6
EMERGING COMPLEXITY IN INTERNATIONAL MANAGEMENT
Joo-Seng Tan
Introduction
In the twenty-first century, there have been seismic shifts from stability to instability, and from predictability to non-predictability, in every area of life. Extraordinary political, economic, social, and technological transformations and disruptions have unleashed “tsunamis” of chaos, volatility, and complexity in all aspects of life. Recent studies by IBM (2010) and KPMG (2011) spotlight the importance of managing complexity in the global marketplace. Key findings reveal that businesses face increasing complexity that presents both management challenges and opportunities. In fact, in the IBM study, one key finding was the significant gap between the need for complexity management and the organizational resources as well as management capabilities of businesses to address that need.
When we reviewed past and recent research in international business and international management in the leading academic journal publications in the field, there appears to be a paucity of research on complexity management. Although this might appear to be the case, upon closer scrutiny, in viewing past research from a complexity perspective, we could identify certain emerging possibilities and potential new areas of research. This is not meant to be an exhaustive review of all past research in international business and international management; neither is it meant to be a detailed review of complexity theory and complexity management. The field of complexity science has attracted many adherents from a variety of backgrounds and motivations. For those interested in finding out more about complexity science and complexity management, a selection of related resources is available in the Bibliography (at the end of this chapter). A major intent of this chapter is to identify new possibilities and areas for complexity research in international business and international management, and to discuss the potential implications for international management education.
Complexity management and organizations as complex adaptive systems
Olmedo (2010) provides a concise and systematic account of complexity management, explaining the evolution of management paradigm from “Newtonian” (Stage 1) to “Randomness” (Stage 2) to “Complexity” (Stage 3) (Table 6.1).
| Stage 1 Newtonian paradigm | Stage 2 Randomness paradigm | Stage 3 Complexity paradigm | |
Assumption |
Strong causation |
Weak causation |
Chaos, complexity, emergence |
View of organization |
Organization is unique and isolated, rigid and hierarchical. Closed system. |
Organization is formed by different agents interrelated at different levels. Interdependent system. |
Organization is a complex adaptive system. Open system. |
View of management |
Cause and effect are linearly related, so perfect knowledge and accurate predictions are possible. Success comes from managers' capacity to anticipate, making perfect forecasting and coming up with fixed rules to guide organization. |
Cause and effect are related approximately linear. Increasing information is necessary to make more accurate forecasting. Success comes from groups rather than individuals. |
Cause and effect are non-linearly related. Sensitivity to initial conditions invalidates perfect knowledge and forecasting. The same starting conditions can produce different outcomes, depending on interactions of elements in the system. Organizations are unstable and dramatic changes can occur unexpectedly. |
View of sources of information and control |
Top-down, anticipative. |
Bottom-up, control emerge through habituation of routines and norms. |
Bottom-up and top-down. There are simple, general adaptive guidelines and rules that emerge from interactions. |
Source:Adapted from Olmedo (2010).
The major shifts in management from the Newtonian paradigm to the emerging Complexity paradigm can be viewed from four key aspects: the underlying or prevailing assumptions, views of organization, views of management, and views of sources of information and control. The Newtonian paradigm of management is underpinned by one major assumption; that is, its belief in strong causation: an action leads to an outcome, and this cause—effect relationship is assumed to be linear. This makes causal analysis and accurate predictions possible. Control is possible as decisions made at the top of the organizational hierarchy cascade to lower levels, and alignment can be achieved by exerting one's power and influence over another. However, with the shift towards the Complexity paradigm, the earlier assumptions that were central to the Newtonian paradigm are no longer tenable.
One significant aspect of the emerging “Complexity” paradigm in twenty-first-century management is the view of organizations as “complex adaptive systems.” A “complex adaptive system” is different from the earlier mechanistic and predictable system in Newtonian paradigm in significant ways. Forecasting the future, mitigating risks, and making trade-offs become more challenging as cause and effect are not linearly related and, having the same starting conditions, can produce very different outcomes depending on the dynamic interactions of components or elements in the system. In contrast to the earlier Newtonian paradigm which viewed organizations as being stable, the Complexity paradigm views organizations as unstable systems that can experience unexpected or disruptive changes. The emergence of the Complexity paradigm has had a major impact on all aspects of management, and here we have just highlighted a few such as managerial control and decision-making.
A “complex adaptive system” is characterized by “emergence”: the interactions between the elements of the system and the environment create new properties (Gleick, 1987). These properties, named emergent properties, create new structures and changes in the roles of the elements and their behavioral patterns. Some defining attributes include the following:
• open, self-organizing systems
• disequilibrium and order/disorder
• non-linear dynamics and unpredictability (Holland, 1992).
In contrast to the earlier Newtonian paradigm that viewed organizations as closed systems, the Complexity paradigm views organizations as open systems with self-organizing properties. It is also evident that the Complexity paradigm's view of organizations as a “complex adaptive system” challenges the earlier Newtonian paradigm's assumption that organizations need to be controlled to achieve order, predictability, and equilibrium (and eliminate disorder, non-predictability, and disequilibrium) . We should point out that order and disorder (equilibrium and disequilibrium) can co-exist in complex adaptive systems, unlike the Newtonian paradigm.
Complexity scientists use the term “complex adaptive systems” to explain how living systems work. All “complex adaptive systems” exhibit a capacity for pattern recognition and employ this to anticipate the future and learn to recognize the anticipation of change. In summary, this chapter follows the Complexity paradigm in viewing organizations as “complex adaptive systems” characterized by instability, disorder, disequilibrium, non-predictability, non-linearity, and emergence and relates it to international business and international management.
Three types of complexity: algorithmic, deterministic, and aggregate
Although complexity as a term is widely used in research, its meaning varies depending on the disciplinary perspective and its underlying assumptions as well as the research focus. Here, we adopt Manson's (2001) classification of complexity research and apply his classification to review past research in international business and international management, and discuss implications for international management education. According to Manson, complexity research can be divided into three types: (1) algorithmic complexity, (2) deterministic complexity, and (3) aggregate complexity. All these three types of complexity are underpinned by different assumptions.
Algorithmic complexity posits that the complexity of a system lies in the difficulty experienced in describing system characteristics. Deterministic complexity contends that the interaction of two or three key variables can create largely stable systems prone to sudden discontinuities. Deterministic complexity in this sense is closely related to chaos theory and catastrophe theory. Aggregate complexity is concerned with how individual elements work in concert to create systems with complex behaviors.
Algorithmic complexity
A key feature of algorithmic complexity is in information theory. In information theory, the simplest computational algorithm can reproduce system behaviors by condensing the myriad interactions between system components into simple measures. Algorithmic complexity could open up new avenues of research in international business and international management. Take the conceptualization of “global mindset” (Kedia and Mukherji, 1999) and its more recent conceptualization and development by scholars at Thunderbird led by Mansour Javidan and associates as an example. The multiple interactions between the components that constitute “global mindset” and the dynamic interactions between “global mindset” and other individual, team, organizational, and contextual components could be approached from an algorithmic complexity perspective. Instead of just focusing on the differentiation between “local mindset” and “international mindset” (Cunha, 2005), we could study the interactions between “local mindset” and “international mindset,” and how these interactions also interact with other mindsets. If we approach “global mindset” as an “emergent” complex adaptive system, we might be able to gain deeper insights on how “global mindset” operates as a complex adaptive system. Algorithms of “global mindset” could be developed with the aid of agent-based modeling and other computational models. Another example of recent research that could adopt an algorithmic complexity perspective is Harvey, Griffith, Kiessling and Moeller's (2011) development of a multi-level frame of reference for global decision-making.
Deterministic complexity
Deterministic complexity, in so far as it relates closely to chaos theory and catastrophe theory, offers interesting research possibilities in international business and international management. The premise that systems can experience large, disruptive, and unexpected changes due to a small change in another is highly relevant to the study of financial, economic, and other kinds of crises in international business and international management. As we experience more major crises and disruptions in international business and international management, we can expect more research that could adopt a deterministic complexity perspective.
Chaotic or catastrophic complex systems could be described and understood in simple mathematical terms. The underlying premise is that a few key variables related through a set of known equations can describe the behavior of a complex system; for example, the equation for the standard logistic model of population growth. Malthussian population theory depends on positive feedback for explosive growth and negative feedback for population decline. Complex adaptive systems are sensitive to small, incremental changes in key variables (under certain conditions) that can produce massive, non-linear effects. The popular term, “butterfly effect,” exemplifies sensitivity to initial conditions, whereby the flapping of a butterfly's wings can influence distal weather systems.
One challenge is obviously the collection of data. A large amount of time series data is needed to prove that a complex adaptive system has deterministic complexity. However, a more important challenge in research in international business and international management is to discover “tipping points” in real data sets. Identifying which events could potentially trigger a “catastrophic” market failure would be a challenge, for instance. The availability of sophisticated computational modeling techniques and algorithmic tools such as minimum spanning tree (MST) can partially help address some of these challenges. Another challenge would be to identify the few key variables that matter, but this could be at the expense of simplifying a very complex phenomenon like a financial crisis or economic recession.
Aggregate complexity
While both algorithmic complexity and deterministic complexity rely on simple mathematical equations and a number of assumptions of how complex systems work, aggregate complexity considers systems of linked components and the synergy resulting from the interactions of system components, rather than individual components. In international business and international management research, aggregate complexity could offer rich possibilities.
According to Manson (2001), aggregate complexity has four key attributes: (1) relationships between entities; (2) internal structure and surrounding environment; (3) learning and emergent behaviors; (4) different means by which complex systems change and grow. All these four key attributes are central to international business and international management.
The heart of aggregate complexity lies in the relationships between components. At the disciplinary level in international business and international management, many “components” such as international business (IB), international management (IM), and international strategy (IS) are interrelated (Eden, Li and Dai, Chapter 4, this volume). How these “components” of IB, IM, and IS interact and change and generate “emergence” could constitute a research domain and help us understand not just the evolution of our discipline but also learn how our discipline has adapted to the changing business environment and increasingly multidisciplinary research landscape.
In aggregate complexity, relationships have different strengths. The relationships of differing strengths between component parts define the internal structure of a complex system. Components with very tight connections form sub-systems such as competitive niches. Any given component can also belong to multiple sub-systems. For example, if we view the development of increasingly complex relationships between the multinational enterprises (MNEs) and their production networks as “biological systems” adapting to the changing “fitness landscape,” we could get new insights on the emergent behaviors and synergies unleashed by the adaptive relationships of MNEs and their production networks (or global supply chain or distribution channel). Another example is the relationships between the internal dynamics of MNEs and their subsidiaries and the processes of strategic coupling, de-coupling, and re-coupling.
A complex system owes its existence to relationships with its environment. The international affects the national as much as the national affects the international in the increasingly intertwined and complex international business and global marketplace, as evident in global strategy, global marketing, global supply chain, global—local interface, and cross-cultural management. Alan Rugman (2012), in a recent editorial in the journal Multinational Business Review, poses the tantalizing question, “Does Country Matter?” It appears that national responses or national responsiveness (and the effectiveness of such national responses) to financial crises is an area of research that would benefit from a complexity perspective.
Viewed from this aggregate complexity perspective, some examples of other past research that could have focused more on learning, adaptability, and emergence would include:
• internationalization process and global expansion of MNEs (e.g., Oladottir, Hobdari, Papanastassiou, Pearce and Sinani, 2012);
• establishment of international joint ventures and strategic alliances (e.g., Merchant, 2005);
• trust-building process in joint ventures and alliances (e.g., Muethel and Hoegl, 2012);
• knowledge transfer in MNEs (e.g., Hong and Nguyen, 2009; Liao andYu, 2012);
• retention of management teams (e.g., Kiessling, Harvey and Moeller, 2012);
• global talent management (e.g.,Tarique and Schuller, 2010);
• risk management (e.g., Bonabeau, 2007; Figueira-de-Lemos,Johanson and Vahlne, 2011), and
• development of international capabilities (e.g., Hsu, Lien and Chen, 2012).
The fourth key attribute of aggregate complexity, that is, change and evolution, is the focus here. The critical value is that aggregate complexity challenges conventional notions of stability and change. Complexity research is interested in “multiple equilibria, non-predictability … historical path dependence, and asymmetry” (Arthur, 1999). Some examples of past research that could have embraced complexity and studied “multiple equilibria” and “path dependence” would include:
• Collings, Scullion and Morley's (2007) study of changing patterns of global staffing in MNEs;
• Chung and Beamish's (2012) study of joint ventures' post-formation change processes, and
• Egri, Khilji, Ralson, Palmer, Girson, Milton, Richards, Ramburuth and Mockaitis's (2012) study of complex values change at national level.
Implications for international management education
In this section, we will focus on three areas: (1) implications for the discipline and research, (2) implications for teaching international management, and (3) implications for leadership and management development in international business.
The emergence of the “Complexity” paradigm in management is in a sense a good “fit” with the discipline of international business and international management. As Eden, Dai and Li (Chapter 4, this volume) point out, “IM and IB have strong eclectic orientations that have been both assets and liabilities for the development … the diversity in theoretical and methodological perspectives makes IM and IB research interesting and open to new ideas.” IM and IB as a discipline that is characterized by “openness,” “eclectic orientations,” and “diversity of perspectives” could be said to possess the characteristics of “complex adaptive systems.” We view these characteristics as “assets” not “liabilities.”
The “Complexity” paradigm would also foster “emerging themes of investigation in international business requiring scholars to build on insights from other disciplines, pushing the frontiers of the field” (Gentile-Lüdecke and Lundan, Chapter 7, this volume). Gentile-Lüdecke and Lundan declare that “[the] future of IB field lies in an open and collaborative effort in which scholars from various disciplines who have an interest in MNEs, mixing different assumptions, causal mechanisms, and levels of analysis.” In essence what is envisioned here is that the IB field truly becomes a complex adaptive system on its own, reflecting the “complex adaptive system” characteristics of the MNEs and the international business context.
There is a critical need to re-examine what is “country,” “MNE,” “international,” “global,” “local,” “business,” “management,” “culture” terms and constructs that we use in our research and teaching. There is also a critical need to re-examine not only our research methodologies but also our teaching pedagogies.
The way we teach IB and IM may need to be reviewed. If students need to embrace and address complexity, one can ask two key questions, (1) “What kind of curriculum and educational experience would be most relevant in teaching IB and IM?,” and (2) “How can we design an optimal learning environment that captures ‘complexity’ in teaching IB and IM?” Other related questions could include: Should the current basis for organizing and structuring the IB and IM curriculum be reviewed? How can students learn about “complexity” as a “threshold concept?” According to Meyer and Land (2006), a “threshold concept” can be compared to a “portal”: it opens up a new and previously inaccessible way of thinking about something.
“Complexity” as a “threshold concept” should represent “a transformed way of understanding, or interpreting, or viewing something without which the [IB and IM] learner cannot progress” (Meyer and Land, 2006, p. 3). We propose that “complexity” should be taught as a “threshold concept” in IB and IM.
Finally, how managers are developed also needs to be reviewed. At the beginning of this chapter, we made reference to the IBM study on complexity (that found the gap between the need to manage complexity and the lagging capabilities), and highlighted the significance of developing leadership capabilities to manage complexity. We ask the question, “How can managers develop skills in ‘managing complexity’?” We think there is a need for organizations to review their current leadership development programs and to embrace “complexity” as the core or heartbeat of any leadership or management development programs.
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