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Pioneer, Consolidate, or Adapt

The Innovation Pathways in Circular Cross-Sector Partnerships

Published onJun 20, 2023
Pioneer, Consolidate, or Adapt
Anna Margolis*
University of Hamburg, Faculty of Business, Economics and Social Sciences, Hamburg, Germany
* [email protected]


Using a multiple-case study of eight circular cross-sector partnerships (CCSPs), this study examines why some CCSPs produce transformative circular innovation, while others do not. Combining process studies and systems thinking, I define a map of the transformative innovation process in CCSPs and identify three innovation pathways: pioneering, consolidating, and adapting.


transformative innovation process, cross-sector partnerships, circular economy, circular oriented innovation, system innovation


Many of the complex ecological and social challenges in today’s world can only be resolved through systemic and transformative innovation, which requires collaboration across disciplines and sectors (Dentoni et al., 2020; Senge et al., 2008; Wasieleski et al., 2021). Cross-sector partnerships (CSPs) represent mission-based collaborations between actors, such as business, government, civil society, and academia, which can support interdisciplinary efforts for systemic transformation (Clarke & MacDonald, 2016; Mazzucato, 2021; Seitanidi & Crane, 2014; Selsky & Parker, 2005). Three interconnected and dynamic elements are crucial in understanding the potential of such collaborations to solve systemic issues (Clarke & Crane, 2018; Giddens, 1984):

(1) agency, which manifests through the actors’ actions or practices;

(2) the socio-technical structural change that these actions bring about; and

(3) the impact this change has on social and ecological issues.

A successful interplay of these three recursive elements results in transformative innovation.

However, we have a limited understanding of the relationship between these three elements in cross-sector partnerships that focus on circular oriented innovation (CCSP). We do not understand in depth (1) which CCSP actions and practices bring about socio-technical structural change;

(2) how these actions and practices connect into innovation pathways shaping the process.

This gap is problematic as the lack of transparency on how to navigate the process adds complexity to cross-sector collaboration (Dentoni et al., 2020; Pedersen et al., 2020; van Tulder et al., 2016). That complexity may throw CCSPs off track and prevent them from achieving their vision. The focus on the relationship between the three elements described above informs our research question:

“What are innovation process pathways for CCSPs that have the potential to transform socio-technical structures and support transformative innovation?”

Transformative innovation requires actors to reconfigure socio-technical structures. To understand socio-technical structural change, I turn to literature on socio-technical sustainability transitions (Köhler et al., 2019; Markard et al., 2012). I build on this literature to define CCSPs as niche actors, which are aiming at destabilising an existing regime from within.

The study design is a multi-case study (Cao, 2007; Eisenhardt, 1989; 2021; Langley, 1999) of eight European CCSPs which want to contribute to making the packaging ecosystem more sustainable and circular. The analysis of more than 2,000 actions covers various sources, including 1,106 pages of public documents, nine interviews, ten videos, three industry events, and one progress report. The findings demonstrate that only CCSPs who engage in experimentation, co-creation, and value redistribution are in a position to follow a learning spiral (Raisch et al., 2018) that can ultimately reconfigure socio-technical structures. In collaboration literature, it is commonly assumed, that the initial design of the partnership is crucial to foster experimentation and co-creation (Pattberg & Widerberg, 2016). However, this study demonstrates that practices associated with experimentation and co-creation may also develop as a result of previous collaboration.

The rest of this paper is structured as follows: first, I present more details on the three elements of analysis in the context of CCSPs. Second, I describe the methodology for the multi-case study. Third, I present key findings on the three elements of analysis and their mutual relationship. Finally, I discuss what these findings mean for transformative collaborative innovation in research and practice.

Background: Transformative innovation in CSPs

In this section, I present insights from CSP, sustainability transitions and systems thinking literature, focusing on the three interconnected dynamic elements in CSPs. I start with (1) innovation actions and practices, followed by (2) socio-technical structural change, and (3) socio-ecological impact. Finally, I discuss how the three elements link together conceptually.

Element 1: Actions and Practices Shape the Innovation Process

Agency contains multiple units of analysis: Actions are trackable activities performed by a single or multiple actors. Practices are social phenomena denoting an organised constellation of many actions performed by multiple actors (Schatzki, 2012). For example, having a meeting is a practice that requires actions such as defining an agenda, sending invitations, coming on time, and listening to others. A sequence of practices constitutes a process (Langley et al., 2013). For example, a weekly meeting can be one practice within the process of partnering for socio-ecological impact.

It is common for researchers to structure practice studies on CSPs around three chronological stages: formation, implementation, and outcomes (Selsky & Parker, 2005). However, while scholars have identified various innovation and collaboration practices in CSPs, there is currently no detailed account of how the innovation process unfolds and how practices relate sequentially.

Element 2: Socio-technical structural change

The second element in systemic change is the reconfiguration of structures (Clarke & Crane, 2018). To understand socio-technical structural change, I turn to literature on socio-technical sustainability transitions (Köhler et al., 2019; Markard et al., 2012). The critical takeaway is that CCSPs engaging in systemic change are comparable to niche players. Developing the novelty is only one part of the challenge. The CCSP must reconfigure the system to diffuse innovation. This process is present in entrepreneurship literature, but largely unexplored in partnerships literature (Raisch et al., 2018; Ries, 2014). Therefore, understanding the innovation process in CSPs in more depth can also shed light on why some CSPs succeed in reconfiguring structures, and others do not.

Element 3: Socio-ecological impact of CSPs

Finally, CSPs have a motivation to exist, which revolves around creating social, ecological, and economic impact. While an impact analysis remains a state-of-the-art tool in sustainable innovation, its use is connected to many uncertainties, as socio-ecological systems are not predictable (Bester & Hermans, 2017; Rounsevell et al., 2021). To that end, process-based models can support sensemaking in CCSPs as they consider evolutionary change and adaptation (Blanco et al., 2017; Senge et al., 2008). Therefore, I turn to process models in the conceptual development.

Conceptual development:
Navigating complexity in the innovation process

This study looks into how to navigate innovation process pathways to create a socio-ecological systemic impact. In this section, I develop an analytical method to answer that research question. I select practice theory as a lens because it supports the assumptions behind the three interconnected elements in a CSP, where practices shape structures and vice versa (Giddens, 1984). The systems thinking lens covers constituents, such as complexity and organisational learning (Argyris, 2011; Davies, 2004; Jackson, 2019; Senge, 1990; Snowden & Boone, 2007). These constituents help to address the question of how to navigate a complex innovation process.

Defining the navigation map:
Complexity of actions and practices

Issues addressed by CSPs have varying levels of complexity (Snowden & Boone, 2007; van Tulder & Keen, 2018). Clear issues require clear, projectable actions based on a static ‘best practice’ approach to change, with evaluation at the end; complicated issues focus on transforming structures, adaptive theories of change, and double-loop learning with ongoing evaluation (ibid). While complex issues require systemic iterative solutions, which involve experiment-driven learning, evaluation, and continuous development of indicators (ibid). The idea of the complex domain rests on the assumption that in systemic change, our actions shape emergent structural change – unforeseen in the initial planning (Jackson, 2019). Actors learn about system dynamics only by experimentation (Snowden & Boone, 2007). These learning outcomes will shape the problem frames and might shift the direction of activities (Klitsie et al., 2018).

While categorising issues into complexity categories is a valid approach (Jackson, 2019), this alone is unlikely to support change in socio-ecological systems. We need to comprehend the change in complex settings as an evolutionary process that is facilitated by continuous reflection and learning (van Tulder & Keen, 2018). A process consists of practices manifested by actions. Correspondingly, we need to understand how actors frame the complexity of actions and practices in the innovation process.

Looking at actions and practices as units of analysis corresponds to how complexity theory views decision-making (Snowden & Boone, 2007). Therefore, I formulate the first research sub-question as:

What are simple, complicated, or complex actions/practices associated with innovation in CCSPs?

Further, we need to understand the sequence of these actions/practices to map the process. Therefore, I formulate the second research sub-question as a process question (Langley, 1999):

Which practices belong together in one phase? What is the sequence of these phases? How do these phases together build up the transformative innovation process?

As this question is not addressed in the literature, I analyse it based on empirical data (see methodology section). In summary, the innovation process acts as a map that indicates two things: if there is a road or paths (i.e. level of complexity) and the direction (i.e. phase in the process).


The analysed phenomenon is the innovation process in CCSPs for sustainable packaging. The research looks at a multi-case study of eight CSPs collaborating to create a more sustainable and circular packaging ecosystem in the European Union (EU). The research design builds on Cao’s (2007) proposition that combining theories helps to increase understanding of the overall phenomenon in qualitative interpretative research. Therefore, the study design consists of four steps and combines inductive (Eisenhardt, 1989; Eisenhardt, 2021; Langley, 1999) and deductive (Cao, 2007; Kuckartz, 2018; Mayring, 2014) methods, depending on the research sub-question (see Figure 1).

Figure 1: Methodology overview

The units of analysis are the actions for sub-question one and practices for sub-question two. For the first research sub-question, deductive qualitative content analysis (Kuckartz, 2018; Mayring, 2014) is used to classify complexity levels of practices. For the second research sub-question, a theoretical framework to inform coding did not exist. Therefore, I performed an inductive process analysis applying temporal bracketing to case histories (Langley, 1999). Both results were linked in a cross-case comparison, where I applied pattern matching (Cao, 2007).

Case selection, sampling, data collection

I select the transition towards more sustainable and circular packaging in the EU as the context of analysis. Since 2016, the EU Commission has increasingly introduced policy measures to foster that goal (European Commission 2018; 2019; 2020). At the same time, there has been a rise in consumer and civil society sentiment towards packaging pollution (Testa et al., 2020). These pressures created a highly dynamic environment that resulted in the foundation of various CSPs pursuing sustainable packaging in the period between 2016 and 2021. This variety of cases focusing on one socio-ecological mission offered an ideal context for the analysis.

I used theoretical sampling, which aligns with the approach for multi-case theory building (Eisenhardt, 2021). All sampled CCSPs were active for more than 12 months and published progress reports. I selected two polar types (Eisenhardt, 2021; Graebner & Eisenhardt, 2004) and controlled for antecedents of impact (Davis & Eisenhardt, 2011; Eisenhardt, 2021). To apply polar types, I studied one focal phenomenon but selected four CCSPs with a broad mission and four with a specific mission. While theoretical, the sample is still representative of large multinational CCSPs in packaging, as it contains the largest initiatives in the EU in the last five years. The analysis covers various sources, including 1,106 pages of public documents, nine interviews with one CCSP, ten videos, three industry events, and one quantitative progress report.

Data Analysis

Step 1 – Deductive Analysis: Linking Actions to Levels of Complexity

In step 1, I analysed how CSPs engage with complexity building on the conceptual framework by van Tulder and Keen (2018). I applied qualitative content analysis (Kuckartz, 2018; Mayring, 2014), a method that looks at quantitative patterns in qualitative data. This approach allowed me to identify the number of clear, complicated, and complex actions per case.

Step 2 – Inductive Analysis: Temporal Bracketing to Construct the Innovation Process

This step looks into how actions and practices connect to a process. First, I constructed case histories for each CSP focusing on key activities (Eisenhardt, 1989). Next, I analysed the innovation process inductively by applying temporal bracketing (Langley, 1999), a technique that divides the overall process into phases. Each phase consists of several practices with underlying actions. I used the phased case histories to perform a cross-case comparison. From this analysis, I derived a ‘master narrative’ of the ‘perfect’ innovation process that covers all phases and results in success. Using the master narrative as a blueprint, I compared it against the existing cases to develop a classification.

Step 3 – Pattern Matching: Developing the TIP

In step 3, I applied pattern matching (Cao, 2007) to compare practices in the innovation process with the data on the complexity levels of practices. I find a strong overlap between complexity categories and phases of the innovation process. This approach leads to further development of the innovation process ‘master narrative’ via systems thinking (Cao, 2007) as I identify complexity levels in the innovation process.

Step 4 – Validating the TIP

I validated the preliminary findings in two ways. First, I applied data triangulation using interview data. I analysed interview data on one CCSP that has already gone through several innovation cycles. The interviews cover eight project members from different parts of the supply chain and one orchestrator. To validate the master narrative, I coded the interview data deductively using the data structure of the master narrative. As a result, I could identify an additional phase in the innovation process.

In the next validation step, I follow the advice of Sætre & Van De Ven (2021) to seek inside-out feedback. To do so, I presented the framework in a workshop with policymakers and NGOs working on circular economy innovation and at two scientific conferences. The feedback from those sessions helped develop the approach.


In this section, I present the two findings from the analysis. First, I demonstrate that CSPs need to pursue practices at all complexity levels. Second, I indicate three process pathways, of which only two are capable of transforming systems.

Finding 1: Transformative innovation requires a combination of clear, complicated, and complex practices

Pattern matching and cross-case comparison for sub-RQ2 resulted in a ‘master narrative’ of how CCSPs pursue innovation. I refer to that framework as the Transformative Innovation Process (TIP) (see Figure 2). TIP consists of six phases that are situated across different complexity levels. Phase 1 focuses on clear practices, like mobilising action through motivational framing, and ends with joining the initiative and addressing potential quick wins. Phase 2 consists of complicated practices aligning knowledge and frames. Phase 3 practices reside in the complex domain of experimentation. Here, actors learn about how socio-technical structures react to their intervention and understand possible constraints and enablers. The objective of complex practices in Phase 4 is to enable a joint solution, which includes configuring the value model and addressing structural gaps. In Phase 5, the CCSP returns to the domain of complicated practices as it attempts to establish and scale the previously defined solution. Finally, in Phase 6, the partners advocate for their scaled practice to become an industry or legal standard, building on clear practices. Additionally, there are some overarching practices that support the cohesion and development of the CCSP, including internal meetings, financing practices, maintaining governance structures, and regular communication to relevant external audiences.

The TIP represents the ideal pathway that can lead to impact. However, there is a flaw in this metaphor – while a road is linear, innovation processes are iterative and cyclical, comparable to a learning spiral (Raisch et al., 2018; Van De Ven, 2008). Accordingly, the journey of CSPs through the innovation process revolves in cycles.

Figure 2: Transformative Innovation Process (TIP)

Finding 2: Three classes of innovation pathways

However, not all cases follow the TIP pattern. Instead, I identify three typical process pathways: pioneering, consolidating, and adapting. In the analysis, three cases (1, 2, 3) correspond to the consolidating pathway and two cases (4, 5) correspond to the pioneering pathway. The remaining three cases (6, 7, 8) start out with the consolidating pathway but adapt their strategy and turn towards the pioneering pathway during the initiative. Therefore, I classify the cases as consolidators (1, 2, 3), pioneers (4, 5), and adaptors (6, 7, 8).

The main goal of a consolidator (cases 1, 2, 3; see Figure 3) is to connect different actors in order to consolidate knowledge and align frames.

Figure 3: Consolidating Process Pathway

On the other hand, pioneers (see Figure 4) tend to focus on specific missions, such as enabling reuse solutions (case 5) or testing digital technology for waste management (case 6). These missions attempt to reconfigure socio-technical structures in order to enable the specific goal.

Figure 4: Pioneering Process Pathway

The final class in the dataset are adaptors (cases 6, 7, 8). Initially, these CSPs had broad aims and focused on publishing a toolkit or report. However, over time the initiatives identified specific structural gaps that needed to be addressed. This led to a reconfiguration in the partnerships’ practices.

All the analysed initiatives are ongoing, so it is too early for a final evaluation. However, there is a clear indication that pioneers and adaptors have the ability to reconfigure socio-technical structures.

Discussion and conclusion

This study contributes to the debate on how to address complex systemic challenges in cross-sector partnerships (cp. Bryson et al., 2015; Dentoni et al., 2020; van Tulder & Keen, 2018). By introducing the process turn to the debate, this study enables a more differentiated understanding of how to navigate and facilitate the collaboration process. Understanding the dynamics of the TIP can make CCSP evolution less of a ‘black box’ for practitioners and support researchers in creating a deeper understanding of critical outcomes in different phases. Therefore, further avenues for research include zooming in on specific TIP phases, analysing phase transition, and inquiring into why CSPs choose certain pathways.

The research findings also demonstrate that not all CCSPs are created equal. Mainly, the data does not confirm the proposition that the design stage is always the most decisive (Bryson et al., 2015; Van Tulder & Keen, 2018). Rather, the three adaptor cases demonstrate that CSP ambition and design may evolve during the partnership. So, while there is no straight road leading to transformative innovation in CCSPs, a systemic process approach can help CCSP actors to embark on a learning journey towards transformative innovation.


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