Organizational theories

Performance feedback and organizational learning: the role of

regulatory focus Shinhye Ahn

Graduate School of Business, Seoul National University, Seoul, Korea

Cecile K. Cho Korea University Business School, Seoul, Korea, and

Theresa S. Cho Graduate School of Business, Seoul National University, Seoul, Korea

Abstract

Purpose – This study investigates how a firm’s regulatory focus (i.e. promotion and prevention foci) affects growth- and efficiency-oriented strategic change, highlighting the role of organizational-level regulatory focus as a cognitive frame within which to interpret performance feedback and its subsequent effects on strategic decisions. Design/methodology/approach – The authors collected longitudinal data on 98 S&P 500 manufacturing firms for a seven-year period. The panel data, which includes texts from the firms’ 10-K filings, were then analyzed using a feasible generalized least squares (FGLS) regression estimator to test the authors’ hypotheses. Findings – A firm’s strategic change orientation is affected by its regulatory focus and performance feedback: a promotion focus increases the magnitude of growth-oriented strategic change, while a prevention focus favors efficiency-oriented strategic change. Furthermore, both foci moderate the effect of performance feedback on the strategic change orientation: under negative performance feedback, a promotion (prevention) focus increases (decreases) the magnitude of growth-oriented strategic change relative to that of efficiency- oriented change. The findings provide robust evidence that regulatory focus can influence how organizations learn from feedback and formulate strategic change. Research limitations/implications – The authors’ examination of regulatory focus and organizational learning process relied on large manufacturing firms in the USA. However, learning process could be quite different in small and/or young firms. Future work should expand to a wider range of organizational types, such as nascent entrepreneurial ventures. In addition, the authors’ measurement of regulatory focus using corporate text has inherent weakness and could be supplemented with alternative research methods, such as surveys, interviews or experiments. All in all, however, the findings of this study offer a novel behavioral perspective while demonstrating that a regulatory focus is an important antecedent of organizational learning. Practical implications – This study highlights the importance of motivational characteristics of the top managers in the process of organizational learning from performance feedback. Furthermore, recruitment of a new top manager should be aligned with the organizational context, values and goals. In addition, corporate governance systems such as managerial compensation schemes need to be carefully designed so as to maximize organizational resilience, especially in the context of performance downturn or environmental change. Establishing a constructive organizational culture so that strategic decisions are not overly swayed by the performance outcomes would also be crucial to the organizational learning process. Social implications – This study highlights the importance of understanding the motivational orientations of top managers in organizational learning. In terms of managerial compensation, for instance, an optimal incentive system should reflect the desired performance output by encouraging managerial behavior that corresponds to its objective. Furthermore, motivational orientation of new recruits should be considered in the context of the composition of the top management team members in order to achieve “optimal fit.” In addition, this study suggests that top executives’ regulatory focus can be a key factor for organizations in balancing goals of different value orientations. Originality/value – The findings of this study demonstrated that a firm-level regulatory focus has a significant effect on organizational learning and strategic change following performance feedback. The authors hope this study provides an impetus for future discussions on the microcognitive mechanisms of

Organizational learning and regulatory

focus

This research has received funding from Institute of Management Research at Seoul National University and Korea University Business School Research Grant.

The current issue and full text archive of this journal is available on Emerald Insight at:

https://www.emerald.com/insight/0025-1747.htm

Received 30 September 2019 Revised 2 May 2020

2 June 2020 Accepted 3 June 2020

Management Decision © Emerald Publishing Limited

0025-1747 DOI 10.1108/MD-09-2019-1319

organizational learning by exploring the relations between organizations’ regulatory foci, performance feedback and strategic change orientations.

Keywords Regulatory focus, Cognitive frame, Performance feedback, Strategic change, Organizational

learning

Paper type Research paper

Introduction The behavioral theory of the firm suggests that organizations learn through formalized, structured processes that continuously and incrementally adjust organizational routines and strategic decisions based on feedback on prior performance (Cyert and March, 1963). Cyert and March (1963) proposed that organizations compare their performance levels against their aspirations as the minimal level of “satisficing” or the threshold between success and failure (Greve, 2003). When a firm’s performance falls short of its aspirations, it initiates a problemistic search, which increases its risk tolerance (Cyert and March, 1963). In contrast, when performance exceeds its aspirations, the firm becomes more risk- averse (Cyert and March, 1963). This performance feedback model (Cyert and March, 1963; Greve, 2003) has provided a growing body of research with a powerful framework through which to explain the associations between organizational learning and various corporate strategic decisions, including allocation patterns of managerial attention (Hu et al., 2017), organizational change (Greve, 1998), acquisition activities (Miller and Chen, 2004), corporate divestitures (Shimizu, 2007), research and development investment (Lim and McCann, 2014), new market entrance (Shapira, 2017), foreign direct investment (Xie et al., 2019), cooperative behavior toward competitors (Makarevich, 2018), bribery expenses (Xu et al., 2019) and fraudulent actions, such as misrepresenting financial information (Harris and Bromiley, 2007).

However, despite the growing body of literature on the performance feedback model, the cognitive-level microprocesses involved in organizational learning remain relatively underexplored. An organization is an open system in which learning occurs not in isolation, but through its continuous interactions with various internal and external environments (Scott and Davis, 2015; Thompson, 2017). As such, scholars have increasingly been interested in the environmental factors that may either amplify or constrain the effect of performance feedback on organizational change. For example, firm size (Audia and Greve, 2006), organizational success and failure experiences (Desai, 2008), executive compensation (Lim and McCann, 2014), outside experience of managers and the independence of boards (Choi et al., 2019), organizational structure (Joseph et al., 2016) and business group affiliation (Vissa et al., 2010) have been found to alter the outcomes of organizational performance feedback learning.

Currently, this literature focuses on mostly how archival measures based on the firm-level affect organizational learning, largely ignoring how cognitive elements can stimulate such learning. This paucity of research is surprising, given that behavioral theory of the firm (Cyert and March, 1963) posits that organizations learn through sociocognitive mechanisms in decision-making processes. This lack of research on the cognitive aspects of organizational learning was also highlighted by Posen et al. (2018), who suggested that research on performance feedback and problemistic searches needs to incorporate such aspects in order to better understand the processual issues in organizational learning.

In this study, we address these gaps in the literature. In particular, we explore the role of regulatory focus at the firm level as a crucial determinant of the cognitive frame used in the performance feedback learning process. Regulatory focus, in general, is defined as an individual’s tendency to self-regulate his/her own behavior through promotion and/or prevention focus (Higgins, 1998). Specifically, we argue that a firm’s regulatory focus serves

MD

as a crucial determinant of its strategic change orientation. Furthermore, we posit that the interplay between the firm’s focus and its performance feedback affects the firm members’ perceptions and interpretation of the feedback. This leads to a shift in the firm’s strategic change orientation that diverges from the outcomes predicted by the behavioral theory of the firm. Hence, we examine two research questions: (1) How does a firm’s regulatory focus directly affect its strategic change orientation? (2) How does performance feedback affect the interplay between a firm’s regulatory focus (as a cognitive frame) and its strategic change orientation? We test our idea empirically using longitudinal data on 98 US manufacturing firms for the period 2011–2017. The results support our prediction, in general.

In summary, this study adds to the organizational learning literature (Cyert and March, 1963; Greve, 2003) by showing that, in addition to archival measures (e.g. financial performance or slack), organizational learning is governed by how the members of the firm perceive and interpret their environmental contexts, based on their cognitive orientations. By clarifying the role of regulatory focus in the relation between performance feedback and strategic change, we hope to provide novel insights that expand our understanding of this role beyond that currently documented in the literature (Gamache et al., 2015; Jiang et al., 2019; Rhee and Fiss, 2014).

Theory and hypotheses Strategic change as a response to organizational regulatory focus Organizational scholars have devoted much attention to drivers of strategic change. As such, various firm characteristics have been found to stimulate such change, including firm size (Audia and Greve, 2006), age (Hannan and Freeman, 1984), successful and unsuccessful prior performance (Audia et al., 2000; Greve, 1998) and external media portrayal of the firm (Bednar et al., 2013). In particular, given that the strategic change process entails much risk and uncertainty (Carpenter, 2000; Zhang and Rajagopalan, 2010), scholars have become increasingly interested in its underlying cognitive mechanisms. As Gioia and Chittipeddi (1991) note, a strategic change involves “the creation of instability in members’ ways of understanding the organization” (p. 434) and, hence, is inevitably influenced by how these members make sense of the world.

As a result, management scholars are increasingly focusing on the cognitive antecedents of strategic change. For instance, studies have identified executives’ entrepreneurial orientation (Cho and Hambrick, 2006), personality (Herrmann and Nadkarni, 2014) and motivational characteristics (Jiang et al., 2019) as predictors of strategic change. However, despite this growing recognition of the importance of such cognitive antecedents, we still lack a comprehensive understanding of the organizational cognition involved in strategic change. In fact, many of the micro-level processes of strategic change remain underexplored, with intermediate-level processes conceptualized as a “black box in the middle.” This lack of research is surprising, given that strategic change, which entails substantial uncertainty and risk of failure, is closely linked to how organizational members perceive, interpret and make sense of their environment.

We contend that a firm’s implementation of strategic change is driven by organizational cognition and, in particular, by its regulatory focus (Gamache et al., 2015; Higgins, 1997, 1998; Jiang et al., 2019; Rhee and Fiss, 2014; Weber and Bauman, 2019). Although this theory originated as an individual-level construct, it is increasingly being applied to teams and organizations (Brockner et al., 2004; Gamache et al., 2015; Johnson et al., 2015). Our premise is consistent with the view that a firm’s collective cognitive orientation can be gauged by assessing that of its top management team. To this end, we briefly describe regulatory focus theory, including recent studies based on this theory, and then explain how we use it to conceptualize the cognitive dimension of organizational learning.

Organizational learning and regulatory

focus

Regulatory focus and individual cognition Regulatory focus theory posits that individuals pursue their goals through distinctive modes of self-regulation, represented by promotion focus and prevention focus (Higgins, 1997, 1998). Although numerous psychological theories (e.g. approach-avoidance theory, expectancy- valence theory and emotional and evaluative sensitivity theories) have been derived from the hedonic principle of “approaching pleasure and avoiding pain,” it remains unclear which end states individuals pursue and what strategies they employ to reach such end states (Higgins, 1997, 1998). Regulatory focus theory proposes two distinctive ways of approaching types of desired end states, based on how individuals perceive and pursue their goals: promotion and prevention focus. Scholars have suggested both biological (Gomez et al., 2013; Higgins, 1997) and environmental factors (F€orster et al., 1998; Higgins, 2000) as constituents of individual regulatory focus. Thus, a regulatory focus may be conceptualized as comprising two dimensions of psychological elements: chronic individual dispositions (e.g. narcissism and core self-evaluations) and situational and context-dependent emotions (e.g. positive and negative moods) (Gamache et al., 2015).

Promotion- and prevention-focused individuals self-regulate their emotions, cognition and behavior through different mechanisms. Following Brockner et al. (2004), promotion- and prevention-focused self-regulations differ along three major dimensions: (1) the underlying motives of behavior, (2) the goals individuals try to attain and (3) the types of salient performance cues or outcomes that sensitize them. First, promotion-focused individuals are motivated by growth and advancement needs, whereas those focused on prevention are motivated by security and safety needs (Higgins, 1997). Thus, the most prominent goals of promotion-focused people are those related to capturing opportunities that align with their aspirations by maximizing gains, minimizing nongains, ensuring hits and minimizing “errors of omission” (missing out on an opportunity). These attributes drive such individuals to initiate actions in response to an opportunity for gain, value speed and quantity over accuracy and quality and tolerate experimentation and risk-taking if this means moving closer to their ideals and aspirations (Higgins and Spiegel, 2004). Thus, individuals with promotion focus are sensitive to positive stimuli in the environment (Higgins, 1997).

In contrast, prevention-focused individuals tend to focus on their ought selves (fulfilling their designated responsibilities), minimizing losses and maximizing nonlosses and preventing “errors of commission” (investing too many resources unnecessarily, owing to a misjudgment), with heavier weight given to negative stimuli (Higgins, 1997). These attributes gear their behavior toward vigilance. As such, they make careful and systematic decisions, emphasize accuracy and quality over quantity and create a sense of security by adhering to rules and conventional routines (Higgins and Spiegel, 2004). To summarize, in the same context, their divergent motivations and preferences mean that promotion- and prevention-focused individuals interpret the environmental stimuli differently and, thus, make different choices.

Regulatory focus and organizational cognition Organization research offers much evidence that regulatory focus can be applied to understanding firm-level cognition and behavior. In their review article, Johnson et al. (2015) suggest that regulatory focus theory can be considered “a multilevel construct” that operates on individuals and at the team and organizational levels (Johnson and Wallace, 2011). Here, teams and organizations are conceptualized as assuming a promotion or prevention focus, offering a distinct way of characterizing their respective goals (Johnson et al., 2015; Rietzschel, 2011). Regulatory focus has been described as a collective cognitive orientation that exerts a direct influence on the formation of group and organizational norms with respect to strategic thinking (Levine et al., 2000). This distinct orientation has been found to affect firm

MD

performance related to innovation (Beersma et al., 2013), creativity (Sacramento et al., 2013) and strategic change (Downs et al., 2006). For instance, a collective regulatory focus has been shown to manifest in organizational change when the focus of the top executives trickles down to employees (Downs et al., 2006). In a macro-organizational context, the collective cognition of the top management team can take on a distinctly promotion or prevention orientation, affecting how a firm identifies its goals (Wallace et al., 2010) and the manner in which these goals are pursued, including its perception of risk and its acquisition decisions (Gamache et al., 2015).

We predict that the distinctive characteristics of firm-level promotion and prevention foci lead to variations in firms’ orientations toward strategic change. The basic premise of our theoretical framework is that organizational-level cognition is, in general, an amalgamation of individual cognitions, aggregated through various processes (Fiske and Taylor, 1991; Walsh, 1995). Thus, in the context of regulatory focus, we contend that a promotion focus shifts a firm’s strategic change orientation toward growth rather than efficiency. Conversely, we speculate that a prevention focus leads to an orientation toward efficiency rather than growth.

The dichotomized strategic change orientation of growth versus efficiency is drawn from two divergent mechanisms of strategic change (Gordon et al., 2000, p. 912). Revolutionary strategic change, on the one hand, creates systematic reconfiguration in organizational design and brings discontinuous and fast-paced changes in firms’ strategies (Gordon et al., 2000, p. 912; Lant et al., 1992; Tushman and Romanelli, 1985). On the other hand, evolutionary strategic change induces the incremental adaptation and refinement of a firm’s current strategy toward consistency and convergence (Gordon et al., 2000, p. 912; Pettigrew, 1987). In this research, we draw on these previous theoretical perspectives on strategic change (Gordon et al., 2000; Tushman and Romanelli, 1985) and refer to them as growth or efficiency orientation.

First, growth-oriented strategic changes (e.g. increased R&D investment and advertising expenditures) necessitate that a firm explores novel domains and confronts future uncertainty. In particular, R&D investment entails substantial risk because it has a longer payoff horizon and significant sunk costs (Lee and O’Neill, 2003). Moreover, because R&D projects usually require external equity and specialized human capital, they increase the risk of diluting incumbent members’ control of the firm (Carney, 2005; Sirmon and Hitt, 2003). In contrast, efficiency-oriented strategic change (e.g. reducing nonproduction overheads and inventory levels; see Haleblian and Finkelstein (1993)) occurs when the firm seeks to exploit familiar domains and reaffirm its learning curve, as dictated by previous routines. Cost inefficiencies, such as increases in operative and administrative expenses and excessive inventory levels (Haleblian and Finkelstein, 1993), arise when a firm deviates substantially from its previous strategic options. Conversely, organizational efficiency is best achieved when organizations follow prior routines based on repetitive trial-and-error processes (Argyris and Sch€on, 1997) and make incremental adjustments to their heuristics and standard operating procedures (Bingham et al., 2007).

Thus, we expect a firm’s promotion focus to facilitate a strategic change oriented toward growth, but to hamper that oriented toward efficiency. In contrast, we expect a prevention focus to favor efficiency over growth. Prior research on regulatory focus suggests that a promotion focus is associated with experimentation and a global and explorative search, whereas a prevention focus is associated with a localized and exploitative search. In their experimental studies, Pham and Chang (2010) found that promotion-focused consumers conduct searches that are more global and consider larger choice sets when making dinner selections from a restaurant’s menu, as compared with their prevention-focused counterparts. This finding is consistent with those of prior related studies (F€orster and Higgins, 2005; Lee et al., 2010; Semin et al., 2005). In addition, Ahmadi et al. (2017) found that promotion- focused individuals pursue higher levels of exploration related to technological change.

Organizational learning and regulatory

focus

This increased scope of search and higher intensity of exploration associated with a promotion focus leads to a greater desire to increase the chance of success by exhausting all alternatives (Higgins, 1998). In contrast, the primary concern of prevention-focused individuals is reducing errors of commission by examining all options in detail (Pham and Chang, 2010). Thus, we suggest that promotion-focused organizations, with their inclination for exploration and experimentation, adopt a strategic orientation of growth, rather than efficiency. In contrast, we expect that prevention-focused organizations emphasize efficiency over growth. Combining these ideas, we propose the following hypotheses.

H1. Firms with a promotion focus undertake greater growth-oriented strategic change relative to efficiency-oriented strategic change.

H2. Firms with a prevention focus undertake greater efficiency-oriented strategic change relative to growth-oriented strategic change.

Regulatory focus and strategic change: the role of performance feedback Although we predict that a firm’s regulatory focus will have a direct effect on the extent of strategic change, this effect may be moderated by its performance feedback. A basic premise of the behavioral theory of the firm (Cyert and March, 1963) is that above-aspiration performance leads to risk aversion and below-aspiration performance leads to risk-taking behavior, at the firm level. The argument is based on the following assumptions. First, top decision-makers focus primarily on a prior level of aspiration, defined as “the smallest outcome that would be deemed satisfactory by the decision-maker” (Schneider, 1992, p. 1053). Thus, an organization’s aspiration is derived from its own past performance history and the performance levels of other comparable organizations within an industry (Cyert and March, 1963; Greve, 2003). Second, decision-makers interpret their organizational performance based on their aspirations. Thus, the firm’s performance is considered successful when it exceeds the aspirations, but is a failure when it falls below these aspirations. Finally, the firm’s performance relative to its aspirations determines its search activities and risk-taking behavior. Those in a failure state take more risks than those in a success state do, because the desire to overcome failure is greater than the desire to maintain success.

However, as noted by Posen et al. (2018), critics of the performance feedback model point out its highly mechanistic prediction on organizational behavior. Indeed, diverse alternative predictors other than performance feedback can account for a firm’s problemistic search and strategic change. Examples include the escalation of commitment theory (e.g. Sleesman et al., 2012; Staw, 1976), threat rigidity theory (e.g. Staw et al., 1981) and shifting-focus model of decision-making (e.g. March and Shapira, 1987, 1992). In this light, organizational scholars have attempted to identify environmental factors that can amplify or attenuate the effects of performance feedback on organizational change. For instance, Audia and Greve (2006) found that firm size significantly moderates the relationship between performance feedback and business expansion in the Japanese shipbuilding industry. Desai (2008) found that operating experience, corporate legitimacy and firm age are boundary conditions for organizational performance feedback learning in the US railroad industry. Other moderators include the structure of executive compensation (Lim and McCann, 2014), outside experience of managers and independence of the boards (Choi et al., 2019), business group affiliation (Vissa et al., 2010), time horizon of strategic decisions (Lehman et al., 2011), level of internal resources (Kuusela et al., 2017), organizational structure (Joseph et al., 2016) and a firm’s status and category distinctiveness (Kim and Rhee, 2017).

Nonetheless, the extant literature has focused mainly on the archival measures, such as organizational resources and financial performance, as the boundary condition, with few studies examining the micro- and cognitive-level factors in the organizational learning process. Although a few works have examined how cognitive attributes (e.g. self-enhancement,

MD

narcissism and internal and external referent orientation) alter the process of learning from performance feedback (e.g. Audia and Brion, 2007; Jordan and Audia, 2012; Short and Palmer, 2003), we still know relatively little about how the sociocognitive attributes of an organization influence its performance feedback learning. Posen et al. (2018) highlighted this paucity of research by stating that “a research agenda premised on a more central role for cognition in the theory and the need for greater emphasis on a process perspective of problemistic search is needed” (p. 208).

In this study, we answer the call of Posen et al. (2018) and contend that an organization’s cognitive frame (how the organization perceives, interprets and makes sense of its environmental change) significantly alters how it responds to its performance feedback. In particular, we suggest that a firm should employ its regulatory focus as a cognitive frame, such that a motivational mindset oriented toward either seeking pleasure (i.e. promotion focus) or avoiding pain (i.e. prevention focus) becomes a fundamental part of its strategic change orientation.

Organizations with different cognitive frames perceive and interpret the environment differently. Research on cognitive frames, which form the unique perceptions and interpretations of a context (Gioia and Thomas, 1996), is increasing among organizational theorists, particularly in relation to positive and negative framing (e.g. Dutton and Jackson, 1987; Ocasio, 1995), temporal framing (e.g. Nadkarni et al., 2018) and emotional ambivalence framing (e.g. Gilbert, 2006; Plambeck and Weber, 2009). Promotion and prevention framing are also the subject of a growing number of organization studies (e.g. Cacioppo et al., 1997; Higgins, 1997; Weber and Bauman, 2019; Weber and Mayer, 2011), based mainly on the Carnegie School’s tenet that how we frame a problem guides our search and matching processes, which causes significant variation in individuals’ risk-taking behavior. Indeed, a firm’s performance is not viewed as a success until its members subjectively interpret and make sense of the event as positive. As such, we predict that how an organization responds to performance feedback is shaped significantly by how it uniquely interprets and makes sense of its prior performance.

First, we predict that under positive performance feedback, a firm’s regulatory focus will not play a significant role in its strategic decisions; in fact, the firm is more likely to maintain its status quo (e.g. Greve, 2003; Iyer and Miller, 2008). Positive performance feedback that surpasses the aspiration level signals to the firm that its current course of strategic actions has been effective. Under a positive signal, the firm reduces its exploration of novel domains by, for example, reducing its R&D and advertising expenses. Moreover, such organizations would not feel it necessary to venture into new areas with high risk, because the net gain from incremental exploitation and adhering to the status quo is greater than that from exploration and adventuring into unknown domains. In fact, experimenting would jeopardize its current advantages that stem from the status quo.

This logic holds even when we analyze regulatory foci separately. On the one hand, for promotion-focused organizations, positive performance feedback suggests that adhering to the prior course of strategic action will best guarantee a continued flow of positive stimuli, which is a strong source of motivation. On the other hand, for prevention-focused organizations, receiving positive performance feedback implies that negative stimuli will be best avoided by maintaining their previous routines and heuristics related to implementing strategic change. Therefore, we expect that both promotion- and prevention-focused organizations respond to positive performance feedback by reducing the magnitude of their strategic change and becoming strategically inactive. Thus, we suggest the following hypotheses.

H3. Under positive performance feedback, firms with a promotion focus maintain the status quo with less strategic change, regardless of whether they are growth- or efficiency-oriented.

Organizational learning and regulatory

focus

H4. Under positive performance feedback, firms with a prevention focus maintain the status quo with less strategic change, regardless of whether they are growth- or efficiency-oriented.

The behavioral theory perspective suggests that when performance falls short of the firm’s aspirations, it initiates a problemistic search, which increases the level of strategic change (Cyert and March, 1963; Greve, 2003). In this study, we propose that this organizational search to find solutions to the negative performance feedback differs substantially by regulatory focus. Traditionally, the previous research has highlighted the search as a fundamental process of organizational learning (Cyert and March, 1963; Levitt and March, 1988; March and Simon, 1958). This search is an organic problem-solving process through which organizations “recombine,” “relocate” and “manipulate” existing knowledge and “create” new knowledge (March and Simon, 1958; Katila and Chen, 2008, p. 593). Firms use the search to approach their aspirations, specifically, by solving impending problems or using slack resources in novel ways (Cyert and March, 1963; Greve, 2003).

Managers are boundedly rational and are overloaded with more information than they can fully perceive or interpret (March and Simon, 1958). Thus, the domain of the search in which the managers allocate their limited cognitive resources becomes a critical issue. While previous research differentiates between local and distant searches (e.g. March, 1991; Martin and Mitchell, 1998; Stuart and Podolny, 1996), we extend this stream of research by distinguishing between two additional types of search: (1) a global search, in which the search terrain spans distant, broad and inexperienced domains with the goal of exploring novel knowledge; and (2) a local search, in which the search terrains are local, narrow and experienced while striving to leverage previously developed knowledge.

In organizations, a search domain depends on its organizational-level cognition. As Li et al. (2013) remarked, a search is a “controlled and proactive process of attending to, examining, and evaluating new knowledge and information” (p. 893). For promotion-focused organizations, negative performance feedback prompts a problemistic search that is more global in scope, because these organizations hope to encounter a positive stimulus by increasing their exploration, confronting uncertainty and attempting novel domains. Hence, we predict that global search activities result in a firm’s strategic change being oriented more toward growth than efficiency. In contrast, prevention-focused organizations under negative performance feedback focus their behavior more toward safety and stability, rather than exploration and experimentation, owing to their fear of encountering even greater losses relative to the status quo (Higgins, 1997). Thus, we predict that prevention-focused organizations initiate searches that are more local in scope, resulting in a firm’s strategic change being oriented more toward efficiency than growth.

Therefore, we propose two hypotheses on the relation between regulatory focus and strategic change under negative performance feedback. Specifically, we contend that a promotion focus is positively associated with strategic change oriented toward growth rather than efficiency. In other words, a firm with a promotion focus is more likely to concentrate its efforts on growth and expansion when performance feedback is subpar. In contrast, under negative performance feedback, a prevention focus is positively associated with strategic change oriented toward efficiency rather than growth. In other words, a firm with a prevention focus is more likely to focus on streamlining operations, minimizing overhead costs and so forth, when performance feedback is below its aspirational level. We thus offer the following two hypotheses.

H5. Under negative performance feedback, firms with a promotion focus will undertake greater growth-oriented strategic change, relative to efficiency-oriented strategic change.

MD

H6. Under negative performance feedback, firms with a prevention focus will undertake greater efficiency-oriented strategic change, relative to growth-oriented strategic change.

Methods Data selection We collected data on US manufacturing firms listed on the S&P 500 in 2017. The Standard Industrial Classification (SIC) codes of the sample firms ranged from 2,000 to 3,999, which means our sample is comparable with those of prior studies on organizational learning (e.g. Chen and Miller, 2007; Lim and McCann, 2014; Miller and Chen, 2004).

The data cover the period 2011–2017 and are taken from three databases: COMPUSTAT, EXECUCOMP and Securities and Exchange Commission (SEC) EDGAR. Firms with any missing data were omitted from the sample. The final sample comprised 98 firms with 447 firm-year observations.

Dependent variables Growth-oriented relative to efficiency-oriented strategic change. Based on prior related research (Bednar et al., 2013; Jiang et al., 2019; Zhang and Rajagopalan, 2010), we measure a firm’s strategic change orientation as the change in its resource allocation patterns in key strategic dimensions representing growth and efficiency. Growth-oriented strategic change is measured in terms of advertising intensity (advertising/sales) and R&D intensity (R&D/ sales). Efficiency-oriented strategic change is calculated using nonproduction overheads (selling, general and administrative [SGA] expenses/sales) and inventory levels (inventory/ sales). All variables are obtained from COMPUSTAT.

We derive our measures as follows. First, for each ratio, we calculate the difference between the focal year and the previous year to capture the variation in a firm’s resource allocation over time. Next, we subtract the median value of the relevant ratio within each two- digit industry to adjust for primary industry effects. Following the operationalization used in prior studies (Bednar et al., 2013; Jiang et al., 2019; Zhang and Rajagopalan, 2010), we standardize the absolute values of the industry-adjusted differences within the sample such that the mean is 0 and the standard deviation is 1. Then, we add the two standardized ratios for each strategic change dimension and use the index as our composite measure of strategic change. This reflects the extent to which firms’ strategies were fixed or changed over time in the respective domains. Thus, a lack of change in this measure represents a degree of persistence in the firms’ strategies (Bednar et al., 2013). For the index of efficiency-oriented strategic change, we multiply the result by �1, reflecting that an increase in nonproduction overheads and inventory levels represents organizational inefficiency (Haleblian and Finkelstein, 1993). The Pearson correlation coefficient between the two strategic change orientation indices is �0.2730 (p < 0.01), indicating that the growth and efficiency orientations are inversely correlated.

We wish to determine how much of the firm-level strategic change can be accounted for by the trade-off between growth and efficiency. Thus, for each firm, we divide the change in growth-oriented change by the sum of growth- and efficiency-oriented change. Because the averages of the standardized values are highly skewed toward 0, we add the value 2 and take the natural logarithm of the result.

Independent variables Organizational regulatory focus: promotion focus and prevention focus. To capture the strength of the promotion and prevention foci at the firm level, we perform a text analysis of the management discussion and analysis (MD&A) section (item 7) in the firms’ 10-K filings.

Organizational learning and regulatory

focus

This section and other corporate documents (including annual reports) are widely used to measure managerial and organizational cognition (e.g. Cho and Hambrick, 2006; Hu et al., 2018; Kaplan, 2008). We use text analysis software called Linguistic Inquiry and Word Count 2015 (LIWC 2015; Pennebaker et al., 2015) to assess the lexicons in the MD&A section of 10-K filings in order to measure the percentages of promotion and prevention words that appear in each document. The dictionaries for promotion and prevention words, 27 and 25, respectively, were developed by Gamache et al. (2015). Table 1 lists these lexicons, along with examples of their usage in the sample firms’ 10-K MD&A sections. The 10-K filings were collected for each year from the EDGAR database provided by the US SEC.

Positive and negative performance feedback. Organizational performance feedback is operationalized using the method of Audia and Greve (2006) and Gaba and Joseph (2013). Here, we calculate the difference between a firm’s performance and its organizational aspirations in a given year. The aspirations are calculated as the weighted sum of the firm’s historical and social aspirations. We measure firm performance using return on assets (ROA), because this is the major accounting-based proxy for firm profitability within the manufacturing industry; see Lim and McCann (2014) and Miller and Chen (2004). The historical aspiration of each firm in a given year is the weighted average of performance and the historical aspiration of the previous year; the social aspiration is the mean performance of all firms in the manufacturing industry in a given year, excluding that of the focal firm (Audia and Greve, 2006; Mezias et al., 2002). The total aspiration of each firm in a given year is a weighted sum of its historical and social aspirations (Audia and Greve, 2006). Eqns (1)–(3) describe how we operationalize these measures, where HA denotes historical aspiration, P is performance, SA is social aspiration, A denotes total aspiration, α and β are weights and the subscripts i and t indicate a firm and a period (year), respectively.

HAi;t ¼ αPi;t−1 þ ð1 � αÞHAi;t−1 (1)

SAi;t ¼ X j≠i

Pj;t

, ðN � 1Þ (2)

Ai;t ¼ βSAi;t−1 þ ð1 � βÞHAi;t−1 (3) The weights used to measure the historical and total aspirations, α and β, were determined following the approach of Gaba and Joseph (2013). Here, each weight increases from 0.1 to 1 in increments of 0.1. We run OLS regressions for each model, predicting the strategic change

Dimensions Key words Examples from sample firms’ MD&A

Promotion words (27 key words)

Accomplish, Achieve, Advancement, Aspiration, Aspire, Attain, Desire, Earn, Expand, Gain, Grow, Hope, Hoping, Ideal, Improve, Increase, Momentum, Obtain, Optimistic, Progress, Promoting, Promotion, Speed, Swift, Toward, Velocity, Wish

(1) AbbVie Inc. MD&A in 2015 10-K “In addition, AbbVie expects to achieve operating margin improvements while continuing to invest in its pipeline in support of opportunities in oncology, HCV, and immunology, as well as continued investment in key products.”

Prevention words (25 key words)

Accuracy, Afraid, Anxious, Avoid, Careful, Conservative, Defend, Duty, Escape, Escaping, Evade, Fail, Fear, Loss, Obligation, Ought, Pain, Prevent, Protect, Responsible, Risk, Safety, Security, Threat, Vigilance

(2) Boeing Co. MD&A in 2016 10-K “During the second quarter of 2015 and of 2014, we recorded reach-forward losses of $835 million and $425 million on the USAF KC-46A Tanker contract.”

Table 1. Regulatory focus words (Gamache et al., 2015)

MD

using different weights, and choose those that yield the highest explanatory power (R2). The results indicate that the highest explanatory power is given by 0.1 for both αand β. Consistent with Audia and Greve (2006), we create separate variables for positive and negative performance feedback using a spline specification. This lets us examine the differential effects of the variables on the dependent variable, depending on whether their values are above or below 0. Positive performance feedback is equal to performance minus total aspiration when the value is above 0 and is 0 otherwise. Similarly, negative performance feedback is equal to performance minus total aspiration when the value is below 0 and is 0 otherwise. All raw data were gathered from the COMPUSTAT database.

Control variables We consider several control variables at the firm, CEO and industry levels that might also affect strategic change. Because a firm’s size might increase its risk-taking behavior (Wright et al., 2007), we measure firm size as the log of total assets. Organizational slack has been identified by prior studies as affecting firms’ risk-taking behavior (e.g. Chen and Miller, 2007). Thus, we measure organizational financial slack as the current ratio, calculated by dividing current assets by current liabilities. We also include the return on investment (ROI) and market-to-book ratio (MTB) to control for the effects of firm performance. Following prior studies that employ text analyses in their research methods, we include the total number of words in the MD&A section of a 10-K filing as a control in our model (e.g. Cho and Hambrick, 2006; Yadav et al., 2007). Previous studies have suggested that CEO tenure affects executives’ strategic choices (e.g. Hambrick and Fukutomi, 1991; Simsek, 2007). As such, we measure CEO tenure as the fiscal data year minus the year in which a CEO joined the firm and add 1. The joining years are taken from EXECUCOMP, company websites and media reports (including the CEOs’ biographies). Based on the information provided by EXECUCOMP, we also include CEO age and gender (1 5 female CEO, 0 5 male CEO) as controls. Given that CEO turnover affects risk-taking behavior (Westphal and Fredrickson, 2001), we include a dummy variable in our model that captures CEO turnover, based on the executive data in the EXECUCOMP database. Here, CEO duality takes the value 1 if the CEO is the chairperson of the board and 0 otherwise. We include multiple CEO compensation variables to account for the possibility that the firm’s risk-taking behavior might be affected by CEO incentives (e.g. Sanders and Hambrick, 2007). We measure CEO total current compensation as the natural log of the sum of his/her cash-based pay (salary) and bonus. We measure CEO stock option pay as the natural log of the proportion of CEO stock option grants in CEO stock option awards, each of which is measured using the FAS-123R valuation (e.g. Sanders and Hambrick, 2007). Although Black–Scholes valuations (Black and Scholes, 1973) are more widely used, we had to use FAS-123R valuations because these were the only data provided by the EXECUCOMP database for our sample period. We measured CEO restricted stock holdings as the natural log of the total market value of the restricted shares held by the executive at the end of the fiscal year. For all compensation data, we add 2 to avoid values of 0 before taking the natural logs. All compensation data were collected from the EXECUCOMP database. Given that managerial discretion is positively associated with strategic change (Hambrick and Abrahamson, 1995; Chen et al., 2015), we include managerial discretion as a control variable, based on an index comprising three variables: capital intensity, product differentiability and market munificence. Each measure is based on annual data at the two-digit SIC industry level, following the operationalization of Chen et al. (2015).

Analysis We use a feasible generalized least squares (FGLS) regression estimator to test our hypotheses, consistent with several prior studies on the linkage between performance

Organizational learning and regulatory

focus

feedback and risk-taking behavior (e.g. Desai, 2008; Labianca et al., 2009). We employ a likelihood ratio (LR) test and a Wooldridge test (Wooldridge, 2010) to detect heteroscedasticity and autocorrelation, detecting significant heteroscedasticity in our data. We corrected for this data characteristic using a corresponding option in Stata 15. In addition, we include year fixed effects and industry fixed effects (in two-digit SIC codes) in our models to control for unobserved heterogeneity effects between periods and industry sectors. All variables included in the interactions are mean-centered to mitigate any potential multicollinearity problem (Aiken et al., 1991).

Results The descriptive statistics and pairwise correlations for all variables are shown in Tables 2 and 3. Because some of the bivariate correlations between the variables are significant, we tested the variance inflation factor (VIF) to check for potential multicollinearity. The mean VIF was 1.30 and the maximum VIF was 1.78 for the independent variable “negative performance feedback.” Thus, in all our models, every variable had a VIF well below 10, which is the widely accepted cutoff value. Thus, no multicollinearity exists among our research variables.

Effects of organizational regulatory focus and performance feedback on strategic change orientation Table 4 reports the results of the FGLS regression analyses that test the effects of organizational regulatory focus and performance feedback on strategic change orientation. Model 1 represents the baseline model and includes only the control variables. Model 2 tests the effects of promotion and prevention focus. Model 3 adds positive and negative performance feedback as controls in the model, and Model 4 investigates the interaction effects of organizational regulatory focus and performance feedback on strategic change orientation.

Variable Mean Std. Dev Min Max

Growth-oriented relative to efficiency-oriented strategic change (growth stg. change)

0.851 0.878 �5.07 4.039

Firm size 4.094 0.523 1.58 5.523 Financial slack 2.185 1.307 0.089 9.592 ROI (return on investment) 0.135 0.139 �2.094 0.557 MTB (market-to-book ratio) 3699.165 44417.71 �210000 1020000 CEO age 55.1 10.099 38 77 CEO tenure 18.958 11.921 1 45 CEO gender 0.043 0.203 0 1 CEO turnover 0.029 0.167 0 1 CEO duality 0.421 0.494 0 1 CEO total current compensation (TCC) 6.942 0.932 �6.908 9.004 CEO stock option pay �2.046 1.61 �8.664 7.905 CEO restricted stock holdings 6.283 3.721 0.693 12.283 Managerial discretion 0 0.462 �0.688 1.955 Total number of words in MD&A 14854.65 6585.531 1.24 66440 Positive performance feedback (PosPF) 0.026 0.04 0 0.292 Negative performance feedback (NegPF) �0.019 0.054 �0.648 0 Organizational promotion focus (ProF) 1.108 0.371 0.28 2.62 Organizational prevention focus (PreF) 0.466 0.169 0 1.13

Table 2. Descriptive statistics of research variables

MD

V a ri a b le s

1 2

3 4

5 6

7 8

9 1 0

1 1

1 2

1 3

1 4

1 5

1 6

1 7

1 8

1 9

1 G ro w th

st g .

ch a n g e

1

2 F ir m

si ze

�0 .2 4 7 *

1 3 F in a n ci a l sl a ck

0 .0 5 4

�0 .3 1 6 *

1 4 R O I

0 .0 3 2

0 .0 5 7

0 .0 1 7

1 5 M T B

0 .0 2 8

0 .0 7 9

�0 .0 3 3

0 .0 9 3 *

1 6 C E O a g e

�0 .0 2 8

0 .0 5 1

0 .0 9 9 *

�0 .0 4 4

�0 .0 2 3

1 7 C E O te n u re

�0 .0 3 5

�0 .0 4 5

�0 .0 5 9

0 .0 3

�0 .0 0 1

�0 .5 9 6 *

1 8 C E O g en d er

�0 .1 1 6 *

0 .1 4 4 *

�0 .1 6 8 *

�0 .0 0 5

0 .0 0 6

0 .0 3 9

0 .0 4 4

1 9 C E O tu rn o v er

�0 .0 2 7

0 .0 0 9

0 .0 1 3

�0 .0 2 8

�0 .0 1 2

�0 .0 4 3

0 .0 2 9

0 .1 1 4 *

1 1 0 C E O d u a li ty

�0 .0 8 4

0 .1 4 4 *

�0 .0 6 9

0 .0 5 6

�0 .0 3 1

0 .1 4 7 *

�0 .0 5 9

0 .1 2 8 *

�0 .0 0 3

1 1 1 C E O T C C

�0 .0 9 7 *

0 .3 2 0 *

�0 .0 2 6

0 .1 1 7 *

0 .0 2 6

0 .1 0 5 *

�0 .1 2 6 *

�0 .0 2 8

�0 .0 8 6 *

0 .1 7 1 *

1 1 2 C E O st o ck

o p ti o n

p a y

0 .1 0 5 *

�0 .1 2 0 *

�0 .0 5 1

�0 .0 2 6

0 .0 5

0 .1 4 1 *

�0 .1 7 4 *

0 .1 5 1 *

�0 .0 1 8

�0 .0 5 5

�0 .1 4 4 *

1

1 3 C E O re st ri ct ed

st o ck

h o ld in g s

�0 .0 7 1

0 .1 0 3 *

�0 .0 2 5

�0 .0 3 3

0 .0 1 9

0 .0 2

�0 .0 7 3

�0 .0 6 4

0 .0 7 9

0 .0 6

0 .0 2

0 .0 4 8

1

1 4 M a n a g er ia l

d is cr et io n

�0 .2 1 7 *

0 .2 5 2 *

�0 .1 0 1 *

0 .0 1 5

0 0 .1 3 3 *

�0 .1 0 7 *

�0 .0 8 8 *

0 .0 4

0 .0 4 6

0 .2 1 4 *

�0 .1 0 0 *

0 .0 0 9

1

1 5 T o ta l n u m b er

o f

w o rd s in

M D & A

�0 .0 1 3

0 .4 0 8 *

�0 .2 0 3 *

0 .0 1 1

0 .0 6 2

0 .0 5 3

0 .0 0 5

0 .2 0 4 *

�0 .0 4 6

0 .0 1 6

0 .1 8 3 *

0 .0 1 9

0 .0 3

0 .0 9 8 *

1

1 6 P o si ti v e

p er fo rm

a n ce

fe ed b a ck

0 .0 7 7

�0 .1 8 7 *

0 .2 9 9 *

0 .5 5 8 *

0 .0 9 9 *

0 .0 9 0 *

0 .0 0 8

�0 .0 8 6 *

�0 .0 2 6

�0 .0 1 8

0 .0 2 9

�0 .0 4 3

�0 .0 2 9

0 .0 3 5

�0 .0 8 5 *

1

1 7 N eg a ti v e

p er fo rm

a n ce

fe ed b a ck

0 .0 0 2

0 .2 2 6 *

0 .1 3 2 *

0 .6 6 4 *

0 .0 1 4

0 .0 3 5

�0 .0 2 2

�0 .0 1 5

�0 .0 1 2

0 .0 9 3 *

0 .3 7 1 *

�0 .2 6 9 *

�0 .0 6 3

0 .0 2

0 .0 4 4

0 .2 2 9 *

1

1 8 P ro m o ti o n fo cu s

0 .2 0 5 *

�0 .1 0 6 *

0 0 .2 4 7 *

�0 .0 2 4

0 .0 6

�0 .0 7 4

�0 .0 4 6

0 .0 4

0 .0 2 5

�0 .0 3 4

�0 .1 4 0 *

�0 .1 1 7 *

�0 .0 7 4

�0 .1 3 7 *

0 .2 1 7 *

0 .2 3 6 *

1 1 9 P re v en ti o n fo cu s

�0 .0 5 9

0 .1 2 1 *

�0 .1 9 2 *

�0 .0 6 1

0 .0 2 3

�0 .1 0 6 *

0 .1 4 1 *

0 .0 2

0 .0 0 7

�0 .0 4 7

0 .0 6 8

�0 .0 1 3

0 .0 9 4 *

0 .0 7 6

0 .2 5 4 *

�0 .0 8 5 *

0 .0 0 2

�0 .2 1 6 *

1

N o te (s ): * sh o w s si g n if ic a n ce

a t th e 0 .0 5 le v el

Table 3. Pairwise correlations of research variables

Organizational learning and regulatory

focus

V a ri a b le s

M o d el 1

M o d el 2

M o d el 3

M o d el 4

F ir m

si ze

�0 .2 1 4 * * * (0 .0 3 0 2 )

�0 .1 6 7 * * * (0 .0 3 4 0 )

�0 .1 7 3 * * * (0 .0 3 5 3 )

�0 .1 4 4 * * * (0 .0 3 6 1 )

F in a n ci a l sl a ck

�0 .0 5 7 7 * * * (0 .0 1 1 5 )

�0 .0 4 9 3 * * * (0 .0 1 2 2 )

�0 .0 4 4 7 * * * (0 .0 1 3 8 )

�0 .0 2 8 9 * * (0 .0 1 3 8 )

R O I

0 .1 8 3 (0 .1 1 9 )

0 .1 8 0 (0 .1 1 0 )

0 .2 8 1 * (0 .1 4 5 )

0 .8 8 6 * * (0 .3 6 6 )

M T B

2 .2 5 e- 0 7 * (1 .3 0 e- 0 7 )

2 .3 5 e- 0 7 * * (1 .1 1 e- 0 7 )

2 .4 7 e- 0 7 * * (1 .2 0 e- 0 7 )

2 .6 7 e- 0 7 * * (1 .1 4 e- 0 7 )

C E O a g e

0 .0 0 8 8 7 * * * (0 .0 0 2 1 3 )

0 .0 0 9 7 6 * * * (0 .0 0 2 2 4 )

0 .0 0 9 7 7 * * * (0 .0 0 2 2 1 )

0 .0 0 9 4 4 * * * (0 .0 0 2 1 4 )

C E O te n u re

�2 .0 0 e- 0 5 (6 .5 3 e- 0 5 )

4 .3 2 e- 0 5 (6 .9 5 e- 0 5 )

4 .4 0 e- 0 5 (6 .8 9 e- 0 5 )

8 .8 7 e- 0 5 (6 .6 7 e- 0 5 )

C E O g en d er

�0 .4 3 1 * * * (0 .1 0 2 )

�0 .3 3 7 * * * (0 .1 0 2 )

�0 .3 3 4 * * * (0 .1 0 1 )

�0 .3 8 3 * * * (0 .1 0 1 )

C E O tu rn o v er

0 .0 9 5 7 (0 .0 6 0 7 )

0 .1 0 0 (0 .0 6 5 7 )

0 .1 0 6 * (0 .0 6 4 5 )

0 .1 0 1 (0 .0 6 7 3 )

C E O d u a li ty

�0 .1 7 2 * * * (0 .0 2 7 4 )

�0 .1 7 3 * * * (0 .0 2 8 3 )

�0 .1 7 1 * * * (0 .0 2 8 4 )

�0 .1 5 7 * * * (0 .0 2 9 8 )

C E O T C C

�0 .0 5 8 3 * (0 .0 3 1 7 )

�0 .0 3 3 4 (0 .0 3 3 5 )

�0 .0 3 4 7 (0 .0 3 4 6 )

�0 .0 2 4 4 (0 .0 3 3 4 )

C E O st o ck

o p ti o n p a y

0 .0 6 1 9 * * * (0 .0 1 0 5 )

0 .0 6 9 3 * * * (0 .0 1 1 7 )

0 .0 6 7 1 * * * (0 .0 1 1 6 )

0 .0 6 7 0 * * * (0 .0 1 1 6 )

C E O re st ri ct ed

st o ck

h o ld in g s

�0 .0 1 9 7 * * * (0 .0 0 3 8 2 )

�0 .0 1 3 3 * * * (0 .0 0 3 8 3 )

�0 .0 1 3 5 * * * (0 .0 0 3 8 4 )

�0 .0 1 2 8 * * * (0 .0 0 3 8 9 )

M a n a g er ia l d is cr et io n

0 .0 3 7 6 (0 .0 5 6 9 )

0 .0 0 5 5 9 (0 .0 6 0 7 )

0 .0 0 1 6 0 (0 .0 6 1 7 )

0 .0 2 9 2 (0 .0 5 4 9 )

T o ta l n u m b er

o f w o rd s

1 .4 7 e- 0 5 * * * (3 .0 8 e- 0 6 )

1 .3 3 e- 0 5 * * * (3 .0 8 e- 0 6 )

1 .3 0 e- 0 5 * * * (3 .1 1 e- 0 6 )

1 .6 1 e- 0 5 * * * (3 .2 8 e- 0 6 )

(H 1 ) P ro m o ti o n fo cu s

0 .2 1 9 * * * (0 .0 3 9 0 )

0 .2 2 8 * * * (0 .0 4 0 0 )

1 .8 5 4 * * * (0 .7 0 4 )

(H 2 ) P re v en ti o n fo cu s

�0 .0 0 1 3 6 (0 .1 3 4 )

0 .0 3 5 0 (0 .1 3 4 )

�6 .2 2 8 * * * (1 .8 9 3 )

P o si ti v e p er fo rm

a n ce

fe ed b a ck

�0 .4 6 8 (0 .4 3 7 )

�0 .9 8 1 (0 .6 7 4 )

N eg a ti v e p er fo rm

a n ce

fe ed b a ck

�0 .0 7 1 6 (0 .0 4 7 7 )

�2 .0 1 9 * * (0 .7 9 3 )

(H 3 ) P ro F 3

P o sP F

�0 .6 3 3 (0 .8 0 6 )

(H 4 ) P re F 3

P o sP F

�2 .1 3 0 (2 .9 9 2 )

(H 5 ) P ro F 3

N eg P F

3 .2 3 3 * * * (1 .0 4 7 )

(H 6 ) P re F 3

N eg P F

�8 .0 0 6 * * * (2 .4 1 9 )

C o n st a n t

�6 .5 1 8

(1 9 .3 4 )

�3 5 .2 7

(2 3 .5 2 )

�3 2 7 .0

(2 0 7 .0 )

8 ,4 6 8 * * *

(2 ,8 6 7 )

Y ea r fi x ed

ef fe ct s

Y es

Y es

Y es

Y es

In d u st ry

fi x ed

ef fe ct s

Y es

Y es

Y es

Y es

W a ld

C h i- sq u a re

1 2 3 5 .4 3 * * *

1 2 6 2 .7 7 * * *

1 4 3 2 .2 6 * * *

1 2 3 5 .5 0 * * *

N u m b er

o f o b se rv a ti o n s

4 4 7

4 4 7

4 4 7

4 4 7

N u m b er

o f fi rm

s 9 8

9 8

9 8

9 8

N o te (s ): S ta n d a rd

er ro rs

in p a re n th es es . * p < 0 .1 , * * p < 0 .0 5 , * * * p < 0 .0 1

Table 4. FGLS regressions predicting growth- oriented strategic change

MD

Hypotheses 1 and 2 test the relation between firm-level regulatory focus and growth-oriented strategic change. Specifically, Hypothesis 1 posits that a promotion focus leads to greater strategic change oriented toward growth rather than efficiency. Hypothesis 2 suggests that a prevention focus leads to lower strategic change oriented toward growth rather than efficiency. The results show that a promotion focus is significantly and positively associated with growth-oriented strategic change (β 5 0.219, p < 0.01 in Model 2 of Table 4). However, a prevention focus is not significantly associated with such an orientation (β 5 �0.00136, n.s., in Model 2 of Table 4). Thus, Hypothesis 1 is supported, but Hypothesis 2 is not.

Hypotheses 3–6 examine the interplay between organizational regulatory focus and strategic change, with organizational performance feedback as a moderator. First, Hypotheses 3 and 4 stated that promotion and prevention foci, respectively, lead to firms maintaining the status quo under positive performance feedback, regardless of their strategic change orientation. In our analyses, both hypotheses are supported. Specifically, the interaction effect of a promotion focus and positive feedback on growth-oriented strategic change is insignificant (β 5 �0.633, n.s., in Model 4 of Table 4). Similarly, the interaction effect of a prevention focus and positive feedback on growth-oriented strategic change is also insignificant (β 5 �2.130, n.s., in Model 4 of Table 4).

Hypothesis 5 predicted that a promotion focus is positively associated with a growth- oriented strategic change, particularly when the performance feedback is negative. Our results support this hypothesis, showing a significant and positive result (β 5 3.233, p < 0.01, in Model 4 of Table 4). Hypothesis 6 states that a prevention focus is negatively associated with growth-oriented strategic change, particularly when performance feedback is negative. This hypothesis is supported because the interaction effect of a prevention focus and negative performance feedback on growth-oriented strategic change is significant and negative (β 5 �8.006, p < 0.01, in Model 4 of Table 4).

Discussion The primary goal of this study was to examine the effects of organizational regulatory focus on strategic change. Our study provides robust evidence to suggest that regulatory focus impacts how organizations learn from feedback and subsequently undertake strategic change. Moreover, it allows us to look “inside the blackbox” and refine the interplay between individual-level learning and strategic formulation. Overall, our results indicate that promotion focus encourages an organization to orient its strategic change toward growth, but less toward efficiency. Furthermore, we found that under positive performance feedback, both promotion and prevention foci increase the tendency of the organization to adhere to the status quo. Under a negative performance feedback, however, firms with a promotion focus tend to pursue more growth-oriented strategic changes, while reducing efficiency-oriented changes. In contrast, firms with a prevention focus tend to pursue more efficiency-oriented strategic changes, rather than growth-oriented. In other words, under negative performance feedback, a regulatory focus functions as a cognitive frame that magnifies its underlying tendencies. In sum, we showed in this study how firms’ strategic responses to performance feedback can diverge from the conventional behavioral theory expectations. By delineating regulatory focus as an important driver of the organizational learning process, we offered a novel perspective on how organization-level psychological characteristics interact with performance feedback leading to strategic changes.

In addition, the findings of our study provide the following practical and social implications for managers. First, this study highlights the importance of understanding the motivational orientations of top managers in organizational learning. In fact, our study indicates that certain motivational characteristics need to be redressed or “incentivized” in organizations (Wowak and Hambrick, 2010) in order for managers to learn effectively from

Organizational learning and regulatory

focus

performance feedback. For example, if a firm’s objective is to achieve growth, it would be critical to promote and facilitate the top managers’ promotion focus by designing an incentive system to reflect this priority. It is duly noted that an individual manager has both promotion and prevention foci, which operate in tandem (Higgins, 1997). An optimal incentive system should reflect the strategically desired (growth or efficiency) performance output by encouraging managerial behavior that corresponds to its objective. Furthermore, when new executives are recruited into the top management team, his/her underlying motivational orientation, that is, regulatory focus, should be considered in the context of the composition of the incumbent team members (e.g. Chen et al., 2018) in order to achieve “optimal fit” (Higgins, 2000). After all, regulatory focus of top managers functions as a cognitive frame that filters information perceived in the environment and dictates the orientation of strategic change. We invite future scholars to take deeper interest in the interrelationship among top managers’ regulatory focus, corporate governance mechanisms and organizational decision-making.

Second, our study suggests that top executives’ regulatory focus can be a key factor for organizations in balancing goals of different value orientations – for instance, a conflict between the growth goal, which encourages experimentation and exploration with longer time horizons, and the efficiency goal, which prioritizes immediate and stabilized profits via exploitation with shorter time horizons. Striking the optimal balance between growth and efficiency orientations in strategic change is critical for firms’ survival and performance. For example, the past failure of Nokia indicates that pursuing efficiency strategy amid heightened industrial competition for innovation can hinder organizational flexibility and innovativeness, which may eventually lead to losing corporate competitive advantage (McCray et al., 2011). In contrast, efficiency strategies of Toyota Motors and Japan Air Lines pursued in the late 2000s during the global economic downturn proved successful, which ultimately led to the firms’ resilient performance (Liker and Franz, 2011). As these anecdotal evidences indicate, strategic orientations need to be adapted continuously in accordance with the environmental conditions. In this light, organizational regulatory focus would play a significant role in firms’ strategic adaptation.

Our study suffers from a number of limitations. First, we examined the role of regulatory foci in organizational learning processes of large manufacturing firms in the USA. However, the process could be quite different in small and/or young firms. Future research should test our ideas with different samples of firms, such as nascent entrepreneurial ventures. Second, we assessed the regulatory focus of organizations using a content analysis of their 10-K filings. Although an increasing number of studies have employed text analyses to measure regulatory focus (e.g. Gamache et al., 2015; Rhee and Fiss, 2014; Stam et al., 2016), this method is prone to issues of construct validity. Employing alternative research methods, such as surveys, interviews or experiments, to measure an organization’s regulatory focus and triangulating the results would add validity to our findings.

To conclude, while scholars have examined a variety of drivers of organizational learning and strategic change, the psychological and cognitive underpinnings in such processes have remained underexplored. In this study, we bridge this gap by exploring the interlinkages between organizations’ regulatory focus, performance feedback and strategic change orientation. We hope this study provides an impetus for opening up future discussions on the microcognitive mechanisms of organizational learning and change.

References

Ahmadi, S., Khanagha, S., Berchicci, L. and Jansen, J.J. (2017), “Are managers motivated to explore in the face of a new technological change? The role of regulatory focus, fit, and complexity of decision-making”, Journal of Management Studies, Vol. 54 No. 2, pp. 209-237.

Aiken, L.S., West, S.G. and Reno, R.R. (1991), Multiple Regression: Testing and Interpreting Interactions, Sage, Newbury Park, CA.

MD

Argyris, C. and Sch€on, D.A. (1997), “Organizational learning: a theory of action perspective”, Reis, Vols 77-78, pp. 345-348.

Audia, P.G. and Brion, S. (2007), “Reluctant to change: self-enhancing responses to diverging performance measures”, Organizational Behavior and Human Decision Processes, Vol. 102 No. 2, pp. 255-269.

Audia, P.G. and Greve, H.R. (2006), “Less likely to fail: low performance, firm size, and factory expansion in the shipbuilding industry”, Management Science, Vol. 52 No. 1, pp. 83-94.

Audia, P.G., Locke, E.A. and Smith, K.G. (2000), “The paradox of success: an archival and a laboratory study of strategic persistence following radical environmental change”, Academy of Management Journal, Vol. 43 No. 5, pp. 837-853.

Bednar, M.K., Boivie, S. and Prince, N.R. (2013), “Burr under the saddle: how media coverage influences strategic change”, Organization Science, Vol. 24 No. 3, pp. 910-925.

Beersma, B., Homan, A.C., Van Kleef, G.A. and De Dreu, C.K. (2013), “Outcome interdependence shapes the effects of prevention focus on team processes and performance”, Organizational Behavior and Human Decision Processes, Vol. 121 No. 2, pp. 194-203.

Bingham, C.B., Eisenhardt, K.M. and Furr, N.R. (2007), “What makes a process a capability? Heuristics, strategy, and effective capture of opportunities”, Strategic Entrepreneurship Journal, Vol. 1 Nos 1-2, pp. 27-47.

Black, F. and Scholes, M. (1973), “The pricing of options and corporate liabilities”, Journal of Political Economy, Vol. 81 No. 3, pp. 637-654.

Brockner, J., Higgins, E.T. and Low, M.B. (2004), “Regulatory focus theory and the entrepreneurial process”, Journal of Business Venturing, Vol. 19 No. 2, pp. 203-220.

Cacioppo, J.T., Gardner, W.L. and Berntson, G.G. (1997), “Beyond bipolar conceptualizations and measures: the case of attitudes and evaluative space”, Personality and Social Psychology Review, Vol. 1 No. 1, pp. 3-25.

Carney, M. (2005), “Corporate governance and competitive advantage in family-controlled firms”, Entrepreneurship Theory and Practice, Vol. 29 No. 3, pp. 249-265.

Carpenter, M.A. (2000), “The price of change: the role of CEO compensation in strategic variation and deviation from industry strategy norms”, Journal of Management, Vol. 26 No. 6, pp. 1179-1198.

Chen, W.R. and Miller, K.D. (2007), “Situational and institutional determinants of firms’ R&D search intensity”, Strategic Management Journal, Vol. 28 No. 4, pp. 369-381.

Chen, G., Crossland, C. and Luo, S. (2015), “Making the same mistake all over again: CEO overconfidence and corporate resistance to corrective feedback”, Strategic Management Journal, Vol. 36 No. 10, pp. 1513-1535.

Chen, G., Meyer-Doyle, P. and Shi, W. (2018), “How CEO and CFO regulatory focus interact to shape the firm’s corporate strategy”, Behavioral Strategy for Competitive Advantage, Vol. 37, pp. 37-74.

Cho, T.S. and Hambrick, D.C. (2006), “Attention as the mediator between top management team characteristics and strategic change: the case of airline deregulation”, Organization Science, Vol. 17 No. 4, pp. 453-469.

Choi, J., Rhee, M. and Kim, Y.C. (2019), “Performance feedback and problemistic search: the moderating effects of managerial and board outsiderness”, Journal of Business Research, Vol. 102, pp. 21-33.

Cyert, R.M. and March, J.G. (1963), A Behavioral Theory of the Firm, Englewood Cliffs, NJ, No. 2, pp. 169-187.

Desai, V.M. (2008), “Constrained growth: how experience, legitimacy, and age influence risk taking in organizations”, Organization Science, Vol. 19 No. 4, pp. 594-608.

Downs, A., Besson, D., Louart, P., Durant, R., Taylor-Bianco, A. and Schermerhorn, J. (2006), “Self- regulation, strategic leadership and paradox in organizational change”, Journal of Organizational Change Management, Vol. 19 No. 4, pp. 457-470.

Organizational learning and regulatory

focus

Dutton, J.E. and Jackson, S.E. (1987), “Categorizing strategic issues: links to organizational action”, Academy of Management Review, Vol. 12 No. 1, pp. 76-90.

Fiske, S.T. and Taylor, S.E. (1991), Social Cognition, Mcgraw-Hill, New York, NY.

F€orster, J. and Higgins, E.T. (2005), “How global versus local perception fits regulatory focus”, Psychological Science, Vol. 16 No. 8, pp. 631-636.

F€orster, J., Higgins, E.T. and Idson, L.C. (1998), “Approach and avoidance strength during goal attainment: regulatory focus and the ‘goal looms larger’ effect”, Journal of Personality and Social Psychology, Vol. 75 No. 5, p. 1115.

Gaba, V. and Joseph, J. (2013), “Corporate structure and performance feedback: aspirations and adaptation in M-form firms”, Organization Science, Vol. 24 No. 4, pp. 1102-1119.

Gamache, D.L., McNamara, G., Mannor, M.J. and Johnson, R.E. (2015), “Motivated to acquire? The impact of CEO regulatory focus on firm acquisitions”, Academy of Management Journal, Vol. 58 No. 4, pp. 1261-1282.

Gilbert, C.G. (2006), “Change in the presence of residual fit: can competing frames coexist?”, Organization Science, Vol. 17 No. 1, pp. 150-167.

Gioia, D.A. and Chittipeddi, K. (1991), “Sensemaking and sensegiving in strategic change initiation”, Strategic Management Journal, Vol. 12 No. 6, pp. 433-448.

Gioia, D.A. and Thomas, J.B. (1996), “Identity, image, and issue interpretation: sensemaking during strategic change in academia”, Administrative Science Quarterly, Vol. 41 No. 3, pp. 370-403.

Gomez, P., Borges, A. and Pechmann, C. (2013), “Avoiding poor health or approaching good health: does it matter? The conceptualization, measurement, and consequences of health regulatory focus”, Journal of Consumer Psychology, Vol. 23 No. 4, pp. 451-463.

Gordon, S.S., Stewart, W.H. Jr, Sweo, R. and Luker, W.A. (2000), “Convergence versus strategic reorientation: the antecedents of fast-paced organizational change”, Journal of Management, Vol. 26 No. 5, pp. 911-945.

Greve, H.R. (1998), “Performance, aspirations, and risky organizational change”, Administrative Science Quarterly, Vol. 43 No. 1, pp. 58-86.

Greve, H.R. (2003), Organizational Learning from Performance Feedback: A Behavioral Perspective on Innovation and Change, Cambridge University Press, Cambridge.

Haleblian, J. and Finkelstein, S. (1993), “Top management team size, CEO dominance, and firm performance: the moderating roles of environmental turbulence and discretion”, Academy of Management Journal, Vol. 36 No. 4, pp. 844-863.

Hambrick, D.C. and Abrahamson, E. (1995), “Assessing managerial discretion across industries: a multimethod approach”, Academy of Management Journal, Vol. 38 No. 5, pp. 1427-1441.

Hambrick, D.C. and Fukutomi, G.D. (1991), “The seasons of a CEO’s tenure”, Academy of Management Review, Vol. 16 No. 4, pp. 719-742.

Hannan, M.T. and Freeman, J. (1984), “Structural inertia and organizational change”, American Sociological Review, Vol. 49 No. 2, pp. 149-164.

Harris, J. and Bromiley, P. (2007), “Incentives to cheat: the influence of executive compensation and firm performance on financial misrepresentation”, Organization Science, Vol. 18 No. 3, pp. 350-367.

Herrmann, P. and Nadkarni, S. (2014), “Managing strategic change: the duality of CEO personality”, Strategic Management Journal, Vol. 35 No. 9, pp. 1318-1342.

Higgins, E.T. (1997), “Beyond pleasure and pain”, American Psychologist, Vol. 52 No. 12, p. 1280.

Higgins, E.T. (1998), “Promotion and prevention: regulatory focus as a motivational principle”, Advances in Experimental Social Psychology, Vol. 30, pp. 1-46.

Higgins, E.T. (2000), “Making a good decision: value from fit”, American Psychologist, Vol. 55 No. 11, p. 1217.

MD

Higgins, E.T. and Spiegel, S. (2004), “Promotion and prevention strategies for self-regulation: a motivated cognition perspective”, Baumeister, R.F. and Vohs, K.D. (Eds), Handbook of Self- Regulation: Research, Theory, and Applications, Guildford Press, New York, NY, pp. 171-187.

Hu, S., He, Z.L., Blettner, D.P. and Bettis, R.A. (2017), “Conflict inside and outside: social comparisons and attention shifts in multidivisional firms”, Strategic Management Journal, Vol. 38 No. 7, pp. 1435-1454.

Hu, Y., Chen, S. and Wang, J. (2018), “Managerial humanistic attention and CSR: do firm characteristics matter?”, Sustainability, Vol. 10 No. 11, p. 4029.

Iyer, D.N. and Miller, K.D. (2008), “Performance feedback, slack, and the timing of acquisitions”, Academy of Management Journal, Vol. 51 No. 4, pp. 808-822.

Jiang, W., Wang, L., Chu, Z. and Zheng, C. (2019), “How does CEO regulatory focus matter? The impacts of CEO promotion and prevention focus on firm strategic change”, Group & Organization Management, Vol. 45 No. 3, pp. 386-416.

Johnson, P. and Wallace, C. (2011), “Increasing individual and team performance in an organizational setting through the situational adaptation of regulatory focus”, Consulting Psychology Journal: Practice and Research, Vol. 63 No. 3, p. 190.

Johnson, P.D., Smith, M.B., Wallace, J.C., Hill, A.D. and Baron, R.A. (2015), “A review of multilevel regulatory focus in organizations”, Journal of Management, Vol. 41 No. 5, pp. 1501-1529.

Jordan, A.H. and Audia, P.G. (2012), “Self-enhancement and learning from performance feedback”, Academy of Management Review, Vol. 37 No. 2, pp. 211-231.

Joseph, J., Klingebiel, R. and Wilson, A.J. (2016), “Organizational structure and performance feedback: centralization, aspirations, and termination decisions”, Organization Science, Vol. 27 No. 5, pp. 1065-1083.

Kaplan, S. (2008), “Cognition, capabilities, and incentives: assessing firm response to the fiber-optic revolution”, Academy of Management Journal, Vol. 51 No. 4, pp. 672-695.

Katila, R. and Chen, E.L. (2008), “Effects of search timing on innovation: the value of not being in sync with rivals”, Administrative Science Quarterly, Vol. 53 No. 4, pp. 593-625.

Kim, T. and Rhee, M. (2017), “Structural and behavioral antecedents of change: status, distinctiveness, and relative performance”, Journal of Management, Vol. 43 No. 3, pp. 716-741.

Kuusela, P., Keil, T. and Maula, M. (2017), “Driven by aspirations, but in what direction? Performance shortfalls, slack resources, and resource-consuming vs. resource-freeing organizational change”, Strategic Management Journal, Vol. 38 No. 5, pp. 1101-1120.

Labianca, G., Fairbank, J.F., Andrevski, G. and Parzen, M. (2009), “Striving toward the future: aspiration–performance discrepancies and planned organizational change”, Strategic Organization, Vol. 7 No. 4, pp. 433-466.

Lant, T.K., Milliken, F.J. and Batra, B. (1992), “The role of managerial learning and interpretation in strategic persistence and reorientation: an empirical exploration”, Strategic Management Journal, Vol. 13 No. 8, pp. 585-608.

Lee, P.M. and O’Neill, H.M. (2003), “Ownership structures and R&D investments of US and Japanese firms: agency and stewardship perspectives”, Academy of Management Journal, Vol. 46 No. 2, pp. 212-225.

Lee, A.Y., Keller, P.A. and Sternthal, B. (2010), “Value from regulatory construal fit: the persuasive impact of fit between consumer goals and message concreteness”, Journal of Consumer Research, Vol. 36 No. 5, pp. 735-747.

Lehman, D.W., Hahn, J., Ramanujam, R. and Alge, B.J. (2011), “The dynamics of the performance–risk relationship within a performance period: the moderating role of deadline proximity”, Organization Science, Vol. 22 No. 6, pp. 1613-1630.

Levine, J.M., Higgins, E.T. and Choi, H.S. (2000), “Development of strategic norms in groups”, Organizational Behavior and Human Decision Processes, Vol. 82 No. 1, pp. 88-101.

Organizational learning and regulatory

focus

Levitt, B. and March, J.G. (1988), “Organizational learning”, Annual Review of Sociology, Vol. 14 No. 1, pp. 319-338.

Li, Q., Maggitti, P.G., Smith, K.G., Tesluk, P.E. and Katila, R. (2013), “Top management attention to innovation: the role of search selection and intensity in new product introductions”, Academy of Management Journal, Vol. 56 No. 3, pp. 893-916.

Liker, J.K. and Franz, J.K. (2011), The Toyota Way to Continuous Improvement: Linking Strategy and Operational Excellence to Achieve Superior Performance, McGraw Hill Professional, New York, NY.

Lim, E.N. and McCann, B.T. (2014), “Performance feedback and firm risk taking: the moderating effects of CEO and outside director stock options”, Organization Science, Vol. 25 No. 1, pp. 262-282.

Makarevich, A. (2018), “Performance feedback as a cooperation ‘switch’: a behavioral perspective on the success of venture capital syndicates among competitors”, Strategic Management Journal, Vol. 39 No. 12, pp. 3247-3272.

March, J.G. (1991), “Exploration and exploitation in organizational learning”, Organization Science, Vol. 2 No. 1, pp. 71-87.

March, J.G. and Shapira, Z. (1987), “Managerial perspectives on risk and risk taking”, Management Science, Vol. 33 No. 11, pp. 1404-1418.

March, J.G. and Shapira, Z. (1992), “Variable risk preferences and the focus of attention”, Psychological Review, Vol. 99 No. 1, p. 172.

March, J.G. and Simon, H.A. (1958), Organizations, Wiley, New York, NY.

Martin, X. and Mitchell, W. (1998), “The influence of local search and performance heuristics on new design introduction in a new product market”, Research Policy, Vol. 26 Nos 7-8, pp. 753-771.

McCray, J.P., Gonzalez, J.J. and Darling, J.R. (2011), “Crisis management in smart phones: the case of Nokia vs Apple”, European Business Review, Vol. 23 No. 3, pp. 240-255.

Mezias, S.J., Chen, Y.R. and Murphy, P.R. (2002), “Aspiration-level adaptation in an American financial services organization: a field study”, Management Science, Vol. 48 No. 10, pp. 1285-1300.

Miller, K.D. and Chen, W.R. (2004), “Variable organizational risk preferences: tests of the March- Shapira model”, Academy of Management Journal, Vol. 47 No. 1, pp. 105-115.

Nadkarni, S., Pan, L. and Chen, T. (2018), “Only timeline will tell: temporal framing of competitive announcements and rivals’ responses”, Academy of Management Journal, Vol. 62 No. 1, pp. 117-143.

Ocasio, W.C. (1995), “The enactment of economic adversity: a reconciliation of theories of failure- induced change and threat-rigidity”, Research in Organizational Behavior, Vol. 17, pp. 287-331.

Pennebaker, J.W., Booth, R.J., Boyd, R.L. and Francis, M.E. (2015), Linguistic Inquiry and Word Count: LIWC 2015, Pennebaker Conglomerates, Austin, TX.

Pettigrew, A.M. (1987), “Context and action in the transformation of the firm”, Journal of Management Studies, Vol. 24 No. 6, pp. 649-670.

Pham, M.T. and Chang, H.H. (2010), “Regulatory focus, regulatory fit, and the search and consideration of choice alternatives”, Journal of Consumer Research, Vol. 37 No. 4, pp. 626-640.

Plambeck, N. and Weber, K. (2009), “CEO ambivalence and responses to strategic issues”, Organization Science, Vol. 20 No. 6, pp. 993-1010.

Posen, H.E., Keil, T., Kim, S. and Meissner, F.D. (2018), “Renewing research on problemistic search—a review and research agenda”, Academy of Management Annals, Vol. 12 No. 1, pp. 208-251.

Rhee, E.Y. and Fiss, P.C. (2014), “Framing controversial actions: regulatory focus, source credibility, and stock market reaction to poison pill adoption”, Academy of Management Journal, Vol. 57 No. 6, pp. 1734-1758.

MD

Rietzschel, E.F. (2011), “Collective regulatory focus predicts specific aspects of team innovation”, Group Processes & Intergroup Relations, Vol. 14 No. 3, pp. 337-345.

Sacramento, C.A., Fay, D. and West, M.A. (2013), “Workplace duties or opportunities? Challenge stressors, regulatory focus, and creativity”, Organizational Behavior and Human Decision Processes, Vol. 121 No. 2, pp. 141-157.

Sanders, W.G. and Hambrick, D.C. (2007), “Swinging for the fences: the effects of CEO stock options on company risk taking and performance”, Academy of Management Journal, Vol. 50 No. 5, pp. 1055-1078.

Schneider, S.L. (1992), “Framing and conflict: aspiration level contingency, the status quo, and current theories of risky choice”, Journal of Experimental Psychology: Learning, Memory, and Cognition, Vol. 18 No. 5, p. 1040.

Scott, W.R. and Davis, G.F. (2015), Organizations and Organizing: Rational, Natural and Open Systems Perspectives, Routledge, New York, NY.

Semin, G.R., Higgins, T., De Montes, L.G., Estourget, Y. and Valencia, J.F. (2005), “Linguistic signatures of regulatory focus: how abstraction fits promotion more than prevention”, Journal of Personality and Social Psychology, Vol. 89 No. 1, p. 36.

Shapira, Z. (2017), “Entering new markets: the effect of performance feedback near aspiration and well below and above it”, Strategic Management Journal, Vol. 38 No. 7, pp. 1416-1434.

Shimizu, K. (2007), “Prospect theory, behavioral theory, and the threat-rigidity thesis: combinative effects on organizational decisions to divest formerly acquired units”, Academy of Management Journal, Vol. 50 No. 6, pp. 1495-1514.

Short, J.C. and Palmer, T.B. (2003), “Organizational performance referents: an empirical examination of their content and influences”, Organizational Behavior and Human Decision Processes, Vol. 90 No. 2, pp. 209-224.

Simsek, Z. (2007), “CEO tenure and organizational performance: an intervening model”, Strategic Management Journal, Vol. 28 No. 6, pp. 653-662.

Sirmon, D.G. and Hitt, M.A. (2003), “Managing resources: linking unique resources, management, and wealth creation in family firms”, Entrepreneurship Theory and Practice, Vol. 27 No. 4, pp. 339-358.

Sleesman, D.J., Conlon, D.E., McNamara, G. and Miles, J.E. (2012), “Cleaning up the big muddy: a meta- analytic review of the determinants of escalation of commitment”, Academy of Management Journal, Vol. 55 No. 3, pp. 541-562.

Stam, D., Van Knippenberg, D., Wisse, B. and Nederveen Pieterse, A. (2016), “Motivation in words: promotion-and prevention-oriented leader communication in times of crisis”, Journal of Management, Vol. 44 No. 7, pp. 2859-2887.

Staw, B.M. (1976), “Knee-deep in the big muddy: a study of escalating commitment to a chosen course of action”, Organizational Behavior and Human Performance, Vol. 16 No. 1, pp. 27-44.

Staw, B.M., Sandelands, L.E. and Dutton, J.E. (1981), “Threat rigidity effects in organizational behavior: a multilevel analysis”, Administrative Science Quarterly, Vol. 26 No. 4, pp. 501-524.

Stuart, T.E. and Podolny, J.M. (1996), “Local search and the evolution of technological capabilities”, Strategic Management Journal, Vol. 17 No. S1, pp. 21-38.

Thompson, J.D. (2017), Organizations in Action: Social Science Bases of Administrative Theory, Routledge, New York, NY.

Tushman, M.L. and Romanelli, E. (1985), “Organizational evolution: a metamorphosis model of convergence and reorientation”, Research in Organizational Behavior, Vol. 7, pp. 171-222.

Vissa, B., Greve, H.R. and Chen, W.R. (2010), “Business group affiliation and firm search behavior in India: responsiveness and focus of attention”, Organization Science, Vol. 21 No. 3, pp. 696-712.

Organizational learning and regulatory

focus

Wallace, J.C., Little, L.M., Hill, A.D. and Ridge, J.W. (2010), “CEO regulatory foci, environmental dynamism, and small firm performance”, Journal of Small Business Management, Vol. 48 No. 4, pp. 580-604.

Walsh, J.P. (1995), “Managerial and organizational cognition: notes from a trip down memory lane”, Organization Science, Vol. 6 No. 3, pp. 280-321.

Weber, L. and Bauman, C.W. (2019), “The cognitive and behavioral impact of promotion and prevention contracts on trust in repeated exchanges”, Academy of Management Journal, Vol. 62 No. 2, pp. 361-382.

Weber, L. and Mayer, K.J. (2011), “Designing effective contracts: exploring the influence of framing and expectations”, Academy of Management Review, Vol. 36 No. 1, pp. 53-75.

Westphal, J.D. and Fredrickson, J.W. (2001), “Who directs strategic change? Director experience, the selection of new CEOs, and change in corporate strategy”, Strategic Management Journal, Vol. 22 No. 12, pp. 1113-1137.

Wooldridge, J.M. (2010), Econometric Analysis of Cross Section and Panel Data, MIT Press, Cambridge, MA.

Wowak, A.J. and Hambrick, D.C. (2010), “A model of person-pay interaction: how executives vary in their responses to compensation arrangements”, Strategic Management Journal, Vol. 31 No. 8, pp. 803-821.

Wright, P., Kroll, M., Krug, J.A. and Pettus, M. (2007), “Influences of top management team incentives on firm risk taking”, Strategic Management Journal, Vol. 28 No. 1, pp. 81-89.

Xie, E., Huang, Y., Stevens, C.E. and Lebedev, S. (2019), “Performance feedback and outward foreign direct investment by emerging economy firms”, Journal of World Business, Vol. 54 No. 6, pp. 1-13.

Xu, D., Zhou, K.Z. and Du, F. (2019), “Deviant versus aspirational risk taking: the effects of performance feedback on bribery expenditure and R&D intensity”, Academy of Management Journal, Vol. 62 No. 4, pp. 1226-1251.

Yadav, M.S., Prabhu, J.C. and Chandy, R.K. (2007), “Managing the future: CEO attention and innovation outcomes”, Journal of Marketing, Vol. 71 No. 4, pp. 84-101.

Zhang, Y. and Rajagopalan, N. (2010), “Once an outsider, always an outsider? CEO origin, strategic change, and firm performance”, Strategic Management Journal, Vol. 31 No. 3, pp. 334-346.

Further reading

March, J.G. (1962), “The business firm as a political coalition”, The Journal of Politics, Vol. 24 No. 4, pp. 662-678.

March, J.G. and Olsen, J.P. (1976), “Organizational choice under ambiguity”, Ambiguity and Choice in Organizations, Vol. 2, pp. 10-23.

Corresponding author Cecile K. Cho can be contacted at: [email protected]

For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: [email protected]

MD