Management control systems in complex settings emerging research and opportunities

complexity approach explicitly takes into consideration the active role of the context under which management control is performed; • From the Management of Organisational Performance to

Management Control Systems in Complex Settings:

Emerging Research and Opportunities

University of Udine, Italy

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Names: Zanin, Filippo, 1974- editor | Comuzzi, Eugenio, 1963- editor |

Costantini, Antonio, 1974- editor.

Title: Management control systems in complex settings : emerging research and

opportunities / by Filippo Zanin, Eugenio Comuzzi, and Antonio Costantini.

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Preface vii Acknowledgment xiii

Section 1 Complexity and Management Control Systems Chapter 1

Complexity.and.Control:.Managing.for.Value.Creation.in.Complex.Firms 79

Chapter 5

Complexity.and.Control:.Forecasting,.Planning,.and.Budgeting.in.Complex Firms 97

Section 3 Empirical Evidence of Managerial Practices in Complex Firms Chapter 6

Strategy.in.Action:.The.Use.of.Visual.Artefacts.for.Strategic.Change 131

Chapter 7 The.Effect.of.Business.Strategy.and.Stock.Market.Listing.on.the.Use.of.Risk Assessment.Tools 145

Related Readings 169

About the Authors 188

Index 189

To reach the point that you don’t know,

you have to take the road that you don’t know.

San Giovanni della Croce (1542-1591)

WHY A BOOK ON COMPLEXITY AND

MANAGEMENT CONTROL SYSTEMS?

Theme, Perspectives, and Guidelines

“Management Control Systems in complex settings Emerging research and opportunities” is a relevant milestone of a research path centered around the evocative topic of “control and firm’s value under complexity conditions”.Complexity and control are analysed with the lens of “economic value” in order to adopt multiple and unconventional perspectives on the topic New lines of reasoning have stimulated the generation of an innovative conceptual framework that is articulated around the following turning points:

• From the Corporate Control to the Complexity Control: This

methodological shift evokes the adoption of a new approach able

to emphasize the pivotal role of the environmental complexity for managing firm’s value The control of complexity becomes the objective of the Management Control Systems (MCS) and, at the same time, the methodological approach for controlling strategy, operations and resources A radical change of perspective is taking place While the classical approaches to management control are targeted to maximize the financial value of the firm and its constituent assets, the

complexity approach explicitly takes into consideration the active role

of the context under which management control is performed;

• From the Management of Organisational Performance to the Management of Complexity: A new conceptual framework is

proposed for managing value in complex settings The relation between complexity and firm’s financial performance is analysed by adopting

a new methodological perspective that encourages the exploration of the intricate cause and effect relationships among firm’s value and its determinants It is difficult to develop a comprehensive understanding

of the value drivers and their impacts on firm’s value The proposed conceptual framework tries to overcome this imbalance by making the causal relationships more explicit;

• From the Functionalist View of MCS to the Perceived View of Complexity Management: Traditional approaches to management

control adopt a narrow and functionalist view of the MCS Consequently, management control is seen as a system of formal and analytical tools that supports rational decision-making by providing financial measures, economic analysis and allowing “management by exceptions” However, decision and control processes are rarely rational and linear Rather, they are complex interconnections of provisional and emerging practices that involve many actors who represent different values, beliefs, biases and competencies This seems consistent with the emphasis on the individual’s perception of the importance and use of specific MCS Thus, MCS and complexity are not objective entities but subjective abstractions beyond the perceived reality of organisational phenomena Following this line of reasoning, it is important to make a distinction between two different conceptualisation

of complexity: perceived complexity and managed complexity The first conceptualization refers to complexity as a individual and social accomplishment The second one, draws attention to the problem of the treatment of complexity It depends on the degree of complexity of the analysed problem If the problem is simple to understand, then it

is possible to apply analytical and rational tools Conversely, when the problem continues to evolve over time taking unpredictable terms, it is manageable by assuming simplifying assumptions Then, the control of complexity is a bundle of multifaceted practices that involves building imperfect cognitive representations of the problem or, alternatively, rational and computationally tractable solutions;

• From Traditional Control Methods and Tools to Advanced Toolkit for the Management of Complexity: Complexity management

requires the use of advanced methods and tools able to capture the dynamics that impact on the creation, conservation or destruction

of economic value In this perspective: a) multidimensional models are becoming increasingly important tools for the control of firm’s strategy in complex settings; b) there is a systematic change of the methods and metrics that support forecasting, planning and budgeting processes; c) increasing level of complexity stimulates the adoption

of more sophisticated measurement tools and techniques; d) advanced management control systems combine different measurement and representation tools (quantification, narrative approach, visual maps, matrix, alternate templates, temporal bracketing)

THEME AND OBJECTIVES OF THE BOOK

Complexity science has emerged across different research fields in recent years

In business management, the term complexity generally evokes a business context involving problems in decision-making, direction, measurement and evaluation, both by managers and stakeholders

On the one hand, complexity implies environmental uncertainty, change, dynamism, heterogeneity These characteristics can be ascribed to the contemporary competitive environment Nowadays, firms have to face challenges driven by a variety of factors: changes in manufacturing and operations, the rise of emergent markets and developing economies, the long-lasting effects of the 2008 financial crisis, evolutions in customer tastes and preferences, relations with buyers and suppliers, rapid innovations, variations in the actions of competitors and growing rivalry, deregulation and globalization issues, the diffusion of information technologies

On the other hand, firms can also be internally viewed and analyzed as complex systems Firms must respond to the expectations of a range of multiple stakeholders Product life cycles are shorter and factors such as knowledge, innovation and intangible assets are increasing their importance Customer loyalty and reputation have become major concerns Overall, complexity influences organizational structures, and makes planning and control more difficult

In such complex settings, firms’ efforts and abilities are oriented to formulate and implement successful business strategies, with the purpose of creating value for their customers and to differentiate from their competitors In turn, appropriate organizational devices, such as organizational design, effective manufacturing processes, and MCS must support business strategies.

In particular, complex settings represent challenging contexts, which are likely to require new approaches to control and MCS

This book depicts complexity theory issues and focuses on MCS as tools that can play a role in coping with complexity concerns to support the attainment of strategic objectives The objective is to provide theoretical insights and managerial implications for managing complexity within and across organizations

STRUCTURE OF THE BOOK

The book consists of three sections, each with a rather different focus on both theoretical and empirical content

The first and second sections provide a theoretical overview about complexity theory, management control systems and value creation, as well as strategic issues in complex firms In particular, the first section explores the various approaches to complexity theory by emphasizing its multidisciplinary roots in business management literature, examining the domain of management control with a focus on different theoretical frameworks, conceptual constructs and approaches A look at contingency theory and its application to management control systems concludes the first section of this book The second section covers specific items of management control, and especially managing economic value and strategic planning in complex firms

Finally, the third section presents the empirical results of two research works, a qualitative case study and a survey-based quantitative study, on emerging and innovative research themes about control in complex settings.The volume has the following structure

Chapter 1 provides the foundations of complexity theory as a new perspective to address the transformative and evolutionary nature of organizational phenomena and system dynamics The chapter then moves from the conceptual framework of complexity theory, which draws assumptions and methodological implications from a variety of disciplines, to focus on its application to business management The effects of complexity on managerial action are also discussed

Chapter 2 delivers an overview of definitions and key concepts of management control Drawing from relevant academic literature, the chapter presents some of the most popular definitions of management control, summarizes different approaches to management control and emphasizes some theoretical frameworks that are influencing the current debate The chapter depicts management control as a tool for tackling strategic and operational issues in a highly complex business environment.

Chapter 3 covers issues regarding a fundamental theoretical approach

to MCS research, i.e., contingency theory Based on a review of the most prominent contingency-based research, the chapter discusses the relationships between contingency factors and the appropriate design of MCS In particular,

it assumes the conventional view that considers MCS as devices designed

to support managerial decision-making and summarizes the effects of contingency variables on the design of MCS and firm performance for the achievement of organizational objectives

Chapter 4 focuses on managing economic value in the complex firm and proposes a methodological framework for the analysis of complex firms, as well as a complexity management model The complex firm is recognized as

a coherent pattern that emerges by the combination of decisions and actions

at the levels of strategy, operations and resources This conceptual framework

is the basis for the construction of a model for managing complexity for business purposes The model takes a holistic view of the firm as complex entity and defines the managerial initiatives for copying with complexity Finally, the measurement of economic value under complexity conditions

is examined, with emphasis on the shift towards integrated value-based management systems

Chapter 5 describes forecasting, planning, and budgeting as managerial activities involving decisions on future actions to pursue strategic objectives First, the chapter emphasizes the importance of complexity and its implications regarding managerial decision-making The discussion then moves to forecasting, highlighting process, main methods and techniques Next, the chapter focuses on traditional approaches to planning, roles and limitations,

as well as alternative frameworks developed to plan under complexity Finally, budgeting is considered, also discussing the use of budgets in uncertain contexts

The empirical section comprises chapter 6 and chapter 7

Chapter 6 presents the results of a qualitative research work It draws on a case study of strategy renewal in an Italian professional service firm, where visual strategy mapping techniques were employed in a collective process of

strategic decision-making The research emphasizes that: 1) visual artefacts reveal the complexity of strategy renewal, rather than reduce it; 2) visual artefacts enact knowledge within strategizing processes; 3) the generated knowledge shapes actions and meanings, hence performing strategic change.Chapter 7 includes a survey-based quantitative study aimed at exploring the effect of business strategy and stock market listing on the use of risk assessment tools The study, that is exploratory in nature, is based on a sample

of large manufacturing firms in Italy First, drawing from academic literature,

it provides an overview of risk management as part of MCS Then, following

a congruence approach as a form of contingency fit, two research hypotheses are developed To test the hypotheses and yield the results, statistical analysis

is carried out

The range of issues addressed in this book is not exhaustive and, inevitably, subjects of interest are omitted However, a variety of areas are included, delivering a picture of the relevant dimensions of complexity, management control and their reciprocal connections

The book is not aimed at providing prescriptive views, but it seeks to offer insights and knowledge that may stimulate debate and further research The bibliography at the end of each chapter will also encourage additional study

The authors would like to thank the two anonymous reviewers for their careful reading of the manuscript and their many insightful comments and suggestions

Complexity and

Management Control

Systems

on business management by providing the “3Vs model” for interpreting both the structuralist and post-structuralist view of complexity in organizations.

INTRODUCTION

Since the open-systems view of organizations was developed, complexity has been a term of reference and a conceptual framework for exploring social and organizational phenomena from a post-structuralist point of view (Anderson, 1999) The science of complexity has helped to generate a great deal of research in organization studies and has significantly advanced knowledge

in the field by developing ontological and epistemological issues that have produced important implications for organizational theory (Tsoukas & Hatch, 2001; Allen, Maguire & McKelvey, 2011) In shifting the focus from the reductionist to the post-structuralist perspective, two different schools of thought on organizational complexity can be broadly distinguished The first one assumes that the world is objectified and conceives the organization as an already accomplished entity, with pre-given properties that can be described, analysed and quantified by adopting the logico-scientific mode of thought (Bruner, 1986) Such a perspective contributes in explaining and predicting

Complexity

the organizational phenomena under investigation by the construction of abstract models that provide explanations in terms of relationships among dependent and independent variables (variance model) Consequently, the intrinsic properties of phenomena may be discerned and it may be possible to state common principles with predictive validity as a guide for interpretation (Hayles, 1990) The second one employs a process perspective and tends to conceive organizational phenomena as an emergent outcome of the process

of sense-making, through which people share meanings and interpretations

of reality The origin of this relational ontology is the recursive relationship among organizational phenomena, that are thought to consist of wholes emerging out of the continuous interactivity of constituent parts, embedded

in broader wholes Accordingly, organization is immanently generated from within and organizational members are both observers and participants in the unfolding of the organizational phenomena Individual and organizational action is performative, because it generates productive and counterproductive effects that create and recreate the practices of the organization while the practices enable action The practices in which organizational members are engaged with their knowledge, experience, values, symbols and languages create the space for new opportunities, making organizational change always possible (Feldman, 2003) In sum, the post-structuralist approach to organizational knowledge views the object of study as inherently complex and, accordingly, seeks to embrace complexity rather than reduce it Embracing complexity implies awareness of the need to expand the focus from the object under investigation (the system) to include the individuals that describe the object as complex The integration of the two perspectives leads towards the assumption that the features of a complex phenomenon are both objectified descriptions and interpretations that observers assign to specific phenomena This assumption has important implications for how we position our approach

to organizational complexity

Following this reasoning, the starting point for the construction of a conceptual framework for complexity in organizational studies can be found

in four fundamental propositions:

1 Complexity can be recognised as a multidisciplinary way of thinking about organizational phenomena, since it draws assumptions and methodological implications from a variety of disciplines (natural science, biology, social systems);

2 Complexity is an umbrella term that encompasses a wide array of meanings related to an assumed objective world and entirely compatible with an interpretative approach, but distinct from complication;

3 Complexity is a constitutive trait of a system and, at the same time, a distinct characteristic of the observer The science of complexity sets

an initial condition: the dynamic interrelationship between the observer and the system under investigation;

4 Complexity is a relative and relational concept, because it depends on the perceptual filters of the observer and is generated in practice when multiple agents interact in open-ended ways

Although the reductionist and post-structuralist schools of thought rely

on specific methodological and theoretical assumptions, there are important connections between them that the scientific debate has considered, in order

to explore complex ways of viewing organizations as complex systems Therefore, adopting a multi-perspective approach, the main objectives of this chapter are:

• To provide a theoretical framework that is able to investigate the complexity of organizational and economic phenomena by adopting a multidimensional approach;

• To illustrate an alternative lens for the analysis of the firm as complex system;

• To propose an overall picture of the firm as economic value generating system

COMPLEXITY THEORY

Complexity science has emerged across different research fields in recent years and various definitions of complexity can be found in literature It is not surprising that different views should emerge and that the attempt to explain complexity should be conceived as an intractable problem As one of the foremost proponents of complexity as a new paradigm in system dynamics

remarks, no one has yet succeeded in giving a definition of complexity which

is meaningful enough to enable one to measure exactly how complex a system

is (Waddington, 1977, p 30) Despite the fact that the scientific literature

contains a wide range of ill-defined concepts of complexity that draw their roots from computation, biology, physics, sociology, economics and management

studies, it is to be noted that entering the domain of complexity leads towards three consolidated conceptualisations

The first one is the recognition of two distinct ways for formulating a definition of complex objects: state and process descriptions (Taborsky, 2014) State description is conventional in science and focuses on the structure of the complex object by emphasizing the number of components of a system,

as well as the number of way they can be related Process description focuses

on the interactive relatedness of the components of a system and emphasises the unfolding character of the interrelations over time

The second one is the identification of five dimensions that explain a complex system These dimensions are proposed to be held in common by natural, biological and social system (Tsoukas & Hatch, 2001):

• Nonlinearity: There is no proportionality between cause and effect

relationships among constituent parts;

• Scale-Dependence: There is no single measure able to capture a true

answer, because it depends on the adopted device;

• Recursiveness: The repetition of the same structure at different

analytical levels of the system;

• Sensitive Dependence on Initial Conditions: A variety of input

stimuli enable systems to change in an unpredictable manner;

• Emergence: The essence of a system is provisional in nature, because

it is the emergent outcome of multiple chains of interactions

Finally, the distinction between complication and complexity (Morin, 2007)

Complication comes from the Latin cum plicum, meaning with folds The Latin etymology of plicum refers to the fold in a piece of paper, which must

be “unfolded” in order to be observed and understood Complex, on the other hand, derives from the Latin cum plexum, meaning with knots, interwoven The Latin etymology of plexum, therefore, refers to knots, the weaving of

a cloth or a carpet, that cannot be unravelled without losing the overall picture that it provides The distinction produces different methodological implications The “complicated” approach to organizational phenomena is analytic in nature: the phenomenon is broken down into its constitutive parts, that are analyzed and recomposed The “complex” approach, however, is relational and based on emergency, aiming at providing a holistic view of the phenomenon Complexity arises when the different parts that constitute

a system cannot be separated, because of the recursive interrelations between the parts and the whole, and the parts among themselves Table 1 reports a

synthetic explanation of the structuralist and post-structuralist perspectives

on complexity

The attempt to frame the most important scientific works on complexity is

a very difficult task When a theoretical concept is elaborated using different modes of thought, it is not surprising that different views emerge over time Moreover, the usefulness of this theoretical frame comes from recognizing that the various perspectives on complexity focus on specific issues, helping

to advance our understanding of the complex and unpredictable world The evolution of complexity theory during the twentieth century can be traced back to three generations of theories (Abraham, 2011; Alhadeff‐Jones, 2008)

The first generation embraces the cybernetic movement and operational research Cybernetics is an interdisciplinary field, born during the second world war in the USA, where mathematics, engineers, neurobiologists, anthropologists and social psychologists contributed to expand the possibilities for thought and action through the elaboration of some principles like circular and reticular causality, feedback effects and artificial intelligence Operational research is a field of study focusing on the elaboration of computing systems and methods (algorithms) to support decision processes under uncertain conditions

The second generation encompasses computer and engineering sciences, general system theory, system dynamics theory, studies on non-linear dynamics and evolutionary biology Computer and engineering sciences are involved

in the control of systems perceived as complex By adopting sophisticated computers, different mathematical models were elaborated and the notion

of algorithmic complexity was defined with the aim to allow a quantitative evaluation of complexity General Systems Theory is the European counterpart

Table 1 Complexity theory: Structuralist and post-structuralist perspectives.

View Complexity as a constitutive property of a

system Complexity as the interactive relatedness of emergent and nonlinear processes Definition Number and differentiation of parts and

irregularity of their arrangement Intricacy of emergent processes that are qualified by recursive interrelations and

feedback loops Methodology State complexity Description of the

components of the system and their interrelations

Process complexity Description of the actions that are involved in the emergent construction

of the provisional properties of the system Tools Analytical, rational, quantitative Synthetic, paradoxical, qualitative

Source: The Authors

to the cybernetic movement that imposed the open system as a new approach

to social sciences The main principles of this new approach were extrapolated from the biology sphere, like organicism and holism System Dynamics Theory is a large branch of Mathematics, that had a great impact on physics, biology, medicine and social sciences by widening the application of digital computers The studies on non-linear dynamics gathered together different concepts like dissipative structure, catastrophe, chaos and fractality, with the aim to contribute to the elaboration of frameworks for understanding the behavior of complex systems Finally, evolutionary biology has emerged as a new theory on living systems, based on the concepts of evolution, adaptation, emergence, self and autonomy

The third generation is concerned with the studies of complex adaptive systems and the development of epistemological reflections around the concept of complexity Studies on complex adaptive systems have emerged as

a multidisciplinary research collaboration for the advancement of knowledge

in the fields of natural, artificial, and social systems within the Santa Fe Institute in New Mexico, while the epistemological debate on complexity has constituted a conceptual research field on organized complexity that has involved a redesign of the various concepts of complexity from the 1940s

STUDIES OF COMPLEXITY THEORY

Drawing on these theoretical roots, researchers applied complexity theory to the study of physical, natural, social and economic systems The lack of an adequate definition of complexity theory, even in the physical and natural sciences where it was originally developed, continues to stimulate research efforts, specifically targeted at applying concepts and methodological implications from complexity theory to organizational issues In management and organizational literature, a growing number of researchers are beginning

to approach the central notions of change, evolution, adaptive and emergent behavior with the alternative lens deriving from complexity science (Mathews, White & Long, 1999; Allen, Maguire, & McKelvey, 2011) In shifting the focus from a traditional approach to complexity theory for the explanation of organizational change and transformation, it is important to explore the studies

on complexity theory that have significant implications for the advancement

in organizational and management research fields These studies are listed in Table 2, which shows the author, concepts and methodological implications relied upon in each study

Table 2 Studies of complexity theory

Studies Field Concepts Methodological Implications

Bohm (1951, 1980) Theoretical Physics

• The causal interpretation of quantum mechanics

as a new version of quantum theory: A system

is described in part by its wave function and it is completed by the specification of the actual positions

of the particles

• A new path for indeterminism, unpredictability and application of probability in processes characterized

by chance rather than causality

Each particle of a system is not separate or autonomous but it is part of a timeless and universal order

Asby (1961) Cybernetics

• Cybernetics principles offer a scientific method for exploring systems that are intrinsically complex

• Parallelisms between machine, brain and society

• Cybernetics is a prominent scientific methods for dealing with complexity

The image of organisation as a complex system (black box)

• A new conceptual approach that was applied to diverse disciplines (biology, economics, psychology, demography) concerned with understanding open and closed, complex, dynamic systems acting as regulatory devices

• The approach focuses on the concept of

“open system” which emphasises the constant interchanges of resources with the environment This interdependence makes the systems always open to change

• Other fundamental concepts include: parts/

wholes/sub-systems, system/boundary/environment, structure/process and emergent properties

The recognition of the multiple interrelations between organisation and its environment starts to have greater relevance for management studies.

Varela, Maturana,

Uribe (1974) Biology and Neuroscience

• Autopoietic organization By drawing on biology studies, organizations are conceived as complex systems, qualified by reproduction and evolution The system is a unity that reproduces itself spontaneously without disintegration

• Complex organization Complexity results from the reproduction and self-reproduction of a system and it is influenced by the perturbations that affect the components of the system Each component participates recursively in the same network of relations which produced it.

Six keys for determining whether or not a given system

is autopoietic

Morin (1984, 1999,

2007) Philosophy and Sociology

Definition of complexity Many ways to understand what complexity is

• Irreducibility of chaos and disorder, the irruption of irreversibility and unpredictability

• Overcoming the limits of abstraction for embracing singularities and the embeddedness on time and space

• Complication due to the recognition of the messy nature of the intricacies among the whole and its constituent parts The whole-part relationship generates mutual implications

• Order, disorder and organization Order and disorder are compatible and organization can be related to disorder

• Organization as a complex basis, because the whole

is more and less than the sum of the parts

• Ologrammatic principle The whole is included in the part, the part in the whole.

The complex notion of organization allows a great advance in understanding organizational phenomena

continued on following page

Studies Field Concepts Methodological Implications

• Reconceptualization of science From classical perspective to complex perspective, with emphasis on evolution, diversification and instability

• Dissipative structure An irreversible process (dissipation of energy) is able to play a generative role and become a source of order

• Open system The growing relevance of the recursive interactions between a system and the environment through an entropy flow

• Equilibrium and non-equilibrium From the isolated system at equilibrium to non-equilibrium as a source

Klir & Folger (1988) Computer and system science

• Several categories of complexity, depending on ambiguity and vagueness

• Ambiguity arises in situations where there is a to-many relationship between two entities

one-• Vagueness equals the difficulty to make distinctions clearly

Fuzzy system dynamics for the measurement of uncertainty

Waldrop (1993) Physics

• Conceptualization of complex adaptive systems, based on the ability to bring order and chaos into balance (the edge of chaos)

• Complexity depends on: relevance of interconnections, edge of chaos, impossibility to reach optimal solutions, coopetition and self-organization

The use of computing power and the search for common principles of complexity

Le Moigne (1995) Systems theory and constructivist

epistemology

• Conceptualization of complexity as a scientific concept that emerges from the difficulties arising with the rational application of the complete separability of the observing subject and the observed object

• Complex economic systems Economic systems are open systems and their behavior cannot be understood

by the observer (intelligible unpredictability)

• Complexity and ambiguity Observed object, interpretative model and model builder form a circular interconnection that generates ambiguity

From analytic methodology

to the design of perceived complex phenomena

Stacey (1995, 1996) Management and organizational

studies

• In complex responsive process terms, systems are characterized paradoxically by positive and negative feedbacks, stability and change, predictability and unpredictability, certainty and uncertainty

• Complex systems evolve in a self-organized manner

• Complexity associated with the concepts of variability, unpredictability, uncertainty

• Three types of change: closed (present/future as repetition of past situations), limited (present/future

as a vague repetition of the past situations), open (unpredictable and spontaneous change)

Complexity theory as a third perspective on the strategy process

of complex problems), power of computation and elaboration

From equilibrium approach to complexity economics

COMPLEXITY MANAGEMENT

Building on the puzzle of defining the complexity of a system, some conceptual and methodological assumptions regarding the notion of complexity can be summarized as follows

• The importance to emphasize a concept of complexity that takes into consideration the object under investigation and the observer According

to a central assumption in complexity science, understanding the complexity of a system raises issues of identification of the components that are interconnected in such a way as to make it difficult to isolate them (Klir & Folger, 1988) Therefore, the efforts for describing the properties of the system that are aimed at the precise identification

of the constituent parts and their interrelations is unable to exactly measure the complexity of a system, and the adoption of a holistic approach is necessary (Bertuglia & Vaio, 2013) From a structuralist point of view, the concept of complexity is used to highlight systems that exhibit multiplicity, variety and variability of the component parts and the relationships between them (Simon, 1996) Others use the term complexity to indicate “…the quality of an object characterized

by various interconnected parts that make it difficult to understand its operation” (Klir & Folger, 1988, pp 192-193) Moreover, it is widely recognized that complexity is a relative concept, since it depends not only on the intrinsic properties of a system, but also on how the system is described and interpreted by the observer (Morin, 1984; Prigogine, 1980) The observer also can be interpreted as a complex system, and this affects the level of perceived complexity (Casti, 1986) Therefore, complexity is an observer-dependent phenomenon, because

it is associated with the frames of reference of the observer (that are unique), as well as with the perceived properties of the system This reasoning is an interesting one, for it adds complexity, making it difficult to uniquely define complexity as a measurable entity that can

be analyzed objectively

• The ability to use multiple approaches for understanding the complexity

of a system Complexity is a multi-faceted phenomenon that imposes different appropriate methods and tools (analytic, synthetic, holistic, analogical, paradoxical, discursive), that are able to capture the properties of the constitutive elements of the systems and their

changing nature Despite the fact that the line of demarcation between the complicated and the complex is fuzzy, the observer can increase his or her effectiveness in managing the complexity of the situation under investigation by generating and accommodating a wide array

of methods and tools The choice will depend on the degree of the perceived complexity of the situation that the observer is attempting to manage or to enact To put it another way, any potential situation can become a point of bifurcation by shifting from a low level to a high level of complexity In the first case, the adoption of the structuralist approach is possible and the use of analytical tools is appropriate, because simple and linear causal models are adequate for modelling the system’s behaviour In the second case, the adoption of the process-based approach is preferable and the use of analogical, paradoxical and discursive tools is appropriate, because simple causal relationships are inadequate for capturing the behaviour of a system with nonlinear interconnections and feedback loops In particular, reductions, simplifications and approximations are required when the modelling

of complex behaviour by extrapolating regularity that emerges from the interaction of the components of the system become intractable Obviously, reducing a complex system to a simpler one by abstracting out a model is equal to compressing information by putting it into a smaller picture that is easier to grasp

• The attempt to apply the conceptual framework of complexity theory

to business management, recognizing complexity as a structural variable that characterizes both firms and environments Complexity has become a central concept in business management literature in the 1960s, when the paradigm of the firms as open systems became widespread From the structuralist perspective, a complex firm can

be defined as a set of interdependent parts, which together make up a whole that is connected to the environment in which the firm operates (Thompson, 1967) Thus, the level of complexity of a firm equates to the numbers of subsystems that can be identified within the organization Following this framework, complexity can be measured along three dimensions (Daft, 1992): 1) vertical, that corresponds to the number

of levels in an organizational hierarchy; 2) horizontal, that refers to the number of organizational units across the organization; 3) spatial, represented by the number of geographical locations in which the firm operates through subsidiaries The complexity of the environment

is associated with the number of different elements (actors) that the firm faces while deploying competitive actions (Scott 1992) The distinction between the complexity of the firm and the complexity

of the environment surrounding the firm tends to be relevant for business management because of the stratification of various level of complexity Consequently, managers face a complex object (the firm) that is embedded within an equally complex object (the environment) The two forms of complexity manifest themselves in different manners and it can be useful to identify the general directions along which complexity arises Within this perspective, complexity can be observed initially through three fundamental directions or dimensions: variety, variability and velocity (Comuzzi, 2005; Comuzzi, 2015; Comuzzi, 2016) Variety suggests static analysis and has to do with the number

of the components and the connections between them Variability suggests dynamic analysis and has to do with the extent and the intensity of change within the parts and their connections Velocity suggests dynamic analysis and has to do with the rapidity with which change manifests itself See Table 3

Complexity is not an “intrinsic” property of an object Rather, it is the interaction between the “intrinsic” characteristics of an object and those of the subject (Comuzzi, 2016) Therefore, the complexity of a given object is

a relative concept, since it depends, on the one hand, on the characteristics of the observed object, and on the other hand on those of the observing subject

In this perspective, the characteristics of the subject become important as a basis for sense-making and coordinated actions These include (Comuzzi,

2005, 2016; Corneliessen & Werner, 2014):

Table 3 Complexity framework for business management: the “Three Vs” frame

• Belief Systems: These are the fundamental cognitive structures that

organize knowledge among individuals and guide the subject in the exploration of the surrounding environment Cognitive frames, therefore, represent active guidance structures that direct the process

of perception and knowledge acquisition;

• Variability of Belief Systems: The subject acquires information about

objects and environmental phenomena in such a way as to confirm or modify the original cognitive structures, thus giving rise to a recursive learning cycle (positive and negative feedbacks);

• General and Specific Knowledge: Accumulated knowledge influences

the degree of perception, analysis and interpretation of phenomena in the surrounding environment;

• Experience: The progressive accumulation of knowledge, crystallized

within the memory of every subject, gives rise to a structure of expectations that influence perceptions, interpretations and actions.The problem of complexity management inevitably shifts the focus on a set of managerial actions whose poles of attraction are represented, on the one hand, by reduction, simplification and abstraction; on the other hand, by absorption, direct involvement and acceptance The first pole of attraction identifies managerial actions based on the ontological distinction between two independent kinds of entities: the observer and the observed object (Barad, 2003) The representations, in the form of simplifying models, serve

a mediating function that is supposed to facilitate the knowing process They mediate the access of the observer to the world by describing objects as they really are or objects that are the product of subjective (social and cultural) constructions The problem here is that of the accuracy of the representation: when the observed object is perceived as complex, the observer tends to adopt simplifying models that provide a reductive picture of the inherent complexity The second pole of attraction refers to managerial actions that explicitly take into consideration the complexity of phenomena by assuming

a sort of ontological continuity between the observer and the object They coexist within the space of action in which subjects and objects are inextricably entangled in performing actions It is important to specify that the positioning into the continuum (from simplification to direct involvement) depends on some fundamental elements (Comuzzi, 2016): a) organizational climate; b) organizational culture; c) trade-off between costs and benefits; d) tradeoff between available resources and complexity of the situation

Viewed from this perspective, the management of complexity can generate effects on the subject’s attitudes, choices, decisions and actions In order for managers to be able to act effectively in complex settings, we propose some reflections that are entirely compatible with the structuralist and post-structuralist approaches to complexity (Comuzzi, 2015, 2016):

• Complexity, Subjectivity and the Trade-Off Between Simplification and Absorption: Managers possess limited cognitive frames of

reference and tend to adopt simplified solutions to complex problems because of the perceived difference between available and desired knowledge Therefore, when facing complex problems, the managerial action often results in an exasperation of simplification

• Complexity, Subjectivity and Resistance to Change: Managers

possess limited cognitive frames of reference that they are inherently reluctant to change Subjects tend not to accept problems that reveal ambiguity and surprises that are in contrast with the accumulated knowledge system An increase in complexity can reinforce the resistance to change, since managers respond to complex problems with solutions associated with past experiences;

• Complexity, Subjectivity and Choices: Managers tend to face

complex problems by adopting a selective approach, based on the perceived importance of the specific situation They tend to take into account only one situation at a time or a set of hierarchically-organized situations by sorting various courses of action

THE THREE VS MODEL OF COMPLEXITY

After elucidating the main concepts around the complexity sphere and the effects on managerial action, we propose the “Three Vs” (Variety, Variability and Velocity) complexity framework (Comuzzi, 2016) This framework employs the structuralist perspective of complexity and proposes an alternative view to the identification of the explicative dimensions of complexity The usefulness of this model for the management of complexity comes by recognizing that the structuralist and post-structuralist perspectives provide distinctive ways of conceptualizing complexity that are not irreducible to one another The “Three Vs” model of complexity emphasizes the different conceptual frameworks that are characterized by a different view of organization

and are connected to different types of managerial actions Following this reflection, the model is based on:

• The structural perspective on complexity, based on the distinction between differentiation and connection Differentiation implies variety, heterogeneity and diversity of the component parts of an object; it means that the various parts of a system or an object are different or behave in different ways Connection implies variety, heterogeneity and diversity

of the relationships between the component parts of an object The parts are not independent from one another and the relationships move

in a circular and recursive way

• The process perspective on complexity, based on the distinction between variety and variability Complexity emerges from variety (number of parts and connections, differentiation) and variability (change) Variety is a static/structural dimension of complexity, while variability takes explicitly into consideration change and evolution over time, representing the dynamic/procedural dimension of complexity

• The perceived perspective on complexity, based on the emphasis on space, time and scale The perception of complexity stimulates a careful reflection on the main dimensions along which complexity can be perceived by managers These are: space (the morphological structure of the complex object); time (the evolution of the morphological structure over time); scale (the relationship between the units of measure and the corresponding real measurements of the complex object)

The “Three Vs” model recognizes three fundamental macro-dimensions

of complexity: variety, variability and velocity See Table 4 and 5

Variety refers to the static dimensions of the structure of a complex object and it can be operationalized in the following dimensions:

• Uniqueness of the constituent parts and their relationships (in opposition

• Ologrammatic form, that refers to the existence of complex states in which the parts are within the whole and the whole is within the parts.Variability refers to the changing nature of the parts and their relationships and it can be operationalized in the following dimensions:

• Non-variability of the parts and their relationships (stability and equilibrium over time)

• Known variability of the parts and their relationships (closed change: present and future as repetition of the past)

• First-order unknown variability of the parts and their relationships (limited change: the future is rationally unknowable, but the evolutionary trajectories of phenomena are predictable)

• Second-order unknown variability of the parts and their relationships (open change: the future is unpredictable)

• Edge of chaos (order and disorder, contradictions and ambiguity coexist making the future predictability a chaotic issue)

Finally, velocity refers to the compression of time As already pointed out when presenting the key variables of the model, nowadays time is increasingly compressed, since phenomena manifest themselves and change with increased pace Velocity is actually an irreversible phenomenon, strongly influenced

by the increasing capacity to learn

Table 4 Variety, variability and velocity

Variety

Static Dimension of Analysis Dynamic Dimension of Analysis Variability Dynamic Dimension of Analysis Velocity

Vagueness

Imperfect recognition of the boundaries

of the observed object

Known variability Repetitive change Steady velocity Variety of the constituent parts of the

observed object

Differentiation

Unknown variability Repetitive and open change with limited impacts Velocity discontinuityVariety of the relationships among

constituent parts

Interconnections

Unknown variability Open change ArhythmiaAmbiguity

One to many relationship Unknown variability Open and radical change

Cause and effect relationships

Causal ambiguity Unknown variability Edge of chaos

Ologrammatic form

Source: adapted from Comuzzi (2015, 2016); Zanin & Comuzzi, (2016)

EMERGING TRENDS AND DIRECTIONS

FOR FUTURE RESEARCH

One of the most important research stream in recent years has been the significant growth in complexity theory across different disciplines Academic research in business management confirms this trend, which looks set to continue with increasing numbers of theoretical and empirical works Despite these developments, a new research agenda is needed to enrich and extend the field beyond its current theoretical foundations, and connect it more closely

to the challenges of contemporary management practice The main directions for possible future research, which focus on the development of new concepts and approaches identified by both researchers and practitioners as critical to the management of complexity, are threefold:

• Theoretical Advancements: The interdisciplinary and multifaceted

nature of the scientific work about complexity requires a greater focus

in future research on concepts and methods closely resonating with new approach more in alignment with contemporary thinking For example, the adoption of a paradox lens to understand anarchy and chaos in organizations (Schad et al., 2016)

• Theory in Practice: This direction emerges from the need for the

adoption of a pragmatic approach towards the use of complexity theory in practice In short, there is a high need to make effective use of conceptualizations and theoretical models in practice (De Roo, Hillier,

Table 5 The “Three Vs” frame and managerial actions

Source: The Authors

2016) The aim is to explain the different practical implications of the diverse theoretical approach on complexity theory

• Methodological Implications: It is important that future research

focus on the methodological issues, a conceptual and practical toolkit able to provide managers with rules and principles for the management

of complexity in organisations Most managers continue to believe that complexity is essentially a problem of making reasonable predictions of the internal and external courses of action They tend

to underestimate the problem of prediction because they believe that prediction and control depend upon their ability to identify causal and linear links among the constituent parts of the observed object But many situations are ambiguous and the rational approach to problem finding and solving can lead to a limited engagement with complexity

in practice (Morin, 2016)

CONCLUSION

This chapter focused on complexity theory and its application to business management Starting from a review of the main studies of complexity science, the complexity of a system or object is interpreted in different ways

by assuming specific propositional statements When initial conditions are characterized by stability and equilibrium of a system, state complexity emerges and managerial actions are directed toward technical rational decision-making Since the evolution of the system is predictable, complex systems are interpreted as linear and able to adapt to environmental changes When the nonlinear and multiple interactions of systemic behaviour in complex systems produce positive and negative feedbacks, process complexity emerges Since the evolution of the system is characterized by limited and open change, complex systems are interpreted as predictable only by adopting judgmental decision-making Finally, when conditions lie on the edge of chaos, complex systems are interpreted as chaotic and anarchic

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Control, 2/2016(2), 89–114 doi:10.3280/MACO2016-002005

Several researchers (e.g Abdel-Maksoud & Abdel-Kader, 2007) have suggested that many firms have responded to the challenges of global competition in several ways For example, introducing new management and production techniques; investing in advanced manufacturing and information-processing technologies; prioritizing quality, innovation, and flexibility to meet customer needs; developing capabilities that allow them to provide services and solutions that supplement their traditional product offerings (servitization

in manufacturing) Within an increasingly dynamic context, the importance

of implementing effective management controls is widely acknowledged.From a structural perspective, management control can be considered as part

of the operating systems of a firm Operating systems allow establishing the running procedures complementing the organizational structure, and mainly

Management Control Systems:

Concepts and Approaches

include (Airoldi, Brunetti & Coda, 1994): strategic planning; information systems; management control; human resources management.

The operating systems have the following general purposes:

• To influence, together with the organizational structure, the behavior

of employees, by identifying and assigning objectives to be achieved and resources to be used for each organizational unit;

• To provide information for supporting decisions taken at the different organizational levels;

• To determine policies and practices regarding human resources aspects, including recruitment, appraising, training, competence development;

• To carefully design reward and incentive systems in order to enhance employee motivation

From a process perspective, management control is the back end of the management process Basically, managers in the different functions, using different types of resources, carry out three major groups of activities (work of management) along a process continuum: planning, directing and motivating, and controlling (Noreen, Brewer & Garrison, 2011) These activities shape the so-called planning and control cycle Planning mainly involves how to use the resources (human, physical, financial) to meet organizational objectives:

it results in the selection of a course of action among possible alternatives, and consequent decisions on how to implement the action Directing and motivating involves mobilizing employees to carry out selected plans and perform routine operations Then, control involves ensuring that the plans are actually carried out and are properly modified as required by continuously changing circumstances

Generally, management control takes the perspective of managers and

is concerned with driving firms towards the achievement of organizational objectives It relates to two main issues: design of the information and responsibility system of the operating activities (information issues) and the behavioral concerns of motivating managers and employees to achieve organizational goals (behavioral issues) (Demartini, 2014)

In the extant literature, there are several definitions of management control, and a universally accepted one has yet to come Further, the concept of management control is a multifaceted one and embraces a variety of issues This chapter has the following aims:

• To introduce some of the most popular definitions and conceptualizations

of management control in the academic literature and highlight how the meaning of management control has changed over time, moving from a traditional to a behavioral perspective;

• To summarize different theoretical approaches to management control;

• To provide an overview on some theoretical MCS framework that are highly debated in scholarly literature and are influencing substantial piece of research work in the last years

MANAGEMENT CONTROL: CONCEPTS AND DEFINITIONS

The term management control is sometimes used interchangeably with other terms, such as management control systems (MCS), management accounting (MA), management accounting systems (MAS), and organizational control (Chenhall, 2003) However, despite the fact that these concepts partially overlap, some differences also can be identified According to Merchant and Van der Stede (2007), devices, mechanisms and practices that managers use for control purposes are generally called management controls, while the collection of control mechanisms is generally considered as a management control system

MASs provide information to assist managers in planning and control, and

MA activities mainly include collecting, preparing, processing, analyzing and reporting financial and non-financial information to managers Information should be relevant and designed to support decision-making within the firm (Kaplan & Atkinson, 1998) Chenhall (2003) refers to MASs as the systematic use of MA practices (e.g budgeting, product costing) to achieve some goal, and suggests that MCS encompass MASs, together with other types of control, such as personal or clan controls

Organizational control has been defined as the process of influencing the behavior of people as members of a formal organization, through processes and techniques designed to increase the probability that people will behave coherently with the attainment of organizational objectives (Flamholtz, Das

& Tsui, 1985)

Focusing on the meaning of management control, Strauss and Zecher (2013) noted that the meaning of management control was initially centered upon the provision of formal, financial information to support managerial decision making, while over the years it has embraced broader views

A traditional (conventional) view of management control is that developed

by Anthony (1965), who defined it as “the process by which managers assure that resources are obtained and used effectively and efficiently in the accomplishment of the organization’s objectives” (p 17) In Anthony’s theoretical framework, management control is seen as a link between strategic planning and task (operational) control In particular, strategic planning is concerned with long-term goals and the decisions on the plans to achieve those goals, focusing on environmental issues, while task control is concerned with daily operations within the organization (Otley et al., 1995) Management control is concerned with formal reporting on the performance of all aspects

of organizational activities on a routine basis for the monitoring of resources used to achieve strategic objectives Here, management accounting information provides the foundations of management control, as such information assists

in performance measurement, as well as comparisons between actual and planned performance

Basically, the traditional perspective regards MCS as passive tools providing information to support managerial decision making (Chenhall, 2003) and confines the scope and practices of MCS to economic-based issues and financially measurable aspects, to attain high profits (e.g budgetary control, which was the dominant control technique) Further, the cybernetic concept

of control deeply influences the traditional perspective of management control Cybernetic controls are based on a feedback loop (Malmi & Brown, 2008; Lerner, 2012) Preliminary to the process, measurements are needed that enable quantification of an underlying phenomenon, activity or system Then, as explained by Green and Welsh (1988), the feedback loop involves the following stages:

• There are standards of performance or targets to be met;

• Performances are measured as the outcome of the activities or system;

• Actual performance is compared with the standard;

• The comparison feeds back information about variances (deviations) between actual and standard performance;

• The results of variance analysis determine taking corrective actions to modify the system’s behavior or underlying activities

The cybernetic concept of control also informed Lowe’s (1971) definition

of MCS This author, widening Anthony’s view, depicted MCS (p 5) as “…

a system of organizational information seeking and gathering, accountability, and feed-back designed to ensure that the enterprise adapts to changes in