The insights from the crowd: Drawing inferences from many approaches to key empirical questions in international business

Delios, Andrew et al. Article — Published Version The insights from the crowd: Drawing inferences from many approaches to key empirical questions in international business Journal of International Business Studies Suggested Citation: Delios, Andrew et al. (2025) : The insights from the crowd: Drawing inferences from many approaches to key empirical questions in international business, Journal of International Business Studies, ISSN 1478-6990, Palgrave Macmillan UK, London, Vol. 56, Iss. 9, pp. 1102-1124, https://doi.org/10.1057/s41267-025-00808-9 This Version is available at: https://hdl.handle.net/10419/333221 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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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/4.0/ Vol:.(1234567890) Journal of International Business Studies (2025) 56:1102–1124 https://doi.org/10.1057/s41267-025-00808-9 The insights fromthecrowd: Drawing inferences frommany approaches tokey empirical questions ininternational business AndrewDelios· TianyouHu· ShuYu· NanZhou· EricUhlmann, etal.[full author details at the end of the article] Received: 1 August 2024 / Revised: 29 May 2025 / Accepted: 11 June 2025 / Published online: 5 November 2025 © The Author(s) 2025 Abstract In this crowdsourced initiative, 57 independent analysts used the same longitudinal dataset to address four major empirical questions in international business. For all four research questions, different analysts obtained substantial estimates in opposite directions, meaning that they could have drawn any conclusion at all had they conducted the project alone. Aggregating across the results obtained by different analysts pointed to an overall answer for two of the four research questions, although for one of the two questions, the evidence was more suggestive than conclusive. That said, the variability in results was not simply random, and could in some cases be meaningfully explained. Choices regarding how to operationalize variables played an important role in determining the empirical results, and expert analysts were more likely to report large positive effects. Rather than exhibiting a bias to confirm their pre-existing beliefs, analysts appeared to rationally update their beliefs considering the evidence. Overall, these findings empirically demonstrate the role of subjective researcher choices in shaping results in international business research yet also show that it is still possible to draw meaningful conclusions in science. We advocate for an open science of international business in which the consequences of subjective analytic choices are rendered as transparent as possible. Keywords Crowdsourcing· Many analysts· Open science· Entry mode strategy· Multinational firms Introduction Since 2010, we have seen increasing concerns about publication incentives and researcher confirmation bias negatively affecting the reliability of thescientific literature (Aguinis etal., 2020; Nelson etal., 2018; Nosek etal., 2022). Simulations demonstrate that given sufficient choice points in a dataset, researchers could, in principle, select the path that supports whatever they are (consciously or perhaps unconsciously) biased to conclude (Murphy & Aguinis, 2019; Simmons etal., 2011). In part, because of such opportunities to “p-hack”, p value distributions in published articles reflect substantial numbers of statistically implausible findings (Fanelli, 2010; Goldfarb & King, 2016; Ioannidis, 2005; Simonsohn etal., 2014). Indeed, one predictor of the results of an empirical scientific investigation is the prior intellectual commitments of the research team (Berman & Reich, 2010). The consequent reform movement to increase the reproducibility, replicability, and robustness of scientific findings across disciplines holds value for research on international business (IB) just as it does for other fields (Aguinis etal., 2017, 2022, 2023). Although less prone to the small samples and underpowered tests that characterize many behavioral experiments (Schimmack, 2012; Weingarten etal., 2016), international business studies often rely on complex longitudinal datasets with many choice points and defensible analytic approaches. Meta-scientific investigations suggest concerning rates of scientific errors (Bergh etal., 2017), difficulties reproducing the same findings from the same Andrew Delios, Tianyou Hu, Shu Yu, and Nan Zhou share first authorship. They, together with Eric Luis Uhlmann, formed the core project team that designed and conducted the meta-scientific project and wrote the paper. The remaining 64 co-authors served as analysts who carried out the analyses for the four research questions. Correspondence to: Tianyou Hu, Faculty of Business Administration, University of Macau, E22, Avenida da Universidade, Taipa, Macau. Email: tiany[email protected]; Nan Zhou, School of Economics and Management & Advanced Institute of Business, Tongji University, 1500 Siping Road, Shanghai, China, 200092. Email: [email protected]. Accepted by Arjen van Witteloostuijn, Area Editor, 11 June 2025. This article has been with the authors for four revisions. 1103Journal of International Business Studies (2025) 56:1102–1124 data (Bergh etal., 2017; Delios etal., 2022), and effect size overestimation (Bosco etal., 2016; Goldfarb & King, 2016) in strategy and international business—just as has been observed across all disciplines examined thus far (e.g., Fanelli, 2010; Ioannidis, 2005; Open Science Collaboration, 2015). In this study, we demonstrate how empirical estimates are contingent on subjective analytic choices, which we suggest represents a more significant challenge to the science of international business than p-hacked or suboptimal analyses. Prior theoretical scholarship has raised concerns about epistemic uncertainty in management research (Ketokivi & Mantere, 2010; King etal., 2021; Mantere & Ketokivi, 2013), and the dependency of results on analytic choices has been directly demonstrated for a few specific claims (Berchicci & King, 2022; Goldfarb & King, 2016; Goldfarb & Yan, 2021; Nandialath & Rogmans, 2019). Building on Goldfarb and King (2016), who estimate statistically that false positives are common in strategic management research (as in many fields), we examine whether results that are true-positives under a given specification may not always be robust to the alternative specifications other capable scholars might have used. By organizing a mass collaboration, we provide a birds-eye empirical view of the importance of context in this space as theorized by Ketokivi and Mantere (2010). We selected four unanswered research questions that are intrinsically interesting in-and-of-themselves to exploit the insights of the crowd to clarify the nature of the relationships between key variables in international business studies, such as intangible assets, policy uncertainty, ownership, and firm performance. This first-order contribution to address the research questions themselves, some of which are the subject of dozens of prior publications that rendered mixed and conflicting results, is a key value-add beyond the second-order contribution of turning a meta-scientific lens on the scientific process itself. Our study shows that IB as a discipline is not immune to the subjectivity challenge uncovered in other fields. At the same time, our target questions varied in the degree of theoretical consensus in the field regarding what pattern “should” emerge, allowing us to examine cross-question differences in empirical conclusions. In addition to aggregating results across disparate approaches in an attempt to draw substantive inferences (Landy etal., 2020; Nandialath & Rogmans, 2019), we examine meaningful moderators (Berchicci & King, 2022; Ketokivi & Mantere, 2010; King etal., 2021) such as theoretically laden variable operationalizations, researcher expertise, and beliefs, with more success than in any prior many-analysts initiative from any field. In the Discussion, we present a typology of interventions designed to tackle subjectivity in science while also discussing their downsides. We outline an optimistic vision for an open science of IB that considers both the benefits and costs of potential reforms as well as promising strategies for dealing with heterogeneous estimates, such as aggregation and moderator identification. Research background Subjectivity inscience Even absent analytic mistakes, difficulties reproducing findings, publication pressures, or an intellectual commitment to a theory, there remains some inherent subjectivity in scientific approaches to a problem (Landy etal., 2020; Silberzahn etal., 2018). For complex datasets, this is rendered transparent by a multiverse in which a solo researcher or small team conducts many analyses (Sala-i-Martin, 1997; Simonsohn etal., 2020; Steegen etal., 2016; Young & Holsteen, 2017) or a crowdsourced approach in which many scientists use the same dataset to test the same research question (Silberzahn etal., 2018). Crowdsourcing is far less efficient than a multiverse yet it reveals the naturally emerging strategies of real researchers. A many-analysts approach can directly address the question of what would have happened if another investigator had analyzed the same data and further examined which of their characteristics (degree of topic or statistical expertise, beliefs about the hypothesis, etc.), made a difference in the outcomes of their empirical tests. In one early many-analysts initiative, 29 research teams tested the relationship between the skin tone of soccer players and whether they received red cards from referees. The estimates obtained from the 29 research teams ranged from large positive estimates, consistent with the conclusion that referees are biased against darker-skinned players, to small negative effects reflecting the opposite directional bias (Silberzahn etal., 2018). Most subsequent crowd collaborations find an even greater heterogeneity in results across independent analysts (Botvinik-Nezer etal., 2020; Breznau etal., 2022; Menkveld etal., 2024; Schweinsberg etal., 2021). Schweinsberg etal. (2021) refer to it as “radical effect size dispersion” when different researchers analyzing the same dataset to address the same research question return different effect size estimates in opposite directions. For example, one analyst may conclude that including more women in a scientific debate increases the likelihood that each individual woman will speak, whereas another finds that the presence of more women suppresses female participation (Schweinsberg etal., 2021). Related work reveals such a wide dispersion of results across experimental studies designed by independent research teams (Baribault etal., 2018; Huber etal., 2023; Landy etal., 2020; Tierney etal., 2025). To the extent that such a pattern emerges frequently for the hypotheses tested in a scientific field, the 1104 Journal of International Business Studies (2025) 56:1102–1124 implications regarding the products of traditional science done in small teams are considerable. When reading a typical published paper, the reader is hence left unsure whether to trust the results; if another team had pursued the same idea, they might have reached the reverse conclusion. Two means ofmanaging theuncertainty: Aggregation versusparsing When different scientists using disparate approaches reach different conclusions, how can we adjudicate? The two major approaches to managing variability and uncertainty in science are aggregation and parsing (Berchicci & King, 2022; Cyrus-Lai etal., 2022). Aggregation involves mechanically averaging across the results from the many analyses or many designs, for example, via meta-analysis (Landy etal., 2020) or Bayesian model averaging (Hinne etal., 2020; Nandialath & Rogmans, 2019). Parsing means identifying meaningful empirical moderators of the different estimates across diverse approaches (Berchicci & King, 2022). Perhaps different researchers interpreted the research question differently (Auspurg & Brüderl, 2021; Breznau etal., 2022; Kummerfeld & Jones, 2023) or obtained divergent results because they operationalized variables in distinct ways (Schweinsberg etal., 2021). The associated differences in results could be non-arbitrary and hold theoretical implications that are lost via aggregation. Parsing is consistent with the perspectivist thesis that the opposite of a great truth is also true (McGuire, 1983), and that the task of the scientist is to unravel this web of theoretically rich moderation. Although perspectivism offers a beautiful vision of scientific inquiry, the many-analysts projects thus far have struggled to quantitatively explain much variance in estimates. Silberzahn etal. (2018) failed to identify reliable moderators of the results across analysis teams, finding only null effects of expertise and other variables. Schweinsberg etal. (2021) demonstrated that theoretically laden operationalizations of variables (e.g., how status is conceptualized) accounted for a meaningful portion of the results, but only a small amount in absolute terms. Breznau etal. (2022) considered numerous predictor variables, which combined explained only 4% of the variability in results across analysts. Huber etal. (2023) coded features of their crowdsourced many designs, which together captured 3.1–6.4% of the variance in estimates. Baribault etal. (2018) had more success, demonstrating that supraliminal priming designs that presented stimuli above the threshold of conscious perception produced reliable priming effects, whereas subliminal designs did not. Past crowd initiatives have proved groundbreaking, but also hold major limitations, especially with regards to cracking the parsing problem. Some prior investigations used arbitrarily selected research questions, such as possible racial bias among soccer referees (Silberzahn etal., 2018; see also Landy etal., 2020). Others generated and selected hypotheses to test leveraging a crowd of scientists (Schweinsberg etal., 2021) or chose research questions the project coordinators considered important to their respective fields (e.g., Breznau etal., 2022; Menkveld etal., 2024). We consider these major improvements in the selection process for research questions, since the extremely inefficient process of organizing and coordinating a small army of collaborators is better justified by a high-stakes outcome (Isager etal., 2021). Posing more than one research question in the same project (Landy etal., 2020; Schweinsberg etal., 2021) further affords the opportunity to explore whether the question itself matters. For example, a general research claim with a high latitude of construal could be interpreted differently by different scientists, increasing the diversity of methodological strategies used to tackle the problem (Auspurg & Brüderl, 2021; Kummerfeld & Jones, 2023). Although the number of collaborators involved was far higher than in a small team project, the numbers of data analysts or materials designers in early crowd initiatives were small in absolute terms (e.g., 29 analysis teams in Silberzahn etal., 2018, and 23 individual analysts in Schweinsberg etal., 2021; 15 or fewer teams of materials designers per hypothesis in Landy etal., 2020). Silberzahn etal. (2018) found that team leaders’ prior beliefs about referee racial bias did not predict their estimates, but with 29 units of observation, this does not provide strong evidence against researcher confirmation bias. Such samples make it difficult to assess the role of individual differences, for example, in topic expertise or statistical skills, unless the relationship is very strong. False negatives become uncomfortably likely. More recently, Menkveld etal. (2024) observed a small correlation between expertise and analytic estimates in their sample of 164 teams, in contrast to earlier estimates that were close to zero. Despite the extraordinary coordination effort required, more crowd projects, including as many analysts as possible, are necessary to address the parsing challenge. In particular, does the analyst matter, and if so, which aspects of an analyst’s profile are most important? The present research We crowdsourced tests of four unanswered research questions in international business studies with a group of 57 collaborators using a common complex longitudinal dataset. A reliable finding that is robust to different analytic approaches is necessary, but not sufficient, for scientific credibility. A credible finding should also be theoretically and practically relevant (RRBM, 2024). We selected these four questions because they are important ones in IB, and each has had many prior investigations targeted at them with a diversity of empirical outcomes and no unequivocal 1105Journal of International Business Studies (2025) 56:1102–1124 conclusion. The answers, if attainable, are both theoretically interesting to academics and of practical importance to management practitioners. As emphasized earlier, there is an intrinsic value in attempting to answer the specific research questions themselves, above and beyond the meta-scientific question of variability in estimates due to researcher choices. Further, although these four research questions have been addressed in much empirical research, prior tests have varied in sample size, sample constitution (geography, industry, and time period) and key variables (Shen etal., 2017; Zhao etal., 2004). Thus, it is useful to explore if such variance in results is maintained when data and research questions are fixed across researchers. In our research collective of 57 collaborators, most contributors had direct topic expertise in strategic management or international business, and many had extensive statistical experience. Epistemic uncertainty is increasingly recognized in management research (Ketokivi & Mantere, 2010; King etal., 2021; Mantere & Ketokivi, 2013), as in other fields such as psychology (Nosek etal., 2022) and medicine (Ioannidis, 2005; Musani etal., 2007). It is therefore valuable to empirically examine whether there is a dispersion in estimates when a crowd of capable researchers attempts to tackle the same questions with the same dataset. We do not claim that the problem is more severe in international business studies than elsewhere, nor do we make field-by-field comparisons. However, quantitative research in international business often relies on complex sets of observations that involve numerous analytic choices. The present meta-scientific insights are relevant to any field or subfield that likewise depends on topic experts to navigate their way through a garden of forking paths (Gelman & Loken, 2014) to reach an empirical conclusion. Adding further value, the four focal research questions varied in the level of theoretical consensus regarding the expected outcome based on the past literature. This allows us to explore cross-question differences, such as whether subjective theoretical consensus is related to the objective degree of dispersion in empirical estimates across analysts. Even if high intellectual consensus does not necessarily translate to consistency in empirical results, aggregating across widely disparate estimates could still yield directional answers to questions where established theory points to what “should” happen. Thus, the project’s value-add in potentially answering some of the research questions themselves is intertwined with the meta-scientific contribution of extending the many-analyst approach to strategic management. Research question 1 What is the relationship between entry mode and foreign subsidiary performance? The relationship between entry mode and foreign subsidiary performance is one of the most studied in international business research (Brouthers, 2002; Chen & Hu, 2002; Shen etal., 2017; Wu etal., 2022). Entry mode choice is an important strategic decision in international expansion because it has implications for various critical issues, such as control over foreign operations, investment risk, and resource commitment (Zhao etal., 2004). Past studies suggest it further influences managerial satisfaction regarding subsidiary performance (Brouthers etal., 2003), subsidiary survival (Shaver, 1998), and the objective profitability of the subsidiary (Chen & Hu, 2002). Different theories and perspectives could explain the relationship between entry mode and subsidiary performance, such as transaction cost theory (Brouthers etal., 2003), internationalization theory (Johanson & Vahlne, 1977; Wiedersheim-Paul & Johanson, 1975) and crossnational distance (Tihanyi etal., 2005). Transaction cost theory would predict that sole ownership leads to better performance due to reduced transaction costs. Internationalization theory would expect that shared ownership would lead to better performance due to the reduction of risk and opportunities for learning and resource sharing to either scale resources or link resources along the value chain. Therefore, we expected a high level of theoretical consensus among scholars in the field that entry mode should matter, but conflicting predictions regarding the direction of the effect. Research question 2 What is the relationship between intangible assets and a firm’s level of ownership in its foreign subsidiaries? The possession of intangible assets that are subject to market failure is one of the reasons that firms invest abroad (Buckley & Casson, 1976). Intangible assets are at the core of internalization theory, which has been widely accepted in the international business literature (Henisz, 2003; Morck & Yeung, 1992; Zeng etal., 2019). There is little theoretical controversy that a higher level of intangible assets should lead to a higher level of ownership because foreign subsidiaries need to protect their intangible assets from leaking to partners (Guillén, 2003; Martin & Salomon, 2003). Thus, we anticipate a high level of intellectual consensus among the collaborators that there should be a positive relationship between intangible assets and the level of ownership. This consensus should be greater than that for research question (RQ1). Research question 3 What is the relationship between policy uncertainty and a firm’s level of ownership in its foreign subsidiaries? 1106 Journal of International Business Studies (2025) 56:1102–1124 Institutional theory posits that firm strategy is influenced by the external institutional environment (North, 1990; Scott, 2008). Policy uncertainty in the host country increases the risk of operations, and firms could reduce their level of ownership to reduce risk (Delios & Henisz, 2000; Henisz, 2000a). However, this may not always be the case since some firms may be able to deal with policy uncertainty by other strategic means (Lee, 2018; Sun etal., 2016). Thus, our reading of the literature is that the level of academic consensus for RQ3 is not as high as for RQ2. Research question 4 How does the level of policy uncertainty moderate the relationship between intangible assets and a firm’s level of ownership in its foreign subsidiaries? When the level of policy uncertainty is high, the risk of expropriation by partners is high. An opportunistic local partner would use all available means, such as the manipulation of the political system to seize opportunities for expropriation (Delios & Henisz, 2000). In such a situation, a foreign subsidiary would tend toward full ownership to reduce the risk of expropriation by a local partner (Henisz, 2000a). Accordingly, we expect the relationship between intangible assets and level of ownership to be stronger when the level of policy uncertainty is high. However, this relationship is rarely tested in the literature and does not carry strong explicit assumptions on the part of most scholars (Delios & Henisz, 2000; Henisz, 2000b). Thus, even more so than for RQ3, we treat RQ4 as an open empirical question. Finally, at a meta-scientific level, our project has several value-adds that reflect learnings and best practices gleaned from past crowd initiatives. To our knowledge, this is only the second such project, after Silberzahn etal. (2018), to assess beliefs about the research questions among our researchers both before and after carrying out the analyses. We are interested in post-analysis beliefs because scientists’ views regarding the hypothesis are likely to be shaped by the results they found, and indeed normatively ought to be. If post-beliefs are more strongly correlated with results than pre-beliefs, this suggests belief updating—changing one’s beliefs to fall in line with the empirical results, something a good scientist should do. Our design thus allows us to potentially capture not only confirmation bias but also rational updating of beliefs considering the evidence. We provide a new test of whether analysts and their characteristics matter, examining the potentially dynamic interplay between beliefs and evidence with a sample size approximately double that of Silberzahn etal. (2018). Building on Schweinsberg etal. (2021), we leave variable operationalizations unconstrained and test their potential importance in explaining dispersion in results across analysts. We empirically compare the importance of research question and researcher, as in Landy etal. (2020), but with 57 units of observation rather than 15. Our aim is to provide informative tests of the four unresolved research questions in international business research, and at the same time shed unique new light on the nature of social scientific inquiries based on complex data. Methods Data andcode availability All data and code used in this paper are publicly available on the Open Science Framework (OSF) at https:// osf. io/ e w3vz/? view_ only= 52ab5 518ad a34e0 ca104 6aeab 08a51 22. Our primary supplementary document that contains Supplements 1–9 is accessible on the JIBSwebpage of this paper (https:// doi. org/ 10. 1057/ s4126702500808-9). Pre‑registration Our methods and analyses were pre-registered at https:// osf. io/ 4euaj. See our original plan in Material 1 in “Further Results and Materials”, which is posted on the OSF repository. Supplement 1, located in the supplementary document (onthe JIBS webpage of this paper), summarizes deviations from and additions to the originally planned analyses. Dataset The "Overseas Japanese Companies" dataset, provided by Toyo Keizai Inc, is widely used in international business research. The version of the dataset we used in this study includes information on Japanese firms' foreign subsidiaries operating in 155 countries during the period from 1991 to 2009. This version of the dataset covers over 2100 firms with more than 26,000 subsidiaries operating in various industrial sectors. According to a report from Western University in October 2022, this dataset has been used in at least 161 peerreviewed publications across fields such as management and organization research, international business, political science, accounting, finance, and other social sciences (Ivey, 2015). Analysts Leveraging our personal networks and email advertisements (Material 10 on OSF), we recruited a crowd of 57 researchers based in 20 different countries and territories. Their ages ranged from 24 to 57, with a mean of 34.88 years (SD = 8.16). Twenty-two (38.60%) self-identified as women and 35 (61.40%) as men. The regions with the most analysts were mainland China (13 analysts, 22.81%) and the United States (12 analysts, 21.05%), followed by Australia (five analysts, 1107Journal of International Business Studies (2025) 56:1102–1124 8.77%), Singapore (four analysts, 7.02%), India (three analysts, 5.26%), New Zealand (three analysts, 5.26%), the Czech Republic (two analysts, 3.51%), Hong Kong (two analysts, 3.51%), and Taiwan (two analysts, 3.51%). Austria, Brazil, Colombia, Denmark, Germany, Greece, Ireland, Japan, Malaysia, Thailand, and the United Kingdom had one analyst each. See Supplement 8 for a summary of the analysts’ academic profiles and their demographics. Although analysts came from a variety of disciplinary backgrounds, such as economics, organizational behavior, and finance, the majority (82.46%) of them were from strategic management or international business. They reported an average of 7.01 years of experience with data analysis (SD=4.39). Forty (70.18%) of them had five or more years of experience with data analysis and 34 (59.65%) analysts performed data analyses at least once per week. The 57 analysts included three Full Professors (5.26%), eight Associate Professors (14.04%), 22 Assistant Professors (38.6%), 20 doctoral students (35.09%), one post-doctoral student, two Masters students and one teaching fellow. Our sample of analysts was skewed towards junior academics. Senior representation also dropped from 24 to 19% over the course of the project, perhaps in part due to the higher opportunity costs of time for Full and Associate Professors. The statistics (Supplement 8) obtained from the pre-survey (Material 2 on OSF) show that 37 (64.91%) analysts had published at least one scientific paper, and 12 (21.05%) analysts had published five or more papers in strategic management or international business. A total of 11 (19.30%) analysts had published at least one paper with a primary contribution in methodology or statistics, and eight (14.04%) had taught at least one statistics class. Project website The project website provided detailed information about the project to colleagues potentially interested in taking part (Material 9 on OSF). In the background section, we introduced the purpose of the project and the database that would be employed to test the research questions. The data description portion included an overview and details regarding each variable in the database. In the data analysis section, we provided data in three alternative formats, specifically STATA, SPSS, and Excel. In the FAQ section, we provided answers to common questions analysts might have in mind, alongside one author’s contact information if they had any further queries. Protocol We asked the data analysts to perform their responsibilities independently by completing five steps: (1) complete the pre-survey, (2) access the research questions, the data and the data descriptor, (3) undertake the analyses to address the four research questions, (4) complete the post-survey, and (5) upload their results and statistical code from their analyses using an online portal. In the first step, we received 158 responses for the presurvey, which asked for an analyst’s beliefs about each research question as well as their demographic characteristics (Supplement 8 and Material 2 on OSF). Our review of these 158 responses showed that 35 responses had an accomplishment percentage below 50%, which meant at least half of the questions were unanswered in their responses. In addition, in the completed answers, there were four respondents who submitted the pre-survey twice. Ultimately, 119 unique analysts successfully completed the pre-survey. In Steps 2 and 3, analysts were provided with pooled longitudinal data, which they used to estimate various fitting models, including ordinary least squares, logistic models, tobit models, and generalized linear models, among others. Members of our crowd worked individually—consistent with investigations that are solo-authored and scientific collaborations that include a single data analyst—but in contrast to the broad overall trend across fields towards large teams of collaborators (Wuchty etal., 2007), presumably with multiple members contributing to the analyses. Past many-analyst projects have recruited both teams and individuals, with comparable overall results (e.g., Menkveld etal., 2024; Schweinsberg etal., 2021; Silberzahn etal., 2018), but the use of solo analysts does stand as a limitation of this research. In Step 4, we received 93 responses to the post-survey. Within these responses, four respondents provided an invalid ID number and name; eight respondents submitted their answers twice, and three respondents did not finish their survey. As such, we had 78 analysts with complete information inclusive of the post-survey, which collected individual analysts’ beliefs regarding each research question, and details on how they conducted their analyses. For example, we asked analysts on their methods and theoretical rationale to operationalize important variables (such as Entry Mode, Intangible Assets, Policy Uncertainty, Performance, and Level of Ownership), as well as their reasons for the selection of their statistical technique and control variables (Material 3 on OSF). In Step 5, we found that 72 of these 78 analysts submitted both results and coding files. We checked each submission and carefully reproduced all the analyses and results. Amongst these 72 analysts, two analysts did not submit complete statistical software codes, and 13 had submissions that are irreproducible due to unrecognizable codes, missing regressions, or ambiguous variables (see Material 14 on OSF for details). As such, our focal analyses are based on the 57 analysts whose submissions are complete and replicable. Among these analysts, four used SPSS to process 1108 Journal of International Business Studies (2025) 56:1102–1124 their analysis while the remaining 53 used Stata. These analysts often provided multiple analyses for one or more research questions; hence, we had 227, 126, 98, and 135 specifications for RQ1–4, respectively. Instituting quality control measures, such as excluding results than cannot be reproduced from the same data and code, helps ensure that the heterogeneity in estimates does not emerge as an artifact of substandard work by some members of the crowd. This presents a conservative test of the “many-analysts” phenomenon in which crowds of analysts obtain disparate estimates using the same data to test the same hypothesis. That said, it is important to be transparent about what specifications and results were excluded and why. We have posted on the OSF all analysts’ submissions, including their model specifications and results, along with the data they used and our overall project analysis code. The reasons for exclusions are clearly stated (either “incomplete submission” or “analysis not reproducible”) in Material 14. In the spirit of open science, we welcome further perspectives from additional colleagues. Results Effect size dispersion acrossanalysts Analysts used a wide variety of specifications to conduct their analyses. No two analysts adopted the same approach when we consider the variables, method, and sample selection simultaneously. Material 4 on OSF provides a detailed summary table describing the specifications employed by each analyst for each research question. Supplement 2 summarizes the control variables used by the analysts. To achieve a standardized measure of the effect sizes of the independent variables on dependent variables across all analyses, we computed the marginal effect sizes (Breznau etal., 2022; Fey etal., 2023), which represent the increase in a dependent variable for a unit increase in an independent variable. In Fig.1, we present the distribution graphs of the effect size estimates found in the separate analyses for each research question. For all four research questions, the range of estimates encompasses both negative and positive values and crosses zero. The most used threshold for considering an individual estimate statistically significant is the arbitrary value of p < 0.05. Whether or not an estimate is associated with a p value below or above 0.05 gives a sense of what an individual Fig. 1 Analysts’ reported marginal effect sizes from their tests of four international business research questions. Notes: Quartiles of the number of analyses and 95% confidence intervals of the effect sizes are as indicated in the figure 1109Journal of International Business Studies (2025) 56:1102–1124 stand-alone analysis or a paper built around one primary analysis might have concluded regarding each research question. As seen in Table1, the crowd of analysts produced at least a few positive estimates that crossed the p < 0.05 threshold and at least a few negative estimates that crossed the p < 0.05 threshold for all four research questions. Thus, different researchers, given the same research question and the same dataset came to directly opposite conclusions for each of the four questions posed to the crowd. As shown in Table2, the aggregation of analyses exhibits a high level of heterogeneity in estimates that is already apparent visually. All RQs are associated with a high value of Cochran's Q and I2. Aggregating acrossdiverse approaches andresults Aggregating across all the specifications revealed an overall directional effect with a 95% confidence interval excluding zero for RQ1 (mean=0.044, 95% CI=[0.011, 0.077]) and RQ2 (mean=23.817, 95% CI=[11.967, 35.668]), but not for RQ3 and RQ4 (Table2). Note that the confidence intervals for the aggregated estimate for RQ1 almost includes zero, which means we draw a cautious conclusion on a directional effect (Benjamin etal., 2018). It is important to note that this inference that we draw from aggregating effect sizes is not based on double-counting, as can happen in a meta-analysis that includes multiple results from the same Table 1 Results for research questions 1–4 based on direction and whether they cross the conventional p < .05 threshold All four research questions were stated to analysts in a non-directional manner. For RQ1, a positive effect size means the analyst estimates a positive relationship between wholly-owned subsidiary (WOS) and foreign subsidiary performance, rather than joint venture (JV) or acquisition. A wholly-owned subsidiary is entirely owned and managed by a parent foreign company. A joint venture is a firm created by local firms and foreign firms, generally with shared ownership, returns, risks, and governance. An acquisition is a transaction in which a foreign firm purchases most or all of another company’s shares to gain control of that company. For RQ2, a positive effect size means the analyst estimates a positive relationship between intangible assets and a firm's level of ownershipin its foreign subsidiaries. For RQ3, a positive effect means the analyst estimates a positive relationship betweenpolicy uncertainty and a firm's level of ownershipin its foreign subsidiaries. For RQ4, a positive effect means the analyst finds that high policy uncertainty makes the relationship between intangible assets and a firm’s level of ownership in its foreign subsidiaries more positive. Research question Positive effect size & p < 0.05 Positive effect size & p > 0.05 Negative effect size & p > 0.05 Negative effect size & p < 0.05 1. What is the relationship between entry mode and foreign subsidiary performance? 27.31% (n=62) 28.19% (n=64) 24.23% (n=55) 20.26% (n=46) 2. What is the relationship between intangible assets and a firm’s level of ownership in its foreign subsidiaries? 67.72% (n=86) 5.51% (n=7) 8.66% (n=11) 18.11% (n=23) 3. What is the relationship between policy uncertainty and a firm’s level of ownership in its foreign subsidiaries? 32.65% (n=32) 12.24% (n=12) 25.51% (n=25) 29.59% (n=29) 4. How does the level of policy uncertainty moderate the relationship between intangible assets and a firm's level of ownership in its foreign subsidiaries? 20.59% (n=28) 37.50% (n=51) 32.35% (n=44) 9.56% (n=13) Table 2 Heterogeneity statistics for the four research questions Heterogeneity tests are conducted with random-effects models. Reported effect sizes are aggregated in means. The brackets contain the 95% confidence intervals of the respective statistic. Given the nature of the diverse choice of variables and measures, means provide an overview of collective results. They suggest that the focal subsidiary will have, on average, a chance of 4.4% to be “profitable” when it is a wholly owned venture (RQ1), a 1% increase in the ratio of R&D expenditure to total sales of a parent firm will lead to a 23.77% increase in its ownership in its subsidiaries (RQ2), and so forth. Cochran’s Q is the weighted sum of squared differences between the effect sizes of individual analyses and the pooled effect sizes across analyses. All the Q values in this table have a p value that is less than 0.001 and reflect a high level of heterogeneity. I2 is the percentage of variance across analyses that is due to heterogeneity rather than chance. I2 values higher than 75% are usually considered to indicate high heterogeneity. Statistics for Bayes Factor Bound are generated using p values (Benjamin & Berger, 2019). The means of the partial correlation coefficients between focal independent and dependent variables are stated as a reference for a standardization of effect sizes (Fitzgerald, 2024; Stanley & Doucouliagos, 2012). The numbers of analyses used for this standardization are in parentheses. Research questions Number of analysts Number of analyses Reported effect size means Q I2Bayes factor bound means Partial correlation coefficient means RQ1 52 227 0.044 [0.011, 0.077] 22,835.295 99.05% 138.544 [50.670, 226.417] 0.018 [0.010, 0.026] (222) RQ2 54 127 23.817 [11.967, 35.668] 3,159.866 96.01% 819.416 [309.150, 1329.681] 0.028 [0.018, 0.038] (72) RQ3 52 98 −0.246 [−1.692, 1.201] 5,976.930 98.38% 88.484 [15.739, 161.230] 0.009 [−0.008, 0.025] (61) RQ4 52 136 −3.808 [−12.829, 5.213] 578.095 76.65% 13.178 [7.667, 18.690] 0.002 [−0.005, 0.009] (60) 1116 Journal of International Business Studies (2025) 56:1102–1124 Potential countermeasures Addressing both strategic p-hacking and inherent subjectivity in data analysis is critical to ensuring the reliability and robustness of our science. Although both involve an uncertain garden of forking paths (Gelman & Loken, 2014) through a scientific inquiry, we believe the most effective countermeasures are different. In Table5, we offer a list and typology of interventions and classify them as effective against either strategic or inherent elasticity in researcher decision making. Several interventions are in principle effective at curtailing p-hacking, in particular pre-registration of analyses (Wagenmakers etal., 2012), creating a test and holdout sample (Altman, 1968) and blind analyses (MacCoun & Perlmutter, 2015). In the complex archival data commonly used in international business studies, data must often be explored as part of the process of understanding, rendering pre-registration and blind analyses potentially over-restrictive (King etal., 2021). Thus, a test-holdout approach could be comparatively more useful in quantitative research using complex datasets, to allow exploration while at the same time preventing p-hacking (Goldfarb & Yan, 2021). Although inherent elasticity is arguably the greater challenge for science, it is not interpersonally inflammatory like allegations of p-hacking since the former does not call the ethical integrity of the scientist into question in any way. Demonstrating that subareas of the scholarly literature are characterized by publication bias, p-hacking by some research teams, and, in some cases, a lack of evidentiary value helps other scientists identify the most reliable bodies of knowledge upon which to build (Egger etal., 1997; Goldfarb & King, 2016; Simonsohn etal., 2014; Stanley, 2005). At the same time, accusations of strategic analyses against individual papers and research teams can be problematic and unfair—if we run publication bias or p-hacking tests on enough individual articles, inevitably some will be flagged for containing problematic data, even if none of them do (Simonsohn, 2013). Moving forward, it is also worth considering our collective interest in maximizing the credibility of what appears in the published literature. We therefore suggest that with regard to individual future investigations, it is typically better to curtail strategic elasticity before the fact than attempt to expose it after the fact. Proposed new practices, such as pre-registration and test-holdout also have shortcomings, such as reduced efficiency or the requirement for very large samples and thus should be deployed on a case-by-case basis. The ultimate objective should be an improved research ecosystem, not the unattainable goal of scientific perfection. In contrast, inherent elasticity, such as that revealed here, may be inevitable in scientific inquiry. Different scientists, acting entirely in good faith, will plan out different approaches in advance, analyze data blindly in different ways, and explore different paths through a test sample. The best available options are to render inherent elasticity transparent through multiverse and crowd analyses, expanded robustness tests, and the open posting of data for re-analysis by colleagues. Open data sharing should be encouraged by international business journals on publication, although this, of course, is not possible for datasets entailing confidentiality concerns and proprietary datasets obtained through agreements with partner firms and data providers. To the extent that data can be made open, the community can collectively probe the robustness of findings using alternative specifications (Goldfarb & Yan, 2021) and work together to identify issues post-publication. We therefore present a vision of an open science of international business (Meyer etal., 2020) in which interventions are considered to prevent p-hacking before it happens when this is a significant concern, a single paper reports more analytic approaches and robustness tests than is currently the norm (Sala-i-Martin, 1997; Steegen etal., 2016), and the data and code are made publicly available to the community when possible. When faced with a wide dispersion in estimates across different specifications, international business scholars should seek to anticipate and detect theoretically informative moderators, and failing this, consider aggregation to reach a tentative conclusion. The principles of open science further encourage researchers to replicate analyses across different sets of observations, especially alternative time periods and countries. This helps develop a norm of examining the generalizability versus the context sensitivity of research findings (Delios etal., 2022). The approaches we advocate have advantages and disadvantages, as do traditional practices. However, open science leads to more transparent results and accurate inferences (Nosek etal., 2022) on behalf of the stakeholders we serve. Testing hypotheses in many ways is considerably more work than social scientists are used to doing to make a single claim. Multiverse analyses, crowd analyses, aggregating across numerous robustness tests, and assessments of generalizability across time and geographies represent departures from standard scientific practices and incentive structures. However, a traditional small science paper reporting a single primary analysis of the results of one research design should not be solely relied on to make strong claims or determine policy, even in the absence of any researcher bias towards a desired conclusion. The research community will need to incorporate more reformed practices if we wish to draw strong inferences (Platt, 1964). Conclusions The major contributions of this first crowd science initiative in the field of international business are multifaceted and interrelated. We address four important unresolved 1117Journal of International Business Studies (2025) 56:1102–1124 Table 5 Approaches to preventing and detecting strategic and inherent elasticity in analytic procedures A longer version of this table is available in Supplement 9 Approach Description Aimed at inherent or strategic elasticity Prevents or detects Test-holdout sample approach The dataset is split into two pieces, with exploratory analyses conducted on the first piece and confirmatory analyses on the second piece (e.g., Altman, 1968; Orrù etal., 2020; Shrestha etal., 2021) Strategic Prevents Pre-registration Researcher specifies the analytic plan before collecting or obtaining the data (Wagenmakers etal., 2012) Strategic Prevents Data-blind analysis Original researcher relabels or recodes some of the key variables before conducting her analysis, such that she is unaware of the direction and nature of the observed relationships (MacCoun & Perlmutter, 2015) Strategic Prevents Independent re-analysis Independent researcher uses an alternative analytic approach to test the same theoretical idea (e.g., Simonsohn, 2011) Both Detects Error analysis Independent researcher identifies errors in data handling or analysis, and may show that these errors systematically favor the original hypothesis (Rosenthal, 1978) Both Detects Tests for publication bias and questionable research practices Meta-scientific analysis of a study set or individual study to test for omitted studies and data patterns consistent with questionable research practices (Ioannidis & Trikalinos, 2007; Simonsohn etal., 2014) Strategic Detects “Chrysalis” analysis Comparison of earlier vs. later versions of research reports (e.g., doctoral dissertations vs. published reports of the same work) (O’Boyle etal., 2017) Strategic Detects Conducting an independent replication study Independent research team collects new data using the same methodology and runs the same analyses as described in the original paper (e.g., Open Science Collaboration, 2015). P-hacked original findings are less likely to replicate, but failed replications can occur for many other reasons as well Strategic Detects Red team approach Independent research team attempts to debias and optimize an original investigation before it is submitted for publication (Lakens, 2020) Strategic Prevents Retrospective re-assessment Original researcher decides after-the-fact that the analysis was sub-optimal and a different approach would have been better (Rohrer etal., 2021) Both Detects Comparing employed analyses A single researcher surveys the various analytic approaches used in different studies on the same topic, identifies inconsistencies within and across research groups, and potentially applies all approaches to the same data (e.g., Elson etal., 2014; Harris etal., 2013) Strategic Detects Strategic analysis A crowd of analysts is asked to either produce statistically significant support for the original hypothesis (directional goal) or provided with no goal when analyzing the data (ongoing crowd initiative via the University of Pennsylvania’s Adversarial Collaboration Project) Strategic Detects Multiverse analysis or specification curve One analyst employs numerous specifications (Simonsohn etal., 2020; Steegen etal., 2016) Inherent Detects Many analysts Many researchers analyze the same data to test the same hypothesis (Silberzahn etal., 2018) Inherent Detects 1118 Journal of International Business Studies (2025) 56:1102–1124 questions in the field by leveraging the insights of 57 independent scholars. The insights collectively suggest a directional answer to two research questions, each the subject of numerous prior investigations across decades of study that only yielded mixed and conflicting results. At the same time, we take stock of the small but growing literature on the many-analysts phenomenon (Silberzahn etal., 2018). Choice points matter in research using complex archival data, but we do not yet fully understand the reasons for and consequences of choice point effects. The present empirical results indicate that heterogeneity in naturally emerging approaches and resulting estimates poses a significant challenge for management scholarship (Ketokivi & Mantere, 2010; King etal., 2021; Mantere & Ketokivi, 2013). In terms of parsing this variance in estimates, we report the strongest evidence yet that variable operationalizations and researcher expertise matter. At the same time, we explore cross-question differences, finding that a high theoretical consensus regarding what “should” happen is no guarantee of empirical consistency in estimates. Encouragingly, however, where theory is strong, as for the direction of assets and ownership hypotheses, aggregation reveals an overall estimate consistent with theory, pending further evidence. Likewise, supporting the idea of a community building knowledge together, scholars updated their beliefs considering the empirical evidence rather than allowing their prior convictions to bias their analyses and conclusions. To further strengthen this community, we advocate integrating the knowledge gains from meta-scientific crowd projects like this one into the everyday practices of traditional small teams of researchers. To facilitate open science practices in international business studies, we present a compendium of interventions against analytic elasticity (see Table5) and a novel typology based on whether they target strategic or inherent elasticity and are prevention or detection oriented. Although our present crowd initiative demonstrates the critical role of subjective researcher choices in shaping results in international business research, it also underscores that meaningful inferences remain possible in science. By sampling analyses and stimuli widely, and leveraging the insights derived from a diversity of approaches, we can make real progress on the key scientific questions of interest to a field. Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1057/ s4126702500808-9. Acknowledgements This project was supported by the Multi-Year Research Grant from the University of Macau (Reference No.: MYRGGRG2024-00140-FBA), awarded to Tianyou Hu, and the National Natural Science Foundation of China (Project No.: 72572121,72122016), awarded to Nan Zhou. Eric Uhlmann is grateful for funding from the INSEAD R&D committee. We acknowledge the following analysts as co-authors, with whom we have unfortunately lost contact: Liang Hu (Jinan University), Mahdi Forghani Bajestani (Old Dominion University), Aqi Liu (The Chinese University of Hong Kong), Fatemeh Askarzadeh (Old Dominion University), Benjamin Krebs (Paderborn University), and Jiao Li (China Europe International Business School). We are grateful to research assistance from Jimmy Chen, Yuehan Chu, Nihong Li, Yixiao Han, Yize Liu, Rafael Goldszmidt, Kerui Gao, Lixuan Xu, and Zhiyi Wu for their material organization, data preparation, and administrative assistance. Data Availability Statement Our methods and analyses were preregistered at https:// osf. io/ 4euaj. The supplementary document of thispaperis accessibleat https:// doi. org/ 10. 1057/ s41267025008089.Further Results and Materials are posted on the Open Science Framework athttps:// osf. io/ ew3vz/? view_ only= 52ab5 518ad a34e0 ca104 6aeab 08a51 22. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. 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He received his undergraduate degree from Peking University and his PhD from National University of Singapore. His current research in strategy and international business focuses on firm strategy within alliances and networks, and how multinational firms navigate institutional environments in foreign direct investment. Shu Yu (PhD, National University of Singapore) is Senior Research Fellow at the Suzhou Industrial Park Monash Research Institute of Science and Technology and Senior Lecturer at the Monash University Suzhou Campus. Her research interests lie in corporate and international strategy, with a focus on emerging economies. Nan Zhou is Professor at the School of Economics and Management, Tongji University in China. She received a PhD from the Wharton School, University of Pennsylvania. Her research addresses questions that intersect the fields of corporate strategy and international business, focusing primarily on understanding how firm growth is influenced by firm resources and institutional environments in the context of emerging markets. Eric Uhlmann is Professor of Organizational Behavior at INSEAD. His research centers on stereotyping and discrimination, moral judgments and behaviors, cross-cultural similarities and differences, and crowdsourcing science. He received a PhD in Social Psychology from Yale University in 2006 and was a postdoctoral scholar at the Kellogg School of Management. 1123Journal of International Business Studies (2025) 56:1102–1124 Authors and Affiliations AndrewDelios1· TianyouHu2· ShuYu3· NanZhou4· FaisalM.Ahsan5· MonaBahl6· TaoBai7· MadhurimaBasu8· HanokuBathula9· GeorgiosBatsakis10· JorgeCarneiro11· DwarkaChakravarty12· DanyangChen13· WeihongChen14· Ying‑YuChen15· LuisAlfonsoDau16· ShuDeng17· DesislavaDikova18· XiaominFan19· ViswaPrasadGada20· DongdongHuang21· HyunGonKim22· KyungjoongKim23· AlešKubíček24· ChengguangLi25· WenHelenaLi26· YiLi27· YuanyuanLi28· Yung‑ChihLien29· HuanchenLiu30· WeiLiu31· GrigorijLjubownikow32· RachealLouisVincent33· OndřejMachek34· ParulManocha35· YoichiMatsumoto36· AtthaphonMumi37· ChaoNiu38· N.Nuruzzaman39· VidyaSukumaraPanicker40· K.PraveenParboteeah41· WeiQiao42· XiaoleQiao43· ShyamalaSethuram44· AoShen45· LeiShi46· EvisSinani47· SandeepSivakumar48· Pei‑ShanSoon49· MaximilianStallkamp50· PriitTinits51· DanielTolstoy52· AndranikTumasjan53· MayankVarshney54· AndresVelez‑Calle55· ChrisWagner56· PengWang57· XingangWang58· YongWang59· LiangWen60· TaoWu61· SandeepYadav62· JiajuYan63· JingYuYang64· MeganZhang65· WeihaoZhang66· YamengZhang67· YangZhao68· EricUhlmann69 * Tianyou Hu tian[email protected] * Nan Zhou [email protected] 1 National University ofSingapore, Singapore, Singapore 2 Faculty ofBusiness Administration, University ofMacau, E22, Avenida da Universidade, Taipa, Macau 3 Monash University & Suzhou Industrial Park Monash Research Institute ofScience andTechnology, Suzhou, China 4 School ofEconomics andManagement & Advanced Institute ofBusiness, Tongji University, 1500 Siping Road, Shanghai200092, China 5 Xavier School ofManagement, Jamshedpur, India 6 Illinois State University, Normal, USA 7 The University ofQueensland, Brisbane, Australia 8 Symbiosis Institute ofInternational Business & Symbiosis International (Deemed University), Pune, India 9 The University ofAuckland, Auckland, NewZealand 10 The American College ofGreece & Brunel University ofLondon, Uxbridge, UK 11 FGV EAESP Sao Paulo, School ofBusiness Administration, SãoPaulo, Brazil 12 San Diego State University, SanDiego, USA 13 Shanghai University ofFinance andEconomics, Shanghai, China 14 Guangxi University, Nanning, China 15 National Dong Hwa University, Taiwan, China 16 Northeastern University, Boston, USA 17 The University ofMississippi, Oxford, USA 18 Vienna University ofEconomics andBusiness, Vienna, Austria 19 Nanjing University ofScience andTechnology, Nanjing, China 20 Glasgow Caledonian University, Glasgow, UK 21 Nankai University, Tianjin, China 22 The State University ofNew Jersey, NewBrunswick, USA 23 Northwest Missouri State University, Maryville, USA 24 Prague University ofEconomics andBusiness, Prague, Czechia 25 Technical University ofMunich, Munich, Germany 26 University ofTechnology Sydney, Ultimo, Australia 27 The University ofSydney, Camperdown, Australia 28 California State University, Los Angeles, LosAngeles, USA 29 National Taiwan University, Taipei, Taiwan 30 Nanjing University ofAeronautics andAstronautics, Nanjing, China 31 Qingdao University, Qingdao, China 32 The University ofAuckland, Auckland, NewZealand 33 Monash University Malaysia, SubangJaya, Malaysia 34 Prague University ofEconomics andBusiness, Prague, Czechia 35 University ofAlabama atBirmingham, Birmingham, USA 36 Keio University, Minato, Japan 37 Mahasarakham University andNational Institute ofDevelopment Administration, KhamRiang, Thailand 38 City University ofHong Kong, Kowloon, HongKong 39 University ofManchester, Manchester, UK 40 Loughborough Business School, Loughborough, UK 41 University ofWisconsin – Whitewater, Whitewater, USA 42 Xiamen University, Xiamen, China 43 Chang’an University, Xi’an, China 44 Baruch College, NewYork, USA 45 Xidian University, Xi’an, China 46 University ofInternational Business andEconomics, Beijing, China 47 Copenhagen Business School, Frederiksberg, Denmark 48 Indian Institute ofManagement Raipur, Raipur, India 49 Sunway University, SubangJaya, Malaysia 1124 Journal of International Business Studies (2025) 56:1102–1124 50 East Carolina University, Greenville, USA 51 Permanent Representation ofEstonia totheOECD, Paris, France 52 Stockholm School ofEconomics, Stockholm, Sweden 53 Johannes Gutenberg University Mainz, Mainz, Germany 54 Indian Institute ofManagement Ahmedabad, Ahmedabad, India 55 Universidad EAFIT, Medellín, Colombia 56 ananki.ai GmbH, MenloPark, USA 57 Beijing Normal-Hong Kong Baptist University, Zhuhai, China 58 The University ofAuckland, Auckland, NewZealand 59 Southwest Jiaotong University, Chengdu, China 60 Xi’an Jiaotong-Liverpool University, Suzhou, China 61 The Chinese University ofHong Kong, Shenzhen, China 62 Indian Institute ofManagement Bangalore, Bengaluru, India 63 Baylor University, Texas, USA 64 The University ofSydney, Sydney, Australia 65 Independent Researcher, NewYork, USA 66 Guangxi University, Nanning, China 67 Xi’an Jiaotong-Liverpool University, Suzhou, China 68 Northwest Normal University, Lanzhou, China 69 INSEAD, Singapore, Singapore