances.

Driven by this logic and informed by the existing literature, we argue that overconfident CEOs will seek to enhance the firm’s positive sentiment regardless of whether the firm experiences positive or negative performance feedback. Although the scholars have yet to focus on CEO overconfidence in the context of our study, Schleicher and Walker (Reference Schleicher and Walker2010), using the annual reports of UK companies, show that companies are inclined to use biased sentiment in their communications when faced with a looming financial decline. In addition, Arslan-Ayaydin, Boudt, and Thewissen (Reference Arslan-Ayaydin, Boudt and Thewissen2016) found that equity-based incentives lead strategic leaders to hype their stock and option portfolios’ value using overly optimistic sentiments in their earnings press releases. Finally, research shows that strategic leaders in organizations sometimes exhibit high levels of narcissism and overconfidence in the event of performance decline (Jordan & Audia, Reference Jordan and Audia2012).

This section explores an essential but understudied contingency – CEO overconfidence: How do firms with overconfident CEOs leverage positive and negative performance feedback in their impression management (proxied by firm sentiment)? We used the CEO’s stock options practice to measure ‘CEO overconfidence’. Although we acknowledge that objective measures of CEO overconfidence may lack representation of behavioral traits, we are strained by the absence of primary data for this variable. However, primarily relying on objective data, past and recent literature found strong evidence between CEO overconfidence and CEO stock options practice (Dezsö & Ross, Reference Dezsö and Ross2012; Kang, Zhu, & Zhang, Reference Kang, Zhu and Zhang2021; Lee, Hwang, & Chen, Reference Lee, Hwang and Chen2017; Malmendier & Tate, Reference Malmendier and Tate2005). To mitigate the potential bias and improve the robustness of our results, we have also used two alternative measures of CEO confidence in our analysis.

We propose that overconfident CEOs will overestimate their persuasion skills and craft a more positive firm sentiment regardless of whether their firm faces positive or negative performance feedback. Hence, we propose the following moderating hypotheses:

Sample and data sources

Compustat North America Fundamental Annual – the corporate financial information source – generated our sample. We keep firms with non-missing values of total assets, net sales, and number of employees. Between the 2002 and 2021 fiscal years, firm sentiment data come from Hassan, Hollander, Van Lent, and Tahoun (Reference Hassan, Hollander, Van Lent and Tahoun2019). Next, we collect data on CEO characteristics and compensation between fiscal years 1992 and 2017 using the ExecuComp database. GMI (GMI Ratings, formerly known as Corporate Library, offers annual historical data captures for each proxy year beginning in 2001) is the source of corporate governance features covering the 2004–2018. One-year lag of our dependent variable is included as a control in all regression models. Also, we drop any firm-year observation with missing information on any used variable. Combining all these factors results in an unbalanced panel of 7,182 firm-year observations with 1,300 individual firms for 2005–2017.

Measures

Dependent variable – firm sentiment

Hassan et al.’s (Reference Hassan, Hollander, Van Lent and Tahoun2019) research is the source of the firm sentiment variable. The measure of the variable is taken directly from the authors’ publicly available database. The authors gather transcripts of 178,173 quarterly earnings conference calls held between financial analysts and senior management of 7,357 publicly listed US companies from Thomson Reuters’ StreetEvents over the 2002–2021 fiscal period. First, the authors create training libraries consisting of bigrams and two-word combinations based on closeness to positive words (good, great, etc.) and negative words (loss, difficult, etc.). Next, the authors count positive and negative tone words. Finally, the measure of overall corporate sentiment is the difference between the frequency of positive words and that of negative words scaled by the length of the transcript and multiplied by 100,000:

\begin{equation*}Sentimen{t_{it}} = \frac{{\mathop \sum \nolimits_b^{{B_{it}}} S\left( b \right)}}{{{B_{it}}}}\end{equation*}

where $i$ and $t$ denote an individual firm and fiscal quarter, respectively. $b$ is bigrams in transcripts, $b = 1,\, \ldots ,\,{B_{it}}$. The function $S\left( b \right)$ equals +1 or −1, or 0 if $b$ reflects positive sentiment or negative sentiment or otherwise, respectively. In this paper, sentiment for an individual firm is the natural logarithm of average overall sentiment in a particular year (Choi, Chung, & Wang, Reference Choi, Chung and Wang2022). A higher score indicates greater firm sentiment. More specifically, poorer and better (compared to last year) firm performances deflate and inflate firm sentiment.

Independent variables – Historical negative performance feedback (HNPF) and historical positive performance feedback (HPPF)

We follow several scholars (Lim & McCann, Reference Lim and McCann2014; Miller & Chen, Reference Miller and Chen2004) and their performance measure ‘historical aspiration’ to operationalize our ‘performance feedback’ variable. Return on Assets (ROA), net income divided by total assets, proxies for performance feedback. HAG is the difference between the current year’s ROA and that of the previous year:

\begin{equation*}HA{G_t} = RO{A_t} - RO{A_{t - 1}}.\end{equation*}

Thus, HNPF equals the absolute value of the HAG if HAG < 0 and 0 otherwise, and HPPF equals the value of HAG if HAG > 0 and 0 otherwise. Organizations typically employ target benchmarks to navigate strategic choices, a practice that is particularly relevant to guiding subsequent strategic endeavors (Bromiley & Washburn, Reference Bromiley and Washburn2011; Chen & Miller, Reference Chen and Miller2007; Cyert & March, Reference Cyert and March1963; Greve, Reference Greve2003; Yu, Minniti, & Nason, Reference Yu, Minniti and Nason2019). These benchmarks, or aspiration levels, are established by evaluating the gap between desired targets and actual outcomes (Argote & Greve, Reference Argote and Greve2007). Aspiration levels comprise a desired objective and a benchmark for comparison, which serve as metrics for assessing organizational performance (Shinkle, Reference Shinkle2012). Most research grounded in behavioral theory has concentrated on benchmarks based on historical (own) and industry-wide (peer) performance. Historical performance reflects a firm’s track record and is often considered one of the most reliable indicators of future performance (Vidal & Mitchell, Reference Vidal and Mitchell2015). The premise is that past success can create a positive feedback loop, enhancing investor confidence, employee morale, and customer trust. Firms learn from their historical performances, adapting their strategies and operations accordingly. This learning curve indicates a firm’s ability to evolve and improve, making it an essential component of sustainable growth. As firms adjust and refine their approach based on what has worked in the past, they are more likely to foster a positive sentiment among stakeholders, which can be predictive of continued growth. Historical performance provides a baseline against which current performance is measured. By setting benchmarks, firms can create specific growth targets, fostering a culture of continuous improvement. When historical performance is used effectively as a benchmark, it can signal to the market and internal stakeholders that the firm is on a growth trajectory, positively influencing sentiment. When historical performance is positive, it contributes to a positive sentiment, which can increase stock prices and increase the firm’s market value. This elevated sentiment can lead to growth as the firm benefits from better financing conditions, increased investment, and customer loyalty. As robustness checks of our conclusions, three alternative measures are utilized – another form of historical performance feedback (HPF) and two forms of social performance feedback (SPF). Following existing literature, the SPF measures are based on industry median and mean (Greve, Reference Greve2003; Schumacher et al., Reference Schumacher, Keck and Tang2020). In the post hoc analysis section, investigations involving these measures have been shown in more detail.

Analytical methods

The inclusion of lagged dependent variables creates an autocorrelation issue. Both measures of performance feedback suffer from endogenous bias because a firm’s sentiment may also influence negative performance feedback and positive performance feedback. This situation is further worsened by the lack of relevant and robust external instruments to address the reverse causality problem. Time-invariant firm fixed effects are correlated with right-hand-side variables. Lastly, our unbalanced panel has a greater cross-section dimension (1,300 firms) than the time-series aspect (14 years). In such cases, Arellano and Bond’s (Reference Arellano and Bond1991) dynamic panel estimation technique is recommended because it takes care of fixed effects and endogeneity of covariates (even with multiple endogenous variables), evades Nickell bias, and is applicable to unbalanced and short-wide panels with one dynamic regress and (Arellano & Bond, Reference Arellano and Bond1991; Mileva, Reference Mileva2007; Nickell, Reference Nickell1981; Roodman, Reference Roodman2009a). Consequently, we employ the system generalized method-of-moments (GMM) method to assess the dynamic firm sentiment-performance feedback relationship (Roodman, Reference Roodman2009b). To be more precise, we measure the following equation to establish the causational relation:

\begin{equation*}{Y_{it}} = {\beta _0} + {\beta _1}HNP{F_{it}} + {\beta _2}HPP{F_{it}} + {\beta _3}HNP{F_{it}}*\,{M_{it}} + {\beta _4}HPP{F_{it}}*\,{M_{it}} + {\beta _5}{Y_{it - 1}} + {\beta _6}{X_{it}} + {\varepsilon _{it}}\end{equation*}

where $i$ and $t$ stand for firms and years, respectively, $Y$ denotes firm sentiment, $HNPF$, $HPPF$, and $M$ signify historical negative performance feedback, historical positive performance feedback, and overconfidence, respectively, $X$ includes all controls including the moderator variable and lagged sentiment, and $\varepsilon $ is regression coefficients and error terms, respectively. We set ${\beta _3} = 0 = {\beta _4}$ to test the first two hypotheses.

CEO age, firm age, CEO gender, CEO change, board size, family ownership status, regulated industry status, median ROA, median sentiment, and year dummies are assumed to be strictly exogenous or self-instrumented. To avoid instrument proliferation, all other variables are instrumented by their respective second lag only (Roodman, Reference Roodman2009a). All models include two-step and robust options. Statistically weak second-order serial autocorrelation (AR2) and statistically insignificant Hansen tests with p-values well under 1.000 (ranging between .10 and .30) should prove the validity of the models (Roodman, Reference Roodman2009a, Reference Roodmanb).