Fatal attraction: A systematic review and research agenda of the dark triad in entrepreneurship – The dark triad has been getting a lot of attention in the recent years. It refers to these three related traits: narcissism, Machiavellianism, and psychopathy. In this review in the field of entrepreneurship, the researchers find that people who score high on the dark triad are likely to be drawn to entrepreneurship. This is unsurprising since these individuals can thrive in highly competitive and ambiguous environments where questionable tactics are allowed or even rewarded. Despite having high entrepreneurial intention, however, those who score highly in psycopathy and Machiavellianism do not seem to have ventures that perform as well. While they can get a way in one-off transctions, business is built on trust and thus, disincentivizes destructive behaviors over time.

Adaptation or Persistence? Emergence and Revision of Organization Designs in New Ventures – quite an extensive study on how organization designs (task division, task allocation, reward distribution, information exchange and exception management) evolve over time. Tracking eight ventures, they find that the founders tend to entrench their logics of organizing despite feedback indicating that they should not. What they tend to update frequently however are the design solutions, though these changes are incremental.

The impact of managerial job security on corporate entrepreneurship: Evidence from corporate venture capital programs – managers won’t risk doing something so innovative if it would cost them their jobs. As the study finds: “firms are more likely to launch a CVC program after their state enacts antitakeover protection.” Although it may seem that guaranteeing job security may make people more complacent, this study finds that it would actually make people experiment more.

Attentional Engagement as Practice: A Study of the Attentional Infrastructure of Healthcare Chief Executive Officers – I just learned about the attention-based view of the firm a week ago, and now, I’ve been seeing more about it in my casual reading of the literature. Quite fitting to have the Baader-Meinhof Phenomenon when we’re talking about attention. This ethnographic research is quite fascinating, they shadowed seven CEOs from NHS organizations and explored the CEOs continuous struggle to prioritize and manage various attentional demands. I feel like it somehow mirrors the Eisenhower matrix of classifying tasks to urgent/not urgent and important/not important.

A New Measurement Conception for the ‘Doing-Using-Interacting’ Mode of Innovation – proposes alternate measures to innovation. Previously, when innovation is measured, it typically aims to capture formal innovation processes in R&D. However, innovation goes beyond that and also occurs in informal processes of doing, using and interacting. The researchers proposes measuring knowledge flows and facilitators.

Why Are Firms with Lower Performance More Volatile and Unpredictable? A Vulnerability Explanation of the Bowman Paradox – first time I’ve heard of the Bowman paradox. It was the finding from empirical data that firms with lower accounting performance also took higher risks. This was paradoxical in that it went against the prevailing assumption that individuals would only take risks if it might correspond to higher rewards.

The Decline of Computers as a General Purpose Technology – introduces the concept of fragmenting cycle where a general purpose technology evolves into loosely-related siloes towards divergent applications. The problem is that improvements might only occur in popular application areas, leaving behind other areas. This cycle occurs when technology advancement slows down, which then causes slower adoption from users, which then makes improving the technology much more expensive.

Patent Quality: Towards a Systematic Framework for Analysis and Measurement – researchers compared different measures of patent quality and find disagreements among them. Thus, the measurement of the quality of a patent should always be tied to context.

What can Strategy Learn from the Business Model Approach? and A Business Model View of Strategy – Two papers that respond to one another about the whether the concept of business models offer anything new to strategy. The response was that business model literature focuses on these questions:

What activities should be connected? How can we develop interdependencies among activities that cannot be imitated? How can we develop superior interdependencies, especially when resources and capabilities are widely available and not differentiated?

Current trends in, and future potential of, crowdfunding to finance R&D of treatments for neglected tropical diseases – Couldn’t access the article but found it fascinating. Reminds me of the meme on how GoFundMe has become the main healthcare provider in the US. Memes aside though, with the number of societal challenges facing society, we need to explore all possible funding mechanisms to develop technologies to address them. Is crowdfunding the best way to solve neglected tropical diseases? Probably not, but they are better when you have no other alternative.

Strategic and cultural contexts of real options reasoning in innovation portfolios – Options thinking is something I’ve heard a lot recently. From what I understand, it’s about maximizing a company’s flexibility in making decisions, trying to delay committing into something as much as possible. This article is a good introduction to what it is about. They explain that it includes three main aspects:

• Approval of project budgets per phase instead of the entire project
• Setting of project goals is based on demonstrating feasibility to inform whether opportunity is worth committing
• New and ongoing projects should compete for resources

Trials and Terminations: Learning from Competitors’ R&D Failures – firms can integrate outside information to avoid making the same mistakes that other firms are doing. As the study finds: “competitor failure news from within the same market and same technology area [leads to ] more than doubling their propensity to terminate drug development projects in the wake of this type of information”

Avi Loeb podcast interview on Skeptic Michael Schermer’s show – Avi Loeb recently published a book on Oumuamua, hypothesizing that it’s evidence for alien life. Therefore, he argues that more resources should be allocated to fund this type of research. This interview is witnessing science as a process live. Michael and Avi are debating his claims in the context of the Sagan standard – extraordinary claims require extraordinary evidence.

Economic inequality – Is entrepreneurship the cause or the solution? A review and research agenda for emerging economies – Entrepreneurship has been pushed by many policymakers as the solution to societal challenges. However, it is important to also stop and reflect whether entrepreneurship can also cause new problems previously unanticipated. This review finds for instance that:

“Within emerging markets, entrepreneurship within the formal sector tends to benefit those who are already advantaged, while exclusionary institutions prevent those in the informal sector from participation.”

Why Do Wealthy Parents Have Wealthy Children? – Typical explanation is good genes, better parenting and safety nets. This study however controls for genes by studying children born in Korea but adopted by Norwegian parents. I cannot access the article itself but the abstract is already informative: “We show that family background matters significantly for children’s accumulation of wealth and investor behavior as adults, even when removing the genetic connection between children and the parents raising them.”

A wealth of discovery built on the Human Genome Project — by the numbers – It’s the 20th anniversary of the human genome project. Researchers are then revisiting its impacts on science and medicine. This article notes that instead of researchers exploring widely the genome, attention was given only on a few promising ones (probably responding to incentives). As always, the visuals produced Barabasi’s group are incredible to look at.

Global citation inequality is on the rise – Following the Matthew principle. the rich becomes richer, also in the sciences. The study finds that the most cited scientists (top 1%) have grown their share of citations from 14% to 21% from 2000 to 2015. Not really surprising considering successful researchers are able to attract better collaborators, brighter graduate students, more funding and goodwill from publishers. The only problem though is when these inequalities hinder more novel research areas from being explored.

Reinventing Innovation Management: The Impact of Self-Innovating Artificial Intelligence – the author introduces the concept of self innovating AI, which refers to the use of AI to create or improve new products from continuously assembling and analyzing data from multiple sources. With data attracting more data, this is once again a source for future inequality across firms. Those who are already leveraging data now would be better prepared for this future.

Deep Tech: The Great Wave of Innovation – just a great primer on deep tech by Hello Tomorrow and BCG. With deep tech going to transform the future, businesses and governments needs to invest in these technologies to avoid getting left behind.

A review of data analytics in technological forecasting – comprehensive review on how data analytics has been used for technological forecasting. Data has been used to inform the following:

• Technology trend analysis – extract clusters and topics
• Emerging trend analysis – identify promising patents
• Product diffusion – examine progression in life cycle
• Roadmapping – uncover relationships between markets, products and tehcnologies
• Opportunity analysis – recommend new business ideas
• Scenario analysis – generate different concepts
• Technology risk – evaluate returns and risks

Robots, AI, and Related Technologies: A Mapping of the New Knowledge Base – a patent study of AI and robotics. The researchers find that AI and robotics’ knowledge base builds on previous paradigm instead of being a technological revolution altogether. Interesting too is that the two are very much coupled that the authors argue that the two should just be considered as one general-purpose technology.

Universities and start-up creation by Ph.D. graduates: the role of scientific and social capital of academic laboratories – 6.7% of Italian PhD graduates engage in startup activities. Their involvement with entrepreneurship is related to whether they had business experience during their PhD and whether their lab had some interest with business. Following one of the articles in the previous week showing that those who worked in small companies tend to have lower human capital, I was already thinking that those who become entrepreneurs were going to be those who cannot find other options. However, it’s great that the researchers in the study controlled for unemployment rate in the region to account for this.

What is the Eisenhardt Method, really? – in any bibliometric analysis of the management literature, one paper will show up whatever topic or domain you are studying, the eisendhardt paper. Useful reflection by the author, especially the different ways to choose cases – common antecedents, matched pair, racing, polar types .

The Digital Transformation of Search and Recombination in the Innovation Function: Tensions and an Integrative Framework – management researchers love 2×2 matrices. In this case, the researchers propose a framework relating digital transformation with search and recombination. The two axes were (1) whether digitalization replaces/complements existing competencies and (2) whether digitalization reinforces/overturns existing knowledge structures.

Fifty years of information management research: A conceptual structure analysis using structural topic modeling – A bibliometric analysis of the information management field. Their framework grouping the various topics under different levels (information retrieval, information systems, information context and information environment) is useful in navigating the myriad of topics under this broad field.

Cancer research needs a better map – introduces an ambitious project called the Cancer dependency map that would cost 30-50 USD over a decade to complete. On a meta-level, I just find it fascinating how scientists also have to be good lobbyists in order to mobilize resources to fund their projects.

Technology Forgiveness: Why emerging technologies differ in their resilience to institutional instability – the researchers introduce a new term – technology forgiveness, which refers to “resilience of the adoption process for a new technology in the face of institutional instability.” They propose that it is dependent on technology (investment, uncertainty, range of applications) and industry (risk aversion, entry barriers, geographical concentration) – related factors.

Data sovereignty: A review – It’s a term that has been thrown around a lot. This review explores the various perspectives by which this term has been used in research.

Knowledge diversity and team creativity: How hobbyists beat professional designers in creating novel board games – with families stuck home, there has been a surge in interest in board games. In this study, the researchers compare board games’ novelty and usefulness on whether they were developed by hobbyists or professional designers.

Do Startup Employees Earn More in the Long Run? – I would have thought that startup employees would earn more, but this study in the Danish context found that startup employees ended up with less 17% less salary over the next 10 years. They attribute these to the following factors: selection effects of people who go to startups, the high failure rates causing unemployment periods and inability to return to larger employers.

Ten computer codes that transformed science – fascinating article on how computing has transformed the scientific process.

The Sequence Effect in Panel Decisions: Evidence from the Evaluation of Research and Development Projects – the book “When” summarizes how timing affects how people evaluate things like who would get a scholarship or who will win a competition. If you are evaluating 10 things and you already said yes three times in a row, you would probably tend to say no to the next one just to “balance” things out. Even experts are not immune to the sequence effect as shown by this study.

Artificial intelligence and management: The automation–augmentation paradox – When technologists discuss AI, they talk about how many jobs would be replaced due to automation. When economists discuss AI, they typically focus on how such transformational technologies are not new and that as history suggests, it would probably just augment the performance of workers. This article talks about how these distinction between automation and augmentation is false as these two are interrelated.

An empirical meta-analysis of the life sciences linked open data on the web – talks about the Life Sciences Linked Open Data cloud which enables researchers to seamlessly discover and integrate data from multiple data sources that had been openly available online but had previously been disconnected from one another.

Evaluating impact from research: A methodological framework – proposes a typology of research impact evaluation methods. They identify the following designs: experimental and statistical methods, systems analysis, textual, oral and arts-based methods, indicator-based approaches, evidence synthesis approaches.

Appraising research policy instrument mixes: a multicriteria mapping study in six European countries of diagnostic innovation to manage
antimicrobial resistance – before I started my postdoc, I wrote a grant for a pharmaceutical research consortium to address AMR. This study is interesting just to check out what their method called multicriteria mapping is. As for the content, it is interesting as it explores policymakers’ preferences and uncertainties with various policy interventions against AMR. The policy options they explore were categorized in the following:

• incentivizing diagnostic firms with financial rewards
• funding R&D
• coordinating stakeholders to make it easier to bring new tests to market
• the government provisioning resources to lead R&D and testing themselves
• incentivizing healthcare providers to use tests more appropriately
• establishing IP regimes to support the development of tests based on demand

Deep Learning applications for COVID-19 – explores how deep learning is used in COVID. They categorize the application to four main areas: computer vision (ie. to analyze medical images and robotics), life sciences (ie. for drug repurposing and protein structure prediction), epidemiology (ie. for forecasting and contact tracing) and natural language processing (ie. literature mining).

Psychological factors influencing technology adoption: A case study from the oil and gas industry – introduces the Psychological Technology Adoption Framework (P-TAF) which categorizes various factors that facilitate or hinder adoption: personality (innovativeness, risk aversion), motivation (personal incentives, fear of technology failure), attitude (technology attitudes, trust), cognitive (risk perception, technical knowledge, certainty perception, previous experiences), social (social influence, subjective norms) and organizational (leadership, collaboration culture, technology adoption culture).

Legitimation of a heterogeneous market category through covert prototype differentiation – introduces the idea of covert prototype differentiation where entrepreneurs communicate how the category that they are in is united while at the same time creating prototype variants that attract entrants to their own camp.

Happy new year everyone! Here are my interesting reads from the holidays up to today.

Developing a unified definition of digital transformation – studies that aim to define a certain concept are always fascinating. In this study, they review the digital transformation literature and complement it with a survey of practitioners and academics to get a unified definition of DT:

A fundamental change process, enabled by the innovative use of digital technologies accompanied by the strategic leverage of key resources and capabilities, aiming to radically improve an entity* and redefine its value proposition for its stakeholders.

Organizing for innovation: a contingency view on innovative team configuration – study on how a team’s expertise should be tailored according to the domain they are trying to innovate in. The researchers explore 4 domains varying by modularity and breadth of application – MRI, RFID, stem cells and nanotubes. Modular domains like MRI and RFID do not need a lot of overlap in knowledge across their inventors. Domains with broad applications benefit from teams with wide knowledge breadth as well.

The front end in radical process innovation projects: Sources of knowledge problems and coping mechanisms – It’s worth the read even just for the review on the difference between complexity, uncertainty, equivocality and ambiguity. These are terms that are thrown around interchangeably and this article builds on previous works to explain them more clearly:

• Uncertainty – refers to the lack of sufficient information needed to get to the desired outcome. Put simply, not knowing the answers.
• Complexity – related to the number of parts in a system and the difficulties in predicting the interactions among them. In other words, not knowing how to find the answers to the question.
• Ambiguity – the inability to interpret. In other words, not knowing what questions to ask in the first place.
• Equivocality – potential for multiple interpretations, even when information is available. Put simply, having multiple answers.

Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet – despite moving away from pharma research, I still enjoy reading news about the industry. The summary of this article is the classic line garbage in, garbage out. The conclusion perfectly illustrates this: “with our current ways of generating and utilizing data, we are unlikely to achieve the significantly better decisions that are required to make drug discovery more successful… we need to understand what to measure, and how to measure it… Only once these data are available for AI approaches can the field be expected to make real progress.”

Impact for whom? Mapping the users of public research with lexicon‑based text mining – When I moved to the social sciences, the impact of my research became much more difficult to define and perceive. This study maps the various communities that such research can have impact to. Found the visualizations fascinating.

Who Contributes Knowledge? Core-Periphery Tension in Online
Innovation Communities – explores what makes a contribution highly valued in the forum Stack Exchange. They talk about social embeddedness which refers to how connected socially an individual is to their community and epistemic marginality which refers to how different an individual’s knowledge base is compared to their community. Those who are both socially embedded and epistemically marginal are able to contribute highly valued insights as these insights are generally novel compared to the community and at the same time, they are able to communicate well their ideas to their community.

Recommender systems in the healthcare domain: state-of-the-art and research issues – always interesting to see the overlap across healthcare and AI. This review looks at the application of recommender systems in food, drug and exercise recommendation, health status prediction and health care professional search.

It did not work? Unlearn and try again – Unlearning success and failure beliefs in changing environments – it’s important to learn from your mistakes, but it’s also important to unlearn the false beliefs you have formed from previous mistakes. Cool insight considering that people generally focus on learning and not on unlearning.

Experience base, strategy-by-doing and new product performance – When we think of strategy, we usually think of getting a whiteboard and brainstorming on the best plan forward. In contrary, there is strategy-by-doing which is defined as “actions of iterative search for what works in the market.”