Mind the gap: the effect of cultural distance on mergers and acquisitions—evidence from glassdoor reviews

Brede, Marius; Gerstel, Hannes; Wöhrmann, Arnt; Bausch, Andreas Article — Published Version Mind the gap: the effect of cultural distance on mergers and acquisitions—evidence from glassdoor reviews Review of Managerial Science Provided in Cooperation with: Springer Nature Suggested Citation: Brede, Marius; Gerstel, Hannes; Wöhrmann, Arnt; Bausch, Andreas (2024) : Mind the gap: the effect of cultural distance on mergers and acquisitions—evidence from glassdoor reviews, Review of Managerial Science, ISSN 1863-6691, Springer, Berlin, Heidelberg, Vol. 19, Iss. 8, pp. 2279-2326, https://doi.org/10.1007/s11846-024-00811-8 This Version is available at: https://hdl.handle.net/10419/323708 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by-nc-nd/4.0/ Vol.:(0123456789) Review of Managerial Science (2025) 19:2279–2326 https://doi.org/10.1007/s11846-024-00811-8 ORIGINAL PAPER Mind thegap: theeffect ofcultural distance onmergers andacquisitions—evidence fromglassdoor reviews MariusBrede1 · HannesGerstel1· ArntWöhrmann1· AndreasBausch1 Received: 4 August 2023 / Accepted: 10 September 2024 / Published online: 5 October 2024 © The Author(s) 2024, corrected publication 2025 Abstract This paper investigates the impact of differences in organizational culture on M&A outcomes during the transaction and post-merger integration phase. Using a state- of-the-art large language model, we construct a novel measure of organizational cultural distance based on employee reviews from Glassdoor.com covering 243 M&A deals from 2008 to 2021. First, we hypothesize and find that organizational cultural distance between acquirer and target firms lead to cultural frictions during the transaction and post-merger integration phases of the deal, which negatively affect shortterm capital market reactions and long-term synergy gains. Second, our results suggest that cultural differences are positively related to the acquisition premium paid by the acquirer, supporting the hypothesis that cultural distance reduces the acquirer’s ability to accurately assess the true value of the target, leading to an overestimation of the realizable synergy potential. Third, we show that cultural distance is negatively related to the long-term innovativeness of the acquiring firm. Specifically, our results suggest that patent growth and new product development, measured two years after the deal, are significantly lower for firms that acquire a culturally distant target. We then examine the underlying cultural dimensions that drive these effects. Consistent with previous studies, we find that performance effects are driven by differences in market orientation, while innovation effects are driven by differences in hierarchy or adhocracy orientation, depending on whether innovation is measured in terms of tangible or intangible assets. Our findings contribute to the broad M&A literature and have practical relevance for firms engaged in M&A transactions. Keywords Organizational culture· Cultural distance· Mergers and acquisitions· M&A performance· Innovation· Natural language processing JEL Classification M140· G340· C450 Extended author information available on the last page of the article 2280 M.Brede et al. 1 Introduction Mergers and Acquisitions (M&A) have become one of the most significant strategic initiatives for firms, with global transaction volumes reaching $3.7 trillion in 2017 (Cho and Chung 2022). However, despite their importance, nearly half of all M&A deals fail to meet their objectives (Cartwright and Cooper 1993). A major factor contributing to this high failure rate is the cultural differences between acquiring and target firms (Stahl and Voigt 2008). For example, a survey of senior executives found that almost 50% would avoid pursuing an acquisition if the target company’s culture did not align with their own. This reluctance to engage with culturally incompatible partners reflects well-documented cases where cultural clashes were cited as reasons for the failure of high-profile deals, such as HP’s acquisition of Compaq and Amazon’s purchase of Whole Foods (Oberoi 2020). These findings suggest that cultural misalignment significantly reduces M&A success, underscoring the critical role of cultural fit. Management literature typically distinguishes between national culture and organizational culture (Rottig 2017). Both are considered distinct phenomena, each with unique manifestations and different implications for an organization’s actions (Kirkman etal. 2006). While numerous studies have empirically examined the impact of national cultural distance on M&A outcomes (e.g., Ahern etal. 2015; Lee 2018; Lim etal. 2016), research on the impact of organizational culture is scarce and largely inconclusive, primarily due to limitations in measuring organizational culture (Renneboog and Vansteenkiste 2019; Rottig 2017). To fill this gap, we analyze the impact of organizational cultural distance between firms involved in an M&A transaction on economically important outcomes such as M&A success, acquisition premiums, and post-deal innovation of the acquiring firm. We derive our proxy for organizational cultural distance directly from Glassdoor.com, where employees anonymously rate their employers and provide textual feedback about their workplace experiences. This method allows us to overcome some of the limitations of previous studies that often rely on subjective measures such as self-reported surveys, which reduce the comparability and objectivity of the results. Using this novel approach, we hypothesize that the capital market reacts negatively to announcements of M&A transactions between firms with high organizational cultural distance (H1a) and that organizational cultural distance leads to lower post-merger synergy realization (H1b). Previous research has provided mixed evidence on the impact of cultural differences on M&A success (Rottig 2017; Stahl and Voigt 2008). On the one hand, the cultural learning hypothesis suggests that different cultures offer learning potential and opportunities for resource recombination (Pesch and Bouncken 2017; Sørensen 2002). On the other hand, the cultural friction hypothesis (Hofstede 1980) argues that cultural differences between acquirers and targets increase integration and coordination costs, thereby reducing M&A performance (Vaara 2002; Weber 1996). We expect that the frictions associated with cultural differences will outweigh the potential learning benefits, leading to lower capital market reactions and reduced post-merger synergies. 2281 Mind thegap: theeffect ofcultural distance onmergers and… Second, we hypothesize that organizational cultural differences lead to higher acquisition premiums (H2). Similar to the relationship between cultural distance and M&A success, the link between organizational cultural distance and acquisition premiums remains underexplored and contradictory (Lim etal. 2016). We argue that differences in processes, structures, and strategies resulting from organizational culture differences hinder the acquirer’s ability to accurately assess the target’s value, leading to overpayment and higher premiums. Third, as one of the first studies to examine organizational cultural distance and long-term innovation outcomes in M&A, we hypothesize that cultural differences negatively affect the acquirer’s long-term innovativeness. Following the cultural friction hypothesis, we propose that organizational cultural differences hinder knowledge sharing, collaboration, and coordination during the innovation process, ultimately reducing the acquirer’s post-acquisition patent growth (H3a) and the rate of new product development (H3b). Past research, including Rottig’s (2017) meta-analysis, has highlighted the complexity of the culture construct and attributed conflicting findings to the methodological weaknesses of previous studies, which often rely on small-scale surveys of high-level employees to assess organizational culture. Other methods, such as analyzing corporate values on company websites, have demonstrated low internal validity (Graham etal. 2022) and predictive power (Guiso etal. 2015). To overcome these limitations, we apply deep-learning-based natural language processing to approximately 400,000 employee reviews from 437 firms (243M&A deals) on Glassdoor, spanning the years 2008–2021. This approach leverages individual employee experiences to provide a more granular view of organizational culture (Corritore etal. 2020; Ji etal. 2022). In the literature, organizational culture is primarily studied using the Competing Values Framework (CVF, Cameron etal. 2006; Quinn and Rohrbaugh 1983), which conceptualizes culture along four dimensions: Adhocracy, Clan, Market, and Hierarchy. These dimensions are arranged along two continuums, reflecting the organization’s preference for flexibility or control, and its internal or external focus. Using state-of-the-art language models (Bochkay etal. 2023; Vaswani etal. 2017), we apply Culture-BERT (Koch and Pasch 2022) to Glassdoor reviews to measure the salience of each cultural dimension. We then adapt Kogut and Singh’s (1988) popular measure of cultural distance to compute the organizational cultural distance between acquirers and targets. This measure of organizational cultural distance serves as the key variable for explaining M&A outcomes. Our analyses offer several important insights. First, in line with the cultural friction hypothesis, we find that organizational cultural distance is negatively associated with both capital market reactions and long-term synergies. Further analysis shows that differences in market orientation between acquirers and targets primarily drive this negative performance effect, aligning with previous findings by Deshpandé and Farley (2004) and Eisend etal. (2016) that emphasize market orientation as a key driver of firm performance. Second, our results suggest that organizational cultural distance leads to higher acquisition premiums by impairing the acquirer’s ability to accurately value the target and assess synergy potential. Third, we find that cultural differences negatively affect the acquirer’s post-deal innovativeness, with reduced 2282 M.Brede et al. patent growth and new product development two years after the acquisition. Additional analyses indicate that this effect is mainly driven by differences in adhocracy and hierarchy orientation for patent growth and new product development. These results suggest that target firms that place significantly less emphasis on values such as innovativeness or agility may hinder the acquirer’s post-deal inventiveness, whereas target firms that place greater emphasis on values such as efficiency or timeliness (i.e., hierarchy culture) may hinder new product development. Finally, we perform several robustness checks to validate our results. To our knowledge, our study is the first to use transformer-based natural language processing on a large sample of Glassdoor reviews to infer the impact of organizational cultural distance on M&A outcomes. In doing so, we contribute to the broader M&A literature in several ways: First, we address a notable criticism of the widespread reliance on small-scale surveys that use subjective measures (e.g., self-reported surveys and interviews) to assess organizational culture and its impact on M&A outcomes (Rottig 2017; Teerikangas and Very 2006). To mitigate the risk of methodological bias, increase objectivity, and improve the comparability of results, we derive our cultural distance proxy from thousands of voluntarily written employee reviews. This approach provides a more representative and nuanced understanding of organizational culture (Campbell and Shang 2021). By doing so, we contribute to the management literature that analyzes organizational culture using rich textual data from sources such as annual reports and employee reviews (e.g., Campbell and Shang 2021; Corritore 2018; Li etal. 2021). Additionally, we utilize a state-of-the-art transformer model that has shown up to 28% higher accuracy in inferring organizational culture compared to previous methods (Koch and Pasch 2022), addressing several limitations of earlier analytical methods. Second, we address the previously inconclusive findings on the impact of organizational cultural differences on short- and long-term M&A performance (Rottig 2017; Stahl and Voigt 2008). Our results indicate that the cultural friction hypothesis outweighs the cultural learning hypothesis, with differences in market orientation between acquirers and targets emerging as the main driver of performance effects. Unlike recent studies examining cultural differences in M&A outcomes (e.g., Alexandridis etal. 2022; Bereskin et al. 2018), our study does not focus on specific aspects of organizational culture (e.g., CSR orientation). Instead, we use a widely accepted framework for assessing corporate culture, drawing insights from a diverse and complex set of employee reviews. Additionally, our methodology avoids subjective third-party assessments, resulting in more nuanced and representative findings. Third, our study contributes to the literature on acquisition premiums and their role in the success or failure of M&As (King etal. 2021). To our knowledge, there is limited evidence on the effect of national cultural differences on acquisition premiums (Lim etal. 2016) and no evidence regarding the effect of organizational cultural distance. Our results suggest that organizational cultural distance may contribute to the misvaluation of target firms, providing a possible explanation for the inconsistent empirical findings on M&A success in the existing literature. Finally, we are among the first to explore the long-term consequences of organizational cultural distance on the acquirer’s post-deal innovativeness. Consistent with previous studies on the effects of national cultural distance (e.g., Bauer etal. 2016), 2283 Mind thegap: theeffect ofcultural distance onmergers and… we find that organizational cultural distance between the acquirer and the target has a detrimental effect on the acquirer’s post-deal innovativeness. This effect holds true for both patent growth and new product development, demonstrating the robustness of our findings across different measures of innovation. However, a more detailed analysis reveals that specific cultural dimensions shape these outcomes: differences in adhocracy culture drive the negative effect on patent growth, while differences in hierarchy culture explain the negative effect on new product development. In conclusion, our research extends the managerial literature by deepening the understanding of the impact of organizational cultural distance on M&A success. 2 Hypotheses development 2.1 Organizational cultural distance, capital market reactions, andpost‑deal synergies Culture is generally defined as “the collective programming of the mind which distinguishes the member of one group or category of people from another” (Hofstede 2001, p. 9). It is often conceptualized as an informal institution that consists primarily of unwritten social rules, values and norms that are shaped by the shared history and experiences of its members (Louis 1981; Schein 1985). As such, culture significantly influences social interactions by shaping expectations and determining acceptable social behavior (Hofstede 1980). Moreover, culture serves as an organizational mechanism for coordinating the activities of a large number of individuals by providing a control system for setting goals, evaluating deviations, and providing feedback to individuals. These social control mechanisms can effectively ensure predictable behavior among organizational members through norms or social expectations (Chatman and O’Reilly 2016). Culture is generally viewed as a multilevel construct that operates at both the national and firm levels, with variations in organizational culture often reflecting differences in national culture (Chakrabarti etal. 2009; Schneider 1988). However, Dauber (2012) suggests that because M&A transactions involve firms rather than countries, differences in organizational culture may be a more accurate predictor of M&A performance than differences in national culture. Research generally supports this idea, showing that organizational culture affects various organizational practices, including behavioral norms, decision making, and strategic initiatives such as outsourcing (e.g., Dahlgrün and Bausch 2019), CSR (e.g., Chen and Liu 2022), innovation (e.g., Büschgens etal. 2013) and M&A (e.g., Bhagat and McQuaid 1982; Kirkman etal. 2006). Regarding the impact of culture on M&A performance, numerous studies and several meta-analyses conducted over the past decades have failed to reach a consensus on whether cultural differences between the acquirer and target have a positive or negative effect on M&A performance (e.g., King etal. 2021; Rottig 2017; Stahl and Voigt 2008). However, there is general agreement that the cultural fit between acquirer and target influences M&A performance, independent of other strategic considerations (Bauer and Matzler 2014). In an attempt to disentangle the complex dynamics of M&A transactions, the literature often 2284 M.Brede et al. differentiates between short-term performance effects, typically measured by market reactions around deal announcements (Aktas etal. 2011), and long-term performance effects, usually measured using accounting-based metrics (Zollo and Meier 2008). Following this convention, we separately hypothesize the effects of organizational cultural distance on short-term market reactions (H1a) and longterm synergies (H1b). Theoretical arguments for the positive impact of cultural differences on M&A performance are primarily based on the theory of interorganizational learning (Sørensen 2002), according to which M&As involving culturally distant firms provide significant learning opportunities that enable the profitable recombination of resources. Acquiring a firm with a different culture facilitates knowledge transfer and provides access to new practices and techniques (Chakrabarti etal. 2009; Morosini etal. 1998; Sarala and Vaara 2010), creating a sustainable source of value creation (Bouwman 2013; Haspeslagh and Jemison 1991). This view is shared by the information processing hypothesis, which argues that both acquiring and target firms can benefit from the multiple perspectives associated with greater cultural distance (Kogut and Singh 1988; Watson etal. 1993), leading to enhanced problem solving, creativity, innovation, adaptability, and ultimately improved performance (Chakrabarti etal. 2009; Morosini etal. 1998). For example, Krishnan etal. (1997) find that differences in the skills and competencies of the acquiring and target management teams positively affect M&A performance, as the weaknesses of one firm can be offset by the strengths of the other. However, contrary to the cultural learning hypothesis, organizational cultural distance between acquirers and targets may be a source of friction in the post-merger integration phase (Vaara 2002; Weber 1996). These frictions can arise from personality clashes among senior executives, incompatible organizational structures and processes, or challenges in transferring core competencies and knowledge between firms (Ahammad etal. 2016; Rottig 2017). For example, Datta (1991), in a study of domestic M&A transactions in the United States, shows that incompatible management styles of acquiring and target firms can lead to reduced market performance and realized synergies. Drawing on social identity theory (Tajfel 1981; Turner 1982), Stahl and Voigt (2008) further suggest that organizational members are biased towards in-group members and tend to evaluate out-group members negatively in order to enhance the relative status of their own group. This negative impact on the internal cohesion of the workforce can reduce intergroup trust (Sitkin and Stickel 1996), thereby increasing the potential for conflict (e.g., Ahern etal. 2015; Jehn etal. 1999; Martin 1992). In addition, internal tensions within the workforce can negatively affect the flow of information between members of the acquirer and the target. As a result, employee and stakeholder resistance can escalate and impede efficient decision-making (Akerlof 1997; Arrow 1974; Renneboog and Vansteenkiste 2019), resulting in higher coordination costs and ultimately reduced firm performance (Barkema etal. 1996; Rahahleh and Wei 2013; Weber and Camerer 2003). For example, Cartwright and Cooper (1993) show that administrative conflicts can arise from the acquisition of a culturally distant firm. Moreover, Buono etal. (1985) show that the acquisition of a culturally distant target can lead to hostilities between employees of the acquirer and the target during the post-merger integration phase. 2285 Mind thegap: theeffect ofcultural distance onmergers and… In addition to studies that measure acquisition performance primarily through accounting metrics, there is evidence that culture affects investor expectations and behavior (Fang etal. 2023). Capital markets have been shown to view M&A transactions between firms with high cultural distance negatively, anticipating frictions from low cultural fit. For example, a meta-analysis by King etal. (2021) finds a negative effect of national cultural distance on the acquirer’s short-term stock performance. Moreover, studies that analyze the combined stock performance of the acquirer and target, such as Ahern etal. (2015) and Aybar and Ficici (2009), also find a negative effect, as do studies that analyze the effect of organizational cultural distance on stock performance (e.g., Chatterjee etal. 1992). These findings are further supported by studies that measure organizational culture using alternative measures of organizational cultural distance. For example, Alexandridis etal. (2022) and Bereskin etal. (2018) find that differing CSR orientations of the acquirer and target lead to lower announcement returns and lower long-term operating growth, while culturally similar firms earn higher abnormal returns around the deal announcement date. Taken together, these findings suggest that investors expect the organizational cultural distance between the acquirer and the target to reduce the likelihood of realizing synergies, resulting in negative announcement day returns. Overall, the empirical evidence on the relationship between cultural distance and M&A performance is mixed. However, cultural differences seem to be a particularly important contributor to social conflict between acquiring and target employees (Vaara etal. 2012). Moreover, this effect appears to be stronger than the positive impact on knowledge sharing between the two firms, which is crucial for the positive outcomes predicted by the information processing and interorganizational learning hypotheses, suggesting that cultural frictions may outweigh the benefits of knowledge sharing. Consistent with these findings, the results of numerous studies (e.g., Ahern etal. 2015; Alexandridis etal. 2022; Bereskin etal. 2018; Chatterjee etal.) and meta-analyses examining short- and long-term M&A performance reflect this relationship (King etal.; Rottig 2017; Stahl and Voigt 2008). For example, the meta-analysis by Homberg etal. (2009) finds that several measures for cultural distance, are negatively associated with synergy realization in both the short and long term. Consequently, we argue that the friction between culturally distant acquirers and targets during the (post-) merger phase outweighs the potential benefits of learning and resource recombination. As a result, we propose a negative impact of organizational culture differences on both short-term stock market reactions and long-term synergy gains. This leads to the following hypotheses: H1a Organizational cultural distance between acquirer and target firms is negatively associated with capital market reactions to M&A announcements. H1b Organizational cultural distance between acquirer and target is negatively associated with long-term synergy gains. 2286 M.Brede et al. 2.2 Organizational cultural distance andacquisition premiums Cultural differences not only affect post-merger outcomes, but also have a significant impact during the pre-transaction and transaction phases (Renneboog and Vansteenkiste 2019). Specifically, this is reflected in the acquisition premium that the acquirer is willing to pay for the target. The acquisition premium represents the additional amount paid by the acquirer over and above the pre-transaction value of the target. It is the result of negotiations between the acquirer and the target, with the acquirer seeking to minimize the purchase price and the target seeking to maximize it. The premium therefore plays a key role in the success of the transaction, as it is directly related to the synergies that the acquirer must achieve for the transaction to be considered successful (Schweiger 2002; Sirower 1997). Therefore, studies suggest that high premiums may negatively affect the post-acquisition performance of acquirers (King etal. 2021). The acquisition premium is affected not only by tangible valuation factors, but also by various intangible factors (Aktas etal. 2011; Chatterjee and Hambrick 2011; Jentner and Lew-ellen 2015). For example, increased growth pressure can increase the acquirer’s reservation price and positively affect the acquisition premium (Kim etal. 2011). In addition, an overconfident management of the acquirer positively affects the premium (Hayward and Hambrick 1997). Furthermore, positive CSR involvement of target firms increases the deal premium (Ozdemir etal. 2022). Conversely, strategies that increase the bargaining power of the acquirer, such as earnings management, negatively affect the premium (Baik etal. 2015). Studies also show the influence of the acquirer’s network partners in determining the premium (Haunschild 1994). In addition to these acquirer characteristics, various aspects and behaviors of the target also influence the acquisition premium. For example, resistance from target management or the withholding of negative information can lead to an inflated target price (Akerlof 1970; Bange and Mazzeo 2004). Targets may also use certain observable signals to increase their value, such as an increase in alliance activity, the selection of a high-profile investment bank, and the backing of prominent venture capitalists. These strategies may be particularly useful insituations where the target is acquired by firms from different industries or countries (Reuer etal. 2012). In such cases, acquirers must use all available information to fill the information gap and properly assess target quality and deal synergy potential (Connelly etal. 2011). Culture has been shown to play an important role in shaping the acquisition premium by exerting normative pressure on firms. Rossi and Volpin (2004) find systematic differences in the level of acquisition premiums for UK and US targets. Hope etal. (2011) observe that non-US acquirers often bid higher for US targets than US acquirers. Li and Haleblian (2022) find that acquirers with low uncertainty tolerance tend to pay lower premiums, while acquirers from countries with high future orientation tend to pay higher premiums, as managers take a long-term view on synergy realization. Lim etal. (2016) find asymmetric effects between national cultural distance and the size of the acquisition premium, attributing these effects to higher information costs and systematic differences in structural uncertainty across countries. According to the authors, cultural distance hinders the ability to accurately 2293 Mind thegap: theeffect ofcultural distance onmergers and… However, as CARs represent the ex-ante expectations of investors and not the ex-post synergies realized, there is no guarantee that they correspond to the actual synergies. Therefore, we use an accounting proxy to measure long-term synergy benefits (Barraclough etal. 2013; Renneboog and Vansteenkiste 2019). Consistent with previous studies on post-acquisition performance (e.g., Morosini etal. 1998; Suk and Wang 2021; Woo etal. 1992), we use sales growth (acq_2yr_sales_growth, acq_4yr_sales_growth) as a proxy for long-term synergistic gains. We measure longterm performance as the sales growth two and four years after the M&A announcement compared to sales in the year before the announcement. Our choice of two- and four-year intervals is based on the understanding that while most integration efforts are completed within two years (Jemison and Sitkin 1986), cultural integration in particular can take considerably longer (Homburg and Bucerius 2006). By including both time frames, we can effectively capture longer-term effects. Table 1 Daily abnormal returns to M&A announcement This table presents the mean abnormal daily returns of acquiring firms for the ten-day event window around the M&A announcement date. The descriptive statistics are based on the daily abnormal returns of 243 deal observations for the years 2008–2021 Day Mean abnormal return (%) Standardized t-statistic p-value Patell Z p-value − 10 0.20 1.605 0.110 0.816 0.414 − 9 0.12 0.972 0.332 0.014 0.989 − 8 − 0.11 − 0.915 0.361 − 1.538 0.124 − 7 − 0.02 − 0.168 0.867 − 0.872 0.383 − 6 0.08 0.672 0.502 0.109 0.913 − 5 0.09 0.721 0.471 0.275 0.783 − 4 − 0.11 − 0.893 0.373 − 1.322 0.186 − 3 0.15 1.231 0.219 1.234 0.217 − 2 0.07 0.573 0.567 − 0.186 0.852 − 1 − 0.10 − 0.827 0.409 − 0.884 0.377 0− 0.25 − 10.984 0.049 − 2.175 0.029 1− 0.11 − 0.924 0.357 − 0.994 0.320 2− 0.10 − 0.816 0.415 − 0.959 0.337 3 0.04 0.300 0.764 − 0.279 0.780 4− 0.11 − 0.871 0.385 − 0.217 0.829 5− 0.07 − 0.627 0.532 0.144 0.885 6 0.05 0.413 0.680 0.134 0.893 7− 0.05 − 0.432 0.666 − 0.836 0.403 8 0.10 0.805 0.421 1.076 0.282 9− 0.12 − 0.989 0.324 − 1.410 0.158 10 − 0.05 − 0.390 0.697 0.378 0.705 2294 M.Brede et al. 4.2.2 Acquisition premiums As a proxy for transaction phase outcomes, we use acquisition premiums, which we measure as the difference between the acquirer’s payment and the target’s market value, divided by the target’s market value (deal_premium_1day; Lee etal. 2019). Prior research uses extended time periods to mitigate the potential impact of information leakage immediately prior to the announcement (Reuer etal. 2012). Therefore, we assess the target’s market value at three consecutive points in time (one day, one week, one month) prior to the deal announcement. 4.2.3 Post‑acquisition innovation To examine the impact of cultural distance on the post-M&A innovativeness of acquiring firms, we employ two widely accepted measures that have been associated with firms’ innovation performance: patenting and new product development (Cordero 1990). Patents serve as a popular indicator of a firm’s inventiveness (Gallini 2002) and have been extensively used to assess the effect of acquisitions on the subsequent innovation performance of acquirers (e.g., Ahuja and Katila 2001; Haucap etal. 2019; Hitt etal. 1991), as they reflect the firms’ technical knowledge base (Prabhu etal. 2005). Therefore, our first measure is the patent growth, measured as patents filed two years after an M&A announcement compared to the number of patents filed in the year before the announcement (acq_2yr_patent_growth). However, we recognize that patents may not capture all forms of innovation, as certain innovations may not be patentable, and firms may choose not to patent ideas for strategic reasons (Hall etal. 2005). Therefore, we consider an alternative measure of firm innovativeness that captures the marketable output of the innovation process and has been used in previous studies examining the effects of M&A on firm innovation (e.g., Grimpe 2007; Hitt etal. 1996). Our second measure captures product innovation by assessing new product growth by comparing the number of product launches two years after the M&A announcement to the number of product launches in the year before the announcement (acq_2yr_npd_growth). 4.3 Control variables Similar to Ahmed etal. (2023), Bereskin etal. (2018), and Suk and Wang (2021), we add a set of control variables to our regression models that capture deal, acquirer, and target characteristics that may affect M&A outcomes (for a detailed variable description, see Appendix B). 4.3.1 Deal controls Bereskin etal. (2018) find that larger deals are associated with lower acquirer CARs. In addition, larger deal values are associated with higher deal complexity, which increases deal duration (Lawrence etal. 2021). Therefore, we include the natural logarithm (to mitigate skewness) of the deal value (deal_value_ln) as a 2295 Mind thegap: theeffect ofcultural distance onmergers and… control. Next, we include two dummy variables indicating whether the deal was all cash (deal_all_cash_dummy) or all equity financed (deal_all_stock_dummy), as Loughran and Vijh (1997) find that equity-financed acquisitions generate lower returns than cash-financed ones. In addition, deal financing affects acquisition premiums (Ghosh and Ruland 1998). As Chen etal. (2018) find that tender offers are positively associated with acquisition synergies, we control for whether the deal involves a tender offer (deal_tenderoffer_dummy). However, tender offers may also lead to higher acquisition premiums as the target may initially resist the offer (Raghavendra and Vermaelen 1998). Moreover, Schwert (2000) finds that acquirers earn lower abnormal returns in hostile deals. Thus, we include the deal_ friendly_dummy. The deal_relatedness_dummy captures whether the acquirer and the target are active in the same two-digit SIC code. Industry familiarity may reduce deal uncertainty (Morck etal. 1990). As our sample includes international deals, we control for national cultural differences between the acquirers and the targets (deal_hofstede_distance) using the cultural distance measure developed by Kogut and Singh (1988), which is based on Hofstede’s (2001) six cultural dimensions of individualism, power distance, uncertainty avoidance, femininity, indulgence, and long-term orientation (Lawrence etal. 2021). 4.3.2 Acquirer andtarget controls Moeller etal. (2004) find that firm size of the acquirer has a negative impact on its announcement returns due to an increased likelihood of engaging in value-destroy- ing mergers induced by managerial entrenchment. Therefore, we control for the natural logarithm of the size of the acquirer (acq_assets_lastyear_ln). Similarly, we also include the target’s size (tar_assets_lastyear_ln), which directly affects the target’s attractiveness and deal outcomes (Chen etal. 2018; Lee etal. 2019). We also include the operating performance of the acquirer (acq_roa_lastyear) because Morck etal. (1990) find that firms with higher operating performance are more successful acquirers. Similarly, we also control for the operating performance of the target (tar_roa_lastyear) because of its potential positive impact on post-merger synergies. Higher target profitability also increases the attractiveness of the target, potentially increasing acquisition premiums (Hayward and Hambrick 1997). We also include the acquirer’s R&D intensity (acq_rd_intensity), which captures the size of the acquirer’s knowledge base as well as its innovativeness. The research literature has shown that there is a direct relationship between the acquirer’s R&D intensity and its acquisition intensity (Hitt etal. 1996) as well as its innovative performance (Hitt etal. 1991). To control for the acquirer’s general industry performance, we add the acquirer’s industry growth in the 12months prior to the deal announcement (acq_industrygrowth) in addition to the industry fixed effects (Ellis etal. 2011). Additionally, we control for the target’s market-to- book ratio (tar_mbratio) because a high market valuation makes it more difficult to realize growth opportunities after deal completion (Laamanen 2007). Finally, Zollo and Singh (2004) find that the acquirer’s prior deal experience has a positive impact on M&A performance. Therefore, we include the acquirer’s prior deal 2296 M.Brede et al. experience in the last three years as control (acq_dealexperience). Since older firms may have more M&A experience, we also include the age of the acquirer and the target as controls (Naranjo‐Valencia etal. 2011). 5 Empirical results 5.1 Descriptive statistics Table2 shows the number of deals by year of announcement. Most of the deals in our sample occurred after 2011, as Glassdoor launched in 2008 and firms were slow to receive reviews. Table3 shows the descriptive statistics for all the variables used in our specified models. The four cultural affiliations of the firms (represented by probability scores between 0 and 1) have their minimum (maximum) values for each dimension below the threshold of 0.08 (above the threshold of 0.51). The means of each dimension are comparable for acquirers and targets. However, the standard deviation is lower for the acquirer dimensions because there are approximately five times more acquirer Glassdoor reviews available than target reviews. The average absolute difference between the acquirer’s and the target’s cultural dimensions is around 0.08. In 57% of the deals, the dominant culture (the culture with the highest probability value) differs between the acquirer and target. In 34% (41%) of the deals, the acquirers’ (targets’) most dominant culture is reflected by the market dimension. Our main independent variable deal_culturaldistance, which represents the Kogut Table 2 Deals by announcement year This table presents the number of deals per year. The descriptive statistics are based on 243 deal observations for the years 2008–2021 Announcement year Deals Percentage Total percentage 2008 2 0.82 0.82 2009 5 2.06 2.88 2010 3 1.23 4.12 2011 4 1.65 5.76 2012 11 4.53 10.29 2013 10 4.12 14.40 2014 15 6.17 20.58 2015 36 14.81 35.39 2016 36 14.81 50.21 2017 22 9.05 59.26 2018 22 9.05 68.31 2019 23 9.47 77.78 2020 21 8.64 86.42 2021 33 13.58 100.00 Total 243 100.00 100.00 2297 Mind thegap: theeffect ofcultural distance onmergers and… Table 3 Descriptive statistics Variable N M SD Min Max Acquiring firms culture affiliation acq_clan_culture 243 0.259 0.084 0.036 0.511 acq_adhocracy_culture 243 0.203 0.104 0.013 0.626 acq_market_culture 243 0.276 0.098 0.077 0.580 acq_hierarchy_culture 243 0.262 0.083 0.051 0.516 Target firms culture affiliation tar_clan_culture 243 0.257 0.100 0.025 0.570 tar_adhocracy_culture 243 0.220 0.117 0.036 0.691 tar_market_culture 243 0.293 0.117 0.038 0.603 tar_hierarchy_culture 243 0.230 0.097 0.027 0.570 Deal cultural distance deal_culturaldistance 243 0.201 0.103 0.029 0.580 deal_culturaldistance_js 243 0.144 0.072 0.023 0.401 deal_clan_culture_abs 243 0.083 0.068 0.001 0.404 deal_adhocracy_culture_abs 243 0.079 0.075 0.0002 0.433 deal_market_culture_abs 243 0.093 0.075 0.0001 0.419 deal_hierarchy_culture_abs 243 0.091 0.069 0.001 0.333 Dependent variables acq_car5 243 − 0.035 0.313 − 3.598 0.705 acq_car10 243 − 0.051 0.563 − 6.463 1.324 deal_car_weighted 243 − 0.016 0.135 − 1.634 0.318 acq_2yr_sales_growth 182 0.227 0.298 − 0.214 1.856 acq_4yr_sales_growth 131 0.128 0.154 − 0.117 0.653 deal_premium_1day 226 0.328 0.306 − 0.312 2.367 deal_premium_1week 226 0.356 0.313 − 0.307 2.438 deal_premium_1month 226 0.380 0.306 − 0.341 2.625 acq_2yr_patent_growth 189 3.573 8.458 0.000 101.187 acq_2yr_npd_growth 154 0.136 0.153 0.000 0.913 Deal controls deal_value_ln 243 21.568 1.672 17.272 25.156 deal_hofstede_distance 243 0.031 0.081 0.000 0.345 deal_all_cash_dummy 243 0.473 0.500 0 1 deal_all_stock_dummy 243 0.152 0.360 0 1 deal_tenderoffer_dummy 243 0.181 0.386 0 1 deal_friendly_dummy 243 0.992 0.091 0 1 deal_relatedness_dummy 243 0.687 0.465 0 1 Acquirer controls acq_age 243 69.984 49.831 6 232 acq_assets_lastyear_ln 243 23.081 1.773 18.594 27.496 acq_roa_lastyear 243 0.419 0.946 − 0.330 10.077 acq_industrygrowth 243 0.009 0.051 − 0.037 0.551 acq_dealexperience 243 1.535 0.937 1 6 2298 M.Brede et al. and Singh (1988) adapted organizational cultural index, ranges from Min = 0.029 to Max = 0.58, with a mean of M = 0.201. Table 4 shows the Pearson product moment correlation coefficients for all dependent and independent variables in our analysis. Since none of our independent or control variables show very high linear dependence (r > 0.8), we conclude that multicollinearity is unlikely to affect the results of our regression analysis (Sheth etal. 2011). 5.2 Results ofH1—organizational cultural distance, capital market reactions, andpost‑deal synergies For H1a, we argue that for acquirers and targets with high organizational cultural distance, the anticipation of cultural frictions leads to negative capital market reactions in the form of lower announcement returns. Models 1–3 of Table5 use our organizational cultural index adapted from Kogut and Singh (1988) between the acquirer’s and target’s four CVF culture dimensions (clan, adhocracy, market, hierarchy) as our main independent variable (deal_culturaldistance). In Model 1 (2), we center the acquirer’s CAR [− 10, 10] ([− 5, 5]) days around the deal announcement. For both event windows, the results show a significant negative relationship between the cultural distance between the acquirer and the target and the acquirer’s respective CAR (t = − 2.278, p < 0.05), indicating that the capital market expects lower synergies for culturally distant firms, lending support to hypothesis H1a. We also use the [− 5, 5] combined (value-weighted) CAR of the acquirer and target as an additional measure of capital market reactions (Model 3). The coefficient of deal_car_weighted (t = − 1.718, p < 0.1) confirms that the capital market expects greater synergy gains from acquirers and targets that are more similar in organizational culture. The coefficients of the control variables are generally consistent with the expected directions suggested by the literature. For example, deal_value_ln has a positive coefficient (Suk and Wang 2021), deal_all_cash_dummy has a positive coefficient (Alexandridis etal. 2022; Bereskin etal. 2018; Chakrabarti etal. 2009), and deal_relatedness_dummy has a positive coefficient (Ahmed etal. 2023; Alexandridis etal. 2022; Conn etal. 2005). Table 3 (continued) Variable N M SD Min Max acq_rd_intensity 243 0.059 0.097 0.000 0.642 Target controls tar_age 243 52.119 40.360 7 191 tar_assets_lastyear_ln 243 21.209 1.847 16.507 27.386 tar_roa_lastyear 243 0.017 0.163 − 0.511 2.028 tar_mbratio 243 2.363 4.503 0.000 59.870 The descriptive statistics are based on 243 deal observations for the years 2008–2021. A detailed variable description can be found in Appendix B N = 243. M = Mean. SD = Standard Deviation. Min = Minimum. Max = Maximum 2299 Mind thegap: theeffect ofcultural distance onmergers and… Table 4 Correlation matrix # Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 1 deal_culturaldistance 1 2 acq_car2 0.03 1 3 acq_car5 0.02 0.96 1 4 acq_car10 0.01 0.94 0.98 1 5 deal_car_weighted 0.04 0.98 0.95 0.92 1 6 acq_sales_2yr_growth − 0.17 − 0.11 − 0.09− 0.09 − 0.101 7 acq_sales_4yr_growth − 0.21 − 0.14 − 0.13− 0.12 − 0.120.89 1 8 deal_premium_1day 0.14 − 0.07 − 0.04− 0.002− 0.08 − 0.08 − 0.09 1 9 deal_premium_1week 0.15 − 0.09− 0.05 − 0.01 − 0.11− 0.03− 0.03 0.97 1 10 deal_premium_1month0.17 − 0.06 − 0.03 − 0.01 − 0.08− 0.11− 0.15 0.85 0.87 1 11 acq_2yr_patent_ growth 0.04 − 0.01− 0.03− 0.03 − 0.01 0.01 0.06 0.04 0.09 0.08 1 12 acq_2yr_npd_growth 0.06 − 0.11 − 0.06− 0.05 − 0.100.04 0.02 − 0.12− 0.11− 0.11 − 0.081 13 deal_value_ln − 0.19 0.08 0.07 0.05 0.06 0.11 0.06 − 0.08 − 0.08− 0.090.06 − 0.17 1 14 deal_hofstede_distance− 0.02 − 0.02 0.05 0.06 − 0.03 0.004 − 0.01 0.05 0.04 0.04 − 0.05− 0.04− 0.01 1 15 deal_all_cash_dummy 0.26 0.03 0.04 0.05 0.04 − 0.25 − 0.20 0.28 0.30 0.30 0.12 − 0.12 − 0.31 0.12 1 16 deal_all_stock_dummy− 0.21 − 0.06− 0.07− 0.08 − 0.070.24 0.28 − 0.22 − 0.22 − 0.22− 0.050.01 0.004 − 0.12− 0.40 1 17 deal_tenderof- fer_dummy 0.21 0.02 0.02 0.02 0.03 − 0.13− 0.13 0.13 0.12 0.17 − 0.060.07 − 0.14 − 0.02 0.35 − 0.20 1 18 deal_friendly_dummy − 0.16 0.005 0.01 0.02 − 0.020.04 0.01 0.11 0.11 0.12 − 0.040.05 − 0.03 0.04 − 0.005 0.04 0.04 1 19 deal_relatedness_ dummy − 0.01 0.03 0.03 0.04 0.02 − 0.07− 0.005 0.09 0.07 0.11 − 0.13− 0.06− 0.005− 0.08 − 0.09 0.14 0.13 0.04 1 20 acq_age − 0.06 − 0.03− 0.03− 0.03 − 0.05− 0.26 − 0.37 − 0.01 − 0.05 − 0.06− 0.08− 0.05 0.15 0.08 − 0.12 − 0.002 − 0.18 − 0.050.005 1 21 acq_assets_lastyear_ln − 0.07 0.05 0.04 0.02 0.04 − 0.30 − 0.39 0.03 0.02 0.04 0.08 − 0.150.62 − 0.030.02 0.004 − 0.06 0.01 − 0.09 0.27 1 22 acq_roa_lastyear 0.08 − 0.07 − 0.06− 0.06 − 0.080.53 0.45 − 0.06− 0.02− 0.04 − 0.040.02 0.10 − 0.02− 0.17 0.05 − 0.06 0.02 − 0.05 − 0.13 − 0.231 23 acq_industrygrowth 0.15 0.08 0.09 0.09 0.09 0.01 0.01 − 0.06− 0.06 − 0.06 0.004 0.07 0.03 0.15 0.15 − 0.06 − 0.02 0.04 − 0.17 − 0.080.18 0.001 1 24 acq_dealexperience − 0.06 0.13 0.10 0.10 0.13 − 0.12− 0.12 0.03 0.03 0.06 − 0.06− 0.17 0.26 − 0.08 0.16 − 0.08 0.20 0.05 0.09 − 0.050.41 − 0.120.22 1 25 acq_rd_intensity 0.04 − 0.04 − 0.06− 0.05 − 0.030.19 0.23 0.03 0.07 0.09 0.08 − 0.07 0.05 − 0.090.09 0.02 0.19 0.06 0.13 − 0.29− 0.210.24 − 0.050.17 1 26 tar_age − 0.17 − 0.10− 0.10− 0.09 − 0.11− 0.03 − 0.02 − 0.03 − 0.06 − 0.05− 0.07− 0.12 0.15 0.05 − 0.17 − 0.002 − 0.22 0.07 0.05 0.39 0.13 − 0.050.06 0.06 − 0.281 2300 M.Brede et al. Table 4 (continued) # Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 27 tar_assets_lastyear_ln − 0.26 − 0.05 − 0.06 − 0.08 − 0.07 0.14 0.10 − 0.12 − 0.14− 0.170.04 − 0.19 0.77 − 0.01 − 0.37 0.22 − 0.28 − 0.030.08 0.29 0.63 − 0.010.08 0.16 − 0.210.36 1 28 tar_roa_lastyear − 0.13 − 0.05− 0.07− 0.05 − 0.06 − 0.01 − 0.03 − 0.14 − 0.13− 0.160.01 − 0.02 0.13 − 0.01 − 0.08 − 0.08 − 0.12 − 0.05− 0.140.20 0.06 − 0.04 0.01 − 0.03 − 0.18 0.17 0.13 1 29 tar_mbratio 0.10 0.04 0.05 0.04 0.04 − 0.02 − 0.02 − 0.02 0.001 0.01 − 0.010.10 0.12 − 0.020.11 − 0.10 0.08 0.02 0.02 − 0.12 0.05 0.10 0.03 0.10 0.21 − 0.16 − 0.20− 0.071 This table presents correlation coefficients for the variables used in the main part of our analysis. Correlations are based on 243 deal observations for the years 2008–2021. A detailed variable description can be found in Appendix B 2301 Mind thegap: theeffect ofcultural distance onmergers and… Table 5 H1a: Capital market reactions (CARs) (1) (2) (3) (4) (5) (6) acq_car [− 10, 10] acq_car [− 5, 5] deal_car_weighted [− 5, 5] acq_car [− 10, 10] acq_car [− 5, 5] deal_car_ weighted [− 5, 5] deal_culturaldistance − 0.900** (− 2.278) − 0.436** (− 2.081) − 0.138* (− 1.718) deal_clan_culture_abs − 0.380 (− 0.687) − 0.199 (− 0.715) − 0.033 (− 0.300) deal_adhocracy_culture_abs 0.195 (0.516) 0.029 (0.138) − 0.006 (− 0.069) deal_market_culture_abs − 1.536*** (− 4.346) − 0.796*** (− 3.947) − 0.342*** (− 3.684) deal_hierarchy_culture_abs 0.037 (0.088) 0.172 (0.796) 0.178 (1.577) deal_value_ln 0.139** (2.425) 0.079** (2.406) 0.034** (2.116) 0.153*** (2.654) 0.086** (2.626 0.037** (2.338) deal_hofstede_distance 0.221 (0.619) 0.051 (0.293) − 0.042 (− 0.467) 0.169 (0.539) 0.032 (0.220 − 0.045 (− 0.606) deal_all_cash_dummy 0.030 (0.336) 0.029 (0.548) 0.007 (0.326) 0.012 (0.141) 0.018 (0.356 0.001 (0.064) deal_all_stock_dummy − 0.121 (− 0.991) − 0.068 (− 1.098) − 0.028 (− 1.114) − 0.113 (− 0.911) − 0.061 (− 0.971 − 0.024 (− 0.934) deal_tenderoffer_dummy − 0.071 (− 0.866) − 0.033 (− 0.672) − 0.018 (− 0.798) − 0.069 (− 0.785) − 0.031 (− 0.605 − 0.017 (− 0.717) deal_friendly_dummy − 0.186 (− 0.926) − 0.081 (− 0.672) − 0.028 (− 0.597) − 0.099 (− 0.483) 0.0002 (0.002 0.027 (0.539) deal_relatedness_dummy 0.020 (0.240) 0.001 (0.023) − 0.003 (− 0.167) 0.037 (0.442) 0.010 (0.224 0.001 (0.073) acq_age 0.001 (1.371) 0.001 (1.421) 0.0003 (1.507) 0.001 (1.257) 0.001 (1.343 0.0003 (1.442) acq_assets_lastyear_ln − 0.001 (− 0.031) − 0.001 (− 0.044) − 0.005 (− 0.748) − 0.006 (− 0.230) − 0.003 (− 0.220 − 0.006 (− 0.997) 2302 M.Brede et al. Table 5 (continued) (1) (2) (3) (4) (5) (6) acq_car [− 10, 10] acq_car [− 5, 5] deal_car_weighted [− 5, 5] acq_car [− 10, 10] acq_car [− 5, 5] deal_car_ weighted [− 5, 5] acq_roa_lastyear − 0.078 (− 1.366) − 0.043 (− 1.307) − 0.020 (− 1.343) − 0.078 (− 1.336) − 0.042 (− 1.241 − 0.018 (− 1.253) acq_industrygrowth 2.717** (2.330) 1.623** (2.632) 0.729** (2.644) 2.838** (2.370) 1.710*** (2.743 0.764*** (2.815) acq_dealexperience − 0.0003 (− 0.008) − 0.006 (− 0.252) 0.005 (0.524) − 0.008 (− 0.206) − 0.012 (− 0.483 0.002 (0.262) acq_rd_intensity − 0.580* (− 1.783) − 0.336** (− 2.188) − 0.149*** (− 2.875) − 0.835*** (− 3.093) − 0.485*** (− 3.706 − 0.225*** (− 4.164) tar_age − 0.002* (− 1.912) − 0.001** (− 2.012) − 0.001** (− 2.036) − 0.002* (− 1.841) − 0.001** (− 2.017 − 0.001** (− 2.148) tar_assets_lastyear_ln − 0.148* (− 1.786) − 0.079* (− 1.708) − 0.031 (− 1.436) − 0.147* (− 1.764) − 0.079* (− 1.708 − 0.031 (− 1.442) tar_roa_lastyear − 0.377 (− 1.216) − 0.245 (− 1.376) − 0.108 (− 1.425) − 0.336 (− 1.110) − 0.229 (− 1.332 − 0.105 (− 1.452) tar_mbratio − 0.008 (− 1.376) − 0.005 (− 1.237) − 0.002 (− 1.239) − 0.011 (− 1.567) − 0.006 (− 1.461 − 0.003 (− 1.529) Observations 243 243 243 243 243 243 Adj R2 0.179 0.191 0.207 0.187 0.199 0.222 Industry FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes This table reports fixed effects linear OLS regression results predicting the effect of organizational cultural distance between acquirers and targets on deal announcement cumulative abnormal returns (CARs). The dependent variable for Models 1, 2, 4 and 5 is the acquirer’s cumulative abnormal return, with the day range being centered around announcement given in brackets. The dependent variable for Models 3 and 6 is the market value-weighted cumulative abnormal return of the acquirer and target. The main independent variable in Models 1–3 is deal_culturaldistance, with higher values indicating higher cultural dissimilarities between the acquirer and target. The main independent variables in Model 4–6 are the absolute cultural differences (in the four Competing Values Framework categories clan, adhocracy, market, hierarchy) between the acquirer and target. A detailed variable description can be found in Appendix B. Constant terms are estimated but not reported. We include the acquirer’s industry and year fixed effects in all models. We cluster standard errors at the 2-digit SIC industry level. t-statistics s are reported in parentheses below the coefficients *, **, *** denote statistical significance at the 0.10, 0.05, and 0.01 levels (using two-tailed tests) 2309 Mind thegap: theeffect ofcultural distance onmergers and… Table 8 H3: Innovation (1) (2) (3) (4) acq_2yr_patent_growth acq_2yr_npd_growth acq_2yr_patent_growth acq_2yr_npd_growth deal_culturaldistance − 0.051** (− 2.573) − 0.028* (− 1.827) deal_clan_culture_abs − 1.117 (− 0.967) 0.218 (0.184) deal_adhocracy_culture_abs − 5.036* (− 1.959) − 0.621 (− 0.782) deal_market_culture_abs − 0.100 (− 0.085) − 1.128 (− 1.526) deal_hierarchy_culture_abs − 0.232 (− 0.192) − 2.008*** (− 3.085) deal_value_ln − 0.057 (− 0.508) 0.034 (0.261) − 0.102 (− 0.830) 0.021 (0.157) deal_hofstede_distance − 0.109 (− 0.082) − 0.679 (− 1.267) 0.468 (0.368) − 0.627 (− 1.330) deal_all_cash_dummy 0.099 (0.400) − 0.253* (− 1.666) 0.045 (0.250) − 0.258 (− 1.590) deal_all_stock_dummy − 0.159 (− 0.413) − 0.178 (− 0.642) − 0.136 (− 0.381) − 0.182 (− 0.681) deal_tenderoffer_dummy − 0.081 (− 0.288) 0.480*** (2.812) − 0.048 (− 0.185) 0.472*** (2.623) deal_friendly_dummy − 4.371*** (− 5.665) 0.000 (0.000) − 3.844*** (− 5.098) 0.000 (0.000) deal_relatedness_dummy − 0.115 (− 0.570) − 0.133 (− 0.907) − 0.206 (− 1.031) − 0.143 (− 0.930) acq_age 0.001 (0.429) − 0.001 (− 0.789) 0.002 (0.864) − 0.001 (− 0.777) acq_assets_lastyear_ln 0.182* (1.859) − 0.021 (− 0.369) 0.231** (2.364) − 0.017 (− 0.284) acq_roa_lastyear 0.319*** (3.096) 0.010 (0.217) 0.351*** (3.437) 0.007 (0.146) 2310 M.Brede et al. Table 8 (continued) (1) (2) (3) (4) acq_2yr_patent_growth acq_2yr_npd_growth acq_2yr_patent_growth acq_2yr_npd_growth acq_industrygrowth − 29.689** (− 2.281) 8.346 (1.241) − 37.101*** (− 3.068) 8.285 (1.329) acq_dealexperience − 0.251* (− 1.929) 0.055 (1.598) − 0.304** (− 2.176) 0.078* (1.953) acq_rd_intensity 0.460 (0.445) − 1.163 (− 1.181) 0.548 (0.419) − 1.042 (− 1.163) tar_age − 0.001 (− 0.314) − 0.003 (− 1.225) − 0.003 (− 1.139) − 0.002 (− 1.083) tar_assets_lastyear_ln 0.036 (0.330) 0.006 (0.067) 0.008 (0.069) 0.002 (0.022) tar_roa_lastyear − 0.110 (− 0.345) 0.165 (0.488) − 0.388 (− 1.072) 0.153 (0.444) tar_mbratio 0.019*** (2.733) − 0.010** (− 2.115) 0.027*** (5.762) − 0.010** (− 2.174) Observations 187 148 0.185 0.072 Adj R20.176 0.069 0.231 0.142 Industry FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes This table reports fixed effects negative binomial regression results predicting the effect of organizational cultural distance between acquirers and targets on innovation. The dependent variable for Models 1 and 3 is the acquirer’s patent growth two years after deal completion compared to one year prior the deal. The dependent variable for Models 2 and 4 is the acquirer’s new product development growth two years after deal completion compared to one year prior the deal. The main independent variable in Model 1 and 2 is deal_culturaldistance, with higher values indicating higher cultural dissimilarities between the acquirer and target. The main independent variables in Model 3 and 4 are the absolute cultural differences (in the four Competing Values Framework categories clan, adhocracy, market, hierarchy) between the acquirer and target. A detailed variable description can be found in Appendix B. Constant terms are estimated but not reported. We include the acquirer’s industry and year fixed effects in all models. We cluster standard errors at the 2-digit SIC industry level. t-statistics are reported in parentheses below the coefficients *, **, *** denote statistical significance at the 0.10, 0.05, and 0.01 levels (using two-tailed tests) 2311 Mind thegap: theeffect ofcultural distance onmergers and… gains (acq_sales_4yr_growth) and combined acquirer-target performance (deal_ car_weighted), the results remained statistically significant across all analyses. Third, although we controlled for national cultural distance between acquirer and target countries (deal_hofstede_distance), we additionally removed 34 crossborder deals from our sample to account for potential international effects, such as regulatory or country-level institutional confounders. The main analyses using deal_culturaldistance remained robust, except again for long-term synergies (acq_sales_4yr_growth), even after excluding these cross-border deals. Fourth, we explored the possibility of a nonlinear relationship between deal_ culturaldistance and our main dependent variables by including a quadratic term (deal_culturaldistance_quadr) in all models. After thoroughly examining the results, we found no evidence of a nonlinear relationship, suggesting that the linear model adequately captures the relationship. Fifth, we included the acquisition premium (deal_premium_1week) as a predictor in all models where it was not already included as a dependent variable. This ensured that the observed effects were not driven by an omitted variable not previously included in our analysis. Sixth, we re-examined the hypothesized negative relationship between organizational cultural distance and long-term synergy gains (H1b) using propensity score matching, a method designed to minimize differences between treatment and control groups based on specific covariates, making inferences about treatment effects more robust and unbiased (Rubin 2006). Matching ensures that differences in the dependent variable are due to the treatment variable, not to pre-existing sample differences (Connelly etal. 2013). Building on previous research on organizational cultural distance and M&A (Bereskin etal. 2018) and following established guidelines (Narita etal. 2023; Stuart 2010), we first operationalized our treatment variable by creating an indicator variable (deal_high_culturaldistance), which denotes significant organizational cultural distance between acquirers and targets. M&A pairs with a cultural distance above the 80th percentile were assigned a value of 1, while those below were assigned a value of 0. We then computed relevant covariates for each M&A transaction, drawing on variables known to influence both synergy realization and organizational cultural differences (Andrade etal. 2001; Arikan and Stulz 2016; Bauer and Matzler 2014; Kumar 1985; Ramaswamy 1997). These covariates, selected for their stability over time or collected prior to the transaction year (Rosenbaum and Rubin 1983, 1984), include deal_size_difference, deal_roa_difference, deal_age_difference, deal_sic_difference, and deal_hofstede_distance (see Appendix B for detailed descriptions). Propensity scores were derived through logistic regression using absolute differences in size, age, performance, industry, and national cultural distance. Given the significantly larger number of non-high cultural distance deals, a three-to-one nearest neighbor matching approach was applied (Stuart 2010). This matched all high cultural distance M&A deals (N = 38) with similar non-high cultural distance deals. Standardized mean differences below 0.1 and variance ratios below two after matching confirmed that the propensity scores of the control group closely resembled those of the treatment group. To measure the effect of high organizational cultural distance on long-term synergies, we conducted a linear regression with acq_2yr_sales_growth as the outcome 2312 M.Brede et al. and deal_high_culturaldistance as the exposure. Using cluster-robust variance to estimate standard errors, our analysis revealed that high cultural distance reduced long-term synergies by − 0.089 points (95% CI [− 0.157, − 0.021], p < 0.05), consistent with the results of our primary analysis. Notably, the inclusion of additional controls in the regression did not significantly reduce bias or increase significance. Finally, we conducted a synthetic counterfactual analysis to further support our hypothesis regarding H1b. Following the approach used by Bereskin etal. (2018), we created a synthetic dataset of pseudo-M&A deals by assigning all possible targets to each acquirer for each year. From this, we took a random sample to test our hypothesis on a larger set of unrealized M&A deals, examining whether combined sales growth (comb_2yr_sales_growth) is lower for M&A deals with high cultural distance. Using propensity score matching with a one-to-one nearest neighbor matching strategy, we achieved a very high balance between the high and low cultural distance groups (N = 600), with all post-match standardized mean differences below 0.05. Using linear regression with combined long-term synergies as the outcome and high organizational cultural distance as the exposure, we observed a reduction in long-term synergy gains of − 0.122 points (95% CI [− 0.3729, − 0.0209], p < 0.01) associated with deal_high_culturaldistance. As in our previous analyses, the inclusion of additional controls did not significantly reduce bias or increase power. Moreover, the results remained consistent across different sample sizes, further reinforcing the robustness of our findings. 6 Conclusion Using a novel sample of 243M&As between 2008 and 2021, this study provides evidence on the impact of organizational cultural differences between acquirers and target firms on M&A outcomes. We applied state-of-the-art deep learning techniques, specifically the large language model Culture-BERT (Koch and Pasch 2022), to analyze over 400,000 Glassdoor employee reviews and infer an organizational cultural distance score. This approach addresses the methodological weaknesses of prior studies, which often relied on subjective measures to assess organizational cultural distance and M&A success, thus limiting the comparability and objectivity of results (Rottig 2017). Our study provides several key insights. First, we confirm that organizational cultural distance negatively affects both announcement day market returns (H1a) and post-merger synergy realization (H1b). These findings align with the cultural friction hypothesis (Hofstede 1980), which posits that cultural distance increases coordination and integration costs, thereby hindering M&A performance. The negative relationship between organizational cultural distance and short-term market reactions (H1a) remains robust across various alternative explanations and analytical methods, as demonstrated by numerous robustness tests. Consistent with the cultural friction hypothesis, this negative effect is mainly driven by differences in market cultures, which emphasize autonomy, profitability, and key value drivers such as goal attainment and market share (Cameron etal. 2006). Shareholders tend to evaluate M&A transactions based on the compatibility of firms’ competitive and 2313 Mind thegap: theeffect ofcultural distance onmergers and… performance cultures, responding negatively when the target is unlikely to enhance profitability. Further examination of the relationship between organizational cultural distance and long-term synergies at two- and four-years post-transaction (H1b) supports our initial findings, again showing a negative impact driven by market culture differences. Notably, the results for the four-year period show a slight reduction in robustness, suggesting that cultural differences may diminish after two years, likely due to full cultural integration (Jemison and Sitkin 1986). However, the two-year post-transaction results remain clear, showing a significant negative effect of organizational cultural distance on long-term synergies. Additionally, a quasi-experiment conducted during robustness tests further supports these conclusions. Using propensity score analysis, we find that deals with high organizational cultural distance yield lower long-term synergies compared to those with lower cultural distance, even when deal partners are randomly assigned. Moreover, these high cultural distance deals show significantly lower combined acquirer-target sales growth (e.g., Bereskin etal. 2018). Second, our study offers insight into why organizational cultural distance negatively impacts M&A performance. We show that acquirers dealing with targets exhibiting high cultural distance tend to pay a premium for the target (H2). High premiums, widely recognized in the literature as a potential driver of value-destroy- ing acquisitions (King etal. 2021), are primarily influenced by differences in market culture. This suggests that acquirers with specific cultural values, such as those discussed earlier, struggle to accurately value targets that do not share these values. In such cases, acquirers incur additional costs for information gathering and contracting, which increases uncertainty and leads to the mispricing of the target’s true value (Giannetti and Yafeh 2012). High organizational cultural distance during the pre-deal phase may also contribute to an information gap caused by poor communication and lack of trust—factors central to our findings in H1a and H1b. This gap further increases uncertainty and ultimately leads to overpayment and inflated acquisition premiums (e.g., Alnahedh and Alhashel 2021; Smeulders etal. 2023). Third, we examine the relationship between organizational cultural distance and the acquirer’s post-deal innovativeness. Our findings reveal a negative impact of organizational cultural distance on the acquirer’s long-term innovativeness, consistent across various measures of innovation. The cultural differences driving these effects vary: differences in adhocracy culture primarily influence patent growth, while differences in hierarchy culture affect new product development. This distinction stems from the differing nature of innovation processes: patenting requires creativity and vision, traits associated with adhocracy culture, whereas new product development demands coordination, timeliness, and consistency, which are aligned with hierarchy culture (Cameron etal. 2006; Child etal. 2003). Future research could explore whether the effects of organizational cultural distance between acquirers and targets vary depending on the level of target integration. Previous studies on national cultural differences have shown that integration levels can lead to different outcomes (Slangen 2006). However, an examination of this relationship in the context of organizational cultural distance is still missing. Therefore, future research should investigate the conditional factors that influence how organizational cultural differences affect M&A outcomes. 2314 M.Brede et al. Our study contributes to the M&A literature by providing evidence on the relationship between organizational cultural distance, M&A success, acquisition premiums, and innovation. However, certain limitations must be acknowledged. First, our analysis of short-term M&A success assumes near-instantaneous, complete, and unbiased market reactions, which rely on the semi-strong form of the efficient market hypothesis (Fama 1970). While there is evidence that markets account for cultural differences when evaluating transactions (e.g., Aktas etal. 2011), it is possible that this information could be misinterpreted or influenced by other confounding factors. Second, our study focuses on M&A transactions between firms in major English-speaking economies, which limits the generalizability of our findings to other contexts. Future research could address this limitation by expanding the geographic scope and incorporating more representative data sources, such as employee reviews from local platforms. Third, our research is confined to completed transactions. Future studies could explore whether acquirers and targets with high organizational cultural distance are more likely to withdraw from announced deals or examine self-selection issues in the relationship between cultural distance and acquisition outcomes. Lastly, while data from Glassdoor.com provide valuable insights into organizational culture, there are potential shortcomings. Employee reviews are voluntary and may not fully represent the entire workforce, introducing possible biases. Additionally, employees who choose to submit reviews may have particularly strong positive or negative opinions, which could skew the data. Moreover, the textual nature of these reviews may not always capture the full complexity of organizational culture. Future research could consider triangulating these findings with additional data sources to provide a more comprehensive view of organizational culture. Our findings have significant economic implications for practitioners involved in M&A transactions. First, our analysis underscores the importance of considering both national and organizational cultural differences. These differences can negatively impact M&A performance and reduce the acquiring firm’s innovativeness. To mitigate these risks, acquiring firms must thoroughly analyze the target firm’s organizational culture and compare it to their own. In particular, our research reveals that differences in market culture can have a detrimental effect on the acquiring firm’s performance. Second, our results emphasize that cultural differences should align with the goals of the acquiring firm. Not all cultural differences have the same impact: for instance, market culture discrepancies are linked to negative effects on both short- and long-term performance, while differences in adhocracy culture hinder patent growth, and hierarchy culture disparities impede new product development. Third, our analysis highlights the critical need for a comprehensive evaluation of organizational culture, offering insight into why these effects occur. Acquiring firms often overvalue targets with high cultural distance, resulting in an inflated acquisition premium. To address this, practitioners should focus on closing information gaps between the firms involved in the transaction. These efforts can improve the accuracy of the target firm’s valuation and increase the likelihood of realizing positive synergies in the post-acquisition phase. 2315 Mind thegap: theeffect ofcultural distance onmergers and… Appendix Appendix A—glassdoor review examples associated withthefour dimensions ofthecompeting values framework Culture Score Sample review text Adhocracy (create) An adhocracy culture focuses on adaptability and flexibility to achieve growth and innovation within the organization 0.0003 This review is for the office 365/outlook team.. * great people and management. * family friendly (great work-life balance). * great benefits. * great facilities (medical facilities, sports fields, i hear they even have a treehouse now). * free drinks cooler i’ve heard nightmares in certain teams, so ymmv depending on the team.. no free food 0.9956 Fast paced, new products and technology, exciting opportunities and ability to try new and different things, support from management and colleagues constant re orgs. inconsistent messaging at times. travel. difficult/laborious to get someone promoted. too many systems and logins. holiday schedule Clan (collaborate) The clan culture emphasizes collaboration, teamwork, and employee development 0.0003 Discounts on services. decent pay. the company has changed a lot, they are only interested in pushing sales and not disclosing the proper information to the customer. providing good customer service is not a concern for them anymore. the information the call center gives customers is not the same as the stores. they are eliminating the need for full time employees. they are eliminating many jobs in the united states. sadly, you deal with a lot of angry and frustrated people. retail hours 0.9979 Amazing work life balance, 1 on 1 sales training, friendly work environment, and opportunity to move up. the people here are very nice and the ages of everyone varies from mid twenties and up in a balanced matter. everyone here wants to succeed and that energy is passed on to all employees. The only thing i wish we had were nicer bathrooms, but i can deal with that! commission structure could be better as well. not the best, but modest Market (compete) A market culture tries to maximize business or production performance by focusing on task completion and goal achievement 0.0003 culture management good learning compensation and benefits policies flexibility cafe vaccination drive well equipped gym inhouse doctors, nurses, nutrition, gym coach and clinic nothing major i can think of. enjoyed working in the company and a great place to learn. inter-department teams work together 0.9970 Nothing is worth the stress and aggregation they put you through. stay away if you can. salary is competitive. 401k is ok. large company so easy to stay close to home if they permit. Overworked as if they are legally breaking labor laws. horrible management too down. management pushes you to fake numbers to improve metrics. questionable patient safety practices in pharmacy. hazardous work conditions many times on the sale floor as there is not enough hours/work ratio to finish work 2316 M.Brede et al. Culture Score Sample review text Hierarchy (control) The hierarchical culture emphasizes clear rules, explicit instructions, and strict controls 0.0005 The managers are great people, and very kind. i love all of my coworkers, and i love the work. copy center is fast paced and always different. love getting to know the products and learning as i went. the company makes cuts in the wrong places. cutting part time hours to under 25 a week, to save $4 million a year. but sending the higher ups on vacations. not enough hours. obviously, no benefits 0.9931 Great benefits for full time employees. it’s corporate retail, so long periods of standing, and micro managing everything you do. but the biggest problem is stagnant wages, and when you do get a yearly raise it’s in the 1–3% range. not somewhere for a career, unless you want to give up most of your personal time and become a salary slave. then your still going to get small raises, your able to compensate somewhat with the store bonus, depending on your store sales Score represents the probability score inferred from the CultureBERT transformer model, which was manually pre-trained on 2000 Glassdoor reviews (Koch and Pasch 2022). Higher values indicate a high affiliation to the respective Competing Values Framework (Cameron etal. 2006) culture dimension. Since the dimensions are not mutually exclusive, a firm’s review can have high affiliations with multiple dimensions. Appendix B—variable definitions Variable Definition Independent variables acq_clan_culture Probability score is determined by applying the CultureBERT transformer (Koch & Pasch 2022) to firms’ Glassdoor textual reviews. Bound between 0 and 1, with values close to 1 indicating a high clan affiliation. Firms’ culture scores are first averaged by year and then averaged over all years until deal announcement Analogously for the other three competing value framework dimensions (adhocracy, market, hierarchy). Examples for the four dimensions are provided in Appendix A deal_clan_culture_abs Absolute difference between the acquirer’s and target’s clan culture. Analogously for the other three competing value framework dimensions (adhocracy, market, hierarchy) deal_culturaldistance Organizational cultural index, adapted from Kogut and Singh (1988) and corrected for quadratic influences according to recommendations from Konara and Mohr (2019). It measures the distance between the acquirer’s and target’s four cultural Competing Values Framework dimensions. Higher values indicate a higher organizational cultural distance between the acquirer and target 2317 Mind thegap: theeffect ofcultural distance onmergers and… Variable Definition deal_culturaldistance_js Jensen–Shannon (JS) divergence between the acquirers and targets CVF probability distributions. The measure was adapted from Corritore etal. (2020). Higher values indicate a higher cultural divergence between the acquirer and target deal_culturaldistance_eucl Euclidean distance between the acquirers and targets. It is defined as deal _culturaldistance_eucli,j= �∑ k i =1(ai−aj)2 and measures the distance between the four cultural dimensions of the CFV of the acquirer and target. Higher values indicate greater organizational cultural distance between the acquirer and the target deal_culturaldistance_maha Mahanalobis distance measures the distance between the four cultural dimensions of the Competing Values Framework of the acquirer and the target. It was introduced to the cultural distance literature by Berry etal. (2010) and corrects for potential correlation among the CVF dimensions. Higher values indicate greater organizational cultural distance between the acquirer and the target deal_culturaldistance_quadr This variable is the squared value of the deal_culturaldistance variable. It is used to test for nonlinear relationships in our robustness checks deal_high_culturaldistance This binary indicator variable indicates whether the organizational cultural distance between acquirer and target is greater than the 0.8 percentile as measured by deal_culturaldistance Dependent variables acq_car5, acq_car10 Acquirer’s cumulative abnormal returns based on market-adjusted returns measured over 10 (20) days around the acquisition announcement. The daily abnormal return is calculated as: ARi,t =R i,t − ( 𝛼 i +𝛽 i R M,t) where R equals the actual stock return of firm i on day t and Rm equals the market value-weighted return on day t. The firm’s alpha and beta are estimated using the market model from -250 to -50days before deal announcement. Next, the firm’s expected return on day t is predicted using the estimated alpha and beta in combination with the remarket turn on day t, which is then subtracted from the firm’s actual return. Lastly, the daily abnormal returns are aggregated over [-5, 5] and [-10, 10] days around the announcement date Information on stock market reactions was obtained from S&P Global Inc.’s Capital IQ database deal_car_weighted Market value-weighted combination of the cumulative abnormal returns of the acquirer and the target [-5, 5] days around the announcement date, using relative market values as weights deal_premium_1day, deal_premium_1week, deal_premium_1month Difference between the acquirer’s payment and the target’s market value, divided by the target’s market value, measured one day (one week; one month) prior to the deal announcement Information on M&A payments was obtained from S&P Global Inc.’s Capital IQ database acq_2yr_sales_ growth,acq_4yr_sales_ growth Growth rate of sales two years after an M&A announcement compared to sales in the year before the announcement. Financial information was obtained from S&P Global Inc.’s Capital IQ database comb_2yr_sales_growth, Growth rate of average sales two years after an M&A announcement compared to average sales of acquirer and target in the year before the announcement. Financial information was obtained from S&P Global Inc.’s Capital IQ database 2318 M.Brede et al. Variable Definition acq_2yr_patent_growth Growth rate of patents filed two years after an M&A announcement compared to the number of patents filed in the year before the announcement Patent filing data were obtained from the USPTO, the Canadian Intellectual Property Office, AusPat, and the UK Patent Document and Information Service acq_2yr_npd_growth Growth rate of new product launches two years after an M&A announcement versus the number of product launches in the year prior to the announcement Information on new product launches by the acquirer was obtained from S&P Global Inc.’s Capital IQ database Deal controls deal_value_ln Natural logarithm of the total value of consideration paid by the acquirer, excluding fees and reported expenses deal_all_cash_dummy Dummy variable that equals one if the deal was fully paid in cash deal_all_stock_dummy Dummy variable that equals one if the deal was fully paid in stocks deal_tenderoffer_dummy Dummy variable that equals one when a tender offer is launched for the target deal_friendly_dummy Dummy variable that equals one if the deal is marked as friendly deal_hofstede_distance National cultural distance between the acquirer’s and target’s nations, computed as the Euclidean distance of Hofstede’s six cultural dimensions (Individualism, Power Distance, Uncertainty Avoidance, Femininity, Indulgence, Long-term orientation). Each distance is bound between 0 and 1 deal_age_difference Absolute difference of firm_age between acquirer and target deal_size_difference Absolute difference of firm_assets_lastyear_ln between acquirer and target deal_roa_difference Absolute difference of firm_roa_lastyear between acquirer and target deal_sic_difference Absolute difference in 4-digit SIC industry codes between acquirer and target Acquirer/target controls (acq_ / tar_ prefixes) firm_age Difference between the year when the transaction was completed and the year when the firm was founded firm_assets_lastyear_ln Logarithm of the total assets of the firm in the last 12months before the deal announcement firm_roa_lastyear Ratio of the firm’s net income to total assets, measured 12months before the deal announcement firm_mbratio Ratio of firm’s market capitalization to book value of total assets at the end of the fiscal year prior to deal announcement acq_industrygrowth Average percentage change in revenue for the acquirer’s 2-digit SIC industry sector, divided by the revenue reported in the year prior to deal announcement acq_dealexperience Number of deals successfully completed by the acquirer in the last three years prior to the announcement date, including the current deal acq_rd_intensity Ratio of the firm’s research and development (R&D) expenditures to revenues in the year of the deal announcement 2325 Mind thegap: theeffect ofcultural distance onmergers and… Schweiger DM (2002) M&A integration—a framework for executives & manager. 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Authors and Affiliations MariusBrede1 · HannesGerstel1· ArntWöhrmann1· AndreasBausch1 * Marius Brede [email protected] Hannes Gerstel [email protected] Arnt Wöhrmann [email protected] Andreas Bausch [email protected] 1 Giessen University, Licher Str. 62, 35394Giessen, Germany Zollo M, Meier D (2008) What is M&A performance? AMP 22:55–77. https:// doi. org/ 10. 5465/ amp. 2008. 34587 995 Zollo M, Singh H (2004) Deliberate learning in corporate acquisitions: post-acquisition strategies and integration capability in U.S. bank mergers. Strat Mgmt J 25:1233–1256. https:// doi. org/ 10. 1002/ smj. 426 Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.