The Interplay of Institutions and Multinational Internationalization
A Complex System View

Rusty V. Karst1
Texas A&M University, USA

Andrew Johnson
Texas A&M University, USA


The importance and influence of institutions to successful multinational internationalization is well documented in the literature. However, much of this work takes a deterministic view in examining their linear relationships. This study takes a non-linear approach by introducing a complex system model which examines the interplay between institutions and multinational internationalization based on reciprocal relationships of mutual causality. Specifically, this paper positions the multinational enterprise as a complex adaptive system which interacts with institutions of the host environment, forming a complex system whereby the multinational faces institutional complexities at the edge of chaos and alters boundaries through continuous systems learning and optimization of resource dependence relationships internal and external to the MNE.

Keywords: multinational enterprise; institutions; internationalization; complex system; mutual causality


Effects of globalization and semi-globalization are increasingly influential in our knowledge-based economy and provide a more open and interactive landscape for multinational enterprise (MNE) internationalization. Within this context, the importance of resources (e.g., Gaffney et al., 2013; Hillman et al., 2009), knowledge/learning (e.g., Alegre & Chiva, 2008; Gaffney et al., 2016; Yamin & Otto, 2004), and institutions (e.g., Arregle et al., 2013, 2016; Hoskisson et al., 2013; Peng et al., 2008, 2009) are well-documented in extant literature. However, studies have predominantly been causal and correlational in nature, examining linear relationships between operationalized constructs where independent predictors (e.g., institutional effects, knowledge flows, resource positions) are hypothesized to have direct effects on dependent outcomes (e.g., MNE internationalization direction, investment degree, performance), but the latter has no effect on the former. While these relationships are important to furthering the literature, they may not fully capture what is occurring from a broader causality and relationship perspective.

Nascent research has begun to examine how institutions and MNEs may reciprocally influence each other and "co-evolve" through the internationalization process over time (e.g., Cantwell, Dunning & Lundan, 2010). A complex system perspective may be more appropriate within this landscape, in that elements are reciprocal (i.e., mutually causal) and influence each other simultaneously or in an alternating fashion (Maruyama, 1963). Our study adopts a complexity approach (Anderson, 1999; Dent & Powley, 2004; Simon, 1996; Tsoukas, 1998) in positioning a complex system model of MNE internationalization (e.g., Chiva, Ghauri & Alegre, 2014) which illustrates the interplay between target market institutions and MNE internationalization. Using complexity theory as a foundation (Tsoukas, 1998), a complex system framework (Simon, 1996) serves as our model structure, in helping frame all constructs (Miles & Huberman, 1994). Because complex systems are composed of heterogeneous elements that interrelate with one another and with their environment (Anderson, 1999; Simon, 1996), we contend that resource dependence relationships, system continuous learning, institutional complexities, and internationalization exist as a complex system which reciprocally interrelate within and external to the MNE. And within our model, the MNE itself exists as a complex adaptive system (CAS) (Anderson, 1999; Lichtenstein, 2000; Paarlberg & Bielefeld, 2009).

An organization's propensity to differentiate increases as the organization's size increases, which is observed in its many components' dimensions specific to goals, orientation, time horizons, spatial effects and structure formality (Lawrence & Lorsch 1967; Baliga & Jaeger, 1994). MNEs follow in this path in that as they differentiate in dimensionsin facing host environmental conditions during internationalization (e.g., institutional constraints)they become more complex systems as a part of their new environments. Their geographic dispersion magnifies overall system complexity in that they operate in environments of varying degrees of complexity, heterogeneity, stability, and hostility (Fayerweather, 1978). As MNEs set out to internationalize into foreign host markets, they must contend with new environmental contingencies (e.g., formal and informal institutional complexities) and are compelled to balance their need to access tangible/intangible resources with their need to manage organizational connections and relationships in order to optimize resource dependencies internal and external to the firm. Our complex system conceptualization contends that current system boundaries are met when an MNE CAS faces institutional complexities in the host market and when current resource dependency relationships internal and external to the MNE are not fully optimized. Capitalizing on knowledge flows fuel system continuous learning and adaptive processes, which reciprocally facilitate an MNE's capacity to face and overcome (i.e., absorb, reduce and accept) host market environmental barriers to legitimacy and competitionnamely in the form of constraining institutional complexities. Host market institutions delineate the "Rules of the Game" (North, 1990) which the MNE must play by to varying extents in order to be seen as "legitimate" within the foreign host market, and in so playing, reduce their liability of foreignness. In confronting institutional complexities in the host marketat the edge of chaosand overcoming them through absorption, reduction and acceptance processes, a MNE CAS and the entire complex system evolves.

The institutions and internationalization complex system model set out in this paper aligns with Paarlberg and Bielefeld's (2009) contention that the lens of complexity science is most appropriate when a researcher desires to examine complex organizational adaptation. To better explain observed phenomena for which other strategic frameworks struggle, researchers alternatively may examine how complex systems self-organize to adapt and improve their performance in dynamic and potentially volatile environments. We intend to contribute to the literature in three specific ways. First, the complex system model presented in our paper will help extend the nascent body of knowledge on the co-evolution of institutions and MNEs, by illuminating the reciprocal influences of model constructs and how they spur interaction, development, growth and evolution over time. Second, our study develops and extends theoretical constructs in a novel and yet important manner for the conceptual model. Complexity theory tenets are instrumental in examining reciprocal relationships of associated model constructs, which are constructed based on tenets from resource dependence theory, internationalization theory, and institutional theory. Third, there are significant implications for practical use of this model and paper, for MNEs as organizations, MNE managers, as well as for country level and region level policy development to attract, support and perpetuate initial and recursive IFDI from MNEs. Moving forward, we will first provide theoretical underpinning for complex system modeling and adaptation, and then justify the MNE as a complex adaptive system. Next, we will introduce the actual model, frame its reciprocal relationships, and extend propositions. We will finalize with a discussion of implications for research and practice, limitations and future research avenues.

Theoretical background

The multinational enterprise (MNE) is an organization that operates in two or more countries with multiple subunits linked through shared policies or strategy (Kostova and Zaheer, 1999). The OLI - ownership, location, internalization paradigm is a uniformly held theory of the MNE (Dunning, 1980, 1988). It delineates the means by which an MNE can overcome the costs/disadvantages of competing with domestic competitors by internationalizing and capitalizing on sources which optimize its access to value chains globally, while maximizing its internalized asset transfers. General systems theory (Boulding, 1956; Von Bertalanffly, 1950, 1972; Von Bertalanffy & Rapoport, 1956) extends that all systems are governed by general system laws, irrespective of the particular system properties and incumbent elements. This is an antecedent to the isomorphic nature of systems across the sciences, yet there is much variance in the principles that govern their behavioral pathways. A complexity view first requires an initial discussion of general systems, due to its assertion of the need to not only examine system components, but also relationships of organizations of components which result from dynamic interaction and manifests in behavioral outcomes (Von Bertalanffly, 1950, 1972). An open-systems organizational view led to a complexity view, namely that systems are any interconnected components which work together and are considered open, when there is an exchange of resources with the environment.

A complex organization is a set of interdependent parts, which together make up a whole that is interdependent with some larger environment. These organizations are depicted as systems with open, dynamic, and nonlinear attributes on which internal and external forces exert influence (Thiétart and Forgues, 1995). Complex systems and organizations alike display divergent nonlinear behavior that is difficult to predict (Casti, 1994). While general systems theory set out to discover principles underlying all systems with interrelated components, complexity theory contends that certain systems composed of many parts and interactions might behave in a simple, predictable manner, while other much simpler systems might behave in ways that are more complex and unpredictable (Anderson, 1999). Complexity theory helps us to recognize the multi-dimensional aspects of competencies, strategies, efficiency, and value decisions. Organizational complexity has been examined by a number of means, such as by number of subsystems and their specific alignment, denoted as vertical, horizontal or spatial complexity (Daft, 1992). Likewise, complexity science has given rise to organizational behavior modeling.

MNEs as complex adaptive systems

Modeling nonlinear organizational interactions is now feasible through the development of one particular conceptual and computational modeling tool, the complex adaptive system (CAS) (Anderson, 1999; Lichtenstein, 2000; Paarlberg & Bielefeld, 2009). CAS models deconstruct and simplify the complex, enabling strategic modeling of rapidly evolving systems which adapt and create new pathways. In CAS modeling, regularity in complex behavior emerges from the interaction of component parts/subunits, and at any level, order emerges from the aggregate in lower levels. Complex organizations of seemingly very complex phenomena can be explained more simply with CAS models (Anderson, 1999; Lichtenstein, 2000; Paarlberg & Bielefeld, 2009). First we confirm that an MNE constitutes a CAS by examining the MNE based on two extant CAS characterization frameworks: (1) four elements of a CAS (Anderson, 1999) and (2) three rules of interaction inherent to a CAS (Paarlberg & Bielefeld, 2009). Next we extend CAS theoretical principles to support MNE internationalization behavior (Lichtenstein, 2000) (see Table 1). According to Anderson (1999), CASs are comprised of four elements: 1) agents with schemata; 2) self-organizing networks sustained by importing energy; 3) coevolution to the edge of chaos; and 4) recombination and system evolution. Next, Paarlberg and Bielefeld's (2009) framework uses an agent oriented model to posit rules of interaction for the component units which comprise the system. To qualify as a CAS, the system must be: 1) complex as described by the many independent actors who interact in a variety of ways; 2) adaptive; and 3) "self-organizing" in that order may emerge in a number of ways due to long-term interactions among actors (Paarlberg & Bielefeld, 2009).

In line with CAS framework elements, at the organizational level of analysis MNEs are an embodiment and outcome produced by the dynamic system of their cross-border subsidiaries' (i.e., agents') location specific idiosyncratic behavior at a lower level of aggregation (Holland & Miller 1991). MNE agent's schema dictates their individual behavior contingent on the specific contextual environment (e.g., isomorphism, legitimacy, resource dependence) within which each subsidiary operates. Cognitive structure variance between agents exists at this lower level of aggregation, which is then amassed at the system level in the MNE. MNE agents are partially tied to one another, so that the behavior of an individual subsidiary depends in part on the behavior (or state) of some subset of all the subsidiaries and home base in the MNE system. This

Complex Adaptive Systems
Theoretical Principles
Multinational Enterprise
Change and transformation are inherent qualities of dynamic systems MNEs demonstrate incremental, adaptive and/or generative evolutionary change, transformation and recursive growth via outward FDI, resource dependence position changes, system organizational learning, and overcoming institutional complexities at the "edge of chaos”.
Organizational behavior is inherently nonlinear which may result in a range of responses from nonproportional to corresponding MNE global mindset and strategic focus affords consistent processes yet may follow conflicting non-linear paths with widely divergent outcomes.
Inputs do not cause outputs; elements of a system are interdependent and mutually causal MNE antecedent and outcome relationships are correlational but not causal and may follow nonlinear paths as a result of modeled system construct interdependencies and interconnectedness.
Organizations are defined according to their underlying order and principles MNEs are defined by their global mindset, strategic orientation, and internationalization arrangements; agents and interaction mechanisms afford structure at lower levels of aggregation
Change is encouraged by embracing tension, increasing information flow, and pushing authority downward MNEs face institutional complexities in host markets which serve as constraining boundary conditions for the MNE and subsidiaries. Tension is embraced through institutional complexity absorption, reduction and acceptance. Information flows internal and external to the MNE and subsidiaries facilitate MNE system organizational learning, which increases individual subsidiary authority as an agent.
Success is determined by optimizing resource flow and continuous learning FDI in host markets and system continuous learning interactively reduce firm resource dependency on externalities and increase resource dependencies by external entities on the MNE. MNE internationalization success is achieved via dynamic processes created through optimizing these resource dependence relationships, associated knowledge flows, and resultant organizational system learning - through tacit knowledge networks, direct and reverse knowledge spillover between home and host markets.

Applied and extended from Complex Adaptive SystemsTheoretical Principles (Lichtenstein, 2000)

Table 1. MNEs as Complex Adaptive Systems

connectedness is further reinforced and influenced by resource dependence relationships internal and external to the MNE in host markets, as well as the strength and timeliness of knowledge flows within the open system. MNE CAS agents are connected to one another by feedback loops (e.g., direct and reverse knowledge spillover, resource dependence relationships). Each agent observes and acts on location-specific information and knowledge, which is reciprocally influenced from the other agent subsidiaries and home organization to which it is connected. As such, no single component dictates the collective behavior of the system because it self-organizes (Drazin & Sandelands, 1992). The MNE's self-organized state requires importing energy into the system (Prigogine & Stengers, 1984), which is accomplished via the reciprocal relationships of this study's model (i.e., system continuous learning, resource dependence relationships, internationalization, and institutional complexities). As such, we extend the following.

Proposition 1: The multinational enterprise constitutes a complex adaptive system.

Reciprocal relationships

System continuous learning*

A valuable aspect of the MNE internationalization process is acquisition of experiential knowledge which is location-specific to target markets and their associated networks. It is a time-dependent process but critical to MNE success because they must overcome entry and operating obstacles stemming from their liability of foreignness or outsidership (Johanson & Vahlne, 2009). In this respect, organizational learning has been defined in relevant literature as the process whereby a firm modifies or otherwise changes its mental models, rules, processes or knowledge, and how this may maintain or improve organizational performance (Argyris & Schön, 1978; Brown & Duguid, 1991; Senge, 1990). This paper adopts this view and positions the construct system continuous learning as a continuous and interactive process within the complex system presented, reciprocally influencing and being influenced by participating agent constructs.

MNE CAS continuous learning may be optimized by identification of knowledge flows internal and external to the MNE, as established and strengthened through experience in international operations, past and present (see Figure 2). All other model constructs have varying degrees of reciprocal relationship with the system continuous learning construct (see Figure 1). Important delineations within the system continuous learning map of knowledge flows (i.e., Figure 2) are made based on whether the knowledge flow emanates from an internal or external source, and further, whether they are from direct sources, spillover effects or reverse spillover effects. As illustration, an MNE's acquisition of a cross-border subsidiary agent in the foreign host market, which fills a critical core competency, may provide access to both direct knowledge flowsnew technology or knowledge workers for exampleand reverse spillover effectsmarket institutional knowledge for example, by virtue of now having an institutionally legitimate position in the foreign host market. The interactive relationship of system continuous learning and institutional complexity reinforces that once this channel opens within the complex system, interactions perpetuate circularly. In other words, increased knowledge/learning means more institutional complexity absorption/reduction. More institutional complexity absorption/reduction fuels additional flexibility and capacity to learn within the host market due to widening of all associated knowledge flows and expansion of complex system boundaries.


Figure 1: Reciprocal relationships of constructs

In aggregate, knowledge development through systems continuous learning is cumulative and requires some degree of continuity and proximity between old and new competences. Increased MNE CAS absorptive capacity (Cohen & Levinthal, 1990), coupled with broad and deep network relationship ties, strong and weak, (Granovetter, 1973, 1983) aid in system-based organizational learning through knowledge flow optimization. These mechanisms for MNE system continuous learning can have both adaptive and generative characteristics, contingent on environmental context, which ultimately impacts internationalization degree and pace (Chiva et al., 2014). Further, continuous learning can occur through interaction with the external environment and participation (Chiva & Alegre, 2005, 2009), with sequential entry in a host market (Chang, 1995) acting as a catalyst.


Figure 2. Knowledge flows of MNE system continuous learning

From a geographic perspective, there are also spatial aspects of knowledge acquisition in that information flows are spatially constrained (Arregle et al., 2013; Buckley & Ghauri, 2004). Geographic proximity is conducive to increased/improved knowledge flow and transmission of organizational practice within MNE (Chang & Park, 2005; Strang, 2003). Thus, a MNE with foreign subsidiaries that are proximally closer will be able to share knowledge and organizational routines with more ease and less risk. Through specified host market-based incremental learning, a MNE can augment investment expansion costs, while enhancing absorptive capacity and reducing risk of time bound diseconomies. MNE CAS continuous learning is compounded by direct and indirect knowledge flows emanating from reverse spillover effects within the host market (Figure 2). Perri and Peruffo (2016) confirm the magnitude which externalities and knowledge spillover can have on MNE learning and development. As complex adaptive systems, MNEs can adapt to prevailing circumstances resulting from rapid experiential learning (Anderson, 1999; Houchin & MacLean, 2005). The iterative learning process (i.e., specific knowledge (tacit/explicit) acquired directly through foreign direct investment and indirectly through spillover) has a reciprocal relationship with resource dependence relationships internal and external to the MNE within the host market. Here the interplay of system continuous learning and optimization of resource dependence relationships spurs synergistic energy for the MNE CAS. Resource dependence theory (RDT) (Pfeffer & Salancik, 1978) tenets clarify how continuous learning may alter MNE resource dependence on the external environment for critical resources and how these resource dependence decisions may reciprocally serve as energy for improved breadth and depth of organizational learning.

Resource dependence relationships

RDT posits organizations to be open systems which are dependent on external environment contingencies. Though constrained by the external environment, organizations endeavor to decrease environmental uncertainty and interdependence through strategies that are environmentally enactive. As illustration, Gaffney and associates (2013) set forth a framework which reinforced the liner link between emerging MNE (EMNE) foreign direct investment (FDI) strategy and resource dependence positions. From a complexity perspective, however, there are three significant underpinnings of RDT at play in this paper's complex systems model of MNE internationalization: (1) the importance of an organization's ability to acquire and maintain resources (tangible and intangible) to its survival; (2) that the organization exists within a network of organizations that affect access to and the flow of needed resources; and (3) that firms strive to reduce dependence on other organizations (in terms of access to resources), while simultaneously trying to make other organizations more dependent on them (Pfeffer & Salancik, 1978).

Hillman et al.'s (2009) examination of RDT highlights how it has been used to demonstrate that organizations have options that they can enact to minimize environmental dependence. For example, RDT tenets support three reasons why a firm would engage in mergers and acquisitions (M&As): (1) to reduce competition by absorbing important competitors (i.e., industry consolidation); (2) to manage interdependence on supplier or buyers (i.e., vertical integration); and (3) to diversify operations to reduce dependence on its present organizational network (i.e., horizontal diversification). In each instance, system continuous learning better equips the MNE, through previously identified knowledge flows, to optimize resource dependence decisions both internally and externally. As each resource dependence decision is made and carried out over time, the knowledge flow process, which fuels continuous system learning, becomes more reciprocal and recursive in nature.

Sequential foreign direct investment (FDI) in a specific host market (e.g., Luo & Tung, 2007) spurs agent subsidiary co-evolution with one another. Each MNE cross-border subsidiary adapts to its idiosyncratic environment by striving to "increase a payoff or fitness function over time" (Holland & Miller, 1991), which is influenced in a mutually causal fashion by adaptive and generative learning (based on environmental contingencies) (Chiva et al., 2014), coupled with optimizing resource dependence relationships (i.e., reduce MNE resource dependence on external entities and increase resource dependencies by external entities on the MNE CAS). The reciprocal interaction effects of continuous learning, resource dependence relationships and MNE CAS development are further strengthened in that each agent subsidiary's payoff function is contingent on preferences and decisions made by other MNE agents, because their specific adaptive domains change (i.e., agent co-evolution) (Levinthal, 1997), and in the aggregate changes the MNE CAS (i.e., complex system evolution).

This resultant complex system's co-evolved state is dynamic with nonlinear behavior and outcomes which can be explained by a power law (Morel & Ramanujan, 1999), as small changes in behavior at one time point can produce small, medium, or large changes in outcomes at the next. Differentiated from chaotic equilibriasmall behavior changes often result in huge outcome changes, power-law equilibria exist at the "edge of chaos" (Kauffman, 1993). Within the context of my model, boundaries at the edge of chaos are fortified by the varying institutional complexities (formal and informal) which constrain organization behavior in the foreign host market. MNE strategy and behavior in facing these institutional boundary constraints, through complexity absorption, reduction, and acceptance, significantly stimulate an MNE, as a complex adaptive system, to evolve within, and as an agent within the large complex system itself. Agent subsidiaries are transformed, as well as the home-base operation, through system continuous learning, resource dependence structure changes (i.e., internal/external reliance shifts), and internationalization elicited in recursive FDI from one targeted acquisition to another. As such:

Proposition 2: Within our complex system model, reciprocal relationships exist between system continuous learning, resource dependence relationships, and internationalization, which collectively contribute to MNE CAS development.

The influence of institutions, both formal and informal, on organizational conduct is well established in the literaturewhether emanating from an economic or sociological derivation (e.g., Broom & Selznick, 1955; DiMaggio & Powell, 1983; Meyer & Rowan, 1977; North, 1990; Scott, 2001; Selznick, 1949, 1957; Williamson, 2000). Institutional theory has particularly deep roots in underpinning MNE strategy and conduct. For example, the Institution-based View (i.e., Peng, 2002, 2006; Peng et al., 2009; Peng et al., 2008) is one seminal prescription which fills a perceived research gap between the resource-based view (i.e., Barney, 1991) and the industry-based view (i.e., Porter, 2000). North (1990) portrayed institutions as being established by formal constraints such as rules, constitutions, and laws, as well as informal constraints such as customs, traditions, norms of behavior, conventions, taboos, sanctions and self-imposed codes of conduct, and lastly by their individual and collective enforcement characteristics. These constraints, influenced by the manner and degree of their enforcement, contribute directly to the institutional effectiveness of a geographically bound society. Aptly captured by North's (1990) original abstraction that they are "the rules of the game", institutions delineate a society's incentive framework, and by extension, dictate and bind constituent participant behavior (e.g., MNEs). MNE internationalization strategy and localization decisions are influenced to varying degrees in relation to the presence, strength, and visible effect of each of these informal and formal institutional structures, independently, and in combination (Holmes et al., 2013). Institutional complexity (e.g., Arregle et al., 2016) is reflected in this study as the degree of institutional diversity and number of factors within the host market environment; its constraining boundaries are established through formal institutions such as political, economic and regulatory, as well as informal institutions such as culture (societal and organizational).

When facing boundary conditions in the form of institutional complexities at the edge of chaos, the adaptive processes whereby an MNE CAS faces institutional complexities in the host market is best illustrated through application of Boisot and Child's (1999) paradigm, which posits that organizations adapt to new and complex environments through two modes: complexity reduction and complexity absorption. For the MNE CAS, institutional complexity absorption, reduction, and acceptance (see right side of Figure 3) is accomplished through an increased understanding, as facilitated by continuous learning, of the market environment complexities (e.g., informal/formal institutions) and directly addressing it through environmental enactment (Boisot & Child, 1999) in order to earn legitimacy and cope with the liability of foreignness (i.e., absorb, reduce, accept).

In accepting certain levels of institutional constraints in the host market, a MNE CAS establishes a varying level of stability for the complex system. Complexity absorption is facilitated in part by the MNE CAS through its resource coordination mechanisms (firm-specific advantages, home and subsidiary network advantages, and continuous systems learning), which create real options, arbitrage of individual country intra-market factors and risk-hedging strategies between entities within the host market (e.g., Arregle et al., 2013). System complexity is further reduced when the MNE CASS (complex adaptive subsidiary system) begins to optimize resource dependencies within the foreign market, internal and external to the CASS, and through exercising the many combinational deployment/redeployment possibilities of their fungible host market-based resources/capabilities/coordination mechanisms. Increased complexity absorption is achieved by simplifying CAS inputs through cognitive structure adoptionas carried out by home and subsidiary network advantages and continuous systems learningin capitalizing on complex system knowledge flows (Figure 2). It is thus equally important for the MNE to not only identify all potential sources of knowledge (knowledge flows) within the complex system, but also to simplify the processes whereby system organizational learning occurs as a result of these knowledge flows (i.e., cognitive structure adoption). MNE CAS development (to include the subsidiaryCASS) is positively augmented through both adaptive and generative learning (e.g., Chiva et al., 2014) within the host market, through respective movements which are incremental (adapt) and larger scale disruptive (generate). Absorbing, reducing and otherwise accepting institutional complexities in the host market influences the degree and pace of internationalization there, by expanding boundary conditions beyond the edge of chaos, thus creating a new reality through MNE CAS evolution. Accordingly, initial and recursive internationalization in this specified market further facilitates increased adaptation to institutional complexity within the host environment, thereby further reducing the liability of foreignness and increasing perceptions of legitimacy there. Concatenating energy flows from these interactive relationships between internationalization, MNE CAS development, and environmental institutional complexityas well as the other constructs in this model.

A complex system model of MNE internationalization

In non-linear combination (e.g., Chiva et al., 2014), the reciprocal interaction of all model constructs (i.e., MNE CAS development, system continuous learning, resource dependence relationships, internationalization, and institutional complexitiesFigure 1) comprise a complex system (Figure 3). The modeled complex system is a culmination of individual construct


Extended from Chiva, Ghauri, and Alegre (2014)

Figure 3: A complex system model of institutions and MNE internationalization

interactions with each other in both linear and non-linear reciprocal ways, and in aggregate on the MNE complex adaptive system itself. Synergy is achieved for the MNE through a systems approach to internationalization, recognizing this framework and method results in greater optimization of resources and associated dependence relationships, heightened overall system organizational learning, increased ability to face and overcome (i.e., absorb, reduce and accept) bounded host market institutional complexities at the edge of chaos, and a more efficient and effective internationalization process which is reciprocal and recursive in nature. Functioning as a complex system, the model can explain MNE internationalization behavior which may not be explainable through more deterministic methods rooted in linear causality. As such:

Proposition 3: A reciprocal relationship exists between MNE CAS development and institutional complexities faced during internationalization, which collectively forms a complex system.

Although examination of linear relationships between institutions and MNE internationalization informs the literature, in overall consideration of our discussion of construct reciprocal relationships within the complex system model, and their influence on the co-evolution of institutions and MNEs via the internationalization process over time, we extend the following final proposition.

Proposition 4: Our complex system model of institutions and MNE internationalization affords a more holistic relationship view over one based on aggregated linear correlational relationships.


The influence of resources, learning, and institutions to MNE internationalization is well documented in extant literature. Yet studies which examine similar constructs and linear relationships often have results/findings which are inconsistent and even contradictory. These inconsistencies might be a result of the nature of linear hypotheses and the tests themselves (e.g., variance in operationalization, controls, unaccounted nuisance variables, multicollinearity, multidimensional and multilevel data, etc.). Taking direct effects correlational snapshots at specific points in time between predictors and outcomes is somewhat static and may not accurately capture what actually occurs from a behavioral relationship perspectivewhich undoubtedly is more dynamic and less deterministic (e.g., Hitt et al., 1994; Hitt et al., 1997; Leelapanyalert & Ghauri, 2007; Molero, 1998). For example, understanding the influence of host market institutions (X) on MNE internationalization degree of investment (Y) may inform the literature. And yet, additional questions may further our understanding, such as: (1) Does MNE internationalization degree (Y) influence host market institutions (X)? (2) Is there a reciprocal relationship? (3) Does the relationship change over time and due to successive FDI?, and so on.

Researchers increasingly recognize that institutions and MNEs may indeed co-evolve in accordance with the internationalization process over time (i.e., Cantwell, Dunning & Lundan, 2010), considering institutions as a "response to complex forms of uncertainty associated with the rise in global economic interconnectedness" (North, 1990; 2005). This study has taken a more holistic and circular approach in examining concept reciprocal relationships based on mutual causality, where X has an influence on Y but Y also influences X (e.g., Chiva et al., 2014). By using complexity theory tenets to model the interplay between institutions and MNE internationalization using a complex systems approach, and in framing the reciprocal interaction of interrelated constructs, this study provides a more dynamic representation of MNE internationalization behavior. Further, it also provides insight into how institutions may serve as both antecedents and outcomes to MNE internationalization. When an MNE internationalizes based on perceived antecedent institutional constraints, the reciprocal process will change the institutions themselves. Organizations operating in our current global environment - a dynamic one characterized by hyper-competition, globalization, innovation, disruptive technologies, and the rise of the MNE, among others -- might necessitate examination with approaches better equipped to evaluate more reciprocal and mutually causal relationships, in other words, a complexity theory lens.


In practice, organizations can use this model as a learning tool to analyze past MNE behavior in order to identify and explain decisions and occurrences that went well, and also those that did notin consideration of model constructs (i.e., system continuous learning, resource dependence relationships, internationalization, the MNE as a complex adaptive system, and institutional complexity). Using a dynamic model such as this to examine past organizational behavior may help the MNE identify and focus on reciprocal relationships which add increased value relative to others. Modeling past behavior with a dynamic model of mutual causality also helps the MNE gain a greater overall understanding of complex systems models in general and hands-on experience of just how they may provide benefit in addition to a linear cause and effect and more static correlational examination of direct relationships. This complex systems model has implications for all organizations intending to internationalize, but may be specifically advantageous for MNEs from emerging, developing or transitioning markets. Arriving as latecomers to the game, they may use a complex systems approach to internationalization in an effort to make generative leaps over their competition. Further, due to its spatial characteristics, this model may additionally inform MNEs who have a geographic aspect to their internationalization strategy, for example geographic region-based localization.

Managers would benefit from a complex system perspective as well, in understanding that their organizations exist within a broader system including the environment (e.g., home and host institutions) and does so in a reciprocal and mutually causal way. This may help managers, leaders, and by extension their organizations, break away from continuously playing on a two-dimensional field of X and Y, and increase awareness that decisions made in any one area will influence many others, and may do so in a reciprocal manner. From a strategic perspective, learning how to play chess in a multidimensional systems field, where there are compounding reciprocal effects of each move, may not only help them win, but may also increase their understanding of exactly how to change the very environment within which they play.

Specific to policy development, country leaders can use our complex system model to help formulate strategy to attract, sustain, and perpetuate successive inward foreign direct investment (IFDI) from MNEs. Policies can center on the reciprocal relationship between country level formal institutions (e.g., regulatory, political and economic institutions) and MNE FDI, understanding that they co-evolve over time, as driven and facilitated by initial and successive FDI from individual MNEs, as well as in aggregate from all MNEs. Further, a more fine-grained understanding of each construct's reciprocal role (resource dependence relationships, system continuous learning, internationalization, and institutional complexities), internal and external to MNEs within the particular market might help policy development to attract, support, and perpetuate FDI. Effects of semi-globalization, over true globalization, may be disparately important for certain regions over othersin attracting recursive FDI. In these areas, leaders of geographically proximate countries would be well served in developing region-based policies using our complex system model. MNEs may increasingly incorporate a region-based strategic component to internationalization and FDI, whereby they explore/exploit region-based firm specific advantages, region network advantages and region-based organizational learning. Thus, country leaders may be inclined to collaborate across geographically proximate country borders in developing region-based policy, using on our complex system model, to compel recursive IFDI into the region. In so doing, they can build bridges in support of initiatives such as regional economic integration.

Limitations and future research

As a conceptual paper supported by a complex system model and propositions, certain limitations exist. Although our study is firmly grounded in theoretical constructsusing complexity theory to bridge and illuminate reciprocal construct relationships underpinned by institutional theory and internationalization theorythe lack of empirical investigation to clarify concepts results in certain limitations in the methodology of concept analysis. As such, the literature would be informed by future studies which operationalize presented constructs and test hypotheses in specific international laboratory condition contexts. For example, a cross-lagged structural equations model, built from latent or formative constructs and using longitudinal data, might be one way to examine the reciprocal influence and interplay between institutions and MNE internationalization. Additionally, case studies of individual MNEs and comparative studies between MNEs, within our specific complex system context, would likewise inform research and practice.


Whether the competitive landscape of our knowledge-based economy is characterized by globalization, semi-globalization, or even anti-globalization, international business is a dynamic environment characterized by multi-dimensional and multilevel agent interactions. Within this context, our study contributes to the growing literature on co-evolution of institutions and MNEs by presenting a complex system model which captures the reciprocal interplay between institutions and MNE internationalization, based on the simultaneous and alternating influences of resource dependence relationships, system continuous learning, and institutional complexities. Taking a nonlinear approach to the relationships between these international business phenomena informs the literature through an important paradigm, the complex system.



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