Causal attribution for peer performance and international joint venture divestment

Journal of International Management 26 (2020) 100754

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Causal attribution for peer performance and international joint venture divestment

T

Kent N. Huia, G. Tomas M. Hultb, , David J. Ketchen Jrc ⁎

a b c

School of Management, Xiamen University, Xiamen, Fujian 361005, China Eli Broad College of Business, Michigan State University, East Lansing, MI 48824, USA Harbert College of Business, Auburn University, Auburn, AL 36849, USA

ARTICLE INFO

ABSTRACT

Keywords: Organizational learning Outcome-based learning International joint venture Causal attribution Divestment

We theorize that compared to peers' consistent subsidiary performance (i.e., negative subsidiary performance of unsuccessful peers and positive subsidiary performance of successful peers), firms are more likely to attribute peers' inconsistent subsidiary performance (i.e., negative subsidiary performance of successful peers and positive subsidiary performance of unsuccessful peers) to external factors that would also influence their own international joint venture (IJV) in the same market. As a result, peers' inconsistent subsidiary performance is more likely to make the observing firms adjust the expected prospects of their own IJV, thereby changing the likelihood of divestment. We also examine the boundary conditions for the effects of peers' inconsistent subsidiary performance, namely the stability of the external environment and the presence of local partner(s) in the focal IJV. Analysis of 460 Japanese IJVs established from 1996 to 2015 provides support for the importance of causal attribution in learning from peer performance.

1. Introduction Firms typically do not have complete knowledge when they make decisions because of issues related to bounded rationality, search costs, and environmental uncertainty (Cyert and March, 1963; March and Simon, 1958). This is particularly the case for decisions about international markets (Johanson and Vahlne, 1977; Xu and Shenkar, 2002). Organizational learning theory suggests that firms often draw on peer experience to fill the knowledge gap (Huber, 1991; Levitt and March, 1988). In parallel, research suggests firms can learn from the international strategic decisions of peers, such as decisions regarding location choice (e.g., Henisz and Delios, 2001; Jiang et al., 2014; Li and Yao, 2010; Li et al., 2015), entry mode choice (e.g., Li et al., 2007; Lu, 2002; Xia et al., 2008; Yang and Hyland, 2012; Yiu and Makino, 2002), and divestment (e.g., Soule et al., 2014). In addition to peer decisions, peer performance can also reveal important information. Specifically, firms may be able to evaluate their own prospects in a market or strategy by observing and interpreting peer performance (Conell and Cohn, 1995; Feinberg, 2008; Haunschild and Miner, 1997; Shen and Xiao, 2014). We refer to this type of vicarious learning as outcome-based learning. Compared to research on learning from peer strategic decisions, research on outcome-based learning is limited in the international context (except Fabian et al., 2009; Fernhaber and Li, 2010; Hsieh et al., 2015; Lu, 2002). These studies in general conclude that the better the peer performance, the better the prospects expected by the observing firms in the same international market and/or using the same strategy. As a result, the positive outcomes of peers tend to encourage observing firms to invest in the same international market and/ or strategy, whereas the negative outcomes of peers tend to make observing firms avoid or quit the market and/or strategy. ⁎

Corresponding author. E-mail addresses: [email protected] (K.N. Hui), [email protected] (G.T.M. Hult), [email protected] (D.J. Ketchen).

https://doi.org/10.1016/j.intman.2020.100754 Received 16 December 2018; Received in revised form 19 March 2020; Accepted 21 March 2020 Available online 02 April 2020 1075-4253/ © 2020 Elsevier Inc. All rights reserved.

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However, attribution research suggests that firms tend to make causal attributions for peer performance (Laamanen et al., 2016; Wagner and Gooding, 1997). These attributions can affect whether firms expect a positive relationship between peer performance and their own prospects. For example, a peer's performance in a host-country market can be driven by external factors such as country and industry-level factors, as well as internal factors at the firm and subsidiary levels (Chan et al., 2010; Makino et al., 2004). If observing firms believe that the peer's performance in a market is largely caused by external factors such as industry demands and policies, the observing firms will expect to be subject to the same factors. The observing firms' expected prospects should therefore be comparable with the peer's performance, holding other factors constant. On the contrary, if observing firms assume that the peer's performance mainly results from its unique internal factors such as its firm-specific resources, then the observing firms are unlikely to expect to be influenced by the same factors and unlikely to expect parallels between the peer's performance and their own prospects. This illustrates the potential value of research on outcome-based learning that takes into account the attributions observing firms make about peer performance. In this study, we integrate insights from research on outcome-based learning, attribution, and international joint ventures (IJVs) to theorize about how firms make causal attribution for peers' subsidiary performance and how such causal attribution influences the likelihood of divesting their own IJV in the same host-country market. We seek to make three core contributions. First, we develop a more fine-grained theory of outcome-based learning in the international context by underscoring the importance of causal attribution for peer performance. Second, we enrich the field's understanding on why firms divest their IJVs. Third, we advance the literature on organizational attribution that largely focuses on causal attribution for self-performance by theorizing about causal attribution for peer performance and its impact on an international strategic decision. 2. Theoretical background 2.1. Outcome-based learning Organizational learning refers to encoding inferences from history into forms, rules, procedures, conventions, strategies, and technologies that guide future behavior (Levitt and March, 1988). It is particularly vital under conditions of high uncertainty (Cyert and March, 1963; Dodgson, 1993). Organizations can learn directly from their own experience or vicariously from peer experience (Dodgson, 1993; Huber, 1991; Levitt and March, 1988). Our focus is on a type of vicarious learning – outcome-based learning. Outcome-based learning refers to drawing inferences from peer performance to estimate one's own prospects (Haunschild and Miner, 1997; Hsieh et al., 2015). Outcome-based learning is especially useful when organizations do not have any direct experience in a market or with a strategy and peer performance is the only type of performance data available for decision making. Incumbent organizations, however, may also engage in outcome-based learning because of the imperfect knowledge about the environment and the causal ambiguity pertaining to their self-performance (Gaba and Terlaak, 2013; Lant and Mezias, 1992; Mosakowski, 1997). Although it is by no means easy to analyze peer performance, this analysis can provide valuable insights (Hsieh et al., 2015). For example, due to limited attention and information-processing capabilities (March and Simon, 1958; Ocasio, 1997), decision makers may not be aware of new trends and events that have already started to affect other organizations (Milliken, 1990). Analyzing how peers have performed may direct the attention of decision makers to those new trends and events that may later influence their own organization. Accordingly, outcome-based learning can be useful for both potential entrants and incumbents. Scholars have studied outcome-based learning in a variety of contexts such as strikes in coal mines (Conell and Cohn, 1995), investment bank selection by firms (Haunschild and Miner, 1997), and market selection by fast-food chains (Shen and Xiao, 2014) and banks (Feinberg, 2008). In the international strategy context, Lu (2002) found that peer performance involving an entry mode is positively related to a Japanese firm's propensity to use the same entry mode in the same host country. Fernhaber and Li (2010) found that firms tend to follow to internationalize if peer performance in internationalization is good. Generally speaking, the literature suggests that positive peer outcomes increase the propensity of an organization to commit resources to the same market or strategy, whereas negative peer outcomes increase the likelihood of avoiding or quitting the same market or strategy. The underlying expectation is that there is a positive relationship between peer performance and one's own expected prospects. However, as noted above, organizations may not always expect a positive relationship between their own prospects and peer performance because they make causal attributions for peer performance. In the next section, we introduce the decision context of IJV divestment in which we will consider the role of causal attribution in outcome-based learning. 2.2. The context of IJV divestment We define an International Joint Venture (IJV) as a joint venture with at least one parent being headquartered outside the venture's country of operation (Geringer and Hebert, 1989), and IJV divestment as the focal parent firm quitting an IJV by selling its ownership stake or liquidating the IJV (Dhanaraj and Beamish, 2004; Lu and Beamish, 2006; Lu and Xu, 2006). We conceptualize the decision to divest an IJV to be a forward-looking cost-benefit decision (Berry, 2013; Chi, 2000; Gaba and Terlaak, 2013). The benefits of sustaining an IJV refer to the value that the IJV directly and indirectly creates, whereas the costs include but are not limited to operating costs and the opportunity costs associated with the capital and resources locked into the IJV. A firm quits an IJV when the expected benefits no longer compensate for the expected costs of staying in the IJV. Such a cost-benefit conceptualization is either explicitly or implicitly adopted by prior studies on IJV divestment. For instance, prior studies suggest that conflict between partners is one of the common reasons for IJV divestment (Hennart and Zeng, 2002; Inkpen and Currall, 1998; Westman and Thorgren, 2016). The conflict may impede the goal attainment of the IJV and increase the costs of monitoring against partner opportunism. Eventually 2

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the expected costs outweigh the expected benefits of staying in the IJV, leading to IJV divestment. Firms tend to first rely on their private information such as the performance of the focal IJV for conducting the cost-benefit calculation. However, they usually face significant uncertainty in international markets because of insufficient local knowledge (Johanson and Vahlne, 1977; Xu and Shenkar, 2002). Indeed, firms may form an IJV in the first place due to the uncertainty of the host-country market and their unwillingness to make a larger investment by establishing a wholly-owned subsidiary (Erramilli and Rao, 1990; Hill et al., 1990; Morschett et al., 2010). These notions suggest that firms are likely to have incomplete information about their IJVs' prospects. As organizational learning is particularly common in the case of high uncertainty (Cyert and March, 1963; Dodgson, 1983), we argue that outcome-based learning is important for the cost-benefit calculation of staying in an IJV. Firms can learn from peer performance that provides useful insight into their IJVs' prospects (Li and Yao, 2010). We define peers as other firms from the same home country and industry, and focus on the performance of these firms' subsidiaries in the same industry and host country as the focal IJV, for three reasons. First, the focal IJV and these peer subsidiaries share two identity dimensions (i.e., country of origin and industry type) and thus face similar situations in the host-country market (Li et al., 2007). Second, home-country environments impose similar if not identical constraints on organizations from the same home-country. Third, the focal IJV and peer subsidiaries compete in the same industry and host-country markets and hence tend to draw each other's attention. Our approach of defining peers and their performance is consistent with prior organizational learning studies in the international context (e.g., Henisz and Delios, 2001; Hsieh et al., 2015; Lu, 2002). Given that unconsolidated financial reports at the subsidiary level are seldom public, a firm can typically at best have access to relatively coarse performance measures of peers' subsidiaries. Consequently, we follow prior studies and focus on whether peers' subsidiary performance is positive or negative (e.g., Hsieh et al., 2015; Lu, 2002; Makino and Beamish, 1998). 3. Hypothesis development 3.1. Perceived locus of causality of peer subsidiary performance Attribution research suggests that performance causes vary in the dimensions of locus of causality, stability, and controllability. In our research context, perceived locus of causality refers to whether the cause of a peer's subsidiary performance in a host-country market is believed to be external (i.e., factors from outside the firm, such as competition and government policies) or internal (i.e., factors residing within the peer's organizational boundaries, such as leadership and strategy – Ford, 1985). In light of evidence that internal factors contribute to differences in subsidiary performance across firms (Chan et al., 2010; Makino et al., 2004), we argue that observing firms are unlikely to expect the prospects of their IJV to be comparable with a peer's subsidiary performance that mainly results from internal factors. On the contrary, observing firms are likely to expect parallels between their IJV's prospects and a peer's subsidiary performance that is mainly caused by external factors, holding other factors constant. External factors tend to provide a certain level of performance consistency across firms operating within the same environment (Chan et al., 2010; Makino et al., 2004). Although it may not be possible for firms to know the exact causes of peers' subsidiary performance due to bounded rationality and restricted access to peers' internal information, attribution research suggests decision makers exhibit common patterns of making use of available information for developing their perceptions. In fact, it is the subjective attribution of decision makers rather than the actual performance cause that determines organizational responses (Ford, 1985; Lant et al., 1992; Mone et al., 1998). Of particular relevance to our study are two general patterns of developing perceptions of performance causes – the discounting and consensus principles (Kelley, 1967, 1971, 1973; Kelley and Michela, 1980). Kelley (1971, p.8) states that if an attributor “is aware of several plausible causes, he attributes the effect less to any one of them than if he is aware only of one as a plausible cause. In other words, he makes his attributions according to a discounting principle: the role of a given cause in producing a given effect is discounted if other plausible causes are also present.” Alternatively, the consensus principle states that an attributor is more likely to attribute a behavior or performance to external causes if there are a greater number of actors that share the same behavior or performance (Kelley, 1967). These two attribution principles have received strong support in a substantial number of experiments (Kelley and Michela, 1980). When they are applied in our research context, the discounting and consensus principles suggest that a firm tends to attribute a peer's subsidiary performance in a host-country market to external causes if there are a smaller number of plausible internal causes, and if there are a greater number of other peers reporting similar subsidiary performance, respectively. We argue that an observing firm pays attention to the consistency between the parent and subsidiary performance of peers in order to identify plausible internal causes of the peers' subsidiary performance. Prior studies suggest firms distinguish between successful and unsuccessful peers based on parent performance in vicarious learning processes (Haunschild and Miner, 1997; Lu, 2002). One would expect firm-level factors such as firm-specific resources and capabilities to deliver a certain level of consistency between parent and subsidiary performance (Chan et al., 2010; Makino et al., 2004). Consider two scenarios. The first case is a successful peer reporting negative subsidiary performance in a host-country market, and the second case is an unsuccessful peer reporting negative subsidiary performance. In the first case, there are a limited number of plausible internal causes at the firm-level that can simultaneously explain the negative subsidiary performance and the superior parent performance. In the second case, in contrast, firm-level weaknesses such as inferior resources and poor management can reasonably explain both poor parent performance and poor subsidiary performance. According to the discounting principle, observing firms are more likely to attribute a successful peer's negative subsidiary performance (i.e., the first case), compared to an unsuccessful one's (i.e., the second case), to external factors because there are fewer plausible internal causes at the firm-level (Kelley, 1971, 1973; Kelley and Michela, 1980). It should be noted that internal factors at 3

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the subsidiary-level such as poor subsidiary leadership may also explain negative subsidiary performance of a successful peer, and some external causes such as luck may not be generalizable to the observing firms' IJV. However, when there are more examples of negative subsidiary performance experienced by successful peers at the same time, the consensus principle indicates that the observing firms should be more convinced of the existence of external causes that may also adversely influence their IJV (Kelley, 1967; Kelley and Michela, 1980). Firms are not likely to revise the cost-benefit calculation of staying in their IJV immediately after observing successful peers suffering poor subsidiary performance. Instead, we argue that the observing firms will direct more attention to the external environments to analyze the plausible external causes. Here observing firms may discover new trends, events, and developments that are overlooked but can possibly explain the negative subsidiary performance of successful peers (Milliken, 1990). The perceived magnitude and generalizability of external threats that have been noticed may also escalate due to a greater number of successful peers suffering poor subsidiary performance. In addition, the impact of some external events may be ambiguous at first. If observing firms consider these events relevant for the negative subsidiary performance of successful peers, they are likely to view the events as threats rather than as opportunities (Dutton and Jackson, 1987; Jackson and Dutton, 1988). As a result, observing firms tend to expect lesser prospects for their IJV. Worse still, with poor expected prospects for the IJV, the expected costs of monitoring and the possibility of inter-partner conflicts may increase because IJV partners may try to maintain their profits by engaging in opportunistic behavior such as shirking responsibilities, deferring or withdrawing investment, withholding or distorting information, and grafting joint earnings (Luo, 2007). Taken together, we argue that the negative subsidiary performance of successful peers is related to lower expected benefits and higher expected costs of staying in an IJV in the same host-country market. The propensity to divest the IJV will thus increase. On the other hand, according to the discounting principle (Kelley, 1971, 1973; Kelley and Michela, 1980), the negative subsidiary performance of unsuccessful peers may be taken as less indicative of external threats owing to a greater number of plausible internal causes of the subsidiary performance. The observing firms are less likely to expect their IJV prospects to fall. Thus, we make the following predictions: H1. a. Successful peers' negative subsidiary performance is positively related to the likelihood of a firm divesting an IJV in the same host-country market. b. Successful peers' negative subsidiary performance has a stronger positive effect than unsuccessful peers' negative subsidiary performance on the likelihood of a firm divesting an IJV in the same host-country market. We offer a parallel argument regarding causal attribution for peers' positive subsidiary performance. Imagine two cases: one is an unsuccessful peer reporting positive subsidiary performance and the other is a successful peer reporting positive subsidiary performance. The parent and subsidiary performance are less consistent in the first case. There are a limited number of internal factors at the firm-level that can reasonably explain both positive subsidiary performance and poor parent performance. In the second case, however, internal strengths at the firm-level such as superior resources and capabilities can explain the strong performance of both the subsidiary and the parent. Drawing again on the discounting and consensus principles (Kelley, 1967, 1971, 1973; Kelley and Michela, 1980), we argue that observing firms should be more convinced of the presence of external causes of positive subsidiary performance when there are more examples of positive subsidiary performance reported by unsuccessful peers at the same time. The observing firms will then examine the external environment to identify plausible causes, thereby increasing the possibility of noticing external opportunities that were neglected, raising the expected significance and generalizability of some already noticed external opportunities, and labeling ambiguous events as opportunities rather than as threats (Dutton and Jackson, 1987; Jackson and Dutton, 1988). The stronger expected prospects of their IJVs may also reduce the concern of partner opportunism that is usually more severe when the vested interests of the partners are at risk (Luo, 2007). Consequently, the expected benefits of staying in their IJVs increase and the expected costs decrease, leading to a lower chance of divesting the IJVs. The expected prospects of their IJVs are thus positively related to the subsidiary performance of unsuccessful peers. In contrast, according to the discounting principle (Kelley, 1971, 1973; Kelley and Michela, 1980), successful peers' positive subsidiary performance is viewed as less indicative of external drivers owing to a greater number of plausible internal causes. Here the observing firms are not likely to expect their IJVs' prospects to increase. Based on this theorizing, we predict: H2. a. Unsuccessful peers' positive subsidiary performance is negatively related to the likelihood of a firm divesting an IJV in the same host-country market. b. Unsuccessful peers' positive subsidiary performance has a stronger negative effect than successful peers' positive subsidiary performance on the likelihood of a firm divesting an IJV in the same host-country market. Fig. 1 presents a 2 × 2 matrix summarizing the effects of the consistency between peers' parent and subsidiary performance on the expected prospects of an observing firm's IJV. 3.2. Perceived stability of the external environment H1 and H2 center on the locus of causality of peer performance. Attribution research suggests that performance causes also can vary in perceived stability, which is defined as “a continuum ranging from temporary to permanent that indicates the relative duration that decision makers attach to a cause” (Ford, 1985, p.773). Organizational responses tend to be different for stable and unstable causes. For example, organizations are more likely to increase innovation in response to stable rather than unstable causes of self-performance decline (Mone et al., 1998). In addition, temporary strategies are used to respond to unstable causes of self-performance decline because these situations are expected to return to normal shortly, whereas permanent or radical strategies are required to tackle stable causes (Ford, 1985). Although the impact of perceived stability has been well considered in research on 4

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Peers’ parent performance Successful

Unsuccessful

Negative

prospects of its IJV

H1b. a firm is less likely to decrease the expected prospects of its IJV

H2b. a firm is less likely to increase Positive

Peers’ Subsidiary performance

H1a. a firm decreases the expected

the expected prospects of its IJV

H2a. a firm increases the expected prospects of its IJV

Fig. 1. 2 × 2 matrix summarizing the ideas from H1a to H2b.

causal attribution for self-performance (e.g., Barker III and Patterson Jr, 1996; Bettman and Weitz, 1983; Ford, 1985; Mone et al., 1998), we are among the first to extend it to research on causal attribution for peer performance. We previously argued that firms update the forward-looking cost-benefit calculation for their IJVs when they expect the IJVs to be subject to the same external factors that engender peers' inconsistent subsidiary performance. External factors, however, can be unstable (Weiner, 1985, 1986): Government policies can shift arbitrarily in some host-country markets (Henisz, 2000), technology changes frequently in many industries, and new competitors can emerge quickly in industries with low entry barriers. If the external factors that produce peers' inconsistent subsidiary performance are unstable and changeable, they may offer little value for predicting the future performance of the observing firms' IJVs. In contrast, the information value of peers' inconsistent subsidiary performance is higher if decision makers expect the external causes to persist. Our theorizing proposes that when there are a greater number of peers reporting inconsistent subsidiary performance, an observing firm may attempt to identify previously unnoticed external factors and revise the significance and interpretation of some noticed ones. It should be noted that different observing firms may eventually focus on different external factors due to the difficulty in knowing the actual causes of peer performance and the subjective interpretation of observers (Hambrick and Mason, 1984; Kelley and Michela, 1980). Some observers may attribute the poor subsidiary performance of successful peers to actions of pressure groups, for example, while others may attribute them to fierce competition. Different perceived causes are likely associated with different degrees of perceived stability, and it is impossible for our theorizing to predict the specific causes of peers' inconsistent performance perceived by each observing firm in each period of time. With that said, all perceived causes of peers' inconsistent performance share an important feature according to our theorizing; that is, they reside in the external environment. Therefore, we argue that if the external environment in general becomes more unstable, decision makers, on average, are more likely to consider the external causes of peers' inconsistent subsidiary performance more unstable, all else being equal. In an unstable environment, it may not be meaningful for decision makers to predict their IJV's future performance on the basis of the causes of peers' current performance because their IJV will not likely subject to the same factors in the future. It follows that the tendency of decision makers to use peer performance for predicting the prospects of their IJV is lower. They may instead focus on other information sources such as consultants and academics who provide forecasts about the future market environments (Gaba and Terlaak, 2013). For these reasons, we argue that the impact of peers' inconsistent subsidiary performance on IJV divestment decisions should be weaker in an unstable external environment. Stated formally: H3. The greater the instability of the external environment, the lesser the positive effect of successful peers' negative subsidiary performance on the likelihood of a firm divesting an IJV in the same host-country market. H4. The greater the instability of the external environment, the lesser the negative effect of unsuccessful peers' positive subsidiary performance on the likelihood of a firm divesting an IJV in the same host-country market. 3.3. Perceived controllability of the external environment Attribution research also suggests that performance causes can vary in perceived controllability, which is defined as “a continuum that reflects decision makers' perceptions of their ability to influence directly a cause” (Ford, 1985, p.773). Some scholars suggest that organizations are more likely to change if their self-performance decline is caused by controllable rather than uncontrollable factors (Mone et al., 1998). Other scholars, however, suggest that decision makers may attempt to maintain the illusion of control by claiming responsibility for declines caused by uncontrollable factors and then taking aggressive remedial measures (Ford, 1985). Although the debate on whether organizations will change more radically in response to controllable or uncontrollable causes of performance decline continues, we can conclude that decision makers take the controllability of causes into account when deciding subsequent actions. The discussion of perceived controllability has been limited to causal attribution for self-performance in the 5

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literature, and we extend it to causal attribution for peer performance. Ford (1985) suggests that external factors are considered more controllable when decision makers have greater power, more knowledge about those factors, and greater access to the sources of the factors. Building upon this notion, we argue that firms may have greater perceived controllability of the external causes of peers' inconsistent performance if there is at least one local partner in the focal IJV as compared to having no local partner. Partners in an IJV may come from the same home country, the host country, or a third country (Makino and Beamish, 1998). Local partners in general know better about consumers' needs and tastes, the strategies and capabilities of competitors, social structures and norms, local technological developments, the preferences of the local government, and the availability and characteristics of suppliers and labor in the host-country market (Lu and Beamish, 2006; Makino and Delios, 1996). The social identity and legitimacy of local partners can also provide the focal IJV with local network connections and access to scarce resources. For example, in countries such as Japan, some distribution channel resources are exclusive to local firms. Although firms may be able to accumulate local knowledge and resources over the course of the IJV's operations, some essential knowledge, network, and resources are only possessed by local partners and not readily transferrable (Luo, 1998; Makino and Delios, 1996). Valuable knowledge, network, and resources offered by local partners are important for an IJV to cope with the external environments in the host-country market. For instance, if fierce competition from local competitors is the reason for the poor subsidiary performance of successful peers, then knowledge about local competitors and resources offered by local partners will be vital for an IJV to succeed. If a host-country's policies are the cause of peers' inconsistent subsidiary performance, an IJV is more capable of lobbying a host-country government to sustain favorable policies and alter unfavorable ones with the superior political connection and legitimacy provided by local partners (Henisz, 2000). We acknowledge that some external factors, such as macro-economic conditions, are hard to influence. But overall the perceived controllability of external factors, on average, should still be higher with the local knowledge and resources provided by local partners. Therefore, we argue that firms expect an IJV that has at least one local partner, compared to an IJV without any local partner, to suffer less from the external threats that lead to the negative subsidiary performance of successful peers, and benefit more from the external opportunities that beget the positive subsidiary performance of unsuccessful peers. Formally stated: H5. The positive effect of successful peers' negative subsidiary performance on the likelihood of a firm divesting an IJV in the same host-country market is weaker if there is at least one local partner than if there is no local partner in the focal IJV. H6. The negative effect of unsuccessful peers' positive subsidiary performance on the likelihood of a firm divesting an IJV in the same host-country market is stronger if there is at least one local partner than if there is no local partner in the focal IJV. 4. Methods 4.1. Data We tested our hypotheses using data on IJVs of Japanese manufacturing firms. An IJV is defined as a foreign subsidiary in which the focal Japanese parent maintain < 95% equity ownership (Franko, 1971; Lu, 2002). We identified IJVs from Kaigai Shinshutsu Kigyou Souran (Japanese Overseas Investments) published annually by Toyo Keizai, Inc. Toyo Keizai Inc. developed Japanese Overseas Investments by conducting mail and telephone surveys with major listed and unlisted Japanese firms and collecting their archival data. A major advantage of Japanese Overseas Investments is that it basically covers all foreign subsidiaries of the Japanese firms that have responded to the surveys (Henisz and Delios, 2001). We constructed a longitudinal dataset in which each observation represents a unique IJV–Japanese parent–year combination. In cases of an IJV with more than one Japanese parent, the IJV appeared more than once in a given year in the dataset so that each observation referred to only one Japanese parent. As such, the only Japanese parent in each observation was the focal firm referenced in our hypotheses. While Japanese Overseas Investments provided the data at the IJV level, we acquired the data at the parent level from Nikkei NEEDS tapes. The data about the host-country environments in which the IJVs operated came from multiple sources as described below. The studied period is from 1996 to 2015. We only included IJVs that were formed in 1996 or later to avoid the problem of left censoring.1 The dataset for the final analysis covers 460 IJVs. 4.2. Measures 4.2.1. Dependent variable The dependent variable, IJV divestment, is a binary variable. We identified IJV divestment when the focal IJV was no longer listed as a foreign subsidiary of the focal parent firm in the Japanese Overseas Investments (Dhanaraj and Beamish, 2004; Lu and Beamish, 2006). This variable was measured in year t + 1 (i.e., the 1997–2016 period) as we maintained a one-year lag structure between independent variables and the dependent variable, which is consistent with prior studies on IJV divestment (e.g., Dhanaraj and Beamish, 2004; Hennart and Zeng, 2002). There were 108 divestment events for the final analysis. Table 1 shows the breakdown of the divestment events into countries and industries. 1

We appreciate the guidance of an anonymous reviewer to avoid left censoring. 6

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Table 1 The breakdown of IJV divestment events into countries and industries. Countries/regions

Number of IJVs

Number of divestments

Australia Austria Belgium Brazil Canada China Czech Republic France Germany Hong Kong Hungary India Ireland Indonesia Italy Malaysia Mexico Morocco Netherlands New Zealand Philippines Poland Russia Singapore South Africa South Korea Spain Sweden Switzerland Thailand Turkey United Arab Emirates United Kingdom United States Vietnam Total

3 1 1 6 5 135 2 4 5 8 2 11 1 32 1 8 15 1 3 1 14 1 3 4 2 16 2 2 1 67 1 1 14 70 17 460

0 0 0 0 1 29 0 2 0 3 1 3 0 5 0 2 1 0 0 0 6 0 0 1 0 5 1 0 0 26 0 0 4 16 2 108

IJV Industries

Number of IJVs

Agriculture Mining Construction Grocery Fabric Wood and furniture Pulp and paper Chemical/pharmaceutical Petroleum and coal Rubber and leather Ceramics, earth and stone, glass Steel Non-ferrous metal Metal Machinery Electrical and electronic equipment Transportation equipment and shipbuilding Automotive and parts Precision mechanical equipment Other manufacturing Agricultural and marine products and food wholesale Chemical and pharmaceutical wholesale Non-ferrous metal wholesale Machinery wholesale Electrical and electronic equipment wholesale Transport equipment wholesale Automotive and parts wholesale Precision equipment wholesale

1 4 5 1 6 1 15 22 6 2 9 19 11 20 37 69 2 130 3 1 1 5 3 18 25 6 10 1

Number of divestments 0 0 3 1 1 0 4 7 3 1 3 3 3 3 10 20 1 13 1 1 1 0 0 8 9 2 1 0

(continued on next page) 7

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Table 1 (continued) IJV Industries

Number of IJVs

Other wholesale trade Warehouse and logistics related business Other transportation services Leisure and entertainment Information service industry (including software) Rental/lease business Consulting and market research Planning, development and research Other services Shareholding/holding company Other Total

4 3 3 1 4 3 1 4 2 1 1 460

Number of divestments 2 1 0 1 1 2 2 0 0 0 108

4.2.2. Independent variables We defined peers as other Japanese parent firms in the same industry, and peer performance as the performance of these Japanese peers' subsidiaries in the same industry and host country as the focal IJV. Scholars suggest that firms may consider peers with competitive advantages as successful, and these competitive advantages are often associated with above-average returns (e.g., Schoemaker, 1990). We thus defined successful peers as those with return on assets (ROA) above the industry mean level, and unsuccessful peers as those with ROA below the industry mean. Regarding subsidiary performance, we used a perceptual assessment reported by the general manager of the foreign subsidiaries to Japanese Overseas Investments. This measure had three ordinal levels—“gain”, “breakeven”, and “loss.” The classification was an absolute assessment of profitability of the focal subsidiary without reference to other subsidiaries under the same parent (Delios and Beamish, 2001). The validity and reliability of this measure was established by Delios and Beamish (2001), and many studies have adopted this measure (e.g., Lu, 2002; Lu and Beamish, 2006; Makino and Beamish, 1998). We measured the number of negative subsidiary performance episodes reported by successful peers and the number of positive subsidiary performance episodes reported by successful peers using the total number of “loss” and “gain” subsidiaries, respectively, that were in the same industry and host country as the focal IJV and owned by Japanese peers with an ROA above the industry mean. We measured the number of negative subsidiary performance episodes reported by unsuccessful peers and the number of positive subsidiary performance episodes reported by unsuccessful peers using the total number of “loss” and “gain” subsidiaries, respectively, that were in the same industry and host country as the focal IJV and owned by Japanese peers with an ROA below the industry mean. We tested H1 to H2 using these four variables. H3 and H4 are about the instability of the external environment faced by the focal IJV. Because our theory does not point to any specific aspect of the external environment, we needed a variable capturing the general external environment. We followed prior studies (e.g., Haleblian and Finkelstein, 1993) and developed a proxy, measured as the average coefficient of variation of sales from year t-4 to year t of all Japanese subsidiaries in the same industry and host country as the focal IJV, for four reasons. First, the instability of subsidiary sales should reflect the instability of most relevant aspects of the external environment. Second, using all Japanese subsidiaries in a local industry to measure instability can reduce the concern that the instability was caused by factors specific to individual firms. Third, we considered Japanese subsidiaries rather than all firms in a local industry because the external environment faced by the focal Japanese IJV might not be the same as that faced by firms from other countries and industries (Li and Yao, 2010; Li et al., 2007). Fourth, a coefficient of variation embraces predictable and unpredictable instability, both of which are relevant for the focal firm to determine whether the external factors that produce peers' inconsistent subsidiary performance will persist. Regarding H5 and H6, we developed local partner(s), a binary variable with 1 indicating that there was at least one local partner in the focal IJV and 0 otherwise. We used number of negative subsidiary performance episodes reported by successful peers, number of positive subsidiary performance episodes reported by unsuccessful peers, instability of the external environment, and local partner(s) to create interaction variables for testing H3 to H6. 4.2.3. Control variables We first included a set of control variables for the attributes of the focal IJV. Because parent firms might be more likely to divest an IJV in a noncore industry (Reuer, 2002), we included noncore IJV, a binary variable with 1 representing that the IJV was in a different industry from the focal Japanese parent's primary business and 0 otherwise. As current performance likely influences the expected prospects of the focal IJV (Berry, 2013; Chi, 2000), we included two dummy variables, positive IJV performance (i.e. “gain”) and negative IJV performance (i.e., “loss”), based on the data of Japanese Overseas Investments. We included IJV size, measured as the number of employees of the focal IJV relative to that of the focal Japanese parent, because it might reflect the importance of the IJV to the parent. We included majority-owned IJV, measured as 1 if the focal Japanese parent held > 50% of the equity ownership and 0 otherwise, because the ability to impose control over the IJV tended to be higher if the Japanese parent assumed a dominant ownership position (Geringer and Hebert, 1989). We also considered whether the strategic purposes of the IJV included knowledge seeking because the lifespan of knowledge-seeking IJVs was usually short (Makino et al., 2007). Lastly, we included IJV age to take the liability of newness and operational experience of the IJV into account (Singh et al., 1986). We next entered a set of controls for the attributes of the focal Japanese parent. We included parent performance that was 8

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K.N. Hui, et al.

measured as ROA minus the industry mean because firms with poor performance might conduct strategic changes that affect the expected benefits of staying in the focal IJV (Franko, 1971; Lant and Mezias, 1992). We controlled for R&D intensity that was measured using R&D investment divided by sales because intangible assets might be important for the focal IJV to compete. We included the ratio of debt to equity to account for potential slack resources of the focal Japanese parent (Cyert and March, 1963). We controlled for parent size, measured as the natural logarithm of the number of employees, as it might reflect the market power and resource availability of the focal Japanese parent. We also controlled for the international experience of the focal Japanese parent firm by including length of international operation that was measured using the number of years since the establishment of the first foreign subsidiary, length of host country operation that was measured using the number of years of operating in the focal host country, and the number of subsidiaries in the host country invested by the focal parent in year t. We also entered a set of controls that serve as the cues of the favorability of the external environment in the host-country market. Some of them were developed based on the data of the World Development Indicators, including the natural logarithm of population, GDP per capita, GDP per capita growth, inflation, FDI intensity that was measured using the total foreign direct investment (FDI) flows divided by GDP, and net bilateral aid from Japan. Additionally, we included cultural distance between Japan and the focal host country, which was measured as the composite index developed by Kogut and Singh (1988) based on Hofstede's national culture index (Hofstede, 1980). The value of an IJV in terms of sharing risk and uncertainty was high in a culturally distant host country (Kogut and Singh, 1988). However, a parent firm tended to have conflicts with local partners from a culturally distant country (Hennart and Zeng, 2002). Last, using the subsidiary profitability data of Japanese Overseas Investments, we calculated the number of peers' improving subsidiary performance episodes and the number of peers' worsening subsidiary performance episodes to account for the yearly change (from year t-1 to t) in the performance of Japanese peers' subsidiaries that were in the same industry and host country as the focal IJV. 4.3. Analytical approach We used Cox proportional hazards models to test the hypotheses as we were interested in the timing of IJV divestment. Cox models can effectively handle right-censored observations (i.e., those related to IJVs that had not been divested in the studied period). Cox models also do not require to make priori assumption about the baseline hazard function because they can allow for a variety of possible underlying hazard functions. An important issue to address here is tied data—events occurring at the same time interval. Tied data require additional statistical treatment because Cox models assume each event to occur at a unique time interval. Given that we identified IJV divestment events from the annual editions of Japanese Overseas Investments, we adopted the discrete-time method that assumed tied events occurred at the same time interval (i.e., one year in our case) (Allison, 2010). We also controlled for the unobserved heterogeneity across parent firms by adding parent-fixed effects. In essence, we used the method of stratification to allow each parent to have different baseline hazard functions while constraining the coefficients to be the same across parents (Allison, 2010). This method could control for all time-invariant unobserved heterogeneity across parents and thus avoid omitted-variable bias. Last, we entered a set of year dummies to control for macro-environmental changes over time. 5. Results Table 2 reports the descriptive statistics and correlations for all variables. Table 3 reports the results of the Cox models. In Table 3, we first entered all variables except the independent variables about peers' subsidiary performance in Model 1 as the base model. The likelihood ratio chi-square increases when adding the independent variables in Model 2. Among the control variables, local partner(s), noncore IJV, majority-owned IJV, knowledge seeking, IJV age, parent performance, number of subsidiaries in the host country, GDP per capita, GDP per capita growth, net bilateral aid from Japan, and number of peers' worsening subsidiary performance appear to be predictors of IJV divestment (p < 0.10). H1a and b are not supported due to the insignificant coefficient of number of negative subsidiary performance episodes reported by successful peers (B = 0.012, p > 0.10), which is not significantly different from the coefficient of number of negative subsidiary performance episodes reported by unsuccessful peers (chi-square = 0.47, p > 0.10). These results suggest IJV divestment is not related to the negative subsidiary performance of both successful and unsuccessful peers. For H2a, the coefficient of number of positive subsidiary performance episodes reported by unsuccessful peers is negative as expected (B = −0.158, p < 0.05) in Model 2. Nevertheless, in a nonlinear model such as a Cox model, the marginal effect of an independent variable—the effect of a unit change in an independent variable on the dependent variable is not directly indicated by the model coefficient (see Hoetker, 2007; Wiersema and Bowen, 2009 for detailed discussion). We therefore used the procedures recommended by Wiersema and Bowen (2009) to analyze the direction and significance of the marginal effect of number of positive subsidiary performance episodes reported by unsuccessful peers. The analysis shows that the marginal effect is negative across all observations (maximum = −0.000, mean = −0.020, minimum = −0.039, all p < 0.05). The marginal effect is also negative and significant when all variables are held at their mean or mode (p < 0.01). The analysis of economic significance indicates that the probability of divesting an IJV decreases by 11.363% as the number of positive subsidiary performance episodes reported by unsuccessful peers increases by one standard deviation from the mean. H2a is therefore supported. In addition, the coefficient of number of positive subsidiary performance episodes reported by successful peers is significantly positive (B = 0.124, p < 0.05) and it is less negative than the coefficient of number of positive subsidiary performance episodes reported by unsuccessful peers (p < 0.05). H2b is thus supported. We conclude that the positive subsidiary performance of unsuccessful peers reduces the likelihood of divesting an IJV to a greater extent than the positive subsidiary performance of successful peers. We entered the different sets of interaction variables separately in Model 3 and Model 4 and reported a full model in Model 5. For 9

6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30.

5.

4.

3.

1. 2.

IJV divestment (binary) Number of negative subsidiary performance episodes reported by successful peers Number of negative subsidiary performance episodes reported by unsuccessful peers Number of positive subsidiary performance episodes reported by unsuccessful peers Number of positive subsidiary performance episodes reported by successful peers Instability of the external environment Local partner(s) (binary) Noncore IJV (binary) Positive IJV performance (binary) Negative IJV performance (binary) IJV size Majority-owned IJV (binary) Knowledge seeking (binary) IJV age Parent performance R&D intensity Debt to equity Parent size (logarithm) Length of international operation Length of host country operation Number of subsidiaries in the host country Population (logarithm) GDP per capita GDP per capita growth (%) Inflation (%) FDI intensity Cultural distance Net bilateral aid from Japan Number of peers' improving subsidiary performance episodes Number of peers' worsening subsidiary performance episodes

Variables

Table 2 Descriptive statistics.

3.10

2.39

0.90

0.07 0.77

0.33 0.37 0.44 0.53 0.25 0.03 0.31 0.18 5.73 −0.23 0.03 2.58 4.12 34.79 16.11 7.03 8.31 12,962.86 4.42 3.99 0.05 2.80 166.81 0.99 0.63

Mean

4.95

3.83

1.79

0.25 1.94

0.14 0.48 0.50 0.50 0.43 0.15 0.46 0.38 3.87 3.73 0.02 9.58 0.61 16.80 12.82 11.38 0.66 16,083.79 3.93 5.45 0.07 0.74 534.44 1.99 1.55

S.D.

−0.02 0.01 0.09 −0.07 0.04 −0.03 −0.07 −0.01 0.02 −0.01 −0.02 0.02 0.00 0.03 0.04 0.11 −0.02 −0.01 0.00 −0.01 0.01 0.01 0.01 −0.01 0.00

−0.01

−0.05

−0.01

1.00 −0.01

1.

0.59

0.42

0.63

1.00

−0.04 0.03 −0.28 −0.11 0.14 0.04 −0.04 0.01 −0.19 0.00 0.08 0.09 −0.04 −0.04 0.01 0.04 0.07 0.19 −0.13 −0.07 −0.03 0.01 0.09 0.47 0.45

2.

0.60

0.56

1.00

−0.04 0.03 −0.34 −0.06 0.10 0.09 −0.06 −0.03 −0.14 0.03 0.11 0.00 0.01 −0.05 0.04 0.08 0.14 0.17 −0.05 −0.11 −0.07 0.02 0.10 0.63 0.44

3.

0.71

1.00

−0.01 0.03 −0.41 0.08 −0.02 0.10 −0.02 −0.05 −0.11 0.01 0.13 −0.02 0.03 −0.01 0.02 0.08 0.19 0.08 0.13 −0.04 0.00 0.08 0.04 0.63 0.35

4.

1.00 0.02 0.03 −0.38 0.04 0.02 0.15 0.01 −0.02 −0.15 −0.02 0.06 0.04 −0.03 −0.08 0.01 0.07 0.14 0.08 0.09 −0.05 0.00 0.07 0.03 0.63 0.40

5.

1.00 0.10 0.13 0.04 −0.03 0.02 0.03 −0.03 −0.21 −0.01 0.02 0.00 0.01 0.00 −0.13 −0.01 0.07 −0.33 0.22 0.11 0.01 0.14 0.22 −0.01 −0.15

6.

1.00 −0.12 −0.03 0.07 0.03 0.23 0.05 −0.06 −0.06 −0.16 0.02 −0.16 −0.13 −0.22 −0.01 0.08 −0.23 0.18 −0.03 0.05 0.07 0.17 0.00 −0.09

7.

1.00 −0.05 −0.05 −0.09 −0.03 0.11 −0.08 0.01 −0.05 −0.01 −0.07 −0.01 0.02 0.07 −0.13 −0.02 −0.03 0.02 0.08 0.11 −0.01 −0.35 −0.25

8.

1.00 −0.62 0.05 −0.03 −0.07 0.22 0.09 0.15 −0.08 0.15 0.13 0.06 0.10 0.09 −0.14 0.14 0.11 −0.06 0.00 −0.09 0.01 −0.06

9.

1.00 −0.01 0.01 0.00 −0.26 −0.08 −0.12 0.09 −0.11 −0.14 −0.05 −0.07 0.02 0.10 −0.08 −0.05 −0.03 −0.03 0.13 0.02 0.05

10.

1.00 0.00 0.00 −0.01 −0.01 −0.06 −0.02 −0.25 −0.17 −0.12 −0.07 0.13 −0.07 0.14 0.00 −0.01 0.02 0.08 0.12 0.06

11.

10

1.00 −0.02 −0.11 −0.10 −0.02 −0.30 −0.22 −0.09 −0.11 −0.19 0.23 −0.10 −0.11 0.20 0.07 −0.12 −0.07 0.00

13.

1.00 0.00 0.04 −0.05 0.11 0.13 0.17 0.03 −0.02 0.05 −0.04 −0.05 −0.05 −0.05 −0.38 −0.10 −0.03

14.

(continued on next page)

1.00 0.07 0.06 −0.10 −0.21 −0.04 −0.26 −0.28 −0.30 −0.22 −0.05 −0.09 0.10 −0.03 0.08 −0.02 −0.01 −0.02 −0.03

12.

K.N. Hui, et al.

Journal of International Management 26 (2020) 100754

Parent performance R&D intensity Debt to equity Parent size (logarithm) Length of international operation Length of host country operation Number of subsidiaries in the host country Population (logarithm) GDP per capita GDP per capita growth (%) Inflation (%) FDI intensity Cultural distance Net bilateral aid from Japan Number of peers' improving subsidiary performance episodes Number of peers' worsening subsidiary performance episodes

1.00 0.23 −0.12 0.14 0.16 0.13 0.07 −0.06 −0.02 −0.03 0.02 −0.05 0.04 0.04 0.01 0.01

15. 1.00 −0.10 0.36 0.31 0.22 0.20 −0.08 0.09 −0.09 −0.02 0.00 0.01 −0.04 0.13 0.03

16.

1.00 0.01 0.05 0.07 0.06 −0.03 −0.04 −0.09 0.01 0.00 −0.01 0.03 −0.01 −0.03

17.

1.00 0.59 0.46 0.44 0.01 −0.07 −0.05 0.10 −0.09 −0.05 −0.06 0.02 0.01

18.

1.00 0.48 0.33 −0.01 −0.09 −0.04 0.05 −0.06 −0.01 −0.13 −0.02 0.01

19.

1.00 0.53 −0.09 0.22 −0.23 −0.04 −0.08 −0.08 −0.24 0.05 0.10

20.

1.00 0.05 −0.08 0.07 −0.06 −0.08 0.04 −0.16 0.11 0.08

21.

1.00 −0.30 0.58 0.10 −0.46 −0.02 0.18 0.14 0.13

22.

1.00 −0.48 −0.26 0.23 −0.11 −0.25 0.09 0.23

23.

N = 1552 (IJV-parent-year observations). Correlations with an absolute value > 0.05 have a p value smaller than 0.05 (two-tailed tests).

15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30.

Variables

Table 2 (continued)

1.00 −0.09 −0.03 0.24 0.18 0.05 −0.11

24.

1.00 −0.19 0.03 0.18 −0.09 −0.08

25.

1.00 0.26 −0.06 −0.07 −0.04

26.

1.00 0.15 0.04 0.02

27.

1.00 0.06 −0.07

28.

1.00 0.34

29.

1.00

30.

K.N. Hui, et al.

Journal of International Management 26 (2020) 100754

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Table 3 The results of the Cox proportional hazards models predicting IJV divestment. Model 1

Model 2

Model 3

Model 4

Model 5

−3.455⁎ (1.397) 0.593† (0.345)

0.012 (0.101) −0.106 (0.117) −0.158⁎ (0.075) 0.124⁎ (0.050) −3.632⁎ (1.441) 0.649† (0.348)

0.839⁎ (0.351) −0.028 (0.131) −0.661⁎⁎ (0.240) 0.121⁎ (0.049) −3.737⁎⁎ (1.375) 0.576 (0.352) −2.895⁎ (1.244) 1.482⁎ (0.634)

−0.097 (0.136) −0.122 (0.120) −0.089 (0.081) 0.113⁎ (0.050) −3.695⁎ (1.549) 0.782⁎ (0.380)

0.278 (0.186) −0.188† (0.113)

0.956⁎ (0.378) −0.038 (0.134) −0.835⁎⁎ (0.252) 0.092† (0.049) −4.269⁎⁎ (1.520) 0.906⁎ (0.385) −3.702⁎⁎ (1.341) 2.386⁎⁎ (0.697) 0.390⁎ (0.192) −0.351⁎⁎ (0.116)

1.096⁎⁎ (0.403) −0.575† (0.340) 0.457 (0.367) −9.169 (5.758) −1.236⁎⁎ (0.444) −1.042⁎ (0.424) 0.259⁎⁎ (0.064) −0.099⁎ (0.043) −10.073 (17.960) −0.006 (0.011) −0.097 (1.689) 0.004 (0.017) 0.017 (0.018) 0.072⁎⁎ (0.023) 0.195 (0.383) −0.000⁎⁎ (0.000) −0.197⁎⁎ (0.075) −0.056 (0.037) 2.446 (2.458) −0.049 (0.239) 0.001† (0.000) 0.116 (0.097) 0.234⁎ (0.114)

1.019⁎ (0.399) −0.510 (0.337) 0.389 (0.366) −9.056† (5.448) −1.329⁎⁎ (0.439) −1.079⁎ (0.422) 0.277⁎⁎ (0.065) −0.088⁎ (0.042) −5.934 (17.595) −0.003 (0.013) −0.189 (1.706) 0.003 (0.017) 0.016 (0.018) 0.076⁎⁎ (0.023) 0.283 (0.387) −0.000⁎⁎ (0.000) −0.192⁎⁎ (0.075) −0.057 (0.035) 2.778 (2.497) −0.031 (0.240) 0.001† (0.000) 0.106 (0.093) 0.258⁎ (0.109)

0.999⁎ (0.413) −0.493 (0.345) 0.446 (0.375) −9.779† (5.677) −1.311⁎⁎ (0.447) −1.142⁎⁎ (0.433) 0.277⁎⁎ (0.065) −0.097⁎ (0.043) −8.421 (18.019) −0.003 (0.012) −0.417 (1.710) 0.007 (0.017) 0.019 (0.019) 0.083⁎⁎ (0.024) 0.227 (0.391) −0.000⁎⁎ (0.000) −0.223⁎⁎ (0.076) −0.059† (0.036) 3.270 (2.525) −0.008 (0.241) 0.001⁎ (0.000) 0.170† (0.099) 0.270⁎ (0.124)

Independent variables Number of negative subsidiary performance episodes reported by successful peers Number of negative subsidiary performance episodes reported by unsuccessful peers Number of positive subsidiary performance episodes reported by unsuccessful peers Number of positive subsidiary performance episodes reported by successful peers Instability of the external environment Local partner(s) (binary) Number of negative subsidiary performance episodes reported by successful peers X instability of the external environment Number of positive subsidiary performance episodes reported by unsuccessful peers X instability of the external environment Number of negative subsidiary performance episodes reported by successful peers X local partner(s) Number of positive subsidiary performance episodes reported by unsuccessful peers X local partner(s) Controls Noncore IJV (binary)

1.175⁎⁎ (0.369) −0.543 (0.332) 0.427 (0.355) −7.105 (5.711) −1.202⁎⁎ (0.423) −1.074⁎⁎ (0.411) 0.266⁎⁎ (0.064) −0.086⁎ (0.041) −7.917 (17.629) −0.004 (0.011) 0.180 (1.633) 0.008 (0.017) 0.018 (0.018) 0.067⁎⁎ (0.022) 0.145 (0.377) −0.000⁎⁎ (0.000) −0.168⁎ (0.073) −0.049 (0.037) 1.788 (2.457) −0.052 (0.234) 0.001 (0.000) 0.077 (0.079) 0.206⁎ (0.093)

Positive IJV performance (binary) Negative IJV performance (binary) IJV size Majority-owned IJV (binary) Knowledge seeking (binary) IJV age Parent performance R&D intensity Debt to equity Parent size (logarithm) Length of international operation Length of host country operation Number of subsidiaries in the host country Population (logarithm) GDP per capita GDP per capita growth (%) Inflation (%) FDI intensity Cultural distance Net bilateral aid from Japan Number of peers' improving subsidiary performance episodes Number of peers' worsening subsidiary performance episodes

1.070⁎⁎ (0.394) −0.541 (0.336) 0.414 (0.361) −8.079 (5.456) −1.295⁎⁎ (0.437) −1.043⁎ (0.419) 0.263⁎⁎ (0.064) −0.093⁎ (0.042) −7.103 (17.657) −0.007 (0.012) 0.100 (1.700) 0.002 (0.017) 0.017 (0.018) 0.071⁎⁎ (0.022) 0.246 (0.383) −0.000⁎⁎ (0.000) −0.185⁎ (0.075) −0.054 (0.036) 2.385 (2.463) −0.055 (0.238) 0.001† (0.000) 0.085 (0.094) 0.224⁎ (0.102)

(continued on next page) 12

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K.N. Hui, et al.

Table 3 (continued)

Year dummy variables Parent fixed-effects Number of observations Log likelihood Likelihood ratio chi-square

Model 1

Model 2

Model 3

Model 4

Model 5

Included Included 1552 −196.702 123.70⁎⁎

Included Included 1552 −192.366 132.37⁎⁎

Included Included 1552 −188.229 140.65⁎⁎

Included Included 1552 −190.554 136.00⁎⁎

Included Included 1552 −183.197 150.71⁎⁎

Coefficients instead of hazard ratios are reported. Standard errors are in parentheses. † p < 0.10. ⁎ p < 0.05. ⁎⁎ p < 0.01 (two-tailed tests).

H3, the coefficient of the interaction term between number of negative subsidiary performance episodes reported by successful peers and instability of the external environment is negative and significant in both Model 3 (p < 0.05) and Model 5 (p < 0.01). The additional analysis based on Model 5 shows that 89.240% of the true interaction effects calculated based on the data value of our observations are negative and 70.876% are significant at the 5% level (true interaction effects: maximum = 2.050, mean = −0.473, and minimum = −2.476). The true interaction effect is also negative and significant when all variables are held at their mean or mode (p < 0.05). The analysis of economic significance shows that as the number of negative subsidiary performance episodes reported by successful peers increases by one standard deviation from the mean, the probability of IJV divestment increases by 74.286% (p < 0.05) when instability of the external environment is low (i.e., one standard deviation below the mean), but it decreases by 73.861% (p < 0.05) when instability of the external environment is high (i.e., one standard deviation above the mean). The overall results provide support for H3, which proposes that the negative subsidiary performance reported by successful peers increases the likelihood of IJV divestment to a greater extent in a more stable external environment. Regarding H4, the coefficient of the interaction term between number of positive subsidiary performance episodes reported by unsuccessful peers and instability of the external environment is positive and significant in Model 3 (p < 0.05) and Model 5 (p < 0.01). The additional analysis based on Model 5 shows that 94.974% of the true interaction effects calculated based on the data value of our observations are positive and 84.601% are significant at the 5% level (true interaction effects: maximum = 1.037, mean = 0.332, and minimum = −0.495). The true interaction effect is positive and significant when all variables are held at their mean or mode (p < 0.01). The analysis of economic significance shows that as the number of positive subsidiary performance episodes reported by unsuccessful peers increases by one standard deviation from the mean, the probability of IJV divestment decreases by 70.233% (p < 0.01) when the instability of the external environment is low, but it increases by 38.984% (p > 0.10) when the instability of the external environment is high. Taken together, H4 is supported as these results show that the positive subsidiary performance reported by unsuccessful peers reduces the likelihood of IJV divestment to a greater extent in a more stable external environment. In Model 4, the coefficient of the interaction term between number of negative subsidiary performance episodes reported by successful peers and local partner(s) is not significant (p > 0.10), failing to support H5. On the contrary, for H6, the coefficient of the interaction term between number of positive subsidiary performance episodes reported by unsuccessful peers and local partner(s) is negative and significant at the 10% level in Model 4 and the 1% level in Model 5. We performed additional analysis based on Model 5 to help interpret the results for H6. Using the observations in our dataset to calculate the true interaction effects, we found that 68.686% of them are negative and 58.634% are significant at the 5% level. The maximum, mean, and minimum values of the true interaction effects are 0.474, −0.044, and −0.515, respectively. The true interaction effect is negative and significant when all variables are held at their mean or mode (p < 0.05). The analysis of economic significance shows that as the number of positive subsidiary performance episodes reported by unsuccessful peers increases by one standard deviation from the mean, the probability of the focal firm divesting the focal IJV decreases by 11.025% (p < 0.01) when there is no local partner in the focal IJV, and it decreases by 65.583% (p < 0.01) when there is one or more local partners. Collectively, we find evidence for H6, which states that the positive subsidiary performance reported by unsuccessful peers reduces the likelihood of IJV divestment to a greater extent when there is local partner(s) in the focal IJV. 5.1. Robustness tests We ran a number of additional tests to make sure our results are reliable. First, regarding the sampling procedure, some researchers define an IJV as a foreign subsidiary with < 80% of ownership stakes instead (e.g., Makino and Beamish, 1998). We find that our results remain unchanged qualitatively even though this alternative definition of IJV causes the sample size and statistical power to drop. Second, multicollinearity may be a concern in our data because some control variables have a variance inflation factor > 10, a commonly acceptable standard. We included those control variables as guided by the theories and findings in the literature to reduce the concern of alternative explanations and omitted-variable bias. The conclusion for hypothesis testing, however, is still the same when the control variables that had a variance inflation factor > 10 were excluded. In addition, we added country-fixed effects in order to control for potential unobserved heterogeneity across host countries. Adding country-fixed effects, together with firm- and year-fixed effects, is a very conservative method. We applied this method in robustness tests rather than in the main analysis because this method would exclude a lot of observations in countries in which there was no IJV divestment event, which was a serious statistical concern considering the number of variables and statistical power in our 13

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Cox models. Nevertheless, we find that adding country-fixed effects does not change the conclusion of hypothesis testing. We also added a new variable capturing the number of subsidiary divestments made by other Japanese peers in the same host country and local industry as the focal IJV to account for the possibility of imitation (Gaba and Terlaak, 2013). The coefficient of this variable is not statistically significant (p > 0.10). Importantly, the results are qualitatively the same as in the main analysis. Last, some IJVs appear more than once in the dataset because they have more than one Japanese parent. In a robustness test in which these IJVs were excluded, we find that the results are similar to those in the main analysis. 6. Discussion The results based on analyses of a dataset of Japanese IJVs provide support for the roles of causal attribution in outcome-based learning. We found that the positive subsidiary performance of unsuccessful peers reduces the likelihood of a firm divesting its IJV in the same market, and this effect is stronger than that of the positive subsidiary performance of successful peers. We also find that the negative subsidiary performance of successful peers increases the chance of a firm divesting its IJV when the external environment is sufficiently stable. We performed additional analysis and found that the negative subsidiary performance of unsuccessful peers does not impose the same effect even in a very stable external environment. Overall, these results are consistent with our theorizing that firms are more likely to attribute peers' inconsistent subsidiary performance to external factors that will also influence their own IJV and hence, peers' inconsistent subsidiary performance is positively related to the expected prospects of their own IJV. In contrast, our analysis suggests that peers' consistent subsidiary performance is not positively related to the expected prospects of observing firms' IJVs. In fact, successful peers' positive subsidiary performance even increases the chance of a firm divesting its IJV. One possible explanation is that the observing firms may be pessimistic about the competitiveness of their IJVs because of the plausible internal causes of successful peers' positive subsidiary performance such as strong resources and capabilities. This finding further supports our main assertion that firms do not always expect parallels between their own prospects and peer performance in the same market. Another major finding is that among peers' inconsistent subsidiary performance, the effect on the observing firms' IJV divestment is generally stronger for unsuccessful peers' positive subsidiary performance than successful peers' negative subsidiary performance, which may indicate the presence of observer bias. Observer bias means firms tend to attribute peers' positive performance to external factors and peers' negative performance to internal factors (Wagner and Gooding, 1997). Social psychologists offer an explanation that such bias can reinforce the observers' self-esteem and positive impressions when making comparisons with the peers. If observer bias is present in our research context, it would reduce the likelihood of the observing firms attributing the negative subsidiary performance of successful peers to external factors. However, the observing firms would be more likely to attribute the positive subsidiary performance of unsuccessful peers to external factors. As our theorizing focuses on how firms use information on the consistency between peers' parent and subsidiary performance to make causal attribution about peer performance, future research can integrate our theorizing with research on observer bias to develop a more complete conceptualization. We also found evidence of boundary conditions for the effects of peers' inconsistent subsidiary performance. The instability of the external environment weakened the effects of peers' inconsistent subsidiary performance, which is consistent with our theorizing that learning from peer performance is more valuable in a stable external environment. In addition, the effect of unsuccessful peers' positive subsidiary performance was stronger for an IJV with at least one local partner than for an IJV without any local partner. According to our theorizing, this is because the perceived controllability of the external environment is higher with the presence of local partner(s), and therefore the IJV may be able to benefit more from favorable external factors that are associated with the positive subsidiary performance of unsuccessful peers. However, the analysis revealed that the effect of successful peers' negative subsidiary performance was not different between an IJV with one or more local partners and an IJV without any local partner, which is inconsistent with our prediction that firms expect an IJV with local partner(s) to suffer less from external threats that produce negative subsidiary performance among successful peers. One possible explanation is that partner opportunism is more serious in a difficult external environment (Luo, 2007). Because local partners possess local knowledge and resources (Makino and Delios, 1996; Luo, 1998), the problems of partner opportunism such as withholding or distorting information and withdrawing investment of resources may be more devastating with the presence of local partners. This increased risk of partner opportunism may thus counteract the increased perceived controllability of the external environment. One way to disentangle the effects of the perceived controllability of the external environment would be to directly measure the construct with the use of a survey. Survey research could also directly measure other key constructs in our study. The relative advantage of our study that uses secondary data, nevertheless, is that it could analyze a larger sample of IJVs across time periods. Looking to the future, survey research and archival research can complement each other in shedding light on outcome-based learning in the international context. Overall, our study offers several contributions. First, highlighting the role of causal attribution, we developed more fine-grained theorizing about outcome-based learning in the context of an international decision. While most prior studies propose that the observing firms' expected prospects are often positively related to peer performance in the same market or strategy, we argue that this is not always the case. Our theorizing suggests that observing firms' expected prospects in an overseas market are more likely to be positively related to peers' inconsistent performance than consistent ones because observing firms tend to attribute the former to external factors. The second contribution centered on our theorizing about the boundary conditions for the effects of peers' inconsistent subsidiary performance. Specifically, we found that the effects were stronger in a more stable external environment and for an IJV in which there was at least one local partner. We are also among the first to extend the discussion on the perceived stability and controllability 14

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of performance causes from causal attribution for self-performance to peer performance. Compared to other types of vicarious learning such as frequency- and trait-based imitation (e.g., Henisz and Delios, 2001; Jiang et al., 2014; Li and Yao, 2010; Li et al., 2015; Soule et al., 2014; Xia et al., 2008; Yang and Hyland, 2012), outcome-based learning has received insufficient attention from international management scholars. We therefore call for more research on outcome-based learning in different international decision contexts. One example is the establishment of footholds in host-country markets. A foothold is “a small position that a firm intentionally establishes within a market in which it does not yet compete” (Upson et al., 2012, p.93). Many firms that enter an emerging market that is highly uncertain often manage their risk by creating footholds rather than making large-scale forays (Craighead et al., 2017). These firms may rely on outcome-based learning to decide whether to expand or abandon a foothold. The third contribution of our study is shedding new light on why firms divest IJVs. IJV divestment is a topic that has long drawn the interest of researchers and managers, and the survival of an IJV can be important for continuous development in a host-country market (Steensma and Lyles, 2000). IJV divestment is also consequential to stakeholders. For example, IJV divestment may influence the stock market value of the parent (Meschi, 2005). Therefore, it is important for researchers to identify the determinants of IJV divestment. Although one significant factor related to the value assessment of an IJV is uncertainty and organizational learning is particularly important under uncertain environments (Cyert and March, 1963; Dodgson, 1993), little is known about the roles of outcome-based learning in the decision to divest an IJV. We sought to help fill this void by presenting evidence that peer performance may be taken into account when firms evaluate their IJV's prospects. This finding was obtained after controlling for the IJV selfperformance. Our last contribution is advancing the literature on organizational attribution by theorizing about causal attribution for peer performance and its impact on a strategic decision. Decision makers make causal attribution for performance of both their own organization and other organizations (Laamanen et al., 2016; Wagner and Gooding, 1997). While scholars have proposed that decision makers' causal attribution for their own organization's performance is influenced by the information to which they attend (Ford, 1985; Mone et al., 1998) and decision biases (Barker III and Patterson Jr, 1996; Bettman and Weitz, 1983; Wagner and Gooding, 1997), research on causal attribution for peer performance focuses on decision biases solely (Wagner and Gooding, 1997; Zacharakis et al., 1999). To fill help this gap, we investigated the impact of the information on the consistency between peers' parent and subsidiary performance on the causal attribution for peer performance in a host-country market. Furthermore, while scholars have examined the impact of the causal attribution for self-performance on strategy making (Ford, 1985; Mone et al., 1998), to the best of our knowledge, our study is among the first to investigate the impact of the causal attribution for peer performance on an international strategic decision. Looking to the future, researchers could build on our study in several ways. One is to test the generalizability of our theorizing with a sample of non-Japanese firms, given that firms from different national cultures may interpret external factors differently (Barr and Glynn, 2004). Another way is to develop a theory of different strategic responses to successful peers' negative performance and unsuccessful peers' positive performance because prior research suggests that organizations may use different types of strategies to cope with external opportunities and threats (Dutton and Jackson, 1987). Moreover, we have only considered the role of the consistency between parent and subsidiary performance in the causal attribution for peer performance. However, the temporal consistency of subsidiary performance may also be relevant for causal attribution, which deserves further examination by researchers. 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