In the past few weeks, there have just been tons of interesting articles published in technology management. Instead of giving a summary for each article, I’ll just dump all these fascinating articles I’ve seen. I might revise this blog post in the future and write extended comments on each paper in the future.

Science commercialization

Our paper entitled Systematizing serendipity for big science infrastructures: The ATTRACT project just got published in Technovation. In this paper, we explore how scientists from large research organizations like CERN, EMBL and ESO find alternate applications from their research.

What drives university-industry collaboration? Research excellence or firm collaboration strategy? – not universities’ quality of research but the firms’ inclination to participate

Innovation-driven entrepreneurship – “the markets, technologies and business models employed by these entrepreneurs are such that the nature and parameterization of the probability distribution of outcomes is entirely unknown.”

Dynamics of Disruption in Science and Technology

Careers

Long-term effects from early exposure to research: Evidence from the NIH “Yellow Berets” – exposure to a two-year training program led to sustained impacts on the careers of the participants.

Do Looks Matter for an Academic Career in Economics? Unsurprisingly, yes.

Management

Contextualizing Management Research: An Open Systems Perspective

Navigating the New Normal: Which firms have adapted better to the COVID-19 disruption? – firms that have good internal R&D / good management practices and younger agile firms are more likely to adapt to COVID challenges.

Telling “white lies” within the entrepreneurial firm: How rationalized knowledge hiding between founder CEO and founder CTO influences new product development – Certain types of knowledge hiding by CTOs from their CEOs can help accelerate these firms’ innovation of new products.

Start with “Why”, but Only if You Have to: The Strategic Framing of Novel Ideas across Different Audiences

Digital Transformation

We’re engaged! Following the path to a successful information management capability – information management capability refers to a “firm’s ability to leverage IT, data, and people’s information usage behaviors to provide accurate and valuable information for the firm to improve its business performance”.

Re-examining path dependence in the digital age: The evolution of connected car business models

Applying digital technologies in technology roadmapping to overcome individual biased assessments – a very visual article on how digital technologies can augment roadmapping.

Data sharing

Data sharing practices and data availability upon request differ across scientific disciplines. Social sciences tend to decline requests for data sharing the most. Top reasons for declining across all disciplines include: no time to search data, data lost, data protected by agreements, privacy and people moving.

Governance of data sharing: A law & economics proposal

It’s my second Academy of Management conference. My first time attending in 2019 in Boston, I went to a lot of workshops but I didn’t really absorb a lot of the things. As a PhD student back then specializing in innovations in drug discovery, I didn’t really have a good mental map of what management was and where my place in it was. Now that I’m a postdoc, with a stronger idea of my academic identity, I have a better grasp of what’s happening. And, with this renewed lens, I have been learning a lot, especially with respect to career development and intellectual growth. I summarize here the lessons I’ve learned in the past few days:

Are you the bug or the windshield? Everyone wants to be unique. I, for one, want to think that my pharmaceutical background gives me a unique perspective on things. However, if you’re entering the field of, let’s say, entrepreneurship, scholars in that field wouldn’t really care about your unique background. All the field cares about is your contribution to their theory. If you are producing papers that are too different or that do not engage with prior literature, you just won’t have any success (ie. optimal distinctiveness, institutional theory). Don’t be the bug, the windshield will always win.

Research productivity = Project Count * Resilience * Success rate. The number of papers you have in top journals can be calculated using three factors: the number of projects you are working on, the number of times you are willing to resubmit a paper before giving up and your actual success rate. One trajectory then is to work on a lot of projects at a time (like 15 in 5 years), shop them to 4 journals, and hope to have a success rate of 5% to get 3 publications. Otherwise, you can focus on quality, just working on 5 projects, submitting them to 2 journals with a 25% success rate to get 3 publications.

Idea entrepreneurship – Academics are entrepreneurs, just of a different product. Our products are ideas. Similar to successful entrepreneurs, we have to create differentiated products that provide some value. Accordingly, as an academic, you should think about whether the papers you produce are catering to the correct audience and providing them some unique benefit. Otherwise, who will buy your product?

Read and re-write the top papers in top journals – One way to get better at writing papers is to literally just copy the top papers’ introductions word for word until you have internalized them. This exercise has two benefits: First, highly cited papers are generally written in a very compelling manner. Understanding these papers’ style and flow would inform your writing as well. Second, to write good papers, you have to be aware of all the interesting theories in your field. Writing these papers down can help in memorizing these theories so that you are able to quickly connect the ideas when you need to.

Always contribute to theory – in management academia, the focus is always your contribution to theory. Papers get rejected if they do not contribute to theory. It’s all about theory, theory and theory. This obsession is something that took me a long time to really internalize. Coming from the natural sciences, I tend to focus on the empirical context that I would forget to step back and look at how the insights there can be generalized to theory. For phenomenon-oriented research like those in technology management, the best way to contribute to theory is to treat your empirical context as a case that can inform the literature.

This is a different post from my usual one reviewing the academic literature. It’s the academy of management conference this week and it made me reflect a bit about my research identity and intellectual journey so far. I’ll be joining the junior faculty consortium for TIM and the homework made me think about things like my research positioning, recommendation letter writers, community engagement and impact.

My PhD training was unconventional in that my dissertation centered around an empirical context (the field of fragment-based drug discovery) instead of a social science theoretical lens. I studied various aspects of this niche field in the pharmaceutical sciences from different perspectives – technology transfer, research collaborations, career development, technology adoption and venturing. My research was mainly done with the Science / Innovation department instead of the traditional Business and Economics department. The contacts I gained were mainly from the natural sciences – from academics to practitioners in the pharma industry. Most of my work had been published in practitioner journals in drug discovery. Not knowing any better, I was thinking that if I branded myself as the “business academic with the strong scientific/pharma background” that would be my unique positioning in the job market.

So far, it has worked out. I got my amazing postdoc position at a renowned business school due to my unique background. However, in recent days, with all these talks about the job market in management academia, I’ve been reassessing my research positioning. Subscribed to academic newsletters, I see all these tenure track position ads, making me reflect on whether my profile would be competitive.

For instance, I just learned recently that business schools had their own hierarchy of journals, which tended to discount technology management disciplines. Related to this, business schools do not really hire professors for technology management, you are hired as a researcher in strategy, entrepreneurship, information systems, OB, HR or some other discipline. Thus, I need to start building my name around a particular phenomenon. Moreover, the academic community I’ve built so far has revolved around the pharmaceutical sciences, meaning, that I have to catchup making collaborators in other top business schools.

There are always things that I could have done better in the past. But, finding what those things are generally could only be identified with the benefit of hindsight. Thinking about it, I won’t even be in my current position now if not for my unique trajectory. Moving forward though and knowing better, I have to find the social science topic that I would devote my future research on.

Just to close, I just wanted to share this highlight on ESADE postdocs. I talked about why I chose to do my postdoc in Barcelona and what topics I have been working on. I’m still finding my way in academia but I’m just glad that I am working on projects that I would have never imagined I would be part of before.

“Baby, you can drive my car”: Psychological antecedents that drive consumers’ adoption of AI-powered autonomous vehicles – straightforward study on psychological factors related to potential adoption of autonomous vehicles. They looked at the following factors: effort expectancy, social recognition, hedonism, technology security and privacy concerns.

How Catastrophic Innovation Failure Affects Organizational and Industry Legitimacy: The 2014 Virgin Galactic Test Flight Crash – rich case study relating failure to firm and industry legitimacy. The figure 1 in their article shows an intricate process model of how failures are interpreted to challenge or uphold firm legitimacy.

The rise of ‘ARPA-everything’ and what it means for science – DARPA has always been discussed in the history of innovation as they have been responsible for many advances such as GPS, weather satellites and computing. It functions differently as mentioned in the article:

Its roughly 100 programme managers, borrowed for stints of 3–5 years from academia or industry, have broad latitude in what they fund, and actively engage with their teams, enforcing aggressive deadlines and monitoring progress along the way. By comparison, projects funded by agencies such as the US National Institutes of Health (NIH) typically see little engagement between programme managers and the researchers they fund, beyond annual progress reports

Funding Risky Research – explores the challenges that various actors face in getting risky research funded.

• Research agencies – lack of portfolio approach, interdisciplinary bias, review protocols concealing uncertainty, emphasis on reviewers’ agreement
• Panelists – “insurance agent” view, bibliometric screening, risk-biased reviewers
• PIs – risk and loss aversion,

Artificial intelligence in information systems research: A systematic literature review and research agenda – a systematic review of AI research in IS. They propose the following problems that can be addressed for future studies: lack of consensus around the definition of AI, overemphasis on the technical (instead of social) impact of AI, unhinged use of machine learning as a methodology and more in-depth studies on the impacts of technologies such as robotics, natural language processing and machine vision.

Innovating the product innovation process to enable co-creation – Co-creation is a term I just learned recently and it has been appearing everywhere ever since. In this article, it explores how managers can help transition the product development process towards co-creation. They outline three main phases of unfreezing the organization towards change, co-creation activities with customers and finally, institutionalizing change.

Mapping the “Valley of Death”: Managing Selection and Technology Advancement in NASA’s Small Business Innovation Research Program – With me being involved in a project that aims to commercialize deep tech, NASA’s SIBR program is definitely an inspiration. In this study, the researchers show that the program tends to use varying selection criteria per funding round to manage risk across the portfolio.

Crackpots in science – There’s a fine line between genius and insanity. The term crackpot describes people who believe that they have discovered a new revolutionary theory of reality, without much engagement with academia. I don’t know anything about physics but what’s interesting is how a lot of recent work on a grand theory of everything has been carried out in the public such as Wolfram’s physics project and Weinstein’s Geometric Unity. I think such projects are important though as they can be source of new ideas.

The Economist’s What ifs? Currently, I’m teaching a class on the business of life science. One of our modules is about the future trends in the life sciences and this week’s edition of the Economist explored various future scenarios:

• If biohackers injected themselves with mRNA
• If America tackled its opioid crisis
• If everyone’s nutrition was personalised
• If smartphones became personal health assistants
• If dementia was preventable and treatable
• If an AI won the Nobel prize for medicine
• If germ theory had caught on sooner

How do startups manage external resources in innovation ecosystems? A resource perspective of startups’ lifecycle – Explores how startups manage their ecosystem’s resources at various stages of their lifecycle. They map the different types of resources (innovation, financial, social, human, physical, organizational) across various ecosystem actors (customers, government, university, consultants/mentors, complementors, incubators, business
association and funding Agencies).

Ecosystem management: Past achievements and future promises – explores the management of innovation ecosystems. Reminds me of the recent article on Technovation too defining innovation ecosystems. In the study, they identify various components of an ecosystem: value creation, systematic innovation, actors, interdependence, structure, dynamic, collaboration, competition, hierarchically independence, complementarity, complements, activities, value capture, configuration, flows and identity. They also focus on three perspectives: process view, configurational view and competitive view.

Configurations for corporate venture innovation: Investigating the role of the dominant coalition – fsQCA studies are always fascinating to me. In this study, they explore the different configurations of corporate venture innovation. They identify different ways that the parent firm can relate to the venture subunit, which they name reign, stimulation, sponsorship, and orchestration.

The obesity wars and the education of a researcher: A personal account – I knew that scientific debates can get heated but this story is just on a different level. It’s just one side of the story though so it would be interesting to hear the other side.

Who do we invent for? Patents by women focus more on women’s health, but few women get to invent – Fascinating study in Science from business school academics. The researchers find that all-female inventor teams tend to address problems that women face. This study implies that there is a need for better representation in the sciences across groups; otherwise, major issues faced by marginalized groups may not be addressed adequately.

Venture Idea Assessment (VIA): Development of a needed concept, measure, and research agenda – develops a scale to assess venture ideas, independent of any agents tasked with pushing it forward. I thought the paper would be about various dimensions that one should consider in evaluating an idea. But, the final scale, in the end, was more “academic” than practical. It’s a good thought experiment though to imagine a venture idea devoid of any entrepreneur.

Forecasting AI progress: A research agenda – as someone who doesn’t know much about forecasting, I like how they list all the different methods under three main categories:

• Statistical modeling using indicators or metrics (e.g. extrapolation, simulation, benchmarks, bibliometrics)
• Judgmental forecasting techniques (e.g. delphi, prediction markets, blue team / red team)
• Hybrid methods

Decision making under deep uncertainties: A review of the applicability of methods in practice – didn’t know about DMDU until this paper. It’s a good introduction to the various techniques such as:

• Robust Decision Making (RDM)
• Dynamic Adaptive Planning (DAP)
• Dynamic Adaptive Policy Pathways (DAPP)
• Info-Gap Theory (IG)
• Engineering Options Analysis (EOA)

I am presenting our paper on the Science Mesh this week. FAIR data through a federated cloud infrastructure: Exploring the Science Mesh – it is a research-in-progress on the potential of FAIR data in unlocking new collaborative workflows.

Entrepreneurial space and the freedom for entrepreneurship: Institutional settings, policy, and action in the space industry – a study of the space industry where the researchers introduce the concept of entrepreneurial space. Great play on words. They define entrepreneurial space as the room for entrepreneurial change, which is often limited by policy and institutions.

Never the twain shall meet? Knowledge strategies for digitalization in healthcare – explores digitalization in healthcare through the lens of knowledge strategy. The researchers followed a hospital for almost a decade and then explored how they digitalized, exploring the following components:

• Vision – top management’s understanding of the role of knowledge in the organization
• Knowledge strategy objectives – goals for the organization and the role of knowledge management towards these goals
• Knowledge management tools – methods to enable the creation, application, leveraging and sharing of knowledge
• Implementation support mechanisms – organizational aspects including culture, structure, HR practices

Ecosystem policy roadmapping – combines innovation ecosystems with technology roadmapping. It’s a paper filled with cool illustrations of different frameworks to guide roadmapping. In the end, I really think the value of management research is providing frameworks for thinking about things and this is a great addition to that toolbox.

From ‘publish or perish’ to societal impact: Organisational repurposing towards responsible innovation through creating a medical platform – explores how an academic research project transformed itself to create larger societal impacts. I find it amazing how management academics can see any phenomenon in new light and make everything sound interesting. Definitely will be an inspiration for future studies that I do.

Digital transformation: What we have learned (thus far) and what is next – important review of digital transformation. They identify the following areas needing further research: measurement of success of DT initiatives; skills required for DT; organization of DT and societal impact. They also identified emerging research avenues including: the role of technology, the interaction between physical and digital environment, the interaction between technologies and organizing at a global scale.

The role of skill versus luck in new venture survival – instead of seeing entrepreneurs as gamblers, it’s better to see them as sailors. They cannot change the weather but they can adjust how their sails are positioned, motivate their crew and redirect their ships if needed.

Rapid Validity Testing at the Front-End of Innovation – Researchers explore RVT which aggregates lean innovation, prototyping, design thinking and pretotyping into one framework. It’s the first time I’ve heard of pretotyping which is the stripped-down version of a product to enable rapid testing. RVT’s components include:

• Problem framing
• Prototyping as test
• Prototyping as communication
• User integration
• Product iteration
• Commercial learning
• Business model iteration

Learning on knowledge graph dynamics provides an early warning of impactful research – A paper that got a lot of attention from reddit. The paper aims to predict which papers would be breakthroughs, by aggregating 29 metrics including from author and journal data, paper citations, and network characteristics. Using the method retrospectively, they were able to identify 19/20 seminal biotechnologies from 1980 to 2014. I need to spend more time reading the paper but the first thing that popped into my mind is this joke about economists predicting nine of the last five recessions.

Prestigious European grants might be biased, study suggests – In the study cited in the Nature article, the authors find that “applicants who shared both a home and a host organization with one panellist or more received a grant 40% more often than average.” Not really surprising. Scientists and grant funders are humans in the end.

Waiving IP – I’m teaching a class on life science patents and it’s impossible to talk about patents without discussing the various calls to waive patents for COVID vaccines. I’m also of the opinion that to some extent, patents can be beneficial to society.

Making the hard problem of consciousness easier – the idea of adversarial collaborations is really interesting. It’s when two groups with opposing ideas collaborate to come up with the experiment to test which theory is correct. In this case, it’s two groups having different theories of consciousness. I wouldn’t claim to understand global neuronal workspace theory and integrated information theory though.