These last few years have changed our lives, our societies, and our workplaces, but they have also changed our minds. The only good use of the pandemic would be to let it help us distill some new and better principles for our lives and our professions. How can we learn better from the negative events in our lives? Our study, recently published in the Journal of Management Studies, shows that CEOs encode their learnings from failure into simple rules like “hire for attitude, not for skills” that are easy to remember, to apply, and to share. The negative event challenges an important preconception in the manager’s mind and leads to an aha moment in which she realizes that, although she thought A (that skills must be the most important hiring criterion), in fact B is true (attitude is more important), which she will implement in future similar decisions (“I will hire together with my team”).

An interesting finding of our research is that, often, executives apply the simple rule they have learned even before articulating it. But the rule becomes much more powerful after it is said the first time. Although our research is about top managers, its findings are easily generalizable for all learning from failure. This short article ends with two easy methods which can help us encode our negative experiences into powerful simple rules.

Managers distill their experience into simple rules. Top managers distill the lessons they learn into easy to remember simple rules that guide important aspects of their activity. Ray Dalio even wrote a best-selling book titled Principles: Life and Work, in which he shares his experience in the form of simple rules like “focus on the what-is before deciding what-to-do-about-it.” Jeff Bezos coined a proverb-like simple rule to guide the internal structure of Amazon, the famous two-pizza rule: “every internal team must be small enough so that it can be fed with two pizzas,” and used to season his annual letters to the shareholders with simple (but unnervingly precise) rules for decision-making: “most decisions should probably be made with somewhere around 70% of the information you wish you had. If you wait for 90%, in most cases, you’re probably being slow.” But how do these executives create their simple rules? Our recent paper tries to answer this question. We interviewed 31 CEOs, gathered from them a repository of 202 simple rules that summarize the lessons they learned from experience, and understood how such a rule is created and shaped. While these rules may seem simple, they are in fact powerful tools for managing and leading. Some of their power yields exactly from their simplicity; one respondent said that the simple rule helps him “being concise and inspirational,” while anotherpointed the advantages of brevity: “the shorter, the better accepted; if you explain for 10 minutes, you give opportunity for doubt.”

Simple rules are born in pairs after a triple insight. All the simple rules that we documented were the direct result of a negative experience, be that a big failure (e.g., a 200-million euro project closed mid-way), or just an ongoing bad situation (a constant stream of employees leaving for slightly bigger salaries). After a longer period of intense thinking, during which the problem keeps running in the CEO’s mind, simple rules appear, during aha moments or even “aha periods,” as the result of an insight. In a constant search for cues to solve the puzzle, managers use mundane observations, analogies, inputs from others, or just something they just read to clarify the problem and to spark not a simple, but a triple insight: what flawed assumption led to the problem, what is true instead, and what to do about it.

• What flawed assumption led to the problem. The first insight leads to the flawed assumption being clearly acknowledged and unlearned: “we thought that a client’s creditworthiness can be financially calculated, but during the crisis, usual client appraisal methods failed. I realized that you cannot assess a client from a distance, by looking at numbers”.
• What is true instead. The second insight leads to a new principle, that replaces the old belief and is encapsulated in a conceptual simple rule: “I realized that the person is more important than numbers.”
• What to do about it. The third insight helps translate the principle into daily behavior, yielding the operative simple rule: “we don’t send offers; we meet people face to face.”

This triple insight gives birth not to one, but to a pair of simple rules, a conceptual one that outlines the newly learned principle, and an operative one that helps enact this principle in daily behavior. The fact that simple rules are born in pairs is a key finding of our research. The table below showcases five such pairs of simple rules.

Managerial proverbs and their validation stamps. After managers have these insights, their simple rules go through articulating, testing, adapting, and refining. One important method of refining is proverbialization: managers find concise, memorable forms for their rules – “look at the person behind the position,” “the ratio between praise and criticism must be 3 to 1,” or “don’t make decisions for someone else” – making them easily remembered and shared.The rules are then shared within the organization, formally, in meetings, or informally, by example or by applying them together. However, the problem with sharing is, in the words of another interviewee, that “such rules are easy to hear and hard to implement.” A recipe to bypass this problem is to share the simple rule along with its whole narrative, making it easier to adopt and remember. This principle was discovered (and even turned into a simple rule for sharing simple rules) by another respondent: “if shared without its story, the rule would be ignored.; if the story is there, they pay more attention, and they remember better.” Creating and sharing simple rules is seen as a key role for any top manager: “the role of a CEO is to create systems that work and then to find the metaphor that helps the team vibrate, understand it, and apply it every day.”

The birth and development of a simple rule is accompanied and facilitated by a rollercoaster of feelings, from negative pressure at the beginning of the process, before the insight, to epiphany-like feelings at insight, and to confidence after successfully implementing it. This emotional journey puts a veritable validation stamp on the simple rule and may prevent its author to abandon it even when it is no longer adequate.

How can anyone use our findings? Despite considering them valuable, managers acknowledge, articulate, and share their simple rules less often than they wish. This led us to propose here two useful tools for practitioners, one for individual managers and one for their teams. The first tool helps managers encode their own past experiences into simple rules and consists in writing down the lessons they learned in a format that maps the triple insight:

“Previously, I thought that (…),

but then I realized that (…).

Now, I/we (…).”

This simple framework can be used to acknowledge and articulate past lessons into an ever-growing personal portfolio of simple rules. You can try it anytime. Even now.

The second tool, this time for teams, is meant to help the lessons-learned process, conducted at the end of most projects, to yield lessons that are better remembered and applied in the future. We recommend the project manager conducting the lessons-learned meeting to explore our model and to conclude by guiding the team to articulate a simple, proverb-like rule to encompass each lesson.

Enhancing worldwide learning and global integration

Subsidiaries are increasingly portrayed as important knowledge-creators who play an active role in the firm, developing new technologies and product offerings for global use. Hence, these units are strategically important, serving as vehicles to enter international markets and increase the firm’s knowledge portfolio often through their entrepreneurial undertakings. However, prior research has not fully captured the reality of such entrepreneurial activities, merely capturing the extent to which some individual conditions matter or how strongly they may be related to the corporate headquarters’ approval.

Our fieldwork indicates that managers often convey that their decisions on initiative acceptance, for instance, are seldom based on single factors. Instead, managers configure relevant dimensions—sometimes internal and other times external to the firm—to inform their decision-making. To understand these possible situations, namely how initiatives could manifest, gain traction in the firm, and receive headquarters approval, we adopted a novel empirical technique to tease out these processes. We incorporate key relational elements, such as headquarters attention, subsidiary track-record, decision-making autonomy, and subsidiary-level contextual characteristics of distance to headquarters and its establishment mode.

Published in the Journal of International Management, our study investigates business opportunities identified by the subsidiary that seeks to change, expand, or renew the operations of the multinational firm. We explicitly focus on entrepreneurial activities that exceed the local subsidiary market or affect the firm and its sister units more broadly. We show that while increased headquarters attention is often an important mechanism that may put the subsidiary on the corporate agenda, it may neither be necessary nor sufficient to accept new entrepreneurial pursuits from subsidiaries.

Combining structural and relational attributes

Using fsQCA we highlight five pathways to subsidiary initiative acceptance. These pathways provide insights for practitioners by offering different ways of ‘configuring’ subsidiaries to benefit most from their entrepreneurial activities. More specifically, we find that acquiring high-performing foreign subsidiaries may be a way to leverage key local competencies that would be too costly to obtain from the ground up. Our results also indicate that a strong track-record of prior performance is a strong signal of the initiative quality, driving headquarters likely endorsement initiatives from these units. At the same time, however, for more proximally located subsidiaries, such as a German subsidiary of a Dutch multinational firms, the headquarter may more easily provide attention and allocate autonomy to enable entrepreneurial activities. The broader implication is that reducing information asymmetry may be achieved in several ways, and the headquarters’ proximity to its subsidiaries is a critical determinant in these configurations. We encourage headquarters to balance the cost and benefits of deploying different structural arrangements, such that paying attention to some units may be beneficial while fostering autonomy may similarly unlock entrepreneurial activities.

We are excited about this paper because it offers a novel view of a core issue within large, multinational firms. We provide implications at the intersection of theory and practice by highlighting the context-dependent and interrelated nature of core conditions within the firm.

More than 20 years after the first edition of “The Matrix,” people are still divided about what AI can and cannot do. Some think that AI is the last hope to save the planet and humankind; and others envision our species to be doomed and at the mercy of machines—a la The Matrix. And while we currently simply don’t know which—if any—prediction will come true, there is a lot of unclarity about how AI works, and for which tasks we should use it, or not. In two recent publications in the Academy of Management Learning & Education and MIT Sloan Management Review, co-authored by Frank den Hond and Dirk Lindebaum, we tackle this problem.

What’s behind the smokescreen?

In current scientific but also practitioner articles and news items, AI is being smoke-screened. The acronym is being used and abused for all kinds of things, alluding to the use of algorithms, calculating computers, big data, digital networks, and all other sorts of vaguely related phenomena and technologies. Yet, not everything is AI. Essentially, the concept refers to abilities that were previously the privilege of humans, including cognition and sensing. AI typically uses algorithms, which are simply sets of instructions or rules. In that sense, a cookbook is nothing but a collection of algorithms, specifying rules for the treatment and ordering of manipulating foody ingredients. Over the course of the past decades, since the 1960s, we have witnessed several waves of AI development. The technology has evolved from symbolic representation of data via statistical crunching of data to what is called machine learning. Yet, all dreams of a “general artificial intelligence” where machines are autonomous, like in The Matrix, have not come true.

Human judgment to the rescue

What has come true, however, is the creeping dominance of so-called AI systems in all dimensions of our life. Machine-learning technology controls our food supply chains, plans travels, steers airplanes, drives cars, sets hotel prices, nudges us into spending hours on Instagram, screens job applicants’ CVS, decides on parole decisions, grades exams, discharges patients from intensive care units, determines the quality of our schools and organizations, and so forth—the list is endless. Almost without noticing, we have happily handed over many decisions to machines that use calculus to take these decisions. Oftentimes, this works well: a machine is better at sorting and calculating, especially where much data is involved.

But as soon as morality and ethics are involved, machines can’t compete: this is because they are not built to take into account morality, judgment, and the context and history of human values when taking decisions. Machines search for the optimal decision; but in real life, there is no one optimal solution for many of the decisions we encounter on a day-to-day basis. Moreover, the majority of our decisions depend on where we are, who we are, and what our environment allows us to decide. This means that any decision that disregards context and history and looks for an optimal solution will never be able to do justice to the richness of our human life.

What comes next?

If we go on as we have for the past decades, developing even more advanced algorithms and AI just because we can, we will soon be faced with situations that will be extremely difficult or even impossible to reverse. For example, we already know that it will soon be impossible to diagnose a deep fake video—meaning that soon, we won’t be able to know if a human or a machine created a video. Instead of in a lemming-like way just continue our path, we should carefully think about when and especially why to use AI—and when and why not.

In our two articles, we observe that our decision-making is already intertwined with that of machines. This leads us to call for inaction: we should stop to replace human judgment with algorithmic “reckoning,” or calculating. We also call to action: we need to reconsider in practice and business education which decisions are fit to be delegated to machines, and which decisions will have to be made by human beings, in order to safeguard our human values. We agree with Neo who in the first Matrix movie says “I don’t like the idea that I’m not in control of my life.” Rather, we should take back control and start using AI technology for what it has been built. 

Putting results in meaningful terms

Quantitative social scientists aim to be able to say that some effect is statistically significant or insignificant. However, a growing collective of academics argue that this aim is misleading. For example, this practice is involved in the tendency to overreport support for hypotheses. Leading statisticians argue that, instead of statistical significance thinking, academics should aim to evaluate whether an effect is practically meaningful or not; yes, you read that right, not on top of statistical significance, but instead (and yes, not business leaders, but statisticians say this).

The state of the art

In our open access publication in Social Sciences & Humanities Open our detailed review of recent top journal publications across social science disciplines reveals that statistical significance thinking is unfortunately still dominant. 79% of papers evaluate evidence using statistical significance, and concerns about overreporting support for hypotheses seem valid with an average of 88% of hypotheses supported. So we are not rid of statistical significance thinking yet. Among disciplines, economics is most is moving away from using hypothesis testing, and is moving toward a more exploratory style. That shift makes sense for them as all economics papers we reviewed that do have hypotheses support them 100% of the time. Such high support makes one question whether one might as well write papers without.

Hopeful examples

We also see papers that give hope by evaluating practical meaningfulness in creative ways. For instance, some studies evaluate the meaningfulness of an effect by comparing it to the effect of known important variables. For example, a hypothetical study on charismatic leadership that controls for age could say ‘the difference in job satisfaction between those with leaders with below average charisma and those with leaders with above average charisma is about as big being # years younger’. This helps make it intuitive to understand whether an effect is practically meaningful or not. There is also an important role for data visualizations to intuitively engage with data beyond averages. We encourage the use of such approaches, not only to help other academics understand your results, but also to help connect to practitioners.

Management scholars struggle with making their research more relevant for practice. For instance, their publications are too abstract for practitioners, aim at scientific goals instead of practitioner goals or confirm the obvious for practitioners. Their studies report causalities that are not rigorous when applied in practice, their results are not timely, and they are too complex for practitioners to be useful, or poorly communicated. Consultants have solved many of these issues when working for management practitioners. Still, management scholars might not want to imitate consultants. Instead, they could consider them as intermediary audience.

In a study published in the International Journal of Management Reviews, Onno Bouwmeester, Stefan Heusinkveld and Brian Tjemkes have reviewed the academic management literature engaging in the relevance gap debate. They find that consultants can help academics in reducing the relevance gap by acting as intermediaries in different ways.

Two consultant roles in the relevance gap debate

By creating the relevance gap in management science, academics have also created a market opportunity for intermediaries like management consultants. As intermediary, consultants have developed many abilities to reduce these relevance gaps. As there is a need for applicable management knowledge, consultants search for such knowledge to share with their clients. They look for academic insights that help strengthen their consulting work. By realizing that consultants look for management knowledge, we consider them a relevant audience for management scholars. It may be better sometimes to target such an intermediary audience, than serving managers directly.

Next to this, the study finds that consultants can help management scholars as reviewer with reflecting on the various relevance gaps. They can help to make academic knowledge less abstract or detached. As practitioners themselves they know how to realize more descriptive relevance, goal relevance or timeliness and why it is important in practice. Consultants can also offer support with simplification and better communication.

Still, consultants have their limits as well, as intermediary. They may point at problems with the rigor of assumed causalities in academic theory, but they will not be of great help in improving on them. The same applies to improving non-obviousness: new insights sometimes come from practice, but they also come from science, and often the kind of contributions are different. Fruitful collaboration on scientific projects is hardly reported, and when tried, authors experience many challenges.

Intermediary audiences for management scholars

The important takeaways of this research are first that management scholars do not necessarily need to change into consultants themselves. Some have tried, but many scholars report tensions when they try: the consulting work translates mostly not into good scientific publications, the consulting work does not further the academic career, and consulting work takes time away from those academic activities that are seen as more central to their career, like doing scholarly research and academic teaching.

Secondly, instead of becoming an academic consultant and reaching out to management practitioners directly, management scholars can also focus on intermediary audiences such management consultants. There are more of them. Journalists report on academic research findings and they have a wide readership. Students at business schools are intermediary audience as they prepare themselves for a role as management practitioner. They will bring academic knowledge or an academic mindset to management practice. Practitioner PhD’s and executive students are also an intermediary audience when they study questions raised in their own management practice, and then apply academic research methods and lenses to make sense of these questions.

Management scholars are thus well advised to consider the knowledge interests of intermediary audiences such as consultants, journalists, executive or graduate students, and practitioner PhD’s when they want to make management science more relevant to practice.

Trusting the science

In this time of vaccines and climate change, we need science to be widely trusted. Unfortunately, the social sciences are making themselves look untrustworthy by underreporting evidence against hypotheses. That is, if one would only look at all published evidence, theories appear more supported than if one would look at all collected evidence (i.e., published + unpublished evidence). Too many papers approach evidence by implicitly starting from the question: ‘What evidence could support this theory?’ or even ‘What theory could explain this evidence?’ As a result, the way in which evidence is surprising is underemphasized. The psychological and social reasons for the popularity of that approach I would summarize as: the need for a smooth and catchy storyline.

Recent study discovers obvious fact

Published research is often so strong in creating a smooth and catchy storyline that readers wonder whether the main insight wasn’t obvious from the beginning. My open access publication at the Journal of Trial and Error introduces ‘complementary explanation’; an approach to engage with nonobvious evidence as the main storyline. The paper provides beginners a step by step instruction manual to start thinking about evidence this way.

Complementary explanation brings balance

Complementary explanation takes a piece, or multiple related pieces, of published evidence and invites you to ask yourself: ‘for which theory would this evidence be surprising?’. In this way, that evidence is connected to a theory that it would otherwise not be connected to, and we realize the way in which the evidence is surprising whereas the evidence seemed obvious from the perspective of the theory with the smooth storyline. Crucially, in this way, a theory is connected to evidence against it, forcing an adjustment (i.e., advancement). That counteracts underreporting and leads to an overall more balanced view of the evidence for a theory, which helps make science more reliable. I hope everyone, from professors to MSc students, will have complementary explanation as a tool in their toolbox to engage more deeply with evidence, and to make evidence more engaging for readers.

Example paper

On the open science framework you can find an example paper that applies complementary explanation to a collection of evidence related to organizational theories. That example paper demonstrates the promise of complementary explanation by showing how it helps suggest specific adjustments to some key premises in the theories.

Alliance management teams; unique yet varied

In today’s globalizing and technology-driven world, engaging in strategic alliances and forming alliance teams is becoming increasingly crucial. These teams can be joint venture management teams, buyer-supplier partnership teams, and inter-organizational new product development teams. They differ from intra-organizational teams because they need to balance vested interests across organizations; there is a lack of unified hierarchy and authority in which the team operates; and often team boundaries across organizations are permeable and unstable.  

In our research, published in Human Relations, we systematically reviewed the literature and identified three main characteristics that alliance management teams have in common yet vary on in meaningful ways. First, teams differ in their level of “factionalism”, this is the extent to which interests compete between (groups of) representatives in alliance teams, for instance relating to resources or authority. Second, they differ in their “scope of responsibilities” which can range from clearly defined tasks with relatively few resources to integrate across organizations, to ill-defined tasks with a broad range of resources to integrate. Finally, alliance management teams can have loosely delineated boundaries or clear boundaries, which make the team less or more “groupy”. This we call “team entitativity”, the extent to which the team is one coherent group. Together, these three dimensions suggest a three-dimensional landscape, in which any alliance management team can be positioned.

Practitioner takeaways from our work

In our article, we show how this 3D landscape brings together a range of managerial practices that were previously unconnected. In particular, we illustrate how our framework can be applied to leadership practices:  

1. For instance, in teams that have weak factionalism, a narrow scope of responsibility, and high entitativity, leaders should stimulate a shared team identity to prevent teams from breaking along the factions and form informal connections outside the alliance teams (to favor exchange of knowledge beyond team’s boundaries). When factionalism is high, however, a shared team identity might threaten factional identities and strengthen “us and them” dynamics.
2. Leadership needs to develop as alliance management teams develop within the 3D landscape. As teams transition from narrow to wide scope of responsibility, for instance, shared leadership is likely to become less effective. It magnifies the chance of dysfunctional conflicts in a context of unknown goals, tasks, and roles.

Taken together, alliance management teams are unique, yet they vary widely. Our work shows that it is essential to align the way to manage and lead alliance teams with the teams’ factionalism, scope of responsibility, and entitativity in order for alliance teams to be effective. More broadly, the 3D conceptualization and systematic review, provide a solid foundation for understanding alliance teams, and for further research on this topic.

During the Covid-19 pandemic many people were suddenly reminded how fragile their own health can be. Yet, what has been widely overlooked is how the situation of vulnerable people changed, who are dependent on support from social care organizations, such as homeless people, people with psychiatric and/or addiction issues, sex workers, undocumented, and poor families.

Our research project ‘Vulnerable in Amsterdam’ funded by ZonMW gained insight into the consequences of the corona crisis for various vulnerable groups in the Amsterdam region by investigating how aid was provided and how it could be organized. Through being part of a consortium with four social care organizations we created an inventory of knowledge and experiences.

In our research which was published in the journal Technovation, we found that many social care organizations and their partners faced the challenge to collectively provide care to vulnerable people during the crisis, however, without being allowed to see them in person due to physical-distancing regulations by the government. In consequence, these organizations had to deal with questions such as: How can we continue our work while maintaining physical distance, and what online options do we have that could help realize this? Most social care organizations started adopting digital technologies, often referred to as ‘e-health’. While on a large scale this went well, our research demonstrated that there was also a need to make certain improvements.

Qualitative research methods

We studied the transition towards digital technology by three social care organizations inductively. Within these three organizations we have conducted observations during site visits and interviewed employees from different backgrounds and functional levels, such as: social workers, doctors, managers, and also their clients. We compared the insights that emerged from the three different social care organizations, to generate theoretical and practical implications, which helps us to explain how different factors influence the digital technology adoption process.

Steps to adopt e-health

For organizations that want to adopt e-health or other digital technologies, particularly during crisis situations, we offer some practical recommendations. The first recommendation is that instead of viewing crises as disruptive to the organization, we advise to view crises as an opportunity that could encourage a new way of working, like using e-health. Even if the crisis had a disruptive effect, if innovative solutions are just being used as a quickfix, opportunities for more thorough renewal are forgone.  A sustained use of new technology requires professional identities to be reshaped and to develop a shared vision in the organization that embraces new technology. For example, organizations can promote a shared vision that improves the quality of care by addressing clients’ digital needs, while also encouraging more freedom of choice for clients.

Most social care professionals have strong professional identities as care givers, often influenced by their organizational history based on religion or activism. Each organization struggled with the introduction of e-health, influenced by this professional identity which typically stressed a need for in-person contact with their clients. That is why our second recommendation emphasizes the need for organizations to develop a vision and strategy, which stimulates the adoption of e-health. This vision and strategy should couple technological innovation with employees’ professional identity, like in the slogan “e-health with a heart”. Otherwise, reactions towards the introduction of e-health will have limited or even negative effects.

The third recommendation is to involve champions who can stimulate the innovation process. Champions are people who have affinity with the change and are enthusiastic about it. Involving them to experiment, to educate employees and clients, and to spread enthusiasm, in a way that is in alignment with employees’ professional identity, could bring the organization closer to successfully adopting e-health.

Our research, published in Entrepreneurship Theory and Practice, advocates for a shared conceptualization of rigor, which helps close the gap between scholars and entrepreneurs.

How Scholarly Concepts Fit with the World

Traditionally, entrepreneurship scholarship view concepts as mirroring or corresponding to some entrepreneurial phenomenon in the real world. We can think of this fit as a concept-to-world direction of fit. For example, scholars use the term effectuation to describe the behavior of individual entrepreneurs as they develop goals, create networks and manage investment. In this view, scholarship is rigorous when descriptions correspond to the real world, and less rigorous when concepts have little empirical backing. If a scholar makes a mistake in what happened in the real-world, the scholar can change the account to match what had actually happened. Much of the peer-reviewed publishing process aims to establish this correspondence by ensuring rigor, and thus has been a linchpin in establishing entrepreneurship research as a legitimate social science domain.

Alternatively, we argue that rigor can also be seen through a world-to-concept direction of fit. This view recognizes that entrepreneurs too need to be rigorous. When they make mistakes, unlike the scholar, they cannot simply change their account, they need to change to world to match their intentions. In this direction of fit, scholars are guided by the entrepreneur’s intention, aiming to explain and improve performance in the light of it. Hence, scholars can seek to aide entrepreneurs in improving their performance in order to generate the desired facts.

Discrepancies Between Entrepreneur’s Intention and Outcomes

Because a concept-to-world direction of fit is past-looking, scholars often forget that, for entrepreneurs, their entrepreneurial performance is still unfinished and ongoing. Because concepts are fitted the world, scholars view outcomes as related to performance and judgement only. However, when we adopt a world-to-concept fit, discrepancies between entrepreneurial intention and desired outcomes can be due to (1) a mistake or error in performance (2) an error of judgment in choosing the performance context (3) an error of judgment in acting (4) the entrepreneur changing their mind, and (5) discrepancies can be due to ‘‘bad luck’’. Errors of judgment, importantly, only exist if the entrepreneur had known beforehand what eventually transpires, which, more often than not is not the case.

Furthering Rigor and Relevance

We believe that scholars should adopt a stance of world-to-concept fit, which can assist in improving the rigor and relevance of research. In particular, we suggest future research further both rigor and relevance by (1) paying greater attention to entrepreneurship-related practices (speech acts, performances, reproductions, transformations, and relations), (2) adopting a future-focused research orientation, and (3) strengthening relevance by supporting moments of co-creation between scholars and entrepreneurs.

A world-to-concept fit focuses scholars on the practices being conducted that underlie entrepreneur’s intention and action. Yet, rather than stop there, scholars need to recognize their abilities to create new concepts to shape future practices. In other words, scholarly concepts can also be active interventions that can be picked up by entrepreneurs to help achieve their intentions. To do so, scholars will need to adopt a future-focused stance in research, in which they propose concepts that can be utilized for ongoing or future entrepreneurial performance. A good example of this is the Business Model Canvas, which was developed by Alex Osterwalder from his 2004 PhD thesis titled ‘‘The Business Model Ontology: A Proposition in a Design Science Approach.’’ Osterwalder did not merely develop a theoretical model of business in a past-oriented, correspondence sense, but put forth new concepts (“Canvas”) that opens toward the future so as to become practically useful. Rigor pertains to the extent to which such concepts are meaningful and actionable from a practitioner point of view. Finally, a renewed focus on practices and their diversity suggests that entrepreneurs and scholars might collectively conceptualize and execute research projects.

There are several methodologies already existing that are currently unused in entrepreneurship studies, for example, collaborative ethnography (Fisher & Nading, 2022), ethnotheatre (Saldana, 2016), pop-up laboratory (Staunæs & Kofoed, 2015), serious games (Susi et al., 2007), walking methods (Springgay & Truman, 2017), participatory video-making and diagramming.

Team networks and team innovation

Team innovation, or the generation and effective implementation of new ideas in teams, is becoming increasingly crucial for managers of most organizations – especially those that heavily rely on knowledge work for the products and services they offer. Think of the development of smartphones, electric cars, or the internet. None of these products and services we take for granted today would have been possible without concerted team efforts in their respective organizations that were characterized by innovation. Naturally, team leaders working in industries that depend on team innovation are looking for ways to promote their teams’ creative and innovative potential.

Previous research and anecdotal evidence suggest that a team’s network matters for team innovation. It’s not what you do, it is who you know, right? But does this squarely apply to innovation as well? At the simplest level, team networks can be viewed as either the strength of members’ internal relationships, also called team bonding social capital, or the reach of a team’s external relationships, termed team bridging social capital.

Team conflict as a linchpin connecting team networks to team innovation

The quality of innovative feats in large part depends on the information teams have available. Typically, the more varied information teams can access, the more it promotes their innovative potential. Viewed from a network perspective, having more varied external networks (i.e., team bridging social capital) should be more beneficial for innovation than stronger team-internal bonds (i.e., team bonding social capital). That’s the theory. Research evidence from various studies, however, showed that either 1) both bridging and bonding social capital promote innovation, 2) neither bridging and bonding social capital promote innovation, or even that 3) both bridging and bonding social capital hamper innovation. Thus, the evidence on how team networks affect team innovation was inconsistent and did not lend itself to formulating straightforward practical recommendations.

My work, recently published in Human Relations and in collaboration with Amer Ali Al-Atwi and David de Cremer, suggests that in addition to having varied informational resources available, two things matter when it comes to promoting team innovation through networks.

First, it matters how the information gathered through networks is applied and implemented in teams. Oftentimes, promoting new information, such as best practices from other teams among one’s own team members, is not met with openness but with skepticism that can undermine team innovation efforts in the long run. So, the way team members resolve such initial disagreements has crucial implications for their innovative potential. The literature on team conflict suggests that disagreeing on task content-related matters can lead to more innovative solutions, thus proposing task conflict a functional team conflict process. Apart from task conflict, teams can make disagreements personal (i.e., relationship conflict) or disagree on task coordination-related disputes (i.e., process conflict), both of which slow down team progress and are thus deemed dysfunctional conflict processes. In our work, we looked at whether task conflict proportional to the other two conflict types (i.e., relationship and process conflict), termed proportional task conflict (abbreviated as PTC), can explain how information gathered from networks is applied in teams, thus influencing innovation. The idea is that if teams engage in more task conflict proportional to relationship and process conflict, this should be experienced as a functional team conflict process and promote innovation. Our results showed that although PTC promoted team innovation as expected, both strong internal networks (i.e., team bonding social capital) and varied external networks (i.e., team bridging social capital) undermine PTC, and thus, hamper team innovation.

Team leadership as a regulator of team network effects

The negative effects of team networks on team innovation are not unexpected. There can indeed be problems with information that is concentrated inside teams (i.e., team bonding social capital) or that is brought in from external sources (i.e., team bridging social capital). Highly concentrated team-internal information is most likely very similar and thus redundant and unlikely to benefit innovative efforts. Conversely, information brought in from outside the team is rarely immediately accepted even if it would promote team innovation. The second aim of our work was thus to highlight that team leadership matters for regulating team efforts. We argue that certain leader behaviors should be able to avoid the potentially negative aspects of teamwork. Our findings suggest that team leaders that motivate members to work together and who lead by example can avoid dysfunctional team conflict spirals and their negative impact on team innovation. Additionally, team leaders that intellectually engage with their members ideas and are more people-oriented in their leadership were found to directly promote innovation via fostering PTC.

Practitioner takeaways from our work

My collaborators and I propose the following two takeaways for managers and practitioners wanting to promote team innovation.

1. Our research suggests that team networks themselves are not a catalyst for team innovation. In fact, in the absence of team leadership they can do more harm than good by making dysfunctional team conflict (i.e., relationship and process conflict) more likely. At the same time, we highlight that certain types of team conflict can be good for team innovation, specifically those where functional team conflict (i.e., task conflict) outweighs dysfunctional team conflict.
2. Team leadership is crucial for team innovation. Leaders who are able to regulate the information flow from team-external sources and enable knowledge integration in teams play a key role in ensuring that their teams continue to be innovative.

Taken together, the thing with networks is that it is not only who you know, but also what you do, and how you are led that will determine your team’s innovative potential.