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This semester I had to step in to teaching at Ghent University talking to students in a program with a strong engineering tradition is. Coming from a Nordic IS background, where the focus leans more toward organizations and their complexities, I saw a chance to bridge these perspectives. My lecture on conceptual modeling for Enterprise Architecture students was the perfect moment to do just that.

Conceptual modelling is an approach in multiple courses at Ghent (Project management, process management, databases). Therefore questioning the assumptions baked into use of models, their role as boundary objects in aligning business, IT, and other functions etc. had the potential to trigger reflection with broader impact. Conceptual models are more than diagrams or schemas—they’re tools that bridge gaps, create clarity, and sometimes challenge boundaries. I built this argument using Lyytinen’s “Two Views of Information Modeling” (1987) and Langley et al.’s framework on boundary work (2019). These insights helped us see how models mediate collaboration in a way that’s as dynamic as the organizations using them.

The lecture slides are below—if you’re curious, take a look! I’d love to hear how others approach conceptual modeling in their own work.

Full Citations

• Lyytinen, Kalle. “Two views of information modeling.” Information & Management 12.1 (1987): 9-19.
• Levina, Natalia, and Emmanuelle Vaast. “The emergence of boundary spanning competence in practice: Implications for implementation and use of information systems.” MIS Quarterly (2005): 335-363.
• Langley, Ann, et al. “Boundary work among groups, occupations, and organizations: From cartography to process.” Academy of Management Annals 13.2 (2019): 704-736.

Here are my slides on business models and innovation of business models.

Business models are a fundamental aspect of any organization, shaping how a company operates, creates value, and generates revenue. They can be understood at various levels of abstraction, from the strategic level to the operational level. Business model innovation involves not only creating new business models but also adapting and evolving existing ones to stay competitive. Platforms, such as GoMore, have successfully diversified their business models over time, starting with ridesharing, then introducing peer-to-peer car rental, and later offering business-to-consumer leasing. This diversification has allowed GoMore to address different customer segments and increase the supply of peer providers in the market, ultimately maximizing car usage efficiency and offering a broader range of mobility services.

Innovating business models can be seen as a paradigm shift on a high level and a process change on a lower level. It involves understanding customer needs, creating compelling value propositions, and scaling operations to meet demand. Business leaders need to evaluate whether a new business model innovation is consistent with their existing business model’s current priorities. The process of designing a business model can be systematic, involving initiation, ideation, integration, implementation, and analysis.

Business Models and Business Model Innovation from Michal Hron

This semester I iterated the lecture on digital transformation. I included a broader historical framing, casting digital innovation as one in a historical series of revolution of management models.

If one stays in academia, is it an inevitable professional deformation that one will turn into a ruthless egoist or another version of an asshole? Far from an intellectual exercise, this is quite practical. Is it possible to basically be at least an OK person and an academic? Asking for a friend.

The amount of unpleasant people in academia appears higher than in the general population. Are they there because the environments twists them into… unpleasant individuals (formation)? Or does the environment selectively attracts assholes (i.e. filtering)? As with many dichotomies, the trick is seeing past the binary. Academic jobs provide opportunities to largely determine the environment that shapes us through actions, values, and people.

I have met a share of unpleasant people in universities. Supervisors who refused to supervise. Exploitative management. Liars. But I have also connected with kind mentor figures. What the positive examples from my journey have shared has been they cultivate their environment, often working with students or in an engaged ways with organisations, or being in collegial scholarly networks. A culture of generosity and kindness in a given bubble can override the otherwise ruthless academic climate. Does that fix academia? No, but that is not the goal. The goal is to practice the research craft while having a positive impact on the immediate surrounding.

So, does academia filter for assholes or forms them? As with any environment, it forms people. But what environment we have is largely our own doing. Contrary to appearances, we do not have to compete on every major prestige point.

Open Innovation is a big and abstract concept. In the lecture, I delivered, I tried to make it as tangible as possible through involving a lot of stories and examples. A literature review from January 2023 by Ogink et al in Technovation was useful because they develop specific mechanisms through which open innovation happens.

1. For the mechanism of Governance and politics, I talked about the role of patents as enablers of structured openness. The work by John Howells Inspired this section.
2. For the mechanism of Environmental interaction, I talked about foresight network on the example of Deutsche Telecom described by Rene Rohrbeck in his work.
3. Knowledge skills and capabilities: I talked about how partnering in digital manufacturing is enabled by digital capabilities and data being inspired by Jannis Kallinikos and his perspective.
4. For the mechanism of Learning by doing: I talked about the role of prototypes as a boundary object for opening development to external participants. The case published by Marcel Bogers on prototyping in Danfoss provided a fantastic illustration.

The slides are available on slide share and I am happy to receive feedback.

Lecture: Open Innovation from Michal Hron

When teaching Innovation, lectures about the Stage-Gate process always bugged me. The approach is pretty limited in its application because it works for corporations but is too cumbersome for smaller companies. Stage-gate also has a number of assumptions that are problematic like the idea that we need to generate a lot of ideas and progressively eliminate. What I learned from working on Agile development is that a single incomplete idea may be enough if we continue developing it. In a recent lecture, I managed to reach a way of presenting Stage-Gate to Master students in Innovation Management in a way that is both more theoretically grounded and more practical:

• More theoretically grounded: I embed the topic within a broader context of theory for decision-making under extreme uncertainty (Loch, 2014) which paradoxically allowed a more practical discussion of approaches for settings like SMEs where Stage-Gate models are too cumbersome.
• More Practical: I introduce the notion of project portfolios and especially management of project lineages (Midler, 2013) was a useful extension to counter the criticisms of Stage-Gate. Traditional stage-gate has little to add on learning by-doing or on transferring learning from precious projects. Failed projects may be a foundation for future success!

Stage gate, portfolios, agile systems from Michal Hron

Feel free to get in touch with comments, suggestions, requests for the full file or ideas.

Every text, once written, becomes a message from the past. My time as a PhD student is now becoming a part of the past. What is the message I can send from this time, which can provide value to you, the reader who didn’t skip the opening of this dissertation? You are most likely a friend, colleague, or you are someone who picked up this thesis to help you in your own research. If you are a friend, you have my appreciation and I hope I haven’t forgotten you on the next page.

If you are scanning this thesis in hope of finding help on your PhD journey, I will use this page to recount some mistakes I made. I am doing this because during my PhD, I was swimming in advice that was often conflicting, hard to replicate, outdated, or not compatible my creative style. Yes, PhD is a writing job. Writing is a creative act or art. But I am losing you, sorry. I don’t want to waste my reader’s time. I don’t know how to give good advice, so here are a few things I which I wish I realized sooner:

1 I paid attention to outcomes when I should have focused on developing a solid routine. The discussions I was exposed to tended to be about papers, publications, awards. Generally speaking, the more, the better. The more prestigious, the better. Such way of talking has misled me. Focusing on outcomes was just making me miserable. What lies behind the outcomes is a lot of things which are hard to control, like luck or whether your supervisor decides to be available or not. What can be controlled is the process one engages in and the routines one cultivates. The outcomes follow but focusing on the process and finding enjoyment in the practice of the craft is generally healthier, which took me too long to realize.

2 I focused on collecting data when I should have focused on developing relationships. The language I was exposed to was filled with phrases like “data collection” and “data access”. Thinking in terms of data collection led me to a less productive path than if I focused on development of relationships. Especially in the case of qualitative work with organizations, approaching practitioner experts with the mindset of “I need to secure data access” is not as effective as thinking “I hope I can learn what these experts work on”. While data are what matters in the end, focusing on them is not productive in establishing relationships that eventually lead to data.

3 Focusing multi-disciplinarity is less effective than focusing on specific disciplinary intersections. The idea of multi-disciplinarity is mentioned frequently. I took a while to realize that in practice, there is hardly any such thing multi-disciplinarity. What I was encountering instead were specific intersections of disciplines. When it comes to academic interactions, there is no truly “multi-disciplinary venue”. There are only people, familiar with certain topics and approaches. Being inter-disciplinary usually means introducing one group to an approach taken by another group. Perhaps relatedly, I also realized that sometimes strong disciplinarity is actually the truly renegade path, especially in areas where disciplines bleed together.

4 I didn’t realize how much I know. PhD is partly challenging because the demands, competitive pressure, and constant comparison make people feel inadequate. When I got too embedded in the work and the academic game, I sometimes lost track of my own knowledge and capabilities. Especially now when my PhD time is over and I resumed contact with the outside world, I am often reminded of my level of skills and abilities. If I didn’t forget that I am actually pretty good at this, I would have easier time during the PhD too.

5 I was not idle often enough. For too long, I didn’t set upper limits on work. I worked on too many weekends and even when I did not work, I never really detached from the work. Being idle is a legitimate part of the research process because ideas need to grow once planted. Few hours of writing per working day with enough time to cook and exercise add up over time. On average, people work much less than how much they make themselves appear to work. Nevertheless, I am putting finishing touches in the dissertation, including this sentence, on a Sunday again.

Every text, once written, becomes a message from the past. This text is a message about where I was as an academic writer when my PhD concluded. I hope for chances to grow beyond that. The last three years have been transformative, as a PhD should have been and full of challenges, I am proud of having overcome. As I am closing this chapter, I sincerely thank to those who travelled alongside me. My best effort at a complete list follows on the next page.

IS Academics on Twitter are a bit hard to find. We dont use a shared hashtag like #econTwitter but seeing what everyone talks about is useful. I maintain a list of IS researchers. Please share with colleagues of incoming PhD students!

Follow and send me a message if you want to add someone (including yourself). I manage the list actively but of course I cant catch everyone.

A Twitter List by michalhron

My dissertation explores the organizational and managerial challenges that arise from systematic involvement of digital technologies in innovation. The work in this dissertation primarily contributes to the literature on digital innovation, which identified systematic involvement of digital technologies as an occasion to revise or problematize existing theoretical perspectives on management of technology and innovation.

Digital technologies have been theorized in the Information Systems literature as digital artefacts. Digital artefacts are objects made of algorithms and data. Digital products, profiles on social networks, databases of past transactions are all examples of digital artefacts. Digital artefacts are representative of an unusual type of materiality because they do not occupy physical space, can be duplicated and distributed freely. As a consequence, they can be arranged and recombined to achieve seemingly endless potential of configurations. The process of recombining digital artefacts is digital innovation, which this dissertation studies.

Digital artefacts are also at the centre of born-digital companies, which provide empirical focus for this dissertation. Existing research has often focused on traditional companies in their efforts to learn to effectively exploit possibilities of digital technologies. Such research provides insight into how digital innovation clashes with traditional innovation and offers insights of undeniable relevance. In contrast, the research here joins the less voluminous stream of research on born-digital organizations. With focus on born-digital organizations, we can uncover logics of digital innovation in their distilled form rather than emphasizing how they contrast or clash with organizing logics of industrial or pre-digital innovation.

The dissertation is composed of three papers: a theoretical literature review, a longitudinal case study and, a multiple case study. The first paper argues that consideration of digital artefacts is central to understanding the logic of digital innovation. This argument is developed by a means of a literature review. The theoretical literature review provides assessment of the attention given to digital artefacts in the extant literature and it constructs a research agenda. The second article presents a case study of organizing for innovation in a born-digital company, showcasing how distributable digital artefacts can stifle effectiveness of organizational separation as a vehicle for innovation. The third article investigates, by a multiple case study, how digital artefacts and organizational structures co-evolve as born-digital companies innovate their products. Overall, the dissertation proceeds from a theoretical argument to exploration of an empirical case and development of a more robust theoretical understanding of the case by moving from a single to a multiple case design.

	Paper I	Paper II	Paper III
Short Title	Quest for New theoretical Logics of digital innovation	Innovation Drift	Mirroring and interpreting
Research question	Which properties of digital artefacts do different conceptualisation of digital artefacts uncover?	How do the specific properties of digital artefacts influence organising for innovation?	How do digital artefacts and organisational forms mutually influence each other?
Core concepts	conceptualisation of digital artefacts as resources, knowledge etc.	Organisational integration, separation	Mirroring hypothesis
Method	Literature review	Single Case Study	Multiple Case Study
Data	53 papers	Interviews over 2 years	Interviews from 5  born-digital companies
Digital artefacts	As described in literature	Online housing market place	Five different born-digital businesses
Finding	Different conceptualisations emphasise different properties of digital artefacts. Research agenda proposed	Reuse of digital artefacts can cause drift of innovation from radical to incremental	Organisations mirror the socially constructed understanding of digital artefacts
Co-authors	Single-authored	Nikolaus Obwegeser,  Sune D Muller	Single-authored
Status	Published at HICSS	Published at IOM	Prepared for submission to JSIS

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Empirical studies in the dissertation relate to a practical problem and hold managerial implications. The empirical problem tackled in papers II and III relates to the situation in which a company wants to develop a radical digital innovation. Is it better to develop the innovation in a new, separated group or is it better to continue development in an existing organization? Paper II highlights that focusing only on organizing can be misleading because digital artefacts can freely travel across organizational boundaries and cause drift from radical to incremental. Paper III presents a multiple case study that revisits this problem and finds out that the decision to organizationally separate an innovation effort appears to be more suitable when a new group of users with a distinct need is being targeted. Organizational separation goes hand in hand with development of separate digital artefacts because organizations and products tend to mirror one another. Overall, the dissertation first emphasizes that, when selecting organizational arrangements for innovation, handling of digital artefacts should be considered alongside organizational structures. Second, the dissertation implies that a choice to organizationally separate may be more appropriate when a new set of users with a distinct need is being targeted by the innovation.

Overall, this dissertation uncovered some organizational and associated managerial challenges that are especially salient in born-digital organizations and therefore emblematic of digital innovation. Organizationally, this research highlights the effects that digital artefacts can exert on organizational structures. They can cause drift from organizational separation to integration. They can be re-interpreted and thus interact with identities of especially born digital businesses. Such organizational phenomena are associated with new managerial challenges. First of all, the research calls for more conscious management of digital artefacts for organizing for innovation. Architectural decision regarding to reuse or new development arise as an area of concern that accompanies decisions about organizational structures. A second managerial challenge is connected to unstable identity of a digital artefacts. The identity of digital artefacts is tied to its role in consumption or to the role they play for consumers. The same digital artefacts can provide a core for a product that solves a very different need. Therefore, effective management of digital innovation requires paying attention to the changing needs that the ever- changing digital products are directed to address.

Situation: Phenomenon-driven discourse

One of the promises of studying open source communities has been that open source communities give us a glimpse into workplaces of the future. Developers use GitHub to create innovative products while being disepersed all over the world and without traditional centralized decision making structure. If we understand what works for open source development, we should be able to foresee effective ways of organizing for the remote workplace of the (post-)covid era. Have we used insights from open source to inform how we think about remote workplaces? I dont think we have. Asking why leads to valuable reflections on how research gets done.

The topics that we publish on in Information Systems journals usually closely track what IT professionals worry about. Today, it is Digital transformation programs, because companies talk about them and try to execute them. We talk about Artificial intelligence, as a class of technology that is finding its ways into organizations nowadays. We apply theories to make sense of the technological novelties of the day. A discourse organized around technologies gives us relevance. Courses about digital transformation or AI are in demand. But, do we develop understanding that goes above the individual technology-centered debates in a useful way? I have doubts.

Maybe IS Journals could have a new type of lit review that would make accessible old lit for current buzzwords. Like:

– What can the lit on Expert Systems teach about AI in orgs?
– What can the lit on IT driven org.change teach us about Digital Trans.?

— 😵‍💫Michal Hron,PhD ??? (@michalhron) April 26, 2021

It is not unusual that social sciences are producing insights that are only “true” for the given moment in history. If we discuss technologies, this is maybe more true than if we discuss organizations or political regimes. I am also not so naive as to expect a commulative discourse. Of course topics leave the discussion and new topics pop in. But some practices can help to avoid that we reinvent the wheel with each shiny new gadget. Moreover, some insights should carry across instances. Like the example of open source informing distributed leadership and the “future of work” research. 

I propose to split the diagnosis of the situation in two parts. There is a problem of narrative and a problem of the nature of the intellectual products available. First, our narratives are dominantly technology-driven. We talk about health platforms, or disinformation on Twitter or digital transformation. We are more likely to review a literature on e.g. blockchain than to review a discourse about distributed organizing. The tech-centered narrative then gets in a way of theorizing on a level of generality independent of technology. A lot of our intellectual products are then bifurcated between facts and models specific to technologies on one side and grand theories on the other side. What would be in the middle of those two would be middle range theories. 

Problem: Learning across phenomena 

There is no lack in technology-independent theory. Such theory is however often imported or rather grand. Let’s set aside the problem with importing theory for another day. Suffice to say, imported theories rarely form basis of our research identities and we apply them to problems rather than meaningfully contributing to them. When it comes to grand theories, I mean that in the technical sense of grand theories that provide an universal position. Publications proposing to see technologies as patterns or processes are such general reflections. The usage of affordances provided an grand theory. The whole sociomaterial debate has been as grand as they come. They give us ontology or meta-language to conceptualise what happens but they rarely yield specific insight. 

Theories of the middle-range, in contrast to grand theories, are theories that focus on limited conceptual ranges. The reference to the term goes to Merton and it is probably better to just let the sociologist do the talking: 

Our major task today is to develop special theories applicable to limited conceptual ranges – theories, for example, of deviant behavior, the unanticipated consequences of purposive action, social perception, reference groups, social control, the interdependence of social institutions – rather than to seek the total conceptual structure that is adequate to derive these and other theories of the middle range. 

Robert K. Merton, Social Theory and Social Structure (Glencoe, IL: Free Press, 1968, ISBN 0029211301). 

The examples Merton gives indicate his focus on decontextualised social dynamics. He calls for calls for theories of deviant behaviour, not of excessive drinking. He calls for unanticipated consequences of purposive action and not to theorize “consequences of birth control on age composition and size of siblings with profound consequences of psychological and social character” (this is example given on page 902 in Merton’s actual study of unanticipated consequences.). Middle range theories, to Merton, provide an account of a particular dynamic in the social world, which can be observed across multiple instances. How would this work for technology? My attempt at formulating the problem is this: Can we propose and productively develop middle-range theories of technology use, without tightly linking them to overly particular classes of technology?

Many mid-range theories are available. Technology acceptance models are a mid-range theories. Goal contagion is a mid-range theory as an example and the list of IS theories is full of them. But do we use them as more than sense-making devices for studies? Do we contribute to them meaningfully? What would it look like if we did?

Solutions: Detachment and attachment 

This is the moment in the essay where I did my work at outlining the problem. The more difficult part is left. That is to propose a path forward. I find it useful to think about two processes: detachment (from data to theory). And attachment (applying theories to data). You may be thinking that this is just induction and deduction. Kind of. I have reasons for not using those terms. The main reason is that they imply quite formal and abstract processes of formal logic. In contrast with formal logic and epistemology, I am more concerned with how research is done. A fancy words would be that I want to focus on communicative practices. 

Achieving detachment

How do we develop theory? The corresponding logical operation is induction but this is not an epistemological problem but a problem of social construction. The challenge is to frame the learning from the study as more general than fitting to a particular technology. For example, so not write studies about blockchain but inter-organisational coordination. 

A second strategy would be review articles that aggregate learning for a specific socio-technical dynamic across different technology types like the examples in the tweet above. What can we learn from decision support systems that applies to artificial intelligence? Review articles like this would allow to detach lessons from particular technologies that were discussed at particular time and allow for transfer by application or comparison. 

Achieving attachment

Assuming a mid-range theory exists, how to we use it? The corresponding logical operation is deduction but the practical problem doesn’t lie in formal logic. The problem is to draw on middle range theories or previous instances of technological phenomena that could well inform whatever topic we deal with at the moment. We can frame articles as cases of specific socio-technical dynamics or we can be more creative in how we approach front-ends of our papers. Imagine reading a paper about working from home that takes us through papers on distributed development, to continue with that example as a context we can learn from. The idea is to attach a current phenomenon to a more general (but not grand) theory or draw thoughtful analogies. 

The processes of attachment and detachment merge when we realise that a good empirical paper should probably be framed with a mid range theory but also advance it. If it doesn’t advance a theory, it only applies it. Its novelty then likely lies in the area of application, which takes us back to the problem of phenomenon-centered discourse.

Technological middle range as tool, output, and organizing device 

We write about technologies, which makes our research timely. However, do we learn across instances of technologies in sufficient degree? I believe that re-focusing on middle-range theorizing is a way to help us be more commutative and to transfer insights from one class of technology to the next. 

To do so, it would help if we stopped using middle-range theories as a mere sense-making device that helps us organise empirical material. That is a legitimate pragmatic purpose. Besides that, we should theorize on the middle-range in a way that considers types of socio-technical dynamics. But most of all, if we start organizing our research around types of specific socio-technical dynamics, we could transfer our findings across time and place. 

That’s it. This was fun to write. 

Note: My aim with this text is to put in words something I have felt for a while.
These are ideas in progress (aka a blog) with the aim to share and maybe more stimulation to move this thinking forward. This is not a paper. Don’t be a reviewer but responses are welcome.