Value driven innovation in industrial companies: A complexity approach

This paper was presented at The XXVI ISPIM Conference – Shaping the Frontiers of Innovation Management, Budapest, Hungary on 14-17 June 2015. The publication is available to ISPIM

members at www.ispim.org.

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Value driven innovation in industrial companies: A complexity approach

Erik Lindhult*

Mälardalen University, Smedjegatan 37, 63105 Eskilstuna, Sweden.

E-mail: [email protected]

* Corresponding author

James K. Hazy

Adelphi University, 1 South Avenue, Garden City, NY 11530, USA.


Gerald Midgley

University of Hull, Cottingham Road, Hull, Yorkshire HU6 7RX, UK.


Koteshwar Chirumalla

Mälardalen University, Smedjegatan 37, 63105 Eskilstuna, Sweden. E-mail: [email protected]

Abstract: The purpose of this research is to contribute to the development of an interactive, systemic and ecosystem view of innovation and its management. This emerging interactive and systematic view of innovation labeled as Value Driven Innovation in this research, where enhanced symbiotic value is continuously discovered and realized in interactive processes among stakeholders such as customers, providers, suppliers and related partners. Complexity science is used as a conceptual resource for improving understanding of innovation. The development of a complexity approach to innovation and innovation management has as yet only been accomplished to a limited extent. The main outcome of the research is conceptualization of value driven innovation, which synthesizes and extends to value-driven innovation management recent developments in complexity science. In addition, the findings may provide useful tools to clarify and enhance the manageability of innovation in the face of complexity, uncertainty and unpredictability.

Keywords: Value; Value-in-use; Joint value discovery; Value driven innovation; Service innovation; Complexity; Leadership practices; Systemic innovation; Innovation models.

1 Introduction

Industrial innovation has predominantly been product-centric, understanding value as developed, produced and embedded in products where assessment of worth is made as



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exchange value in market interaction (Normann, 2001). Developments in service innovation research, open innovation as well as radical and discontinuous innovation have indicated that valuation is more complex, contextual and uncertain in advance of experience of utilization. These developments have led to an increased focus on customer experience and value in context of use (Gentile, Spiller & Noci, 2007). Hence there is a need for better and richer understanding on how experience-based valuation is done in use context and how it can drive innovation in order to accurately and quickly come up with improved ways to create value for customers, partners and in own business.

In particular, service research has pointed the way from product-centric to more value-centric or value-driven innovation (De Ana, et al., 2013) as new paradigm in industrial renewal. It points to a neo-Schumpeterian and service dominant approach, integrating and moving beyond the traditional distinction between product and service innovation (Carlborg, et al., 2013), instead targeting value as emergent in customer and ecosystem context: “ultimately, within this view, innovation is driven by collaborative efforts to find or develop new ways to create value” (Vargo, Wieland & Akaka, 2013). This more radical orientation poses challenges of customer resistance, undeveloped network and ecosystem, lack of discovery competences, and restrictive mindsets (Sandberg & Aarikka-Stenroos, 2014).

Value Driven Innovation (VDI), with a focus on value-in-use from the early phases, strives to overcome these barriers. Although the character of value driven innovation as an approach grounded in practice, interactive, systemic and ecosystem view is still in its infancy (Vargo, et al., 2013). Recent research indicates that value driven innovation (De Ana, et al., 2013) can be conceived as circular or spiral processes of (1) engaging in experiential and interaction spaces, networks and ecosystems (Ramaswamy & Ozcan, 2013), (2) joint discovery and creation of value in nexus of interaction (Ng., et al., 2012), and (3) institutionalizing (Vargo, et al., 2013) novel practice assemblage in order to stabilize performance and recreate it in multiple contexts.

The purpose of our research is to contribute to the development of an interactive, systemic and ecosystem view of innovation and its management, characterized by “win more –win more” outcomes where enhanced symbiotic value is continuously discovered and realized in interactive processes among stakeholders (Ramaswamy & Ozcan, 2013). The overall aim of this research is the development of conceptual resources and theoretical assumptions to underlie a complexity approach to value driven innovation when considered in practice. Complexity science has been pointed to as a conceptual resource for improving understanding of a more interactive, systemic and ecological view on innovation (Normann, 2001; Garud, et al., 2011; Goldstein, Hazy & Lichtenstein, 2010; Vargo, Wieland & Akaka, 2013), but the development of a complexity approach to innovation and innovation management has only been accomplished to a limited extent. The paper outlines a complexity approach to VDI in industry, particularly taking advantage of recent developments in complexity organizing and leadership theory, and how generative leadership can create and cultivate ecologies of innovation (Goldstein, Hazy & Lichtenstein, 2010).

2 Research method This research is mainly theoretical work which is intended to synthesize conceptual and theoretical elements from studies of innovation processes, complexity leadership, and service innovation to develop a model which can be operationalized, describe and potentially guide management of systemic innovation in services through joint value

discovery. This theoretical work was accomplished by combining previous work in complexity organizing and leadership, particularly in the context of innovation (Garud, et al., 2011; Hazy & Uhl-Bien, 2013), with the development of a particular complexity conceptualization of VDI. This conceptualization of a VDI approach will be developed in practice during action research with partner companies. The practical aspect of this research is part of ongoing co-creative research projects in service innovation within large industrial companies, e.g., Ericsson and ABB. The aim of the practical elements of the project is to develop, test and improve the parameters and other factors of the approach so that it can be strategically applied to ongoing service and product-service system innovation management to improve outcomes. This in situ aspect of the project adds real world implementation and detailed study and analysis of process dynamics within co-evolving service and product innovation with value discovery aspirations to the theory development.

3 Value driven innovation and complexity approach

A Complexity approach to Innovation

During recent years concepts and assumptions from complexity theory have been translated into the study of organization in general and innovation in particular. Complexity modeling has been used to point to the capacity for self-organization of complex adaptive system as an alternative to traditional management approaches (Goldstein, Hazy & Lichtenstein, 2010). Mainstream understanding of innovation management tends to limit rather than harness the complexity that innovation requires and generates (Garud, et al., 2011). Concepts such as emergence, tipping points, and phase transitions have aided researchers understand systemic networks of value discovery and value creation emerging from human interaction dynamics as a mechanism for adapting to a changing ecosystem.

The ubiquitous presence of an ecosystem at every point in the space containing an organizing system of agents implies that this a system is teaming with differences and variation that arise from unique difference in both makeup and experiences. The dynamism of interaction among differences can enable the emergence of entirely new structural attractors – which go on to become radical innovation – when a seemingly arbitrary event that almost goes unnoticed becomes deeply important to the system as feedback loops (Chirumalla & Lindhult, 2015) propelled by fine-grained multi-agent interactions, together with path dependencies, reinforce and strengthen what once was a weak seemingly inconsequential event.

Experiments like these, when they occur under coarse-grained constraints, can identify previously unknown pockets of value, and when these become structural attractors that grow by being driven by value, becoming important innovations for the firm. This illustrates how contingent events that can irrevocably change the system can also make the system better. But allowing the inconspicuous experiments the space to grow into something significant requires courage on the part of management. Complexity theory points to the interrelated managing endeavors of connecting subsystems and agents to build thriving ecologies of innovation, organizing experiential spaces and interaction communities which can mobilize a broad range of capabilities in ecosystems, and leading continuous emergence through its different phases (Goldstein, Hazy & Lichtenstein, 2010; Lichtenstein, 2014).



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Complexity leadership theory has developed in recent years to identify and clarify constructive leadership functions and practices that must be performed if an organization is to build ecologies of innovation. This would be done, for example, by constructing smart social networks as well as encouraging and supporting experiments in novelty that creates (Hazy & Uhl-Bien, 2013). This approach provides new insights on how individuals might serve to enact VDI in organizations having the character of complex adaptive systems (CAS). This discussion points toward a practical program for enabling “emergent innovation” in a purposeful constellation of actions.

Defining value

To begin to explore VDI, we must first define what we mean by value and value-driven. Consistent with our theoretical framing from complexity science, we start from the perspective of Stuart Kauffman (2008) who uses the example of bacteria responding to a glucose gradient, seemingly moving purposefully toward up the gradient (presumably) to find more glucose. He writes: “Because natural selection has assembled the propagating organization of structures and processes that lead to swimming up the glucose gradient for good selective reasons, glucose has value to the bacterium” (p.87).

Thus value emerges with agency. The bacteria example also shows that there are some doings, in this case swimming, generally work to be done, to realize and create value. Good business offerings can accurately target “the pain” of the customers and effectively and efficiently perform the job to be done to make them “happy” in the sense of better off, willing to pay or contribute other activities and resources, which makes the provider happy or satisfied. Value in a practical sense is in the activity patterns (“work”) and resources/tools which can create outcomes that an agent is happy with. This includes activities which can provide information and knowledge about what is valued outcomes (value discovery) and how they can be achieved (value creation). Knowledge reside in the capacity of agents to direct energy toward realizing outcomes (Simms, 1971) which in practice means to organize and perform activities and mobilizing affordances in creating valued ends (Dewey, 1939, Ng, 2014).

In the bacteria case, value can be deduced through observation of activity, and how it affects survival and development of bacterias. But we cannot get this information through requesting it directly from the bacteria. Taking an evolutionary journey further, agency developed with consciousness, self-consciousness and the ability for pre-linguistic and linguistic communication. This opens up for emic (insider) and etic (outsider) perspectives on value and its assessment which both are important in determining value. Emically, experience, purpose and desires can be expressed, and etically the actual directed activities of agents indicate sources of attractors representing value. Both insider and outsider research and knowledge is generally needed and in practice used in value discovery and creation. Even the best shoe provider has difficulty to fully know where the shoe pinches and in detail how it fits, e.g., design, color, environmental conditions of use, way of walking, status value etc., and the provider normally offers a lot of activities to be performed by the user, e.g., visual assessment and testing of the shoes, put on and take off the shoes, storage of the shoes, maintenance. In general in developing footwear, users have limited knowledge of possibilities and options as well as efficient ways to realize them and providers do not have full knowledge of heterogeneous user contexts and needs. Thus there exist components of conjoint activity, and joint discovery and creation of value in innovation, which are crucial to achieve success.

Thus both etic and emic views on value is important to consider in innovation. An insider view is more common in the business research literature, e.g., marketing and service

science, while economics predominantly favor etic views, e.g., in economic man models, utility and preference functions. An example is a recent state of the art contribution in service research by Ng & Smith (2012) arguing from a phenomenological perspective that value is grounded in raw experience of agents, which can be articulated more or less accurately and accessed in language and communication. In situations where often heterogeneous insider knowledge is more important, e.g., in customized service encounters, or efforts at creating value constellation with a few stakeholders, clarifying outcomes and jobs to be done need to rely on more participatory, dialogical, qualitative and field oriented innovation methods.

From an etic view “value” (to a particular agent) can be assigned to any process or a thing (called the “source” of the value) such that the information that is embedded in its structure causes the agent to alter its direction of action. The rate of change in the agent’s action is a measure of the value of the source felt by the agent. Influence to an agent’s action toward the source is positive value; influence to action away from the source is negative value. Information in a process or thing, the source of value, makes the outcomes of an interaction between that source and the agent more predictable, less random. If an agent is under attack from a tiger, a weapon is valuable to the agent because it enhances the probability of a positive outcome. The agent is therefore willing to pay a price or take a calculated risk to acquire the weapon. Note that the gun has also value to the tiger if it can be denied to the agent. The question is whether the tiger recognizes the value and is able to act upon it. Value is thus a local phenomenon that sets up a potential, which can be bridged with action of some kind.

Under this framing, therefore, “value” can be thought to exert a “force” (magnitude and direction) on individual action that is mediated through informational transit to bridge potential differences through kinetic action. A simple example of this is a follows: Imagine a $100 bill on the sidewalk. This source of value would almost certainly draw some passersby toward it. The $100 bill could be assigned value according to the magnitude and direction of its effect on agents. We take this admittedly sterile definition of value as a starting point. By doing so, we complement the majority of the literature that frames value in the context of subjective psychological processes, for example, as phenomenal and access consciousness (Ng & Smith, 2012).

Here we seek an etic model of value that can be observed as changes to the action patterns within a population. As such, the sensing, recognition and interpretation of information content embedded in products or services are key aspects of the influence processes at work within the population. In commercial usage, therefore, value would be measured by probability distributions that describe purchase decisions or user behavior for a given population of agents, given the information available to individual agents situated as they are at unique positions in social networks (the outside view). Recent developments in big data and business analytics provide considerably enhanced opportunities for innovation based on modelling and prediction of value based on etic knowledge. This is in contrast to the psychological processes that might be presumed to trigger such a decision in a particular agent (in inside view), which often is more resource consuming to extract.

Defining value driven innovation

An important dimension in targeting value in innovation is the topology of actors representing the sources of value as well energy and work capacity to create value (Kauffman, 2008). Positional value in terms of access of resources and relations in ecosystem is important to consider. The location of actors in the ecosystem both indicate distance between them but also potential and actual bridging of these distances in exchange



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and co-productive relations forming complex activity systems producing value for different parties (Ostrom, 1996; Normann, 2001). The exchange of resources between actors and links between their activities represent potential for mutual value creation to enhance the total value in the ecosystem. Value driven innovation is focusing and targeting mutual value creation, which produces value constellations, or value stars where those involved both contribute and extract value. The traditional distinction between products and services is here becoming of limited relevance. Offerings expressed in value propositions embodied in business models, product and services are primarily proposals and codes for organization of value co-creating systems. Products are physical manifestations of a very complex constellation of activities performed by as very large number of actors. It is an institutionalized organization of activities, resources and relations producing mutual value. The same logic is valid also for services although here the constellation of value co-produced is not embodied in a functional object but is instead in a process serving certain focal actor. Thus value propositions are organizing proposals for joint and mutual creation of value.

We are defining valorization as the process of increasing or establishing the value that can be proposed, co-created and facilitated for customers and partners. It means to engaging with customers, partners and other stakeholders in the business ecology and discovering, innovating, creating and capturing value together in interactive, co-evolutionary processes (Normann, 2001; Ramaswamy & Ozcan, 2013; Goldstein, Hazy & Lichtenstein, 2010; Vargo, Wieland & Akaka, 2013, Lindhult & Hazy, 2015). It is not new, but actually a core aspect of business development, but often with too narrow system view focusing on embedding value in products or services to be delivered to customers. It implies moving from product driven/service driven innovation approaches to value driven approaches where jointly discovered value in ecosystem is targeted and translated into innovative activity which can co-create value in context. In moving to service logic, servitization in different ways is becoming contradictory as everything becomes service. Therefore, the distinction between products and services becomes blurred and of less interest. The digitalization of business accentuates this process. In this regard, service is more a perspective on value creation (Vargo & Lusch, 2004). What is then the meaning of more service or serving more? It is creating more value, increasing the amount of value available and realized by actors. Then valorization is a more appropriate concept, in line with Marx use of it (Entwertung). Valorization is referring to the dynamic processes of discovering, creating and capturing successively more and more value. But value creation is not stable as it depends on continuous enactment in experiential and purposive processes and practices. Certain kinds of and ways of value creation (“service”) can come into or out of use, often depending on Schumpeterian innovation processes where new combinations of resources and activities and agency are brought into use contexts and replacing established ones.

If valorization is seen as something that business corporations are focusing on it is often the creative aspect of Schumpeterian dynamics, or gale as Schumpeter says, of creative destruction. Industrialization meant the emergence of a factory system divorcing production from use/consumption, craftsmen from the consummatory value of his/her work. This created a distance between value embedded in products as outputs to production processes to be exchanged in the market and use value in customer context. Valorization is a way of escaping the commodification of business. That is, what a business is creating is basic to any offering, but it is often difficult for the firm to appropriate value from what the market might perceive as its basic skills. On the other hand there are valorization traps: Targeting a limited niche for premium value may limit value appropriation. In these cases, valorization does not match a customer definition of value, or represents value that is too

limited in relation to customer demands. It can also be in overproduction of value, which cannot match what providers are able to appropriate.

Thus we define value driven innovation as complex business offerings that maximize valorization as shown in Figure 1. This process of maximizing the valorization can occur in several stages depends on the business context.

Figure 1. The landscape of Value Driven Innovation.

In the servitization journey, which also is a path of valorization, a first step is services seen as a kind of added product features, e.g., products offered with a warranty or a spare part service. At this stage services are strongly dependent on products and are mainly wrapped around the product as added function. A next step is to add service in the context of use of products. Often this is a phase where services and service business can be developed more independently with integration in sales as customized product-service packages. When the focus is moved to supporting value creation in customer processes, then interdependence tend to increase implying that more integrated development of Product-Service Systems (Mont, 2002) are called for. Moving to co-creation embedded in activity and experience context, then coordination need to be done for creating contextual design of value-creating systems through more systemic innovation (Lindhult & Midgley, 2014). When value is an achievement of network of actors in ecology, coordination need to be made through interactive co-evolution and resonance in the business ecology (Goldstein, Hazy & Lichtenstein, 2010). From a business perspective, it is not to say that industrial companies are advised to move all the way along this ladder of service integration and complexity. In most cases probably it is best to settle for more limited level of integration and complexity, but this is a strategic decision to be made.



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4 Business models for value driven innovation If we look at business models, the mutual, symbiotic view of value is evident. Value propositions need to be combined with interpenetrating activity systems (Zott & Amit, 2010) performed by actors in different roles to co-produce mutual value, and ways for all parties to perform the activities adequately valuable to create a sufficient value. The value proposed is embedded and packaged in service offerings designed in order to support value-creating activities of customers. Secondly, organization of co-creation of value so as to achieve high total value and satisfactory distribution of value. Thirdly, value capture, is implemented by price mechanisms as well as distribution of responsibilities, roles and activities that increase value, including reduction of costs, for the providing company, e.g., IKEA transferring the activity of assembling the furniture to the customers.

A smart business model can increase the total value in the practice constellation, implying more value potentials for each party, a better chance for significant win-win symbioses, which makes for a sustainable constellation of co-production of value. All types of value is positional, agency dependent and contextual. Even money, which has a definite value in exchange, has different value in different use situations and contexts for different agents. It is important also to consider both core and extended service concept (Grönroos, 2007) in analyzing not only value capture but the whole business model. From a symbiotic and inter-innovation perspective it is important to consider the core and extended service concept also for customers. For example, identify unused or underused resources and capacities, which could be mobilized in the business exchange to provide service. Symbiotic value co-created by different agents, which is mutually adapting themselves, their role and even identity to the relationship, is quite common.

In a large industrial company performing innovation in an inside-out fashion it is a risk of running into a complexity trap (“featuritis”) where product options and functionality outrun the capacity of customers and users to innovate their usage. Value creation potentials in products are therefore not realized in customer contexts. Value is also dependent on the situational needs of customers in the use context of product and service offerings. It is also an effect of how innovation is perceived, modeled and practiced. Value is also positional; a pure transaction of goods can increase total value as Pareto early saw, this also need innovation in mutual activity systems. Economic exchange, like all exchange is symbiotic. Important is mutual and total value produced, and increasing density in a certain symbiotic value constellation which can attract both using and producing agents (often they have both roles).

From a service logic perspective everything is service and needs to be looked at from the point of view of the services it can produce for customers and service providers (Grönroos & Helle, 2010). Also products, like any resource, are valued for their expected services they can help to create (Penrose, 1995).

Outside-in innovation logic from customer context can lead to another type of trap, the competence trap. This occurs when a firm innovates offerings, which create value in customer context but do not sufficiently build on distinctive competence of provider. This is one reason why virtual companies with few internal competencies may eventually fail.

When innovation becomes co-evolutionary, however, both providers and users need to innovative in order to create a mutually adaptive match of activities and resources. There are inside-out, outside-in, and co-evolutionary logics in innovations. This suggests a systemic character of innovation as illustrated in Table 1.

Table 1 Overview from Inside-out to co-evolutionary logic

Inside-out

innovation logic

Outside-in innovation

logic

Co-evolutionary logic

Value focus Value embedded in product, exchange

Value in use, value in context

Value in interaction and symbiosis

Risk Complexity trap Distinctive competence trap

Relational complexity trap

Innovation Models State-gate, waterfall, funnel

Customer involvement, ethnology, engagement platforms, service design and mapping

Systemic collaboration methods, activity and outcome system design, mutual adaptation and co-evolution

5 Systemic methods for value driven innovation

We propose that value-driven innovation can be supported through the use of systemic

collaboration methods (sometimes called ‘problem structuring methods’ in the literature). These have mostly been developed since the early 1980s, and innovation in the production of new methods is still on going: see Flood and Jackson (1991), Rosenhead and Mingers (2001) and Midgley (2003) for some useful overviews. Systemic collaboration methods require stakeholders to be brought together in participative workshops to explore problematic situations that they want to address (e.g., the reasons why new products and services might be desirable, and the need to alter wider systems if they are to accommodate new innovations). Crucially, however, systemic collaboration methods go beyond exploring problems; they also facilitate the development of new value propositions, and encourage the exploration of these to see how different stakeholders interpret them. Finally, they encourage stakeholders to come to synergistic agreements on innovations that are broadly perceived as adding value.

There are two crucial differences between systemic collaboration methods and typical meetings with an agenda. First, systemic collaboration is facilitated by someone who is experienced in managing group dynamics and is able to support stakeholders in reaching synergistic agreements (Taket, 2002). Second, and most importantly, the dialogue between stakeholders is structured through the participative development of models as ‘transitional objects’ to structure stakeholder engagement (Eden and Sims, 1979; Eden and Ackermann, 2006) and provides a focus for dialogue (Franco, 2006). These models may use words, pictures and/or numbers to represent, for example, people’s understandings of a problematic situation; the assumptions underpinning a particular stakeholder perspective; and/or the activities that might be needed to improve the situation. ‘‘The model ... plays a key role in driving the process of negotiation towards agreement through discussion and the development of a common understanding’’ (Franco, 2006, p.766). However, a ‘common understanding’ does not necessarily imply consensus or agreement across the



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board: it may be an agreed understanding of the differences between people’s perspectives and what innovations are possible in the circumstances (Checkland and Scholes, 1990). Models have traditionally been produced on flip charts using marker pens, but computer-mediated modelling is increasing in popularity, and this can facilitate remotely distributed and/or anonymous stakeholder participation, bringing advantages compared with face-to-face, pen and paper modelling (Er and Ng, 1995; Fjermestad, 2004; Fan et al., 2007).

Notably, systemic collaboration methods are used to broaden the perspectives of participants in order to facilitate the emergence of new framings, strategies and innovations. Typical questions addressed by the different methods include:

• Whose perspectives on value should be included in discussions, what issues should

be focused upon, and who or what should be excluded? (e.g., Ulrich, 1994; Midgley,

2000).

• What purposes, values and assumptions underpin different perspectives on value

creation/destruction? (e.g., Checkland and Scholes, 1990; Checkland and Poulter,

2006).

• What interactions within and across organisational, social and environmental

phenomena need to be accounted for in thinking about the value of potential

innovations? (e.g., Vennix, 1996; Maani and Cavana, 2007). It is important to be clear that systemic collaboration methods have not been designed using the language of value-driven innovation: they have all been conceptualised as addressing ‘wicked’ (Rittel and Webber, 1973) or ‘messy’ (Ackoff, 1981) problematic situations characterised by significant complexity and stakeholder disagreements. Essentially, they structure the learning of participants in the face of complexity, and in particular the development of better mutual understanding between stakeholders so they can find accommodations (Checkland and Scholes, 1990) and agree ways forward that everybody involved can accept as improvements (Midgley, 2000). In our view, it is only a small step from the language of ‘improvement’ to the language of ‘value’, which is why these methods have great promise. The authors of this paper are currently working on a proposal for research to look at whether and how the use of systemic collaboration methods would need to be modified to support a focus on value rather than improvement.

The following systemic collaboration approaches are arguably useful starting points to assemble a methodological resource for value-driven innovation, but are by no means exhaustive: Soft Systems Methodology (Checkland and Poulter, 2006); Interactive Planning (Ackoff, 1981); Strategic Choice (Friend and Hickling, 2004); Strategic Assumption Surfacing and Testing (Mason and Mitroff, 1981); Critical Systems Heuristics (Ulrich, 1994); Boundary Critique (Foote et al, 2007); Systemic Evaluation (Boyd et al, 2007); Group Model Building (Vennix, 1996); Strategic Options Development and Analysis (Eden and Ackermann, 1998); Total Systems Intervention (Flood and Jackson, 1991); Systemic Intervention (Midgley, 2000); Systemic Mediation (Midgley and Pinzón, 2013); and Issues Mapping (Cronin et al, 2014).

6 Leading value driven innovation A complexity approach urges us to focus on the interactive context, seeing innovation as emerging in multi-agent dynamics of interplay, exchange and interaction. Its novelty and value shows itself in shifts in constellation of practices producing enhanced or different

performances valued by involved actors, particularly in the case of economic ecosystems customers and main providers of service.

What is the character of emergence dynamics? Emergence is a kind of interaction and network dynamics where stability, occurring differences and variation, and shifts to new states, is controlled and enabled by different negative and positive feedback loops (Chirumalla and Lindhult, 2015). Such loops can be described as attractors (fields of negative feedback where fluctuations are petered out bringing practices back to normal) as well as chaotic states (fields with positive feedback) with high sensitivity to initial differences. Emergence provides a perspective on ecogenesis and co-evolution of value constellation (Normann & Ramirez, 1993).

Innovation are often processes of moving in the fitness landscape in the vicinity of and influenced by existing attractor fields (e.g., process attractors like a stage gate development model, or certain purposes and performance measures), from one attractor field to another (e.g., from technology opportunity/inside-out to customer value/outside-in in innovation approach, from mechanical to digital solutions) or moving into a turbulent, unstable field where (“chaos”) where stabilizing feedback is limited leading to extreme variation and risk of collapse (often the area for more radical innovation), only later coming to zones of more influence of stabilizing dynamics.

Generally Kurt Levin’s classical metaphor of unfreezing, moving to a new state, refreezing has its relevance also here. Building and reinforcing momentum for change and innovation can overcome normalizing attractors and build forces. Eventually, this can freeze developments in a new, improved state. Dynamics is contained in different ways, also directing it but with degrees of permeability and exchange.

Leadership is enabling agency in a double sense; it is an agency enabling complexity dynamics of multiple agency. It is also an agency in itself working through influencing complexity organizing. In the complexity approach, “leadership” is not considered to be a person or persons. Rather, it is the recognizable pattern of organizing activity among autonomous heterogeneous individuals as they form into a system of action (Lichtenstein et al., 2006; Hazy, Goldstein & Lichtenstein, 2007a; Uhl-Bien, Marion & McKelvey, 2007). At the same time, for organizing to occur, leadership must perform certain functions, what Katz and Kahn (1966) called the “influential increment.” It is also enabling space for leading dynamics of emergence (Peschl, 2014).

7 Conclusions and future directions

The main result of this research is a complexity conceptualization of value driven innovation. This conceptualization synthesizes and extends to value-driven innovation management recent developments in complexity science. In addition, the work situates innovation management within the functions of leadership of the complex organizations of today (Hazy, 2004, 2006, 2007a,b; Hazy & Uhl-Bien, 2013).

Conceptual clarification of value driven innovation as an innovation management approach in industrial settings, showing how complexity theory can be used for understanding dynamics of this approach, particularly how leading emergence. The development of a complexity approach to innovation is valuable to provide a theoretical basis and conceptual resources for the community of researchers and practitioners who seek to identify and vet opportunities for radical change in services organizations or functions. In addition, the findings may provide useful tools to clarify and enhance the manageability of innovation in the face of complexity, uncertainty and unpredictability. The intent of this research is to construct an approach to the management of innovation



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that is appropriate for interactive and systemic leadership in multi-agent, networked innovation ecologies. Because of the interactive, collaborative characters of service oriented innovation; the approach is particularly relevant for service innovation management.

The theoretical developments are particularly useful guiding leadership of innovation in broader organizational and networked settings. In ongoing action research in industry (e.g. including Ericsson, ABB), a scientific aim is to contribute to development of value driven innovation strategies as well as systemic leadership of innovation activity in services in global industrial companies. In addition, there has been an ongoing initiative to develop systematic processes and practices for organizing experience feedbacks loops in complex innovation settings. Such processes and practices could support companies in analyzing and converting the gathered feedback information into value-based offerings to customers.

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Value driven innovation in industrial companies: A complexity approach