“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)