# Blended Network Action Learning – A Digital Lean Approach to Solving Complex Organizational Problems Across Space and Time

Daryl John Powell, Melanie De Vries, Mitchell Van Roij, Jannes Slomp

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Daryl John Powell, Melanie De Vries, Mitchell Van Roij, Jannes Slomp. Blended Network Action Learning – A Digital Lean Approach to Solving Complex Organizational Problems Across Space and Time. 7th European Lean Educator Conference (ELEC), Oct 2021, Trondheim, Norway. pp.213-224, 10.1007/978-3-030-92934-3_22. hal-03771946

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# Blended Network Action Learning – A Digital Lean Approach to Solving Complex Organizational Problems Across Space and Time

Daryl John Powell<fnref n="1" />,<fnref n="2" />, Melanie de Vries<fnref n="3" />, Mitchell van Roij<fnref n="3" /> and Jannes Slomp<fnref n="3" />

**Abstract.** Network Action Learning has emerged as an innovative development of Action Learning and has been described as a lean approach to collaborative strategic improvement with problem-solving at its core, be it either within- or across organizational boundaries. Virtual Action Learning is also presented as an emergent variety of Action Learning, bringing together geographically dispersed individuals within and across organizations in an online, virtual environment. Given the onset of new, innovative digital technologies – particularly in response of the Covid-19 pandemic – Blended Learning has also emerged as an educational platform that represents some combination of face-to-face and online learning using mobile technologies and cloud-based resources. Though Virtual Action Learning has been discussed as neither better than nor second best to face-to-face Action Learning, in this paper, we suggest that a blended approach may be the most effective method. Thus, the purpose of this paper is to construct a Blended approach to Network Action Learning, where intra- and inter-firm Network Action Learning can take place using a hybrid, physical-virtual approach to promoting collaborative strategic improvement and gemba-based problem-solving.

**Keywords:** Organizational Learning, Blended Learning, Network Action Learning, Lean Strategy, Digital Lean

## 1 Introduction

Network Action Learning (NAL) has emerged as an innovative development of [1]'s theories of Action Learning (AL) and has been presented as a useful and useable means of enabling intra- and inter-firm learning to achieve sustainable, collaborative strategic and operational improvement, specifically in the context of buyer-led lean transformations [2]. For example, [3] present NAL as an approach to collaborative strategic improvement, be it either intra-firm or inter-firm improvement. As such, in this paper, we explore a blended approach to NAL for addressing complex organizational problems across space and time – assisting organizations in deploying a bottom-up, gemba-led lean strategy.

<footnote n="1">¹ SINTEF Manufacturing, Horten, Norway</footnote>

<footnote n="2">² Norwegian University of Science and Technology, Trondheim, Norway</footnote>

<footnote n="3">³ HAN University of Applied Science, Arnhem, the Netherlands
daryl.powell@sintef.no</footnote>
[4] present Virtual Action Learning (VAL) as an emergent variety of AL, bringing together geographically dispersed individuals within and across organizations in an online, virtual environment. Given the onset of new, innovative digital technologies – particularly in response of the Covid-19 pandemic – Blended Learning (BL) has also emerged as an educational platform that represents some combination of face-to-face (F2F) and online learning using mobile technologies and cloud-based resources.

Though Dickenson et al. argue that VAL is neither better than nor second best to F2F AL, we suggest that given the current situation with social distancing measures and limited international travel due to Covid-19, a blended approach may actually be a superior approach. We also suggest that the solving of complex problems demands broad knowledge and an extensive skills base, thus virtual mobility of international experts and problem owners allows for the development of learning networks, in which peers can challenge each other to reflect over concurrent challenges and the underlying assumptions that may be causing them. This will help advance the state-of-the-art, particularly given the onset of Industry 4.0 and the emergence of key enabling digital technologies that allow for greater virtual mobility. Therefore, the purpose of this paper is to construct and present a Blended Network Action Learning (BNAL) methodology, where intra- and inter-firm NAL can take place both physically and virtually to promote collaborative strategic improvement to solve complex organizational problems across space and time.

# 2 Theoretical Background

## 2.1 Action Learning

Action Learning (AL) has emerged as a radical process for increasing organizational knowledge and capacity for better adapting to change [3]. It can be considered as a lever for developing, improving, and assimilating learning in organizations. [1] outlines the following assumptions that underpin AL:

* Learning is cradled in the task and formal instruction is not sufficient,

* Solving problems requires insightful questions,

* Learning involves doing, is voluntary, spurred by urgent problems or enticing opportunities and is measured by the results of action.

[1] formulated his action learning concept around the formula L=P+Q, where L stands for learning, P for programmed knowledge and Q for questioning insight. In his theory of action, [5] presented his science of praxeology of cyclical systems – alpha, beta and gamma. System alpha focuses on investigating a problem. System beta focuses on solving the problem, and the negotiation cycles required to implement the solution. System gamma focuses on the learning as experienced by participants, and involves self-awareness, reflection and questioning. It is important to emphasize that the three systems (alpha, beta and gamma) are neither linear nor sequential, nor entirely discrete. The three are best understood as a holistic system of interlocking yet overlapping parts which deserve differing emphases at different times [3].
At the heart of AL is a distinction between different kinds of issues. As such, [1] distinguishes between puzzles and problems. Puzzles are those difficulties for which a solution exists, and which are amenable to expert advice. For example, experts may swiftly and simply select a solution to "puzzles", thus solving puzzles is not amenable to AL. Problems, on the other hand, are difficulties where no single solution can possibly exist. Most complex organizational change projects fall into the category of a problem – where there is no single solution and where there are many opinions as to what the course of action might be. Problems are amenable to AL as, in response, different people can advocate different courses of action in accordance with their own value systems, past experiences and intended outcomes.

## 2.2 Network Action Learning

[3] suggests that collaborative strategic improvement requires developing a capacity to learn within and across a network, for example, in both the intra- and inter-organizational context. Not just as individuals in organizations, but especially within and between organizations. With roots in action learning, the authors propose Network Action Learning (NAL) as a useful and usable approach to collaborative strategic improvement:

> "Continuous and collaborative improvement are, in essence, processes of action and learning: problems are identified; solutions are created, analyzed, selected and implemented; resulting not only in improved operational performance but also in improved capability (through learning)."

They extend the action learning formula and define NAL as L=P+Q+O+IO. This formulation captures the action learning process in the context of both intra- and inter-organizational learning. Here, P refers to the established knowledge of collaborative improvement, Q relates to the questioning process, and O and IO relate to emerging insights in the organizational and inter-organizational contexts. As such, "the action learning by the network is built on exposing programmed knowledge to questioning, combined with organizational- and inter-organizational insights created in action" (p.69). To increase competitive advantage, however, the network must be capable of exploiting this learning – by demonstrating an absorptive capacity. As such, participants within and across organizations in the network must engage in appropriate learning interventions in a structured way, consistent with [6] who argue that organizational design is critical to building learning mechanisms that develop and sustain learning capabilities.

# 3 Towards a philosophy for Blended Network Action Learning

[7] suggests that researchers are typically encouraged to ground their research in a research philosophy consisting of an ontology (reflecting the researcher's understanding of self, own experience, the nature of the relational world and the nature of knowledge and theory), an epistemology (expressing how the researcher seeks to know), a
methodology (articulating the set of ideas justifying the approach which the researcher adopts for the process of inquiry), and finally a method (for planning enacting, evaluating and understanding research).

In terms of a philosophy for BNAL, ontology is reflected in [1] in that "there can be no learning without action, and no action (sober and deliberate) without learning" (p.83). The classic formulation (equating learning and knowing) L=P+Q provides an epistemological basis. Most significant for this paper is that of methodology, which we base on [5]'s theory of action and science of praxeology of cyclical systems - alpha, beta and gamma:

*   **System Alpha:** In BNAL, system alpha frames the complex organizational problem to be solved. It focuses on identifying and analysing a real organizational problem including analysing the external environment, current organizational performance, and management values (what the managers want to achieve).

*   **System Beta:** Revans' scientific method presents us with a structured means for investigating, understanding, and solving problems, in action. In BNAL, system beta concerns the deployment of the scientific method and involves exploring the problem-solving process, through multiple cycles of action and reflection. Action learners uses appropriate theoretical perspectives to frame the results of the action and reflection cycles, with a view to identifying emergent actionable knowledge.

*   **System Gamma:** The (individual and collective) learning is the focus of system gamma. In BNAL, the active participation of action learners in developing and executing systems alpha and beta has implications for the scope of system gamma. The action learners' involvement in system gamma exposes the process of how their engagement with the problem has challenged their own thought processes, to further inquiry. The interpretation and evaluation of each action learner's own involvement underpins the emergent actionable knowledge, ensuring the quality of BNAL.

As such, the remainder of this paper is structured as follows: we first present a framework for problem evaluation based on the Find, Face, Frame, Form (4F) framework as presented in *The Lean Strategy* [8]. Secondly, we present a framework for BNAL as well as guidelines for its application.

## 4 The Lean Strategy approach to Problem Resolution: Find, Face, Frame, Form

[8] distinguishes between two types of strategy – the traditional, "Porter" style approach strategy formulation and execution, and the "superior" lean strategy. They suggest that lean-thinking executives abandon all preconceptions of traditional management reasoning. For example, *defining* "problems" in the board room, *deciding* what must be done to resolve them, *driving* execution through action plans, and then *dealing* with unexpected consequences (4D) is not an effective means to *grow* a business. Lean leaders must *find* problems by going to the “Gemba” to see the problems faced by workers and customers with their own eyes. This lets them develop a clear understanding of what
factors are preventing them from hitting current targets. Armed with first-hand, specific knowledge, lean leaders then *face* the main challenges (the “elephants” in the room / the obvious problem(s) no one wants to discuss) by creating key operational indicators such as improving quality, speeding up delivery, or reducing incidents. Next, they *frame* the challenges and goals in such a way that everyone in the company can understand them and know how they can contribute - lean leaders will propose lean solution types to problem types, such as pulling (instead of pushing) the workflow to create value faster for clients or by applying value analysis/value engineering (VA/VE) to conceive and deliver products that clients love, over and over again. Finally, lean leaders support and develop people to enable them to *form* their own solutions, so that the sum of all local solutions and ideas forms an effective, collective response to the main challenges.

This forms the basis for BNAL (and a critical success criteria) – where the organi-
zation's leaders must adopt Gemba-leadership to encourage and guide people in their
improvement activities, and must begin by accepting the workplace-based, ground-up
strategic thinking of finding and facing problems at the Gemba, framing those problems
with pre-defined conditions (e.g., just in time, zero defects etc.), and facing them to-
gether with the teams themselves (4F).

5 BNAL Methodology: Framework and Guidelines

Similar to [9], we identify six main components that make up the BNAL framework:

* The problem

* The network

* The questioning and reflective process

* The commitment to taking action

* The commitment to learning

* The learning facilitator

All six core components must be formalized at the beginning of a BNAL initiative, but as BNAL is dynamic and a process of discovery, the elements may evolve as the process develops (for example, the problem may be reframed, and new members may be added to the network, etc.).

**5.1 The problem.**

The starting point for BNAL is the problem encountered in an organization (also re-
ferred to as the task, the project, the challenge, or the opportunity). Without a problem,
there can be no BNAL. The problem should be important and should provide an oppor-
tunity for learning (the best BNAL projects provide rich learning opportunities). Such
examples could be:

* Improving quality

* Reducing waste in operations
* Increasing productivity

* Better leveraging technology to create value for customers

## 5.2 The network.

The core entity on BNAL is the network (also known as the "set"). The individuals that make up the BNAL team are those who are responsible for framing the problem, constructing action, planning action, taking action and reflecting over action – in a blended manner (e.g., a mix of physical and virtual mobility).

## 5.3 The questioning and reflective process.

The questioning and reflective process is a structured means of guiding strategic improvement within the network. For example, network participants can act and learn together in a blended (hybrid physical-virtual) way. The organizations in the network may have similar problems and therefore similar interventions/actions can be taken. They can share the outcome of these results in the network and learn from one another, by asking each other challenging questions about the interventions. This provides the basis for problem solving while satisfying the learning imperative that is central to BNAL. This process demands that a safe and open environment be created so that organizations become comfortable to share their problems, assumptions, and ideas. Together the individuals try to progress further by challenging one another.

BNAL recognizes that problem solving must begin by first diverging through the use of inquiry, before converging on a solution. Some example questions include:

* What is the real issue?

* What evidence do we have?

* What do we need to learn?

* Why are we doing this?

* What are the risks?

* Which stakeholders should we engage?

* How do we ensure that the outcomes are achieved?

* Would the problem be solved after we implement this?

* How do we know we have achieved the results?

* What have we learned?

## 5.4 The commitment to action.

The members of the BNAL set are committed to implementing change in one or more of the participating organizations. This means that members must be committed to working together to improve the collaborative relationship (be it temporary or long-term) and must commit to solving the problem through participation in action.
**5.5 The commitment to learning.**

The learning that occurs in BNAL is of greater strategic value to the participating or-
ganizations than the immediate tactical value of solving the problem at hand. Learning
to learn is a key component of BNAL and emerges through combining action with
questioning and reflection. It is important to create a safe environment where reflection
and learning can occur. Failures in the group must be seen as opportunities to learn
rather than events to be hidden or ignored.

**5.6 The learning facilitator.**

The learning facilitator, coach, or indeed sensei [10], is the catalyst in BNAL. The
learning facilitator must enhance the network's ability to learn and take meaningful ac-
tion. The characteristics of the facilitator should be more in the areas of group facilita-
tion and learning than in the technical expertise required to solve the problem.

# 6 A Theory of Action and Science of Praxeology

Given that the six core components are in place, BNAL is guided further by [5]'s theory of action and science of praxeology of cyclical systems - alpha, beta and gamma:

**6.1 System Alpha – Finding, facing, and framing (or re-framing) the problem**

System alpha concerns the process of constructing action in BNAL. This subtask aims to provide a set of guidelines for constructing a BNAL project to address a problem, including recruitment and initial contact of network participants, selecting the type of participation / mobility (physical or virtual), and arriving at a (broad) definition of learning and improvement needs.

**Gemba visit.** The BNAL approach begins with a process of reflection and questioning insight at the gemba ("the real place") to locate the problem in practice. The gemba visit should be carried out at least by the company representatives (project owner / -sponsor / -manager) and the learning facilitator (sensei). Other representatives from the network can be involved where applicable.

**Find and face the problem.** Participants in the gemba visit have the potential to discover many problems. Some can be solved with existing solutions and programmed knowledge (these problems are referred to as puzzles and, though amenable to experts, such problems are not amenable to action learning), while others require a great deal of reflection and insightful questions (solving such complex, organizational problems are the primary goal of BNAL). Finding and facing problems effectively often requires the local management team to be challenged by the facilitator (learning coach) to think differently about the observed situation. Facing the main issues of the business by starting with the management team's own misconceptions and taking a helicopter view to
find the challenges which impede customer and / or employee satisfaction and limit
organizational growth is a critical part of this phase.

**Frame the problem.** Framing the problem can often mean aligning the entire organization (or indeed network) around compelling learning goals. As such, the facilitator will help company / network representatives to frame the problem and identify the necessary learning and improvement needs (the next step).

**Define learning and improvement needs.** Though the participants in the BNAL initiative may not have prior experience of either blended- or network action learning, they may be familiar with the Deming cycle [11]: plan-do-check-act (PDCA). This well-established cycle of action and reflection is often referred to as the *learning cycle*.

For companies engaging in BNAL, all improvement actions must be rooted in shared concerns – and a shared understanding of the problem(s) where:

1. Improvement and learning go together, with the share objective of overcoming a problem for which there is no single solution.

2. Simply treating the problem as a puzzle and attempting to solve it with (existing) commercial solutions is not a solution. Rather, if seen as a means and rational for engaging with the problem, the puzzle provides a vehicle for engagement with the real problem.

3. Knowledge gaps present the set with learning needs, where the group must engage in action learning. Simply assigning a reading task or a lecture would be to introduce P only. The plan is to take action, thus questioning insight (Q) from the action must be combined with P in order to solve the problem. This process emphasizes the important role of the learning facilitator – who will help the problem-owner to identify whether the organization has the necessary skills and knowledge to solve the problem alone, or indeed whether external parties should be engaged in the action learning process. This then leads to the identification and construction of the network (see the following section).

**Identify and construct network.** The degree of complexity of the problem and the available resources in the organization determine whether the problem can be solved within the own organization or if other actors should be involved. In the latter case, the learning facilitator should assist the organization in sourcing the relevant expertise externally – acting as a knowledge broker to create ties with external stakeholders. Such ties can be formulated both through physical and virtual (blended) communication. Assuming the problem is significantly complex that it cannot be solved by the organization in isolation, the first step for the learning facilitator is to assess the knowledge, competency and capacity of the existing network of the organization. This is because existing ties require little effort to build the mutual trust which is beneficial for knowledge transfer and learning interventions in BNAL. Also, as BNAL is focused on problems with a high degree of complexity that often cannot be solved in the organization due to lack of available resources, the organization should reach out to actors beyond the network to start an alliance. By bundling the knowledge and resources of the actors in the network the complex problem can be more easily solved.
Thereafter, the BNAL set is tasked both with action on the initiative as well as with extracting learning from the experience of action towards a solution for the wider problem. As such, the network needs to include an appropriate mix of levels, affiliations, disciplines, functions, responsibilities and experiences. The network also needs to interact on a regular basis throughout the BNAL initiative, where some of this interaction is through participation in scheduled meetings, each with practical, commercial and learning outcomes. A plan for such interaction is the topic of the next section – forming and implementing the solution(s) to the problem.

## 6.2 System Beta – Forming and implementing the solution(s) to the problem

System beta concerns the process description for planning action. This subtask aims to develop a set of guidelines for selecting programmed knowledge from existing theory and the knowledge and experiences of participants to help form possible solutions to the problem defined in the previous step, and considers how blended learning approaches can be used to provide network participants with the extra fundamental knowledge required in order to address the problem at hand. Important issues to raise here are definition of network roles and responsibilities, assessment of current state, identification and discussion of existing theory, and planning for milestones and performance deadlines.

System beta also concerns the process description for taking action. This subtask will develop a set of guidelines for how the individuals in a network can effectively take action to solve the problem, also with a view to creating new knowledge and learning. Important considerations include identification of emerging issues as well as review of training and facilitation needs.

**Define Network Roles and Responsibilities.** A core part of BNAL is the network (also known as the "set"). The individuals that make up the network are those who are responsible for solving the problems (this might include intra- and inter-organizational representatives, e.g., managers from companies experiencing the same or similar problems) – through constructing action, planning action, taking action and reflecting over the action. After the problem is identified, the foundation for the network should be laid using the following six steps, for which we rely on the work of [12] to further conceptualize the intra- and inter-organizational networks, namely *allocation, regulation and evaluation*, as well as the important role of the *network administrator* (see below).

**Allocation:** Once the partners for the network are selected, the resources, tasks and responsibilities should be allocated and aligned across the network partners. The partners are tied together in the network and strong cooperation is needed to solve the problem. It is important that this is all formalized.

**Regulation:** In this step, rules for the collaboration are formalized and implemented. All network partners should live by the rules of the game (though these rules can be both formal and informal). When a new partner enters the network, she should comply with the existing rules in the network. However, the rules of engagement may change over time as the network evolves.

**Evaluation:** The last step in creating an effective network to solve problems with BNAL is evaluation. The network should be evaluated regularly to see whether it is
going in the right direction. The contributions of the individual partners, the perfor-
mance of the whole network and the relations between the network partners are evalu-
ated. It should be evaluated if actions should be taken to stay on track. On top of that,
it is important that every partners’ opinion is considered in the evaluation. Organiza-
tions weigh up the disadvantages and advantages of being part of the network and this
in turn influences the effort they will make. Effort to maintain quality relationships with
other partners and effort to take action and share knowledge. Thus, the effectiveness of
the network depends on how the partners rate the quality of the network.

*Network administrator:* A network administrator should also be appointed to facili-
tate the network – this is a distinctly different role to that of the learning facilitator. The
network administrators' job is to administer knowledge sharing among partners, while
the learning facilitator strives to enhance the network's ability to learn and take mean-
ingful action. Regarding the evaluation, the network administrator evaluates the net-
work from his perspective. Is the way the network facilitator sees the network equal to
how the individual partners experience it? If not, it is the job of the network facilitator
to find the imbalance and take action. For an effective network in which partners are
willing to share their knowledge, resources and learnings, high levels of trust and reci-
procity are important. In the evaluation it should be considered if the levels of trust and
reciprocity are desirable or that actions should be taken.

**Planning and taking action.** Having established the roles and responsibilities within the network, the set can begin to plan and take action in order to address the problem at hand. This involves using the scientific method as follows:

1. Assess Current State

2. Agree on Target State

3. Plan for Action (Incl. Selection of Programmed Knowledge)

4. Take Action (using loops of PDCA)

Having also found and framed the problem in the previous step, A3 management is
a well-known and well-documented scientific problem-solving process that presents
leaders with a step-by-step approach to plan and take action, closely modelled on PDCA
[13]. The term A3 in fact refers to an international standard paper size (297 x 420 mm).
Toyota adopted the name A3 drawing on insight that every issue an organization faces
can and should be captured on a single sheet of A3 paper. While the basic thinking for
an A3 follows a common logic, the precise format and wording are flexible, and most
organizations tweak the design to fit their unique requirements [14].

A3 management also serves as an important means of communication – such that
countermeasures developed during the problem-solving process can be standardized
and shared with others [15]. [13] present this form of "standardized storytelling" as a
powerful tool to engage and empower leaders as well as front line personnel. They
conclude that it is the thinking behind paper, not the A3 paper itself, that is most im-
portant.
## 6.3 System Gamma – Reflecting over learning and emergent actionable knowledge

System gamma concerns the process description for reflecting over action and learning, which occurs in parallel to the activities defined in system beta. This subtask aims to establish a set of guidelines for reflecting over the BNAL cycle(s), including how the experiences and new knowledge can be shared within and outside of the network using blended learning. This should also include assessments of the scale of the collaborative improvement, and a plan / review as to how the process of change has been communicated within and outside the network.

Regarding the A3 process, the effect confirmation and follow-up phases are critical for system gamma. Here, the participants in the network (set) must study the effects of the action (preferably at the Gemba) and use insightful questioning to identify important lessons learned. Here questions must be prioritized over statements.

Any emergent learning should be documented (on the A3 or otherwise) and communicated within and across the participating organizations, to share and re-apply any emergent actionable knowledge.

## 7 Conclusion

This paper presents BNAL as a means of guiding collaborative problem-solving and learning across space and time. We document both a means for problem evaluation (Find, Face, Frame, Form) and a framework for planning and taking action (including guidelines for reflection and learning). Further work should aim to empirically refine and validate the BNAL methodology through case-based action research. Critical success factors for facilitating BNAL should also be evaluated.

## Acknowledgements

The authors would like to acknowledge support from the European Commission for financing the Erasmus+ Knowledge Alliance LEAN4.0 (Grant number 601227) and the Research Council of Norway for the project *Lean Digital*.

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