9789059961227

Consen sus management

Social and analytic opportunities in organizations

Jurgen Willems & Kenn Meyfroodt

Academia Press

Coupure Rechts 88 9000 Ghent Belgium

www.academiapress.be

ISBN 978 90 599 6122 7 D/2026/45/147 NUR 801

Jurgen Willems & Kenn Meyfroodt

Consensus Management - Social and analytic opportunities in organizations Ghent, Academia Press, 2026

Cover and layout: Atelier Steve Reynders

© Jurgen Willems, Kenn Meyfroodt & Lannoo Publishers https://consensus-management.org

Academia Press is a subsidiary of Lannoo Publishers.

All rights reserved. No part of this publication may be reproduced, stored in an automated database and/or made public in any form or by any means, whether electronic, mechanical or otherwise, without the publisher’s prior written permission. Text and data mining of (parts of) this publication is expressly prohibited.

INTRODUCTION

INTRODUCTION

We all know the feeling of walking out of a meeting with mixed feelings about how it went. So does a team manager, who might reflect on a tense leadership meeting:

• ‘It is a relief that, after weeks of heated debate, the leadership team finally agreed on the restructuring plan. We have spent countless hours discussing our strategic priorities and negotiating trade-offs of the consequences of each strategic priority. It was not easy, but eventually we came to some level of alignment. Now we are moving forward together.’

• ‘I appreciated the intense discussion about which departments should be prioritized for investment, the process is moving us towards reaching our strategic priorities. Our executive team is diverse; finance, operations, HR, and product all brought different lenses. The disagreement was sharp at times, but the process and critical dialog helped us uncover blind spots and challenge assumptions. We now have a clearer view of the risks and opportunities.’

• ‘… But I was caught off guard when no one questioned the decision to terminate the regional manager. It was presented as a done deal, and everyone nodded. I expected resistance, especially from those who had previously defended her performance.’

These three reflections reveal something crucial: consensus plays a vital role in organizational and team dynamics, but too much consensus – or consensus that comes too easily – can be misleading. It may mask deeper social dynamics or cognitive biases that go unnoticed in the moment.

The first observation is straightforward. High consensus on strategic goals or priorities is often a good thing. When leadership aligns on the overall strategic direction, concrete execution becomes more focused and measurable. But reaching that level of agreement often requires significant effort. Leaders must invest time, energy, and resources to skillfully build consensus across competing interests. This book explores both the benefits of consensus and the strategies to achieve it.

The second observation highlights a different angle. Disagreement – especially in high-stakes decisions – is not only inevitable but often valuable. It brings diverse perspectives and fosters critical thinking. This is essential for organizational learning and innovation. However, unresolved conflict can become toxic. To benefit from disagreement, teams need shared norms for how to debate, reflect, and decide. Without these ‘rules of engagement’, even the most vibrant discussions can spiral into dysfunction.

Again, while this is a general insight applicable to many leadership settings, it shows that low consensus can be productive, but only under certain conditions. It also suggests that the impact of consensus evolves over time. These dynamics – varying levels of consensus, their advantages and disadvantages, and conditional relationships – are explored and clarified throughout this book.

Finally, the third observation reveals a deeper layer. No one challenged a controversial decision. That might look like consensus, but it could be something else entirely. Maybe team members disagreed but stayed silent, assuming they were alone in their views. This is known as the false-uniqueness bias, a cognitive bias in the context of consensus management that we deal with in this book. Or maybe they felt social pressure and did not want to appear disloyal or disruptive.

This example shows that managers need more than surface-level observations to understand team consensus. Silence does not always mean agreement. Opinions are cognitive; they are not always visible in behavior. Effective consensus management requires tools – even an entire toolbox – to uncover what people really think.

Consensus is about more than agreement. It is about social behavior, cognitive biases, and the gap between what is said and what is thought. This book offers practical approaches to explore, measure, and analyze opinions accurately so that managers can lead with clarity and confidence.

Consensus (Management)

This book is about consensus and how to manage it effectively.

Consensus refers to the extent to which people in a group share similar opinions on a specific topic. When opinions are closely aligned or even identical, we speak of high or full consensus. When opinions differ significantly, we refer to low consensus.

Importantly, ‘high consensus’ and ‘low consensus’ are not synonyms for ‘good’ and ‘bad’. As the earlier examples show, the value of consensus depends on:

• The topic on which opinions are compared and analyzed.

• The context in which consensus is evaluated and managed.

• The purpose that high or low consensus serves, such as efficiency, creativity, or critical evaluation.

Consensus management is the full set of actions managers use to explore, measure, and analyze consensus in their teams, organizations, and networks. It also includes actions to:

• Make levels of consensus explicit.

• Communicate about consensus with others.

• Derive actionable learning points for improvement.

In the short term, these actions help ensure that improvement efforts align with current levels of consensus so they are effective and avoid unintended side effects. In the medium and long term, consensus management aims to shift consensus levels to create better conditions for future decision-making. This book introduces the Consensus Management Framework, a step-by-step guide that we built from our consulting and research experience, practical examples, and insights from scientific literature.

• Chapter 1 explains the overall Consensus Management Framework.

• All subsequent chapters break down each phase of the framework into actionable steps.

What to expect in each chapter? Each chapter includes:

An action plan: A step-by-step guide for applying that phase of the Consensus Management Framework. These plans are based on our experience in consulting and research projects focused on measuring, analyzing, and steering consensus in teams, organizations, or cross-organizational collaboration networks. The action plans are practical tools for managers and also serve as a basis for communication with peers, team members, superiors, (external) facilitators, as well as representatives from an organization’s Human Resource Management department.

Concepts and background: Each chapter begins with a section that introduces the key ideas behind the action plan. These are drawn from both scientific literature and our own accumulated consulting and coaching experiences. While the action plans offer a solid foundation, managers are encouraged to go beyond the basics.

Iterative application: The Consensus Management Framework is designed to be used in multiple rounds which we call consensus management trajectories. Each chapter also includes a section ‘To the Next Level’, which offers ideas for further exploration and specialization. These sections are for bold, engaged managers who want to deepen their consensus management competencies with each iteration.

Real-world examples: Throughout the book, we include examples from our consulting, coaching, and research work. These stories make the action plans and scientific concepts more relatable and more actionable.

Why this book now?

Consensus management matters now more than ever, both in how people experience work and in how data is used to understand and improve that experience.

The people aspect of consensus management

Jobs matter deeply to people. They shape:

• What people do every day.

• Who they interact and collaborate with.

• Which customers, citizens, clients, students, patients, employees, or shareholders they serve.

• How they are perceived by others at home, in their communities, and in society.

Work is more than a paycheck. It is a source of identity, purpose, and connection. It influences how people see themselves and how they are seen by others. It affects confidence, motivation, and well-being. That is why the way work is organized – and how people experience it – matters so much.

In recent years, both practitioners and researchers have focused heavily on creating positive, supportive work environments. The goal: balance well-being with productivity (a good work-life balance), efficiency, and effectiveness. This includes promoting psychological safety, encouraging open dialog, and fostering a culture where people feel heard and valued.

Skilled managers face the challenge of navigating this balance daily. They must ensure that teams deliver results while also creating space for diverse voices, respectful disagreement, and shared ownership. A strong understanding of consensus management helps them do this more effectively.

As introduced earlier and explored in depth throughout this book, consensus management is about understanding the social dynamics behind differing opinions in teams and organizations. It is also about guiding those dynamics in a respectful, constructive way to build mutual understanding and trust.

This is not easy work. In contrast to what catchy memes or 30-second videos on social media might suggest, there are no silver-bullet solutions for managing team dynamics. Real consensus management is not about quick fixes. It is about recognizing complex trade-offs and knowing how to work with them.

For example, should a manager push for alignment quickly to meet a deadline or allow more time for discussion to surface better ideas? Should they prioritize agreement or encourage dissent to challenge assumptions? These are not either-or decisions – they are balancing acts.

In fact, the more managers understand these trade-offs, the more they realize there is rarely a perfect endpoint. The process is ongoing. It requires constant attention and adjustment, especially in the fast-changing environments where most teams operate.

Consensus management is not about reaching a final state of agreement. It is about creating the conditions for continuous improvement, learning, and adaptation. It is about helping people work together even when they do not always see things the same way.

This book embraces that social complexity. It is about learning to navigate it, not eliminate it. And while this complexity cannot be reduced to a simple formula, the structure and approach of this book are designed to make it as clear and manageable as possible.

We offer a practical framework, grounded in real-world experience and informed by research, to help managers lead with more clarity, confidence, and care. Because when people feel seen, heard, and understood, they do not just perform better – they thrive.

The analytics aspect of consensus management

Today, data is everywhere. Anyone who started working with employee or customer data over 20 years ago knows the biggest challenge used to be getting any data at all. That has changed. Now, it is easy to run a poll or survey within a team or organization. Personalized technology also gives access to a wide range of relevant data from employees, customers, and other stakeholders.

With the right privacy and anonymity safeguards, managers can collect detailed, representative opinion data in just days or even hours. This data can be combined with other key variables related to resources, processes, outputs, and outcomes. It can also be linked to external feedback, such as customer reviews or stakeholder recommendations.

The bottleneck, therefore, is no longer data collection. The real challenge is making sense of the data: turning it into clear, actionable insights that support better decisions.

Despite the rise of Business Intelligence, Big Data, People Analytics, Predictive Modeling, Workforce Insights, and Data-Driven Decision Platforms, many of these approaches still rely on outdated assumptions about how to report, analyze, and communicate data. Traditional statistical models, even those wrapped in modern machine learning, often focus on aggregating data by team, group, profile, or organization. Aggregation can make insights easier to grasp, but if we forget the assumptions behind it, we risk losing valuable information.

One key piece of that information is variation in data: the differences between individual data points. Variation and variance in data are often reduced to technical metrics like standard errors, confidence intervals, or p-values. These help data analysts to estimate how likely it is that their findings reflect reality and to say something about ‘significance of the observations’. But here is the catch: these metrics rely on assumptions that do not always hold up in real-world settings.

For example:

• They assume individual data points are similar or even identical. But consensus management is built on the idea that opinions often differ.

• They treat differences between individuals as ‘random noise’ or ‘error’. Yet in consensus management, those differences reveal important social dynamics within groups.

Rarely is variance used to analyze, report, or learn about consensus. When it is, we move away from the idea that individuals or teams are inherently the same and that variation is just a deviation from some idealized average. Instead, we recognize that differences in opinions, perspectives, expertise, expectations, and competencies are meaningful.

Variance holds rich insights that traditional analysis often overlooks. The idea of treating variance as a valuable source of information is gaining ground in academic management research. But for many managers, consultants, and business analysts, it is still unfamiliar territory.

It is time to bridge that gap, to bring scientific insights into practice and to make them useful in and applicable for decision-making.

Who is it for?

This book is primarily for managers at all levels, consultants, data analysts, team leaders, organizational-development specialists, learning and development managers, change managers, and internal-strategy advisors. It is also highly relevant for professionals working in Human Resources (HR), HR business partners, people (analytics) managers, and workforce analytics, especially those involved in shaping strategic alignment, organizational development, and evidence-based decision-making. It brings together the people aspect and the analytics aspect of consensus management.

Management researchers with a strong, genuine interest in conducting studies and projects with and for practitioners can also find value and encouragement here. We explain key scientific concepts in a practical way, making them relevant for both consensus studies and consensus management in general. For researchers, this book can thus serve as concrete inspiration – with multiple concrete examples – towards designing studies that go beyond traditional averages and aggregates. It encourages a shift in perspective: recognizing that group-level data holds rich, often untapped insights into diversity of opinions, social dynamics, and collective behavior. By embracing complexity and variance within teams and organizations, researchers can develop more nuanced, practice-oriented studies that reflect the real-world challenges and opportunities of consensus building.

Most of the insights we share come from our own consulting, coaching and research projects. We have conducted these projects in a wide range of organizations (private, nonprofit, and public). Throughout the book, we discuss and include examples from these projects to illustrate key points.

What you will gain

By the end of this book, you will be able to:

• Understand consensus (management) in teams and organizations.

• Identify when what level of consensus is desired, in general as well as within your specific context.

• Use a practical toolkit to measure, analyze, and visualize consensus levels.

• Apply a structured framework to guide team discussions and decision-making.

• Navigate the trade-offs in consensus management.

• Lead with greater accuracy, precision, confidence, and care in complex environments.

Whether you are a manager, consultant, or researcher, this book will help you turn the invisible dynamics of team dynamics and, specifically, consensus into visible, actionable insights.

Our take on this

As authors of this book, we deeply value building bridges between scientific insights and practical applications. That is why we began, and continue, working together on various research and consulting projects. In our empirical work, we consistently aim to combine two core elements:

1. We analyze and report data to contribute to broader academic debates. Ideally, we share these findings through scientific journals, academic conferences, and workshops with fellow researchers.

2. We provide tailored feedback and recommendations to the managers and policymakers who support our data collection, often from their own teams, organizations, or networks. This creates a strong, two-way engagement with practitioners. It makes our insights directly applicable to real-world management while giving us valuable feedback from the environments where our results matter most.

Somewhat prophetically (and even ironically), our collaboration on consensus and consensus management began with a moment of self-reflection. During a research brainstorm (over a slice of cake with a beautiful Viennese view), we realized that we had both fallen into the trap of the false-uniqueness effect. For years, we had each been conducting separate projects that aimed to balance scientific rigor with practical relevance. We had both explored ways to report consensus-related insights and discuss them with engaged practitioners. These efforts uncovered valuable but often hidden insights.

Despite our confidence in the practical value of a structured approach to what we now call consensus management, we each assumed we were working ‘alone’ in this space and were ‘unique’. These assumptions had to be challenged before this joint project could begin. A slice of cake with an amazing view and the right critical questions helped us move past it. And that, in many ways, is the perfect start to any good consensus management trajectory.

The consensus management roadmap

This roadmap offers a structured, phase-by-phase guide for organizational experts, consultants, and anyone involved in strategic and change processes within organizations. It integrates 32 actions across the different phases, aligning them with their intended outputs.

The Consensus Management Framework (Chapter 1)

1.1 Keep a holistic overview of the framework’s building blocks .

→ Output: Strategic alignment and balanced consensus management.

1.2 Apply a continuous, iterative approach.

→ Output: Ongoing improvement and responsiveness.

1.3 Track strengths and weaknesses – self-reflect on Consensus Management ability.

→ Output: Personal development and improved leadership.

Monitor

Explore – sensing and scoping (Chapter 2)

2.1 Critically self-reflect on susceptibility to the false-consensus bias.

→ Output: Guidelines for improving internal communication and openness.

2.2 Regularly probe the team’s internal consensus on key topics.

→ Output: Overview of team opinions and consensus levels.

2.3 Actively plan communication channels about consensus.

→ Output: Known and accessible communication tools and routines.

2.4 Keep a dynamic list of consensus topics.

→ Output: Visual/mental map of topic relevance and consensus levels.

2.5 Prioritize topics for further data collection and analysis.

→ Output: Shortlist of high-priority topics.

Collect – structured data gathering (Chapter 3)

3.1 Select the topic of interest, ideally using a validated scale.

→ Output: Finalized scale and topic description.

3.2 Tailor scale items and use clear response labels.

→ Output: Context-specific items and clear labels.

3.3 Identify and select members of targeted (sub)groups.

→ Output: Documented list and sample description.

3.4 Choose between ranking or rating methods.

→ Output: Finalized method and response labels.

3.5 Invite participants and ensure sufficient response rate.

→ Output: Survey responses recorded.

3.6 Ensure at least two responses per subgroup and assess representativeness.

→ Output: Documented representativeness.

3.7 Handle data confidentially and allocate to subgroups.

→ Output: Securely handled and allocated data.

Analyze – insight generation (Chapter 4)

4.1 Operationalize consensus.

→ Output: Interpretable consensus scores.

4.2 Visualize consensus.

→ Output: Visual representations of team dynamics.

4.3 Match mean and consensus scores.

→ Output: Two-dimensional plots for strategic decisions.

Advance

Explicate – theory building (Chapter 5)

5.1 Choose a template for an Implicit Consensus Theory.

→ Output: Initial theory linking consensus to outcomes.

5.2 Add contextuality.

→ Output: Context-specific refinements.

5.3 Add temporality.

→ Output: Timing details for consensus-outcome links.

5.4 Add conditionality.

→ Output: Influencing factors and conditions.

Implement – short(er) -term action (Chapter 6)

6.1 Generate a wide range of learning points.

→ Output: List of confirming, surprising, and discussion-worthy insights.

6.2 Classify learning points by type and impact.

→ Output: Categorized learning points.

6.3 Develop management actions based on learning points.

→ Output: Action list with impact and feasibility assessments.

6.4 Prioritize actions based on impact and feasibility.

→ Output: Ranked list of actions.

6.5 Select and implement the most promising actions.

→ Output: Actions ready for implementation.

6.6 Evaluate and adjust actions as needed.

→ Output: Evaluation reports and revised actions.

Adjust – long(er)-term steering (Chapter 7)

7.1 Identify antecedents of change in consensus levels.

→ Output: Categorized list of antecedents.

7.2 Derive consensus interventions.

→ Output: Targeted interventions.

7.3 Validate interventions and outcome changes.

→ Output: Evidence of impact using qualitative and quantitative data.

7.4 Repeat until optimal consensus is reached.

→ Output: Refined theory and improved alignment.

This roadmap is designed to be iterative and adaptive. We encourage practitioners to revisit different phases as new insights emerge, ensuring that consensus management remains responsive, evidence-based, and aligned with evolving team dynamics.

CHAPTER 1 THE CONSENSUS MANAGEMENT FRAMEWORK

CHAPTER 1 THE CONSENSUS MANAGEMENT FRAMEWORK

This chapter introduces the Consensus Management Framework: a structured approach that helps managers monitor and improve consensus within their teams. The framework offers a bird’s-eye view of the key processes and actions used by experienced managers to manage consensus.

By understanding how core concepts relate to each other, managers can use the Consensus Management Framework as a strategic roadmap and a practical how-to guide. It shows how to leverage consensus to boost team performance.

This chapter also outlines the rest of the book. Each upcoming chapter dives deeper into the individual components of the Consensus Management Framework.

Background and concepts

Figure 1 shows a visual overview of the Consensus Management Framework. This framework offers a structured approach to managing consensus. It gives managers a clear roadmap to understand and guide consensus levels within their teams and organizations.

We developed this framework to provide structure and guidance. It helps managers implement key actions and apply lessons learned from real-world experiences. It reflects what we have learned through various consulting and research projects on consensus management.

The Consensus Management Framework is not just a summary of our insights. It is a practical tool for managers in three ways:

1. Planning a comprehensive consensus management approach.

2. Evaluating progress in consensus management efforts.

3. (Self-)reflecting on personal management skills, both social (e.g., team interaction) and analytical (e.g. data interpretation and reporting).

The Consensus Management Framework also serves as a shared language. It helps managers communicate and exchange ideas with peers. In short, it offers a holistic

view of consensus management, helping managers assess the current state and take action to improve it.

This chapter also sets the stage for the rest of the book. It gives a bird’s eye view of the Consensus Management Framework’s phases and points to the chapters where each phase is explained in depth. You can return to this chapter anytime to see how the pieces fit together.

CONSENSUS MANAGEMENT

EXPLORE

ANALYZE IMPLEMENT ADJUST IMPLICIT CONSENSUS THEORIES

Knowing the building blocks: Understanding the Consensus Management Framework

The Consensus Management Framework has three main building blocks:

• Implicit Consensus Theories (center)

• Monitor Triangle (left)

• Advance Triangle (right)

At the center are the Implicit Consensus Theories, the mental models managers use to understand how consensus affects outcomes like performance, satisfaction, and efficiency. These theories act as a bridge between the two triangles. We explore these Implicit Consensus Theories in an intermezzo section of the book that follows the Monitor Triangle chapters.

The Monitor Triangle (Part 1 of the book) focuses on understanding the current state of consensus in a team or organization. It includes three key actions:

• Explore (Chapter 2): Identify where consensus is high or low.

• Collect (Chapter 3): Gather data through surveys, interviews, feedback, or other tools.

• Analyze (Chapter 4): Interpret the data to generate insights, visualizations, and reports on the consensus levels.

Together, these actions help managers stay informed about team dynamics and spot areas needing attention. With a solid understanding of these three components, we use the intermezzo on Implicit Consensus Theories, in between the two main parts of this book, to transition to the Advance Triangle.

The Advance Triangle (Part 2 of this book) helps managers act on insights to improve consensus. It also includes three key actions:

• Explicate (Chapter 5): Clarify the assumptions behind shared beliefs and shared attitudes within teams.

• Implement (Chapter 6): Take short-term actions that take the current levels of consensus into account, like deriving advanced learning points and management actions that are adjusted to current opinions in the team.

• Adjust (Chapter 7): Apply long-term strategies to manipulate and steer consensus over time, ensuring that the team continues to benefit from optimal levels of agreement on important topics over time.

As mentioned earlier, Implicit Consensus Theories sit at the heart of the framework. These mental models shape how managers interpret consensus and make decisions. They reflect a manager’s beliefs about how different levels of consensus affect key outcomes such as team performance and employee satisfaction. These theories are shaped by actions in both the Monitor and Advance Triangles. In turn, they guide decisions across all six phases of the framework. We elaborate these Implicit Consensus Theories in depth in the intermezzo section, in which we build on various relevant insights from scientific literature. Concretely, we show how managers can sharpen their thinking using these insights from scientific research on consensus.

MAIN REFERENCES AND

RECOMMENDED

READING PER CHAPTER

MAIN REFERENCES AND RECOMMENDED READING PER CHAPTER

Not all references listed below are available in open access. Articles that are openly accessible are marked in bold. For non-open access publications, interested readers are welcome to contact the authors for more information or access options.

CHAPTER 1 OVERVIEW

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Desmidt, S., Meyfroodt, K., & George, B. (2019). Shared strategic cognition in Flemish city councils: The relevance of political and demographic group characteristics. Public Management Review, 21(7), 945–967. https://doi.org/ 10.1080/14719037.2018.1538423

Floyd, S. W., & Wooldridge, B. (1992). Managing strategic consensus: The foundation of effective implementation. Academy of Management Perspectives, 6(4), 27–39. https://doi. org/10.5465/ame.1992.4274459

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González-Benito, J., Aguinis, H., Boyd, B. K., & Suárez-González, I. (2012). Coming to Consensus on Strategic Consensus: A Mediated Moderation Model of Consensus and Performance. Journal of Management, 38(6), 1685–1714. https://doi.org/10.1177/0149206310386489

Kellermanns, F. W., Walter, J., Floyd, S. W., Lechner, C., & Shaw, J. C. (2011). To agree or not to agree? A meta-analytical review of strategic consensus and organizational performance. Journal of Business Research, 64(2), 126–133. https://doi.org/10.1016/j.jbusres.2010.02.004

Kellermanns, F. W., Walter, J., Lechner, C., & Floyd, S. W. (2005). The Lack of Consensus About Strategic Consensus: Advancing Theory and Research. Journal of Management, 31(5), 719–737. https://doi.org/10.1177/0149206305279114

Meyfroodt, K., & Desmidt, S. (2024). All aboard? How Line-of-Sight impacts the strategic commitment of nonprofit employees. Public Management and Governance Review, 1(1). https:// doi.org/10.60733/PMGR.2024.02

Meyfroodt, K., & Willems, J. (2025). Pairing mean scores with consensus metrics: Extending managers’ toolkit for decision-making. Europe-

an Management Review. https://doi.org/10.1111/ emre.70020

Sonenshein, S. (2010). We’re Changing – Or Are We? Untangling the Role of Progressive, Regressive, and Stability Narratives During Strategic Change Implementation. Academy of Management Journal, 53(3), 477–512. https://doi. org/10.5465/amj.2010.51467638

Stouten, J., Rousseau, D. M., & De Cremer, D. (2018). Successful Organizational Change: Integrating the Management Practice and Scholarly Literatures. Academy of Management Annals, 12(2), 752–788. https://doi.org/10.5465/ annals.2016.0095

Wallace, J. C., Edwards, B. D., Paul, J., Burke, M., Christian, M., & Eissa, G. (2016). Change the Referent? A Meta-Analytic Investigation of Direct and Referent-Shift Consensus Models for Organizational Climate. Journal of Management, 42(4), 838–861. https://doi. org/10.1177/0149206313484520

Willems, J. (2016a). Building shared mental models of organizational effectiveness in leadership teams through team member exchange quality. Nonprofit and Voluntary Sector Quarterly, 45(3), 568–592. https://doi. org/10.1177/0899764015601244

Willems, J. (2016b). Organizational crisis resistance: Examining leadership mental models of necessary practices to resist crises and the role of organizational context. Voluntas: International Journal of Voluntary and Nonprofit Organizations, 27(6), 2807–2832. https://doi. org/10.1007/s11266–016-9753-9

Willems, J., den Bergh, J. V., & Deschoolmeester, D. (2012). Analyzing Employee Agreement on Maturity Assessment Tools for Organizations: Analyzing Employee Agreement on MATs. Knowledge and Process Management, 19(3), 142–147. https://doi.org/10.1002/kpm.1389

CHAPTER 2 EXPLORE

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tegic Consensus, and Commitment. Journal of Management, 46(5), 637–665. https://doi. org/10.1177/0149206318811567

Boswell, W. R. (2006). Aligning employees with the organization’s strategic objectives: Out of ‘line of sight’, out of mind. The International Journal of Human Resource Management, 17(9), 1489–1511. https://doi. org/10.1080/09585190600878071

Crucke, S., & Knockaert, M. (2016). When Stakeholder Representation Leads to Faultlines. A Study of Board Service Performance in Social Enterprises. Journal of Management Studies, 53(5), 768–793. https://doi.org/10.1111/ joms.12197

De Jong, A., Song, M., & Song, L. Z. (2013). How Lead Founder Personality Affects New Venture Performance: The Mediating Role of Team Conflict. Journal of Management, 39(7), 1825–1854. https://doi.org/10.1177/0149206311407509

Heimann, A. L., Ingold, P. V., & Kleinmann, M. (2020). Tell us about your leadership style: A structured interview approach for assessing leadership behavior constructs. The Leadership Quarterly, 31(4), 101364. https://doi. org/10.1016/j.leaqua.2019.101364

Joplin, J. R. W., & Daus, C. S. (1997). Challenges of leading a diverse workforce. Academy of Management Perspectives, 11(3), 32–47. https://doi.org/10.5465/ame.1997.9709231662

Kellermanns, F. W., Walter, J., Lechner, C., & Floyd, S. W. (2005). The Lack of Consensus About Strategic Consensus: Advancing Theory and Research. Journal of Management, 31(5), 719–737. https://doi.org/10.1177/0149206305279114

Lorinkova, N. M., & Perry, S. J. (2019). The importance of group-focused transformational leadership and felt obligation for helping and group performance. Journal of Organizational Behavior, 40(3), 231–247. https://doi. org/10.1002/job.2322

Luria, G. (2019). Climate as a group level phenomenon: Theoretical assumptions and methodological considerations. Journal of Organizational Behavior, 40(9–10), 1055–1066. https:// doi.org/10.1002/job.2417

Mertens, S., Meyfroodt, K., & Schollaert, E. (2024). Leader Humility and Affective Commitment: A Cross-Sectional Study Among Hospital Nursing Teams. Journal of Advanced Nursing https://doi.org/10.1111/jan.16664

Meyfroodt, K., & Desmidt, S. (2024). All aboard? How Line-of-Sight impacts the strategic commitment of nonprofit employees. Public Management and Governance Review, 1(1). https:// doi.org/10.60733/PMGR.2024.02

Olson, B. J., Parayitam, S., & Bao, Y. (2007). Strategic Decision Making: The Effects of Cognitive Diversity, Conflict, and Trust on Decision Outcomes. Journal of Management, 33(2), 196–222. https://doi.org/10.1177/0149206306298657 Paté-Cornell, E. (2012). On “Black Swans” and

“Perfect Storms”: Risk Analysis and Management When Statistics Are Not Enough. Risk Analysis, 32(11), 1823–1833. https://doi.org/10.1111/ j.1539-6924.2011.01787.x

Pihl-Thingvad, S., Winter, V., Schelde Hansen, M., & Willems, J. (2022). Relationships matter: How workplace social capital affects absenteeism of public sector employees. Public Management Review, 1–28. https://doi.org/10.10 80/14719037.2022.2142652

Russell, P. A., & Gray, C. D. (1994). Ranking or rating? Some data and their implications for the measurement of evaluative response. British Journal of Psychology, 85(1), 79–92. https://doi. org/10.1111/j.2044-8295.1994.tb02509.x

Willems, J., & Meyfroodt, K. (2024). Group Research: Why are we Throwing Away the Best of our Observations? Group & Organization Management. https://doi.org/10.1177/10596011241246303

CHAPTER 3 COLLECT

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Kellermanns, F. W., Walter, J., Floyd, S. W., Lechner, C., & Shaw, J. C. (2011). To agree or not to agree? A meta-analytical review of strategic consensus and organizational performance. Journal of Business Research, 64(2), 126–133. https://doi.org/10.1016/j.jbusres.2010.02.004

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Willems, J., & Meyfroodt, K. (2024b). Group Research: Why are we Throwing Away the Best of our Observations? Group & Organization Management. https://doi.org/10.1177/10596011241246303

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& Andrus, J. L. (2016). Are Boards Designed to Fail? The Implausibility of Effective Board Monitoring. Academy of Management Annals, 10(1), 319–407. https://doi.org/10.5465/19416520.2016 .1120957

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CHAPTER 6 IMPLEMENT

Beer, M., Voelpel, S. C., Leibold, M., & Tekie, E. B. (2005). Strategic Management as Organizational Learning. Long Range Planning, 38(5), 445–465. https://doi.org/10.1016/j. lrp.2005.04.008

Desmidt, S., Meyfroodt, K., & George, B. (2019). Shared strategic cognition in Flemish city councils: The relevance of political and demographic group characteristics. Public Management Review, 21(7), 945–967. https://doi.org/ 10.1080/14719037.2018.1538423

Dunne, D., & Martin, R. (2006). Design Thinking and How It Will Change Management Education: An Interview and Discussion. Academy of Management Learning & Education, 5(4), 512–523. https://doi.org/10.5465/amle.2006.23473212

Easterby-Smith, M., Crossan, M., & Nicolini, D. (2000). Organizational Learning: Debates Past, Present And Future. Journal of Management Studies, 37(6), 783–796. https://doi. org/10.1111/1467-6486.00203

Edmondson, A. C., Dillon, J. R., & Roloff, K. S. (2007). 6 Three Perspectives on Team Learn-

ing: Outcome Improvement, Task Mastery, and Group Process. Academy of Management Annals, 1(1), 269–314. https://doi.org/10.5465/078559811

Goodman, P. S., & Rousseau, D. M. (2004). Organizational change that produces results: The linkage approach. Academy of Management Perspectives, 18(3), 7–19. https://doi.org/10.5465/ ame.2004.14776160

Haeffner, M., Leone, D., Coons, L., & Chermack, T. (2012). The Effects of Scenario Planning on Participant Perceptions of Learning Organization Characteristics. Human Resource Development Quarterly, 23(4), 519–542. https:// doi.org/10.1002/hrdq.21147

Kettinger, W. J., & Grover, V. (1995). Special Section: Toward a Theory of Business Process Change Management. Journal of Management Information Systems, 12(1), 9–30. https://doi.org /10.1080/07421222.1995.11518068

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Meyfroodt, K., & Willems, J. (2025). Pairing mean scores with consensus metrics: Extending managers’ toolkit for decision-making. European Management Review. https://doi.org/10.1111/ emre.70020

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Wiedner, R., Barrett, M., & Oborn, E. (2017). The Emergence of Change in Unexpected Places: Resourcing across Organizational Practices in Strategic Change. Academy of Management Journal, 60(3), 823–854. https://doi.org/10.5465/

amj.2014.0474

Willems, J., Andersson, F. O., Jegers, M., & Renz, D. O. (2017). A Coalition Perspective on Nonprofit Governance Quality: Analyzing Dimensions of Influence in an Exploratory Comparative Case Analysis. Voluntas: International Journal of Voluntary and Nonprofit Organizations, 28(4), 1422–1447. https://doi.org/10.1007/s11266-0169683-6

Wilson, J. M., Goodman, P. S., & Cronin, M. A. (2007). Group learning. Academy of Management Review, 32(4), 1041–1059. https://doi. org/10.5465/amr.2007.26585724

CHAPTER 7 ADJUST

Beer, M., Voelpel, S. C., Leibold, M., & Tekie, E. B. (2005). Strategic Management as Organizational Learning. Long Range Planning, 38(5), 445–465. https://doi.org/10.1016/j. lrp.2005.04.008

Desmidt, S., Meyfroodt, K., & George, B. (2019). Shared strategic cognition in Flemish city councils: The relevance of political and demographic group characteristics. Public Management Review, 21(7), 945–967. https://doi.org/ 10.1080/14719037.2018.1538423

Dunne, D., & Martin, R. (2006). Design Thinking and How It Will Change Management Education: An Interview and Discussion. Academy of Management Learning & Education, 5(4), 512–523. https://doi.org/10.5465/amle.2006.23473212

Easterby-Smith, M., Crossan, M., & Nicolini, D. (2000). Organizational Learning: Debates Past, Present And Future. Journal of Management Studies, 37(6), 783–796. https://doi. org/10.1111/1467-6486.00203

Edmondson, A. C., Dillon, J. R., & Roloff, K. S. (2007). Three Perspectives on Team Learning: Outcome Improvement, Task Mastery, and Group Process. Academy of Management Annals, 1(1), 269–314. https://doi.org/10.5465/078559811

Gephart, R. P. (2004). Qualitative Research and the Academy of Management Journal. Academy of Management Journal, 47(4), 454–462. https://doi.org/10.5465/amj.2004.14438580

Goodman, P. S., & Rousseau, D. M. (2004). Organizational change that produces results: The linkage approach. Academy of Management Perspectives, 18(3), 7–19. https://doi.org/10.5465/ ame.2004.14776160

Griffin, M. A. (1997). Interaction Between Individuals and Situations: Using HLM Procedures to Estimate Reciprocal Relationships. Journal of Management, 23(6), 759–773. https://doi. org/10.1177/014920639702300604

Haeffner, M., Leone, D., Coons, L., & Chermack, T. (2012). The Effects of Scenario Planning on Participant Perceptions of Learning Organization Characteristics. Human Resource Development Quarterly, 23(4), 519–542. https:// doi.org/10.1002/hrdq.21147

Hoffman, L. (2007). Multilevel Models for Examining Individual Differences in Within-Person Variation and Covariation Over Time. Multivariate Behavioral Research, 42(4), 609–629. https://doi.org/10.1080/00273170701710072

Humphrey, S. E., & Aime, F. (2014). Team Microdynamics: Toward an Organizing Approach to Teamwork. Academy of Management Annals, 8(1), 443–503. https://doi.org/10.5465/19416520.2014 .904140

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