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.”

In Silico documentary – Nature featured this documentary on the fall of the Human Brain Project. I still haven’t watched it but this seems to be an interesting view on the politics behind science.

Exploring the dynamics of novelty production through exaptation: a historical analysis of coal tar-based innovations – previous theories of serendipity explain that it rises from a need or from theory. This paper offers a new route to serendipity – affordances. I did not know this word before but as the paper explains it is “the perceived or actual properties of the thing, primarily those fundamental properties that determine just how the thing could possibly be used”

Social impacts of additive manufacturing: A stakeholder-driven framework – enumerates and classifies possible impacts of a technology like 3d printing. Since I am doing a social and business impact assessment of cloud computing, this is a paper that would be useful model moving forward.

Failing to Learn from Failure: How Optimism Impedes Entrepreneurial Innovation – a fascinating study that links laboratory experiments with firm data. They measured entrepreneurs’ dispositional optimism – the tendency to think that they will have a favorable outcome. They then linked it to patents or product development success. I like their finding that those who are optimistic by disposition tend to not update their beliefs of future success despite negative outcomes. Everytime I meet with my entrepreneur friends, I really see the difference on how they think and perceive things.

Postdoc challenges – For a few weeks now, Nature has been talking about the issues that postdocs have been facing. As a postdoc myself, I can relate to the issues of uncertainty about the future. Despite this, I have been lucky to find a position in a great research group during the pandemic. I am really worried about how academia would proceed moving forward, so we’ll see.

Lab, Gig or Enterprise? How scientist-inventors form nascent startup teams – groups the startup formation strategies by scientists into three clusters. For some inventors, their startup is basically an extension of their academic laboratories where they can experiment with the commercial applications of their research. The gig model occurs when inventors want to pursue an idea quickly and see if it would be viable enough to be an exit. The enterprise model emphasizes creating a team that would enable the creation of a real sustainable business.

Management Research that Makes a Difference: Broadening the Meaning of Impact – every now and then, I have to remind myself what is the point of management research. This editorial provides five forms of impact: scholarly, practical, societal , policy and educational.

Converting inventions into innovations in large firms: How inventors at Xerox navigated the innovation process to commercialize their ideas – a study from June. Really interesting idea of innovators going around other departments to find a more amenable evaluation criteria so that they can later acquire further resources

The Matthew effect and the halo effect in research funding – The Matthew effect refers to the idea taken from the bible that the rich tend to get richer. The Halo effect is the idea that positive impressions in one area can also lead to positive evaluations of other areas. The study shows that researchers gain cumulative advantages, in that getting a funding grant can lead to them getting another one.

Bridging the gap between invention and innovation: Increasing success rates in publicly and industry-funded clinical trials – a study on the role of PI’s expertise on the success of clinical trials. Fascinating that they find that broad experience across multiple disease fields reduce success rates.

Fiction lagging behind or non-fiction defending the indefensible? University–industry (et al.) interaction in science fiction – My first time to come across a study like this. The study explores how science fiction novels view academia-industry interactions, using these novels as kind of a mirror to how society views such interactions. They find that science fiction has been critical of such relationships.

Developing Improvisation Skills: The Influence of Individual Orientations – another unique study using Live Action Role-Playing as the data collection method. As someone who is not really good with thinking on my feet, I found their three types/stages of improvisation skills useful. Imitative improvisation refers to merely taking inspiration from others. When you can then create an original response guided by some structure, you then evolve to reactive improvisation. When you do not need anymore external triggers and can just easily break existing structures, then you are at the generative improvisation stage.

The sandwich game: Founder-CEOs and forecasting as impression management – with so many people posting about their successes on social media, impression management is becoming more and more relevant. In this study, the authors argue that founders are less likely to oversell their company’s forecasts due to their concern with their long-term relationship with investors. I find this quite unintuitive especially when my top of mind founder CEOs are Elon Musk and Elizabeth Holmes.

Applying advanced technologies to improve clinical trials:
a systematic mapping study – comprehensive review of technologies used in clinical trials. I appreciated Table 3 which talked about the possible contributions of a research paper, adapted from Shaw 2003, which I didn’t know before.

The Emergence of New Knowledge: The Case of Zero‐Reference Patents – when we think of science, we talk about standing on the shoulder of giants. Apparently, there are patents that are just so pioneering that they do not reference previous patents.

Engineering Serendipity: When Does Knowledge Sharing Lead to Knowledge Production? – cool study on serendipity. Researchers were randomly selected to interact with other researchers. Their publications and citations were then tracked afterwards.

Organizing Crisis Innovation: Lessons from World War II– talks about the role of the U.S. Office of Scientific Research and Development led by Vannevar Bush during World War 2 in spurring innovations such as radar, atomic fission, penicillin, and malaria

The long wave of the internet – traces the history of the internet and the paradigms occuring across each phase of its development