Multilevel Approach to Research in Organizations

Multilevel Approach to Research in Organizations


  • Micro approach
  • Macro approach
  • Micro-macro approach
  • Hierarchy
  • Multi Scale
  • Multi Level
  • Heterarchy
  • Holarchy
  • Holonomic
  • Holons
  • Networks
  • Agents
  • Interaction
  • Aggregation
  • Disaggregation
  • Emergence
  • Complexity
  • The Fractal Company (Book)
  • The Fractal Organization (Book)
  • Viable Systems Model (Stafford Beer)
  • Self Similarity
  • Power Laws



From Multilevel Theory, Research, and Methods in Organizations

Foundations for Multilevel Theory in Organizations

Conceptual Underpinnings: General Systems Theory

General systems theory (GST) has been among the more dominant intellectual perspectives of the twentieth century and has been shaped by many contributors (e.g., Ashby, 1952; Boulding, 1956; Miller, 1978; von Bertalanffy, 1972). Systems concepts originate in the “holistic” Aristotelian worldview that the whole is greater than the sum of its parts, in contrast with “normal” science, which tends to be insular and reductionistic. The central goal of GST is to establish principles that generalize across phenomena and disciplines-an ambitious effort that is aimed at nothing less than promoting the unity of science.

Systems principles are manifest as analogies or logical homologies. Logical homologies represent identical concepts (that is, isomorphism), and parallel processes linking different concepts (that is, homology), that generalize to very different systems phenomena (von Bertalanffy, 1972). For example, it is noted that open systems counteract the second law of thermodynamics-entropy-by importing energy and information from the external environment, and transforming it, to maintain homeostasis.

Feedback and servo- mechanisms are the basis for the purposive responses of cybernetic systems. Organizational systems are proposed to have analogous structures and processes (e.g., Katz & Kahn, 1966; Miller, 1978).

Whether one takes a more macro (Parsons, 1956, 1960) or micro (Allport, 1954) perspective, the influence of GST on organizational science has been pervasive. Unfortunately, however, that influence has been primarily metaphorical. The bureaucratic-closed systems-machine metaphor is contrasted with a contingent-open systems-living organism metaphor. Although metaphor has important value-virtually all formal theory is rooted in underlying metaphor (Morgan, 1983)-lack of specificity, formal identity, and precise definition can yield truisms that mislead and fail the test of science (Pinder & Bourgeois, 1982; Bourgeois & Pinder, 1983). GST has exhibited heuristic value but has contributed relatively little to the development of testable principles in the organizational sciences (Roberts et al., 1978). It is to this latter concern that the multilevel perspective is directed.

As social systems, organizations are qualitatively distinct from living cells and other concrete physical systems. The goal of the multilevel perspective is not to identify principles that generalize to other types of systems. Although laudable, such an effort must often of necessity gloss over differences between qualitatively different systems in order to maintain homology across systems (compare Miller, 1978). The primary goal of the multilevel perspective in organizational science is to identify principles that enable a more integrated understanding of phenomena that unfold across levels in organizations.

Macro and Micro Perspectives

Fundamental to the levels perspective is the recognition that micro phenomena are embedded in macro contexts and that macro phenomena often emerge through the interaction and dynamics of lower-level elements. Organizational scholars, however, have tended to emphasize either a micro or a macro perspective. The macro perspective is rooted in its sociological origins. It assumes that there are substantial regularities in social behavior that transcend the apparent differences among social actors. Given a particular set of situational constraints and demographics, people will behave similarly. Therefore, it is possible to focus on aggregate or collective responses and to ignore individual variation. In contrast, the micro perspective is rooted in psychological origins.  It assumes that there are variations in individual behavior, and that a focus on aggregates will mask important individual differences that are meaningful in their own right. Its focus is on variations among individual characteristics that affect individual reactions.

Neither single-level perspective can adequately account for organizational behavior. The macro perspective neglects the means by which individual behavior, perceptions, affect, and interactions give rise to higher-level phenomena. There is a danger of superficiality and triviality inherent in anthropomorphization. Organizations do not behave; people do.  In contrast, the micro perspective has been guilty of neglecting contextual factors that can significantly constrain the effects of individual differences that lead to collective responses, which ultimately constitute macro phenomena (House et al., 1995; Klein et al., 1994; Roberts et al., 1978; Rousseau, 1985).

Macro researchers tend to deal with global measures or data aggregates that are actual or theoretical representations of lower-level phenomena, but they cannot generalize to those lower levels without committing errors of misspecification. This renders problematic the drawing of meaningful policy or application implications from the findings.  For example, assume that we can demonstrate a significant relationship between organizational investments in training and organizational performance. The intuitive generalization-that one could use the magnitude of the aggregate relationship to predict how individual performance would increase as a function of increased organizational investments in training-is not supportable, because of the well-known problem of ecological inference. Relationships among aggregate data tend to be higher than corresponding relationships among individual data elements (Robinson, 1950; Thorndike, 1939). This fact continues to be a significant difficulty for macro-oriented policy disciplines-sociology, political science, economics, education policy, epidemiology-that attempt to draw individual-level inferences from aggregate data.

Micro researchers suffer from an obverse problem, which also makes the desire to influence human resource management policy difficult. We may, for example, be able to show that individual cognitive ability increases individual performance. However, we cannot then assert that selection systems that produce higher aggregate cognitive ability will necessarily yield improved organizational performance. Perhaps they will, but that inference is not directly supported by individual-level analyses. Misspecifications of this sort, however, are not unusual (Schmidt, Hunter, McKenzie, & Muldrow, 1979). Such “atomistic fallacies,” in which organizational psychologists suggest team- or organization-level interventions based on individual-level data, are common in our literature.

A levels approach, combining micro and macro perspectives, engenders a more integrated science of organizations. House and colleagues (1995) suggest the term meso because it captures this sense that organizational science is both macro and micro. Whatever it is called, we need a more integrated approach. The limitations that the organizational disciplines suffer with respect to influencing policy and applications can be resolved through the development of more complete models of organizational phenomena-models that are system-oriented but do not try to capture the complexity of the entire system. Instead, by focusing on significant and salient phenomena, conceptualizing and assessing at multiple levels, and exhibiting concern about both top down and bottom-up processes, it is possible to build a science of organizations that is theoretically rich and application-relevant.


A multilevel approach to theory and research in organizations: Contextual, temporal, and emergent processes

Steve W, J. Kozlowski

Katherine J. Klein



Multilevel Theory and Research


Stan Gully




Gilad Chen




Multilevel Issues in Supply Chain Management


Marian Oosterhuis, Eric Molleman, Taco van der Vaart





Modeling consensus emergence in groups using longitudinal multilevel methods


Jonas W. B. Lang


Paul D. Bliese

Alex de Voogt

















Micro/Meso/Macro Levels of Analysis

Joshua B. Barbour






‘Multi-level research into the social: An old wine in an old forgotten bottle?’

Harwood, S







Multilevel Theory, Research, and Methods in Organizations: Foundations, Extensions, and New Directions

Katherine J. Klein (Editor),

Steve W. J. Kozlowski (Editor)

ISBN: 978-0-787-95228-0Aug 2013, Pfeiffer






Complexity Theory and Organization Science 

 Philip Anderson

Source: Organization Science, Vol. 10, No. 3, Special Issue: Application of Complexity Theory to Organization Science, (May – Jun., 1999), pp. 216-232







  Dr.-Ing. Wilfried Sihn






Analysing Enterprise Models from a Fractal Organisation Perspective – Potentials and Limitations

Kurt Sandkuhl and Marite Kirikova






 The Theory of the Organization and the New Paradigms

Aquiles Limone, Milan Marinovic






The Fractal Company: A Revolution In Corporate Culture

by H.J. Warnecke

Springer-Verlag, Berlin, 1993, 228 pp.,





The Fractal Organization: Creating sustainable organizations with the Viable System Model


by Patrick Hoverstadt






Multi Level Analysis

Joop J Hox

Theoretical approaches to managing complexity in organizations: A comparative analysis

Luz E. Bohórquez Arévaloa,∗, Angela Espinosa







From Micro to Meso: Critical Steps in Conceptualizing and Conducting Multilevel Research




Systems and Organizational Cybernetics

Systems and Organizational Cybernetics


From  System Dynamics and the Evolution of Systems Movement A Historical Perspective


The systems movement has many roots and facets, with some of its concepts going back as far as ancient Greece. What we call ”the systems approach” today materialized in the first half of the twentieth century. At least, two important components should be mentioned, those proposed by von Bertalanffy and by Wiener.

Ludwig von Bertalanffy, an American biologist of Austrian origin, developed the idea that organized wholes of any kind should be describable, and to a certain extent explainable, by means of the same categories, and ultimately by one and the same formal apparatus. His General Systems Theory triggered a whole movement, which has tried to identify invariant structures and mechanisms across different kinds of organized wholes (for example, hierarchy, teleology, purposefulness, differentiation, morphogenesis, stability, ultrastability, emergence, and evolution). 

Norbert Wiener, an American mathematician at Massachusetts Institute of Technology, building on interdisciplinary work, accomplished in cooperation with Bigelow, an IBM engineer, and Rosenblueth, a physiologist, published his seminal book on Cybernetics. His work became the trans-disciplinary foundation for a new science of capturing, as well as designing control and communication mechanisms in all kinds of dynamical systems. Cyberneticians have been interested in concepts such as information, communication, complexity, autonomy, interdependence, cooperation and conflict, self-production (”autopoiesis”), self-organization, (self-) control, self-reference, and (self-) transformation of complex dynamical systems.

From System Dynamics and the Evolution of Systems Movement A Historical Perspective


Along the tradition which led to the evolution of General Systems Theory (Bertalannfy, Boulding, Gerard, Miller, Rapoport) and Cybernetics (Wiener, McCulloch, Ashby, Powers, Pask, Beer), a number of roots can be identified, in particular:

  • Mathematics (for example, Newton, Poincaré, Lyapunov, Lotka, Volterra, Rashevsky);
  • Logic (for example, Epimenides, Leibniz, Boole, Russell and Whitehead, Goedel, Spencer-Brown);
  • Biology, including general physiology and neurophysiology (for example, Hippocrates, Cannon, Rosenblueth, McCulloch, Rosen);
  • Engineering, including its physical and mathematical foundations (for example, Heron, Kepler, Watt, Euler, Fourier, Maxwell, Hertz, Turing, Shannon and Weaver, von Neumann, Walsh); and
  • Social and human sciences, including economics (for example, Hume, Adam Smith, Adam Ferguson, John Stuart Mill, Dewey, Bateson, Merton, Simon, Piaget).


From System Dynamics and the Evolution of Systems Movement A Historical Perspective

Levels of Organizations 

In this strand of the systems movement, one focus of inquiry is on the role of feedback in communication and control in (and between) organizations and society, as well as in technical systems. The other focal interest is on the multidimensional nature and the multilevel structures of complex systems. Specific theory building, methodological developments and pertinent applications have occurred at the following levels:

  • Individual and family levels (for example, systemic psychotherapy, family therapy, holistic medicine, cognitivist therapy, reality therapy);
  • Organizational and societal levels (for example, managerial cybernetics, organizational cybernetics, sociocybernetics, social systems design, social ecology, learning organizations); and
  • The level of complex technical systems (systems engineering).


From System Dynamics and the Evolution of Systems Movement A Historical Perspective

Mathematical/Quantitative Strand


As can be noted from these preliminaries, different kinds of system theory and methodology have evolved over time. One of these is Jay W. Forrester’s theory of dynamical systems, which is a basis for the methodology of System Dynamics. In SD, the main emphasis is on the role of structure, and its relationship with the dynamic behavior of systems, modeled as networks of informationally closed feedback loops between stock and flow variables. Several other mathematical systems theories, for example, mathematical general systems theory (Klir, Pestel, Mesarovic & Takahara), as well as a whole stream of theoretical developments, which can be subsumed under the terms ”dynamical systems theory” or ”theories of non-linear dynamics,” for example, catastrophe theory, chaos theory, complexity theory have been elaborated. Under the latter, branches such as the theory of fractals (Mandelbrot), geometry of behavior (Abraham) and self- organized criticality (Bak) are subsumed. In this context, the term ”sciences of complexity” has also been used. In addition, a number of essentially mathematical theories, which can be called ”system theories,” have emerged in different application contexts, examples of which are discernible in such fields as:

  • Engineering, namely information and communication theory and technology (for example, Kalman filters, Walsh functions, hypercube architectures, automata, cellular automata, artificial intelligence, cybernetic machines, neural nets);
  • Operations research (for example, modeling theory and simulation methodologies, Markov chains, genetic algorithms, fuzzy control, orthogonal sets, rough sets);
  • Social sciences, economics in particular (for example, game theory, decision theory); and
  • Ecology (for example, H. Odum’s systems ecology).

Qualitative System Theories

Examples of essentially non-mathematical system theories can be found in many different areas of study, for example:

  • Economics, namely its institutional/evolutionist strand (Veblen, Myrdal, Boulding);
  • Sociology (for example, Parsons’ and Luhmann’s social system theories, Hall’s cultural systems theory);
  • Political sciences (for example, Easton, Deutsch, Wallerstein);
  • Anthropology (for example, Levi Strauss’s structuralist-functionalist anthropology);
  • Semiotics (for example, general semantics (Korzybski, Hayakawa, Rapoport)); and
  • Psychology and psychotherapy (for example, systemic intervention (Bateson, Watzlawick, F. Simon), fractal affect logic (Ciompi)).

Quantitative and Qualitative

Several system-theoretic contributions have merged the quantitative and the qualitative in new ways. This is the case for example in Rapoport’s works in game theory as well as General Systems Theory, Pask’s Conversation Theory, von Foerster’s Cybernetics of Cybernetics (second order cybernetics), and Stafford Beer’s opus in Managerial Cybernetics. In all four cases, mathematical expression is virtuously connected to ethical, philosophical, and epistemological reflection. Further examples are Prigogine’s theory of dissipative structures, Mandelbrot’s theory of fractals, Kauffman’s complexity theory, and Haken’s Synergetics, all of which combine mathematical analysis and a strong component of qualitative interpretation.

System Dynamics vs Managerial Cybernetics

At this point, it is worth elaborating on the specific differences between two major threads of the systems movement: the cybernetic thread, from which Managerial Cybernetics has emanated, and the servomechanic thread in which SD is grounded [Richardson 1999]. As Richardson’s detailed study shows, the strongest influence on cybernetics came from biologists and physiologists, while the thinking of economists and engineers essentially shaped the servomechanic thread. Consequently, the concepts of the former are more focused on the adaptation and control of complex systems for the purpose of maintaining stability under exogenous disturbances. Servomechanics, on the other hand, and SD in particular, take an endogenous view, being mainly interested in understanding circular causality as a source of a system’s behavior. Cybernetics is more connected with communication theory, the general concern of which can be summarized as how to deal with randomly varying input. SD, on the other hand, shows a stronger link with engineering control theory, which is primarily concerned with behavior generated by the control system itself, and the role of nonlinearities. Managerial cybernetics and SD both share the concern of contributing to management science, but with different emphases and with instruments that are, in principle, complementary. Finally, the quantitative foundations are generally more evident in the basic literature on SD, than in the writings on Managerial Cybernetics, in which the mathematical apparatus underlying model formulation is confined to a small number of publications [e.g., Beer 1962, 1981], which are less known than the qualitative treatises.

Positivistic Tradition

A positivistic methodological position is tendentially objectivistic, conceptual–instrumental, quantitative, and structuralist–functionalist in its approach. An interpretive position, on the other hand, tendentially emphasizes the subjectivist, communicational, cultural, political, ethical, and esthetic: the qualitative, and the discursive aspects. It would be too simplistic to classify a specific methodology in itself as ”positivistic” or as ”interpretative.” Despite the traditions they have grown out of, several methodologies have evolved and been reinterpreted or opened to new aspects (see below).

In the following, a sample of systems methodologies will be characterized and positioned in relation to these two traditions:

  • ”Hard” OR methods. Operations research (OR) uses a wide variety of mathematical and statistical methods and techniques––for example of optimization, queuing, dynamic programming, graph theory, time series analysis––to provide solutions for organizational problems, mainly in the domains of operations, such as production and logistics, and finance.
  • Living Systems Theory. In his LST, James Grier Miller [1978], identifies a set of 20 necessary components that can be discerned in living systems of any kind. These structural features are specified on the basis of a huge empirical study and proposed as the ”critical subsystems” that ”make up a living system.” LST has been used as a device for diagnosis and design in the domains of engineering and the social sciences.
  • Viable System Model. Stafford Beer’s VSM specifies a set of control functions and their interrelationships as the sufficient conditions for the viability of any human or social system [cf. Beer, 1981]. These are applicable in a recursive mode, for example, to the different levels of an organization. The VSM has been widely applied in the diagnostic mode, but also to support the design of all kinds of social systems. Specific methodologies for these purposes have been developed, for instance, for use in consultancy. The term viable system diagnosis (VSD) is also widely used.

Interpretative Tradition

The methodologies addressed up to this point have by and large been created in the positivistic tradition of science. However, they have not altogether been excluded from fertile contacts with the interpretivist strand of inquiry. In principle, all of them can be considered as instruments to support discourses about different interpretations of an organizational reality or alternative futures studied in concrete cases.

  • Interactive Planning. IP is a methodology, designed by Russell Ackoff [1981], and developed further by Jamshid Gharajedaghi, for the purpose of dealing with ”messes” and enabling actors to design their desired futures, as well as bring them about. It is grounded in theoretical work on purposeful systems, reverts to the principles of continuous, participative, and holistic planning, and centers on the idea of an ”idealized design.”
  • Soft Systems Methodology. SSM is a heuristic designed by Peter Checkland [1981] for dealing with complex situations. Checkland suggests a process of inquiry constituted by two aspects: a conceptual one, which is logic based, and a sociopolitical one, which is concerned with the cultural feasibility, desirability, and the implementation of change.
  • Critical Systems Heuristics. CSH is a methodology, which Werner Ulrich [1996] proposed for the purpose of scientifically informing planning and design in order to lead to an improvement in the human condition. The process aims to uncover the interests that the system under study serves. The legitimacy and expertise of actors, and particularly the impacts of decisions and behaviors of the system on others – the ”affected” – are elicited by means of a set of boundary questions.

All of these three methodologies (IP, SSM, and CSH) are positioned in the interpretive tradition. They were designed to deal with the qualitative aspects in the analysis and design of complex systems, emphasizing the communicational, social, political, and ethical dimensions of problem solving. Several of them mention explicitly that they do not preclude the use of quantitative techniques.


Key People:

  • Markus Schwaninger
  • Stafford Beer
  • Werner Ulrich
  • Raul Espejo
  • Peter Checkland
  • John Mingers
  • M C Jackson 
  • Peter Senge
  • Russell Ackoff
  • C. West Churchman
  • R L Flood
  • J Rosenhead
  • Gregory Bateson
  • Fritjof Capra
  • D C Lane 
  • Ralph Stacey
  • James Grier Miller
  • Hans Ulrich


Key Sources of Research:


System theory and cybernetics

A solid basis for transdisciplinarity in management education and research

Markus Schwaninger


Intelligent Organizations: An Integrative Framework

Markus Schwaninger


System Dynamics and the Evolution of the Systems Movement

Markus Schwaninger


Methodologies in Conflict: Achieving Synergies Between System Dynamics and Organizational Cybernetics

Markus Schwaninger


System dynamics and cybernetics: a synergetic pair


Markus Schwaningera and José Pérez Ríos


Managing Complexity—The Path Toward Intelligent Organizations

Markus Schwaninger


Design for viable organizations: The diagnostic power of the viable system model


Markus Schwaninger


Contributions to model validation: hierarchy, process, and cessation

Stefan N. Groesser and Markus Schwaninger,%202012.pdf





System Dynamics and Cybernetics: A Necessary Synergy

Schwaninger, Markus; Ambroz, Kristjan & Ríos, José Pérez


System Dynamics and the Evolution of Systems Movement

A Historical Perspective

Markus Schwaninger


System Dynamics in the evolution of Systems Approach

Markus Schwaninger


The Evolution of Organizational Cybernetics

Markus Schwinger


Operational Closure and Self-Reference: On the Logic of Organizational Change

Markus Schwaninger and Stefan N. Groesser



Model-based Management: A Cybernetic Concept

Markus Schwaninger





Raul Espejo 2003



A complexity approach to sustainability – Stafford Beer revisited


A. Espinosa *, R. Harnden, J. Walker





Allenna Leonard with Stafford Beer


Stafford Beer

The Viable System Model:

its provenance, development, methodology and pathology



Cybernetics and the Mangle: Ashby, Beer and Pask

Andrew Pickering


What Can Cybernetics Contribute to the Conscious Evolution of Organizations and Society?

Markus Schwaninger….pdf


Fifty years of systems thinking for management

MC Jackson



Introducing Systems Approaches

Martin Reynolds and Sue Holwell


A review of the recent contribution of systems thinking to operational research and management science

John Mingers
Leroy White


Managing Complexity by Recursion

by Bernd Schiemenz


Hard OR, Soft OR, Problem Structuring Methods, Critical Systems Thinking: A Primer

Hans G. Daellenbach


Anticipatory Viable Systems

Maurice Yolles

Daniel Dubois


Second-order cybernetics: an historical introduction

Bernard Scott


Glanville R. (2003)

Second-Order Cybernetics.


Systems Theory, Systems Thinking

S White


Theoretical approaches to managing complexity in organizations: A comparative analysis

Estudios Gerenciales
Volume 31, Issue 134, January–March 2015, Pages 20–29


Helping business schools engage with real problems: The contribution of critical realism and systems thinking

John Mingers