1 Introduction
1.1 Relevance
The occurrence, speed and degree of penetration of technological achievements, especially in Information and Communications Technology (ICT), has led to a socio-economic shift towards a knowledge-oriented society and service-oriented economy. The interaction and interdependency of these forces fuel each other’s growth, thereby further accelerating the pace of development [1–3]. The implications for organizations of already being “in the midst of a major evolutionary transition that merges technology, biology, and society” [4] are twofold: On the one hand, the changes concerning the continuing progress of ICT create new business opportunities by providing additional possibilities for service innovation and improvement (cf. e.g. [5]) on the other hand, managers of organizations are faced with increasingly complex situations they need to manage efficiently (cf. e.g. [6]). Both aspects require new models and a new understanding of social and economic dynamics [7, 8].
The forthcoming large-scale labor disruption is expected to radically change global workforce structures within the next years due to accelerated deployment of general-purpose robotics, machine learning, artificial intelligence, nanotechnology, biotechnology, genetics, and all their interrelations within the upcoming Internet of Things. In the context of social, political, and economic spheres, this technological revolution will lead to the redefinition of existing socioeconomic notions and organizing principles (e.g. labor, production, distribution, property, value) as well as change the human relation to socioeconomic coordination, which is currently operating “purely on profit-driven monetary logic without consideration for the complex and multi-dimensional spheres of human value” [9]. Current sociopolitical challenges (e.g. demographic change, acceleration of income and wealth inequality, financial crisis, political instability, etc.) require thorough consideration, comprehensive effort and resolute action. The study of service systems (i.e. service science) confirms this view by pointing out society’s need for developing novel holistic models that would help define viable strategies for a sustainable future [1, 10, 11].
Recent developments in the fields of cloud computing and big data increasingly enable organizations to achieve higher performance and provide the basis for value creation, yet the potential for further improvement is still large. Enabled by digital technology new forms of collaboration have emerged, implemented as collaboration platforms and groupware. With this technology and its applications, it is possible to improve the participation of stakeholders by providing a platform for dialogue and mutual learning. This in turn enables organizations to improve anticipation and forecast of changes in demand and enhances their ability to adapt to uncertain shifts in competitive markets [12]. Consequently, the challenge for organizations in the next decades will be to improve their dynamic capabilities, i.e. the ability to develop and reconfigure key-resources and key-competencies in relation to organizational structure and processes [13, 14].
The emerging field of service science began to point out the central role general systems theory and the viable system model play in better understanding the processes by which global functioning of human society can be sustained [15]. These theories are found to offer a coherent methodology for interpreting the networks of service systems by proposing a new relational approach to manage interactions between these complex, adaptive, socio-technical systems [16, 17]. Aiming at better understanding the coordination mechanisms that enhance collaboration of interacting social agents, the Systems Approach, which is based on systems theory and cybernetics, is recognized by more and more disciplines as being a very fertile approach in helping managers cope with a growing number of organizational and ethical challenges [18].
Established by British cybernetician Stafford Beer during the second half of the twentieth century, the framework of organizational cybernetics provides an integrated approach called variety engineering, which is capable of tackling these challenges [19–27]. It aims at facilitating self-organization and self-regulation in organizations to improve their performance by providing managers with a methodology that helps to find better ways of co-creating value [28]. The Variety Engineering Model is the building block for the Viable System Model [24–26], which describes the necessary organizational structure for a service-system to survive in an unpredictably changing environment. This model is found to be at the core of the complex, urgent challenges society is facing [14, 29].
Based on this model, Espejo and Reyes [12] developed a template for complexity management strategies, which allows agents and actors (e.g. consumers and producers) to “learn from each other and co-create value through recurrent communications” [12, 14]. This observer-dependent methodology is able to improve the effectiveness of communications by providing a “meta-perspective”, where actors become aware of their active participation in the larger ecosystem of mutual value creation.
1.2 Goals and Objectives
Despite the many contributions within the field of service science and service-dominant logic (SDL) to (co-)develop a framework that explains the processes by which producers and consumers interact to co-create value, only a small number of studies have developed a holistic account on the mechanisms underlying these interactions [30]. This work aims to contribute to a better understanding of the mechanisms that facilitate value co-creation in social systems by introducing the framework of variety engineering. The focus on systemic approaches for the management of complexity can help both researchers in the disciplines of service science as well as managers and other actors in organizations to reach a better understanding of the specific capabilities organizations need to achieve to enhance value co-creation.
This chapter is structured as follows. First, we deliver insight into the conceptual framework of SDL representing the foundation for service science. After introducing the science of cybernetics, the models and concepts provided by organizational cybernetics, which are considered relevant for improving value co-creation in social systems are explained and discussed. Then, the concept of variety as a measure of complexity is summarized and, on this basis, the model of variety engineering is introduced. As a further contribution, a second-order perspective on Value Co-creation is elaborated to support SDL’s understanding of this inherently dynamic process.
Finally, we discuss how the model of variety engineering can be integrated into the concept of value co-creation; we thereby address the following two research questions: (i) How can second-order cybernetics contribute to a better understanding of the concept of value co-creation as put forward in SDL? (ii) What guidelines can the concept of variety engineering provide for effectively improving self-organization to improve value co-creation? The chapter closes with a summary and an outlook on further work.
2 Theoretical and Conceptual Background
2.1 Service-Dominant Logic: The Foundation for Service Science
The interdisciplinary field of service science is emerging within the larger context of current societal change. The accelerating pace of technological development has brought a fundamental shift in the context in which service is delivered and experienced. Advancements made in ICT have led to a proliferating variety of revolutionary services that change how consumers perceive themselves and their relationship to the world [11, 31, 32]. Parallel to the development and diffusion of digital technologies, which profoundly enhanced both producers’ and consumers’ communication capabilities and thereby their potential for mutual value creation, marketing science has seen a shift in perspective [14, 33, 34].
By integrating views from various disciplines (e.g. consumer culture theory, agency theory, social network theory, viable systems approach, ecosystems theory, etc.) the field of service science has been redefining the “traditional” concepts of marketing such as service, value creation, customer, market, and product [32, 35].
The models of marketing and management theory that prevailed in the last two centuries focused mainly on the efficient manufacturing of large quantities of homogenous outputs of tangible products (goods) in order to sell them to (passively receiving) customers at prices that allow short-term maximization of profits. The tangible good being the main unit of analysis in these models suited academics well in their pursuit of turning economics into a deterministic science that is based on linear cause-effect logic [36].
At the beginning of the 21st century, a paradigm shift occurred in marketing: the paradigm of goods-dominant logic, which inherently fragments the interrelatedness of consumers with producers, is more and more being replaced by a paradigm that focuses on the “togetherness between service provider and beneficiary” [34]. This shift towards a ‘service-dominant logic’—where the focus moves from static transactions to dynamic relationships [36]—has serious implications on how exchange processes in the market arena are perceived and approached. The adoption of a service-oriented logic not only “embraces a focus on working together […] to integrate resources […] for mutual value creation” [34], but also “has the potential of shedding light on the role of exchange between and among service systems at different levels of analysis (e.g. individuals, organizations, social units, nations, etc.)” [37]. As a consequence, novel SDL-based models and concepts have emerged, such as servitization (cf. e.g. [38]) or service-oriented architectures (cf. e.g. [39]). For an overview of the development in the field of SDL cf [40].
Conceptual transitions from goods-dominant logic to service-dominant logic (based on [41])
Goods concepts | Transitional concepts | Service concepts |
|---|---|---|
Goods | Services | Service |
Products | Offerings | Experience |
Value-added | Co-production | Value co-creation |
Profit maximization | Financial engineering | Learning |
Equilibrium systems | Dynamic systems | Complex adaptive systems |
Supply chain | Value chain | Service ecosystem |
Promotion | Integrated marketing communication | Dialog |
Axioms and foundational premises of service-dominant logic (based on [37])
Axiom 1 | FP 1 | Service is the fundamental basis of exchange |
FP 2 | Indirect exchange masks the fundamental basis of exchange | |
FP 3 | Goods are a distribution mechanism for services | |
FP 4 | Operant resources are the fundamental source of strategic benefit | |
FP 5 | All economies are service economies | |
Axiom 2 | FP 6 | Value is co-created by multiple actors, always including the beneficiary |
FP 7 | Actors cannot deliver value but can participate in the creation and offering of value propositions | |
FP 8 | A service-centered view is inherently beneficiary oriented and relational | |
Axiom 3 | FP 9 | All social and economic actors are resource integrators |
Axiom 4 | FP 10 | Value is uniquely and phenomenologically determined by the beneficiary |
Axiom 5 | FP 11 | Value co-creation is coordinated through actor-generated institutions and institutional arrangements |
The foundational premises of SDL specify the core conceptual elements by which value co-creation is understood [37]. For instance, FP 6 and FP 9 as well as FP 11 imply that value co-creation always takes place in the larger context of service networks and service ecosystems [16]. The collaborative nature of service-for-service interactions between multiple stakeholders is reflected in FP 8. The contextual nature of the inherently subjective construction of value is highlighted by FP 10. In Sect. 4 of this work, the concept of value co-creation is viewed from a systems perspective, which further supports SDL’s foundational premises.
2.2 Organizational Cybernetics
The term cybernetics comes from the ancient Greek word for steersman (kybernetes), a term first used by Plato to describe the art of effective government. Hence, cybernetics is closely related to activities of steering and coordination and thereby represents processes of regulation. In the middle of the 20th century, the mathematician Norbert Wiener revived the term by using it to describe that all biological, mechanical and social systems function and operate according to the same underlying laws and principles [42].
Initially, the field of cybernetics was concerned with the engineering aspect of designing control structures in artificial machines. It had great influence on the birth of many modern sciences, e.g. control theory, information theory, computer science, artificial intelligence and artificial neural networks, system dynamics, cognitive science, computer modeling and simulation science [43]. However, when cybernetic thinking was applied in biology and the social sciences in the early 1970s, a new philosophy of science emerged. Expanding the traditional scientific approach, in which “the properties of the observer shall not enter the description of his observations” [44], the novel approach included the observer in the domain of science.
The term second-order cybernetics as the “cybernetics of observing systems”, opposed to the (first-order) “cybernetics of observed systems” was coined by von Foerster (1979, p. 7) [44]. Neurophysiological experiments provided the biological foundation for this pioneering approach as it was shown that observations, independent of the observer’s attributes, are not physically possible [45]. Influenced by this meta-perspective on social systems, Stafford Beer applied the findings of (second-order) cybernetics to the management of social organizations and institutions. Defining cybernetics as “the science of effective organization”, he pointed to the fact that there are fundamental organizational rules set out for all complex systems, which, disobeyed, lead to instability or to a failure to learn, adapt and evolve, and ultimately to a collapse and disintegration of the system as such [46].
In the late 1950s, Beer laid the foundations for a new second-order discipline in the context of Operational Research, which he understood “as the use of transdisciplinary science in tackling ill-formed problems with no known solution” [25]. With this novel approach called Organizational Cybernetics, Stafford Beer tried to model, make transparent and support the design of communication and control mechanisms in social organizations. Beer created an extensive number of cybernetic models, with his Viable System Model (VSM) being the best known in the management science community. This cybernetic model establishes the necessary and sufficient conditions for the viability of any human or social system. First defined in a set-theoretic model [20], the VSM was then revised using neurophysiological terminology instead of mathematics [25] and in the final version was operationalized as a topological version of the original set-theoretic algebra [24, 26].
System: “a set of interrelated variables selected by an observer” [47]. In general, a system is understood as a mental construct providing observers with a device to order, communicate and explore shared perceptions; by naming them, observers bring systems into existence [12].
Black Box: a cognitive device by which an ‘outside’ observer may describe an organization’s transformation of inputs into outputs where the internal mechanisms performing this transformation are not accessible to observation. When a correlation between the input and the output can be established through time, the observer can model the (future) behavior of the black box and therefore “it is not necessary to enter the black box to understand the nature of the function it performs” [24].
Feedback (negative, error-correcting): “the return of part of a system’s output so as to modify its input”, which is changed in a way that reduces variations in the output according to some specific purpose [26]. Feedback is one of the necessary mechanisms of self-regulation in all goal-seeking or purposive (teleological) systems; both in living organisms, artificial systems as well as in higher-level social systems (e.g. balance of supply/demand in economics).
Homeostasis: the special case of a feedback system stabilizing the internal environment of a viable (living) system. The mechanism of a homeostat provides the system with the self-organizing and self-regulating characteristic to hold critical variables within physiological limits. (e.g. the control of blood temperature). This concept also denotes the tendency of a complex system to run towards a dynamically changing equilibrial state.
Ultrastability: a new criterion emerging when describing complex systems that are self-organizing by homeostasis. This concept is relating to the capacity of a system to balance perturbations and adapt to changes in the environment that were unforeseen by the designer of the system.
Self-Reference: a “property of a system whose logic closes in on itself: each part makes sense precisely in terms of the other parts: the whole defines itself” [26]; self-reference provides closure to an organizational system, thereby allowing it to conceive of itself as an integrated whole, a self having identity and purpose.
Structural Recursion: a multidimensional, fractal organizational structure, where autonomous viable systems contain, and are contained in, autonomous viable systems, each having self-regulating and self-organizing characteristics [24]. At lower levels of recursion, all viable systems are composed of autonomous sub-units of functionally differentiated viable systems (e.g. cells, organs, human beings, corporate divisions, branches of industry, states, etc.), each contributing to producing the larger viable system they are embedded in. One key feature of structural recursion for the management of complexity is that the structure is invariant at each level of recursion.
3 Complexity and the Concept of Variety
In the following, we discuss variety as a means to measure complexity, and the law of requisite variety, finally we elaborate on the Variety Engineering Model.
3.1 Variety as a Measure of Complexity
Organizational systems in the business or social domain are complex, the situations to be managed are mostly highly dynamic: the system is unfolding its richness over time, thereby constantly changing its output, which often turns out to be hard to predict. The system’s internal mechanisms generate a proliferating variety that is to be handled by the manager. Likewise, in today’s turbulent business arena, organizations are faced with the need to manage a large number of uncertain environmental changes. When decision-makers are unable to understand or explain all aspects impinging on the decision-process, they associate these “difficult-to-manage-situations” with complexity.
Complexity thereby is regarded to be an attribute ascribed by observers and thereby relates to the observer’s ability to distinguish the parts and relations constituting the relevant system in the world. Following this line of thought, complexity does not reside in an outside world, but rather is determined by an observer using language to describe an outside world. In cybernetics, a system’s complexity is defined as the number of distinctions an observer is able to make in his or her observational domain [12]. As a consequence, the question arises—as complexity is being shaped subjectively: how can this number of distinctions be determined?
A precise measure of systemic complexity was first proposed by the British cybernetician W. Ross Ashby in the 1950s. This simple measure of complexity is called variety denoting the number of different states or modes of behavior a certain system may adopt [48]. To measure the possible states of a system (i.e. its variety), the observer first has to draw a distinction (in language) to establish the system by differentiating it from its environment [49]. The system’s border has to be set up according to the observer’s purposes, which includes making boundary judgments—i.e. setting up the meaning of a proposition by establishing a specific reference system [50]. Measuring variety is thus a self-reflective process by which an observer constructs descriptions of possible states of a system.
To illustrate the process of measuring variety, consider the following: when thinking about the complexity of a standard lighting system—a bulb governed by a switch—we usually count two possible states: ON or OFF. However, as it sometimes happens, the bulb can burn out. This is then considered to be a different state from OFF, because flicking the switch doesn’t light up the bulb; now three possible states are distinguished. If the bulb is replaced and there still is no light, we arrive at further differentiating additional states: the master switch may be turned off, the electricity bill hasn’t been paid, the cable is broken, the bulb socket is defunct or the electricity generating power plant has blown up. In our small example two possible standard states have developed into eight possible states (cf. [24]).
This example shows the importance of including the observer when considering the variety of a system, as he is always a participant, an intrinsic part of the system and thereby sets the context from which it is observed. It also shows that the level of analysis must be accounted for when different purposes are to be considered. Therefore, by ascribing the system a specific purpose, the observer specifies its boundaries and thus determines its variety [24].
3.2 The Law of Requisite Variety
The essential theorem related to the concept of variety is Ross Ashby’s Law of Requisite Variety [48], which states that only “variety can destroy variety”. Ashby’s law is considered to be as fundamental to management as the laws of thermodynamics are to engineering, or the law of gravity is to Newtonian physics. It states that, for a regulator to be effective, it needs to have at least as much variety at its disposal as the system to be regulated. In other words, “the larger the variety of actions available to a control system, the larger the variety of perturbations it is able to compensate” [43]. Indeed, this fundamental cybernetic law has important implications for every managerial situation: Management as the profession of control aims at a reduction of variety, but since the potential variety of disturbances impinging on the system is unlimited, managers have to enhance their internal variety to be flexible enough to react to unforeseen disturbances.
Perhaps the best known and most concise formulation of Ashby’s law is the Conant-Ashby theorem, which states that “every good regulator of a system must contain a model of that system” [51]. This theorem makes clear that “the result of an organizational process cannot be better than the model on which the management of that process is based” [18]. In other words, the models that managers use in a decision process are fundamental to the quality of the attained results. This implies that the relentless rise of uncertainty and volatility in current business environment requires managers to use adequate models for the task, i.e. models that have requisite variety [52].
3.3 The Variety Engineering Model
One model, which offers effective strategies aiming at requisite variety is Variety Engineering as deployed in the viable system model [12, 24–26]. This approach aims at enhancing value co-creation by facilitating analysis and design of the communication structure between producers and consumers.
To understand the concept of variety engineering in the context of service systems—where consumers and producers interact to co-create value—the distinction of actors from agents is drawn. According to Espejo [28] “it is not until social agents coalesce around shared purposes (tacit or explicit) that they constitute themselves as actors of an emergent organizational system”. The difference between actors and agents is thus defined by the contribution they make in producing the organizational system: Various social and economic actors interact in the creation and regulation of policies according to shared purposes and thereby produce, mainly through self-organization, the organization’s structure. Agents in the environment (e.g. customers, suppliers, competitors) who interact with these actors become participants of the organizational system and thereby co-produce the organization’s boundaries [14, 28]. This understanding of service systems is based on the notions of organizational closure and autopoiesis as developed by Maturana and Varela (1992) by which the boundaries of a system are being created by the system’s own activities [53].
Ashby’s law determines that, if actors (e.g. a multinational enterprise, a corporate division, or a project management team) wants to keep their organizational system under control (i.e. maintain dynamic equilibrium within its environment), they have to account for this variety mismatch by two strategies that have to be applied simultaneously: they have to either amplify their individual variety, i.e. develop more differentiated behavioral responses, or attenuate environmental variety, i.e. selecting an appropriate level of complexity they can deal with. According to Beer’s First Principle of Organization,1 these variety-balancing mechanisms emerge naturally as the variety asymmetries “are necessarily going to equate” [24].
Variety Amplification is concerned with redesigning practices in a way that several relevant perturbations impinging on the management system can be taken care of simultaneously. With this strategy actors improve their behavioral repertoire and the resulting rise of individual complexity allows them to match a greater number of perturbations with the same number of responses. In other words, they can achieve “more with less” [28].
Variety Attenuation deals with the categorization and classification of occurring perturbations in order to reduce situational complexity. Any mechanism or procedure that reduces the number of distinctions that management needs to pay attention to in a situation is an attenuator of variety. What aspects should be paid attention to depends on the purpose and performance criteria of the situation and on how much capacity the system has for self-organization. In this context it should be quoted that sheer ignorance is the “lethal variety attenuator” [26].
Variety Transducer is another important aspect of the model. The mechanism of a transducer “leads across” communications between the environment, the organizational system and the management [26]. When information is passed between environmental agents and organizational actors the message transmitted has to cross a system’s boundary. As each system has its own domain-specific language, the transmitted messages have to be “translated” (i.e. encoded or decoded) into the other system’s particular mode of expression [24]. If the transduction process restrains the variety of the message, the information channel is not working properly, and effective communication is therefore inhibited [29].
One must note that both the agents in the environmental ecosystem as well as the actors that constitute the organizational system have themselves capacities to regulate and steer their behavior towards an agreed goal or purpose (see circles of self-organization in Fig. 1). This implies that the emerging processes of self-organization and self-regulation are capable of absorbing much of the variety that is produced by the interactions of agents and actors, thereby leaving management with only having to account for the unabsorbed residual variety [12]. The model of variety engineering aims precisely at the improvement of these self-organizing and self-regulating mechanisms, thereby reducing the relevant variety the organization and managers have to deal with. This is achieved through two possible modes of use [28]: (i) In a diagnostic mode the variety engineering model provides guidance to determine if the variety operators, that asserted themselves through self-organization, are effectively balancing the variety asymmetries between actors and agents. (ii) In a design mode variety engineering helps to formulate the necessary and sufficient variety operators that have to be implemented to achieve a desirable performance.
4 Systems Perspective on Value Co-creation
4.1 Paradigm Shift in the Concept of Market
As already anticipated by Toffler [54], the increasing trend towards a post-modern digital economy, where innovation and customization become the most thriving business drivers, led to a merging of producers’ and consumers’ boundaries. Facilitated by the wide range of advancements made in ICT, the traditional roles of consumers and producers are readjusted through various forms of new capabilities for collaboration, participation and customer engagement [55, 56]. Consumers today are more willing to provide information while at the same time being increasingly empowered to collaborate with producers in exchange for more customized products and services that better satisfy their needs, desires and preferences [35].
With the consumer’s increased capability to participate in the value creation process through enhanced communication technologies, they can co-develop dynamically, together with producers, an active system of shared meanings [33]. These shared meanings are constituted by an ongoing process of reciprocal negotiation between producers and other stakeholders at a symbolic socio-cultural level [31]. As a result, value is constituted as the locus of these shared symbolic meanings [57]. This perspective resonates with FP6 and FP7 of SDL (see Table 2), which highlight the interactive nature of reciprocal value-propositions by producers and consumers.
4.2 Value Co-creation from a Second-Order Perspective
Considering these implications for market interactions, a deeper understanding of the mechanisms by which value is co-created is provided by second-order cybernetics. When viewing interactions between consumers and producers through the conceptual lens of the variety engineering model, the focus shifts to the conversational nature of service-systems constituting value co-creation through reciprocal value-propositions. In this context, Espejo and Dominici specified value co-creation as a dynamic process “through which the human constituents of the value system coordinate themselves in the creation of symbols and artefacts that become part of, and at the same time modify, the relational systems that create them” [33].
Consequently, both—consumers and producers—are conceived as participatory actors forming social networks within a global service ecosystem, which is treated as a black box. These actors are both involved in the dynamics that produce the black box’s outcomes as well as being changed by these. Combined with SDL’s conception of service ecosystems this implies that—as opposed to a first-order perspective, where social and economic actors stipulate each other’s purposes—the second-order perspective provides a view where each participant is seen as an autonomous observer who enters the service ecosystem by stipulating his own purpose. By entering a dialogue, he changes his purpose and at the same time the purposes and orientation of all other actors in the entire global service ecosystem [44].
This circular relationship between market participants (producers and consumers) is what Maturana (2002) explains with the concept of structural coupling [58]. It states that, whenever there is a history of recurrent interactions between systems a structural congruence between these interacting systems emerges, by which they form each other’s behavior by triggering internally generating structural changes that allow adaptation. In the case of a producer interacting with stakeholders in a market, this structural coupling is a learning process, where the result is the formation of shared meanings for products and/or services. The product is thereby “an embodied token underpinned by processes of value creation, which co-create, through recurrent producer–consumer relations, a stable form” [33].
Furthermore, SDL’s conceptual transition from “products” to “experiences” (see Table 1) reflects it’s close relation to the epistemology of radical constructivism, where knowledge is conceived as being actively constructed by an observer by his subjective interpretation of his experiences [59]. Heinz von Foerster’s notion of an eigenform (1976) explains how structural coupling between producers and consumers leads to the emergence of stable points [60]. These eigenvalues and eigenfunctions represent the product as the recursive computation of shared meanings of congruent experiences. The product is thereby understood as a cognitive and symbolic entity, an emergent token of recursive interactions between producers and consumers. From this constructivist’s perspective, a product (as a perceived experience) is necessarily different for every customer. Its value can even differ for one and the same customer over time, as the symbolic meaning of the subjective experience changes dynamically within a “co-ontogenic structural drift” [61].
The circular process of value co-creation resides in the relational domain of producer-consumer interactions and is the fundament for the creation of symbolic value. Hence, value co-creation occurs “when an object is seen as something perceived by an observer and socially valued, the perception being fluid and varying through time and space. […] As such, the co-creation of value within a market is a continuous and dynamic process that needs to be fostered by continuous communication, negotiation, and action of all the agencies in the market arena” [62]. Designing an effective communications structure can be achieved by the adaptation and application of the variety engineering model.
5 Applying Variety Engineering to Value Co-creation
5.1 Effective Structures Through Variety Engineering
Applying a systems perspective to the study of value co-creation reveals that several theoretical and practical gaps are to be identified and need to be eliminated. Mainly because analytic-reductionist thinking still prevails in management practice, the viable system approach, which is based on the variety engineering model, is found to broaden the perspective on service systems, such as business or social organizations [16]. From the point of view of recent literature elaborating on the integration of second-order concepts into service research, this approach is found to be capable of interpreting the systemic dynamics of economic actors in the global business arena at a holistic level [15].
Homeostatic loop in value co-creation [33]
To achieve adequate performance according to a specified performance criterion (in our context the criterion is “effective value co-creation”), the scheme outlined in Fig. 2 is a design template for pairs of variety amplifiers and attenuators that have to be set up to deal with the residual variety that is left unattended by internal self-organizing processes. Both, the input and output transducers, need to be designed to match the variety that is being amplified or attenuated.
5.2 Variety Operators in the Global Service Ecosystem
In the context of global service ecosystem, these instances of variety operators provide guidelines to enhance value co-creation. In the following we discuss selected examples (c.f. [31, 33]).
Internet and Communication Technologies
The variety contained in consumers’ value propositions may exceed producer’s capabilities to implement these communicated customer demands.
Too much variety in product offerings may overwhelm consumer’s cognitive capabilities, which potentially leads to ‘paradox of choice’ and paralysis in consumers.
For the high-variety side, online purchasing platforms (transducer) with integrated search-engines facilitated by semantic web reduce the variety inherent in producer’s value propositions by guiding self-organization. This helps customers choose from a proliferating variety of product offerings (attenuation).
Online collaboration platforms (transducer) allow customers to participate in the design process of products and services (amplification). These conversations constitute a balancing mechanism, by which producers learn to better meet the specific needs, desires and inner values of consumers with their value propositions.
Social media (e.g. blogs, social networks, virtual communities, apps for online-sharing, etc.) represent transducers for both producers and consumers.
Service Oriented Architectures and related concepts are to reduce or even eliminate boundaries among organizational entities (producers, supply chains, etc.) and thereby support for example the integration of producers in supply chains (amplification).
Artificial Intelligence tools and applications (attenuator) may help consumers as well as producers to interpret and translate online conversations. This would provide a conversations platform to negotiate meanings for services, thereby facilitating the emergence of ‘consumer-tribes’ and ‘brands’.
Intermediaries
As autonomous market agents form self-organizing networks in the service ecosystem, they absorb much of the relevant variety contained in consumers’ value propositions.
Collaboration with environmental agents enhances the producers’ response variety enabling them to reach a wider range of customers.
By instituting distribution networks, that connect e.g. retailers, distributors, and subcontractors, producers may increase (i) their flexibility to react on changes in the environment and (ii) their abilities to meet customers’ demands.
Mass Customization
If marketing research (attenuator) is able to identify specific demands and wishes of customers (e.g. via an online collaboration platform or by ‘decoding’ social media platforms) the producer can attenuate the variety necessary to meet the customer’s demands by utilizing advanced manufacturing systems that use standardized components and procedures (‘smart factory’ in industry 4.0), and determine the latest possible point in the supply chain for customizing a product according the needs or wishes of a specific customer (postponement).
To effectively incorporate customer’s requests for product design, the producer’s organizational structure has to be flexible enough to manage the increasing volatility in trends (amplification).
Consumer Tribes
When individual consumers have something in common, they interact to form culturally based tribal collectives; these consumer tribes assign products with specific symbolic meanings. If these idiosyncrasies are understood by the producer, they become susceptible to appropriate marketing practices, facilitating communication between the producer and the consumer tribal groups (attenuation).
By acknowledging the history, tradition, and culture of consumers the producer can better identify products that have the potential to create consumer tribes.
Brands
Brands are creating awareness for the symbolic meaning of products and services (attenuation). Consumers see additional benefits from using the product (e.g. value-in-use, psychological-value, social-value).
Word of Mouth
As actors and agents form social networks and relationships, they amplify producers’ offerings and communications by word of mouth.
Institutions for Quality Assurance
Organizations that manage the quality assurance of producer’s value propositions by instituting subjective values and norms of customers are essential variety attenuators.
Universal Humanistic Value
Sharing socially desirable meanings, values and norms (e.g. the Universal Declaration of Human Rights) may help producers and consumers to better reach consensus in their conversations. Redundancy in the service-system’s structure provided by the agreement on basic humanistic and/or social values effectively increases the channel capacity, thereby making the communications of value propositions more predictable [63].
6 Conclusion
6.1 Synopsis
The proposed framework with its theoretical foundations in constructivist epistemology may be a starting point for developing enhanced models and improved approaches to overcome the limitations of traditional analytic-reductionist logic. With the integration of a second-order perspective into the understanding of the processes of value co-creation, the reference frame transitions towards a view of service systems as being constituted by the relational aspects of human observers, namely, their conversations. Consequently, service systems are understood as self-organizing, adaptive networks of communications among observers who participate in the service system by drawing distinctions and creating relations within it. This wider perspective shows the potential to transform our understanding of the actions needed to enhance value co-creation.
The perspective on self-organizing service systems as described by second-order cybernetics provides the framework of SDL assistance in establishing itself as a new paradigm. This chapter shows that organizational cybernetics could be used as a theory-driven underpinning for the emerging service science. The overview of the proposed concepts and models sheds light on some of the systemic interdependencies of actors and agents participating in service systems. Continuously co-developing in the evolutionary drift of structural coupling, variety operators assert themselves naturally via self-organization as long as lines of communication are maintained between service beneficiaries and providers in service systems. Improving the quality of these variety balances effectively requires holistic models that exhibit requisite variety according to Ashby’s law. This implies, that a better understanding of these self-organizing patterns of interactions, in which value co-creation takes place, will lead to more differentiated models and—as a consequence—to better mechanisms and tools to guide and control the process of self-organization.
6.2 Further Research
The mere focus on the interactions taking place at the market level is found to be insufficient for better explaining and supporting the opportunities for value co-creation. To reach requisite variety of the model aiming at improving value co-creation, it is necessary to include the emotional, cultural, and cognitive-symbolic dimensions of human interaction. By enriching the design of decision-making models with the conceptual device of the Variety Engineering Model these dimensions could be taken into account. The resulting improvement of communications of value propositions enhances value co-creation by enabling shared meanings of all agents and actors to converge.
To enhance the possibilities for participation and collaboration arising with digital technology, variety operators have to be designed and installed to effectively improve value co-creation. It is likely that further development of the variety engineering model in the framework of service science may produce practical design tools and applications that help social actors to better guide the value co-creation process. One question that needs further elaboration in future research is: What measures are suitable for assessing the performance of the value co-creation process within a service system?
Systems thinking could be used to provide an open-minded, participative, pluralistic conversations-platform that creates an open dialogue between a wide diversity of actors in multiple human spheres. This could help redefine the purpose and orientation of the emerging human-technological society beyond current patriarchal institutions and neoliberal-capitalist forms. It provides a meta-perspective by which all social actors are perceived as closely intertwined and interdependent by their recursive communications, residing in the same coherent system.
With the proposed second-order view on service-systems the following question arises: Do we choose to think of ourselves as being apart from the world, deluding ourselves to think we can point to something independent from us ‘out there’, allowing us to be ‘objective’; or do we want to use a model of us being a part of the world, realizing that our actions change us and everyone else, which would enable us to assume responsibility for our actions by consciously entering a co-evolutionary dialog with others?