Action Research at Work

Apr 1, 2025

Discover the impact of Action research to create lasting change, Cut sunk costs and empower organizations by increasing the analytical and problem solving skills of your people.

Abstract

Creating lasting Organizational Change

Action research (AR) is a participatory, iterative methodology that integrates practical intervention with theoretical inquiry to drive organizational transformation. Guided by Kurt Lewin’s assertion, “If you really want to understand something, try to change it,” AR posits that comprehension emerges through active engagement. This article examines AR’s conceptual foundations, methodological rigor, and practical applications, emphasizing its synergy with appreciative inquiry (AI) and alignment with Soft Systems Methodology (SSM).

Drawing on an extensive scholarly corpus, including Greenwood and Levin (2007), Reason and Bradbury (2001), Husserl (1952, 1989), Jaspers (1959), Cooperrider and Srivastva (1987), Cuijpers (2007), Cuijpers & Zinsmeister (2011, 2016), and case study methodologies from Yin (1981, 1994; Yin & Campbell, 2003), it highlights AR’s capacity to empower stakeholders, foster adaptive learning, and achieve sustainable outcomes. Supported by detailed case studies and theoretical synthesis, AR emerges as a robust tool for navigating organizational complexity.

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Background

Introduction

Organizations today navigate a landscape of relentless complexity—technological disruptions, shifting societal expectations, and economic volatility demand adaptive strategies. Traditional change management, often rigid and top-down, struggles to address these multifaceted challenges. Action research (AR) emerges as a transformative alternative, blending empirical investigation with collaborative action. Defined by Greenwood and Levin (2007) as “social research for social change,” AR engages stakeholders in diagnosing problems, designing interventions, and evaluating outcomes, enhanced by appreciative inquiry (AI) and Soft Systems Methodology (SSM).

Kurt Lewin’s maxim encapsulates AR’s philosophy: understanding arises through intervention (Smith, 2001). This article explores AR’s significance for organizational change, focusing on its integration with AI—a strengths-based approach introduced by Cooperrider & Srivastva (1987)—and SSM, a systemic framework for tackling ill-structured problems (Checkland & Poulter, 2006). Phenomenological insights from Husserl (1952, 1989, 1922), Jaspers (1959, 1961), and Cuijpers (2007), alongside empirical studies like Cuijpers & Zinsmeister (2011, 2016), enrich this analysis, while Yin’s (1981, 1994; Yin & Campbell, 2003) case study methodologies enhance its empirical grounding. Through this synthesis, AR fosters empowerment, learning, and sustainability in organizational contexts.

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5 Reasons to use Action Research

1. Empowerment Through Collaboration:
Action research and appreciative inquiry enhance commitment by involving stakeholders as co-creators, not passive recipients.

2. Adaptive Learning Capacity:
Action research’s cycles and Soft Systems Methodology’s testing foster agility through continuous refinement. In a department of 350, one “Snapshotting” session replaced weeks of talks, adapting solutions on the spot—quick learning keeps organizations nimble and effective.

3. Lasting Change:
Appreciative inquiry’s vision and Soft Systems Methodology’s integration yield durable outcomes by rooting solutions in stakeholder realities.

History

Conceptual Foundations

Action research originated with Kurt Lewin, who in the 1940s introduced a cyclical model of planning, acting, observing, and reflecting to address social and organizational issues (Smith, 2001; McGill & Horton, 1973). Lewin’s maxim reflects his belief that knowledge emerges from transformative engagement. Greenwood and Levin (2007) expand this, describing AR as a participatory process that bridges theory and practice through stakeholder collaboration. Unlike traditional research, which prioritizes detached analysis, AR is pragmatic, aiming to solve real-world problems while generating actionable insights (Tripp, 2005; O’Brien, 1998; Cunningham, 1993).

AR operates on three core principles: participation, reflection, and action (McNiff & Whitehead, 2002). Participation ensures that those affected—employees, managers, or community members—actively shape the process (Wadsworth, 1998; Kemmis, 2005). Reflection fosters critical awareness of the context (Reason & McArdle, 2004), while action translates insights into tangible improvements (Dick, 2007; Boog, 2001).

This iterative cycle enables continuous adaptation, distinguishing AR from linear methodologies (Gootzen, 2011). Cuijpers and Zinsmeister (2011, 2016) enhance this framework with ‘Snapshotting,‘ a co-researching method used in the initial phase to capture a real-time picture of organizational dynamics, decreasing the time required for co-researching and addressing long-term investment challenges between customers and consultants.

Philosophically, AR draws from critical theory, pragmatism, and phenomenology. Eikeland (2014) links AR to phronesis—practical wisdom—emphasizing ethical, context-specific decision-making. Husserl’s (1952) Ideen II informs AR’s phenomenological grounding, exploring meaning through lived experience, while Jaspers (1959) in Reason and Existenz highlights rationality and existence, aligning with AR’s reflective-action nexus (Greenwood, 2006).

” No research without action, no action without research.”

– Kurt Lewin –

4. Holistic Problem-Solving:
Action research, appreciative inquiry, and Soft Systems Methodology tackle complexity by blending perspectives into cohesive fixes. “Snapshotting” reveals core issues swiftly—comprehensive solutions endure beyond quick patches.

5. Practical Insight Generation:
Action research delivers actionable knowledge by prioritizing real-time, intersubjective truth over rigid assumptions. “Snapshotting” in leadership development distilled key needs in one go, slashing costs like the $850,000 survey flop—pragmatic insights fuel immediate impact.

Complementary Frameworks

Appreciative Inquiry & Soft Systems Methodology

Appreciative Inquiry (AI), developed by Cooperrider & Srivastva (1987), enhances AR by shifting focus from deficits to strengths. Its four-phase cycle—Discovery, Dream, Design, and Destiny—complements AR’s iterative process by identifying what works well, envisioning a positive future, crafting actionable strategies, and sustaining change (Thomas, 2011). AI fosters a collaborative culture that amplifies AR’s participatory ethos (Boog et al., 2005). Soft Systems Methodology (SSM), developed by Checkland, addresses complex, “messy” problems through a seven-stage process, from problem mapping to action implementation (Checkland & Poulter, 2006, cited in Werkman & Boonstra, 2008). SSM’s “rich picture” phase aligns with AR’s diagnostic stage and complements ‘Snapshotting’ (Cuijpers & Zinsmeister, 2011, 2016), while its modeling and testing mirror AR’s action-reflection cycle (Greenwood, 2006; Tromp, n.d.). Together, AI and SSM enrich AR’s capacity for holistic, strengths-based transformation, as evidenced by Cuijpers & Zinsmeister’s (2011) study of internal communication quality and their (2016) proposal for organizational change (Cunningham, 1993; Gustavsen, 1992).

Principles

Phenomenological Underpinnings

Phenomenology, as articulated by Husserl (1952, 1989), provides a philosophical foundation for AR’s focus on lived experience. In Ideen II, Husserl explores how meaning emerges through intersubjective encounters, a concept central to AR’s participatory ethos (Maso, 2003; Qutoshi, 2018). His (1989) ‘Ideas Pertaining to a Pure Phenomenology’ emphasizes understanding phenomena as they appear, grounding AR’s interventions in subjective realities (Smith & Osborn, 2003; Hoffman, 2014).

Jaspers (1959) adds an existential dimension, arguing that reason and existence are inseparable—a perspective that mirrors AR’s integration of reflection and action (Peursen van, 1972; Inwood, 2018). Cuijpers (2007) extends this by examining professional attention phenomenologically, uncovering essential insights, while Cuijpers & Zinsmeister (2011, 2016) enhance this through ‘Snapshotting,’ a proprietary method capturing real-time organizational dynamics in communication and synergy studies. This phenomenological lens aligns with Lewin’s maxim, supporting AI’s narratives and SSM’s mapping (Maso et al., 2004; Power, 2011).

Action Research

The Relevancy for Organizational Change

AR offers distinct advantages for organizational transformation, particularly when enriched by AI and SSM. These benefits include stakeholder empowerment, adaptive learning, and sustainable outcomes, each explored below.

1. Stakeholder Empowerment

AR’s participatory nature ensures stakeholders are active contributors (Boog, 2001). AI fosters ownership by focusing on strengths (Cooperrider & Srivastva, 1987; Thomas, 2011). Cuijpers (2007) shows AR enabling educators’ engagement, while Cuijpers & Zinsmeister (2011) illustrate staff empowerment in a governmental agency via “Snapshotting” to enhance communication quality. Gootzen (2011) notes similar effects among student-teachers. SSM integrates these perspectives (Cunningham et al., n.d.).

2. Adaptive Learning

AR facilitates continuous adaptation through iterative cycles (Dick, 2007), supported by AI’s positive inquiry (Cooperrider & Whitney, 2005) and real-time feedback (Zambo, 2010). Cope (1981) exemplifies this in healthcare, structured by SSM (Werkman & Boonstra, 2008).

3. Sustainable Outcomes

AI’s strengths and SSM’s systemic approach ensure grounded, aspirational solutions (Charles & Ward, 2007; Stock, 2015). Gustavsen (1992) and Coenen (2005) highlight AR’s lasting impact.

5 Practical Tips

Implementing Action Research

1. Engage Stakeholders Early:

Build trust and ownership (Boog, 2001; Wadsworth, 1998).

2. Leverage Strengths with AI:

Use AI’s Discovery phase for positivity (Cooperrider & Srivastva, 1987; Thomas, 2011).

3. Map Systems with SSM:

Employ SSM’s “rich picture” or “Snapshotting” (Cuijpers & Zinsmeister, 2011, 2016) to align interventions (Greenwood, 2006; Checkland & Poulter, 2006).

4. Iterate with Reflection:

Adapt via reflection cycles (Dick, 2007; Zambo, 2010).

5. Ensure Ethical Rigor:

Protect participants with ethical standards (Boog et al., 2008; Coenen, 2008).

Learning by Doing

Methodological Rigor

AR’s rigor lies in transparency, reflexivity, and utility (Reason & Bradbury, 2001; Woodley & Smith, 2020). Greenwood and Levin (2007) assert its validity stems from actionable knowledge. Systematic data collection and reflective practices ensure integrity (Zambo, 2010; Maso, 2001), while Yin’s case study methodologies (1981, 1994; Yin & Campbell, 2003) and ethical standards (Boog et al., 2008; Boog, 2008) safeguard credibility

The Practice

Case Studies

1: Healthcare Transformation:

Cope (1981) applied AR to address inefficiencies, using SSM (Yin, 1981).

2: Educational Reform:

Gravett (2004) used AR and AI to enhance teaching (Husserl, 1952; Yin & Campbell, 2003).

3: Rural Development:

Charles and Ward (2007) employed AR, AI, and SSM for sustainability (Thomas, 2011; Yin, 1994).

4: Organizational Neglect:

Kampen (2011) addressed neglect with AR and SSM (Coenen, 2008).

5: Leadership Development:

Mclaughin (n.d.) enhanced leadership with AR (McGill & Horton, 1973; Yin & Campbell, 2003).

The Economic Impact

Reducing Sunk Cost

Traditional consultancy often incurs massive sunk costs—expenditures with no return—especially in government. The Interior Department spent $850,000 on a national parks survey with unclear impact, while the Social Security Administration lost $365 million on a failed IT system. Estimates suggest federal fraud losses of $233 billion to $521 billion yearly, with $750 billion in procurement overruns. These stem from rigid, assumption-laden methods piling costs without value, often due to consultants’ inflexibility.

Action research counters this by prioritizing rapid, collaborative insight over costly studies. It avoids unprovable pre-assumptions, seeking a context-specific fit that cuts financial risk. “Snapshotting” exemplifies this, capturing real-time dynamics to slash initial co-researching time. It streamlined communication fixes in a governmental agency and drove synergy with minimal investment, proving action research’s cost-effectiveness. Skilled facilitation minimizes resistance—a consultant skill gap—offering a lean alternative to bloated projects, saving millions where old methods fail.

“Knowledge is context specific as it depends on a particular time and space“

– Friedrich A. von Hayek.-

Critiques

Challenges

While AR, enriched by AI and SSM, offers significant strengths, its application faces multifaceted challenges and critiques that warrant an in-depth examination to fully appreciate its limitations and refine its practice. One prominent challenge is its time-intensive nature, as highlighted by Wadsworth (1998). The iterative cycles demand substantial commitment, often spanning extended periods, which can lead to participant fatigue, particularly in resource-constrained environments (Woodley & Smith, 2020).

Gootzen (2011) observed that student-teachers struggled with sustained involvement, a concern echoed in broader contexts where AR’s duration tests endurance (Wadsworth, 1998). Cuijpers (2007) underscores the reflective depth required, intensifying time demands.

However, Cuijpers & Zinsmeister (2011, 2016) mitigate this by employing ‘Snapshotting,’ a proprietary method that decreases the time required for co-researching during their projects, addressing long-term investment challenges between customers and consultants in communication quality and synergy initiatives. Despite this, sustaining stakeholder engagement beyond the initial phase remains complex, necessitating strategic resource allocation (Coenen-Hanegraaf et al., 2000).

“ We shall not grow wiser before we learn that much that we have done was very foolish.”.

– Friedrich A. von Hayek.-

Another critique is researcher bias, given AR’s subjective nature (Wadsworth, 1998). Researchers as facilitators may influence outcomes (Maso, 2003), requiring rigorous ethical frameworks (Boog et al., 2008; Woodley & Smith, 2020). Gravett (2004) mitigated this with peer debriefing, but the challenge persists. Cuijpers (2007) notes mutual critical reflection is essential for interpretive integrity.

AI’s positivity may overlook structural issues, which SSM addresses (Werkman & Boonstra, 2008). Gustavsen (1992) critiques AR’s limited scalability, evident in Charles & Ward (2007) and Cuijpers & Zinsmeister (2011, 2016), where “Snapshotting” yielded localized insights hard to generalize due to organizational specificity. Yin (1981) counters this with case study rigor.

Ethical dilemmas arise when AR unearths sensitive issues (Boog, 2008; Coenen, 2008). Kampen (2011) navigated this in neglected organizations, requiring care. Coenen (2005) warns of emotional tolls, reinforced by Boog, Slagter, and Zeelen (2008). Cuijpers (2007) and Cuijpers & Zinsmeister (2011, 2016) exposed challenges necessitating ethical sensitivity (Cunningham et al., n.d.; Kemmis, 2005). Methodologically, AR’s qualitative nature faces scrutiny from positivist perspectives seeking objectivity, such as regression-based models (Reason & Bradbury, 2001).

However, AR prioritizes an optimal fit—an intersubjective truth within a specific context and time—over universal objectivity, acknowledging uncertainty (akin to Heisenberg’s principle) and incomplete information for decision-making, as Cuijpers & Zinsmeister (2011, 2016) demonstrate with “Snapshotting.” Yin (1994) and Stock (2015) enhance this with case study rigor, yet Hill-Campbell (n.d.) notes resistance from traditionalists, advocating practitioner research (Zambo, 2010).

” Reality is only real in a specific place, time, and moment.”

– Peter Cuijpers-

Conclusions

AR, guided by Lewin’s maxim, transforms organizational change via AI and SSM. Rooted in Lewin’s work (Smith, 2001) and refined by Greenwood and Levin (2007), AR empowers stakeholders (Boog, 2001; Cooperrider & Srivastva, 1987). AI’s vision (Thomas, 2011) and SSM’s structure (Checkland & Poulter, 2006) synergize with AR, as in Cuijpers & Zinsmeister’s (2011, 2016) work, where “Snapshotting” reduces time and resistance (Werkman & Boonstra, 2008).

Phenomenological insights from Husserl (1952, 1989), Jaspers (1959), and Cuijpers (2007) ground AR in lived experience (Maso, 2003), enhanced by “Snapshotting” (Cuijpers & Zinsmeister, 2011, 2016; Heidegger, 1982). Yin’s rigor (1981, 1994) supports applications across contexts—healthcare (Cope, 1981), education (Gravett, 2004), rural development (Charles & Ward, 2007), neglect (Kampen, 2011), leadership (Mclaughin, n.d.), and government (Cuijpers & Zinsmeister, 2011, 2016).

Challenges—time eased by “Snapshotting” (Wadsworth, 1998), bias (Maso, 2003), ethics (Boog, 2008), and scalability (Gustavsen, 1992)—tie to consultant skills (Boog et al., 2008). AR’s strengths—empowerment, learning, sustainability, problem-solving, and insight (Dick, 2007)—and tips ensure resilience (Coenen-Hanegraaf et al., 2000). Rejecting regression’s unprovable pre-assumptions, AR’s intersubjective fit embraces uncertainty, offering a transformative philosophy (Cunningham et al., n.d.; Cuijpers, 2007).

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