Digital Technologies as External Enablers of New Venture Creation in the IT Hardware Sector – expounds how digital technologies can vary across two properties specificity (how easy to adapt to different applications) and relationality (how interdependent its users are). From this, they explore six mechanisms by which digital technologies enable the development of hardware ventures:

• Compression – Reduces time to perform an action
• Conservation – Reduces resources needed to perform an action
• Expansion – Increases the availability of a resource
• Substitution – Replaces one resource with another
• Combination – Bundles different resources to create new artifacts
• Generation – Creates new artifactsby changing existing ones

Interoperability in the era of digital innovation: An information systems
research agenda – most of the smart products we have now rely on the ability to receive and send data with other devices. However, if these systems cannot interact seamlessly with one another, or, in other words, not interoperable, then, various innovation challenges arise.

Accomplishing the layered modular architecture in digital innovation: The case of the car’s driver information module – layered modular architecture refers to an architecture where the physical components have hierarchical modularity (interdependent components are clustered together for coordination) while the digital components follow layered modularity (core components are at the bottom of the stack, with the optional ones built on top). The question however is how you organize an organization to coordinate such complex product development. The authors suggest the following mechanisms: uncoupling the digital control system from the physical product hierarchy, layering the digital control system and continuously connecting the two architectures

Theorizing the Digital Object – puts forward a theory of digital objects built by distinguishing between material and nonmaterial bearers. Not very used to these kinds of very philosophical/abstract papers, but it helped me understand better what makes digital technologies unique. According to this, its their feature of “repeated layering of nonmaterial objects, facilitated by the capacity of bitstrings to act as bearers.” To illustrate, a hard drive (material bearer) can hold a zip encoding (non-material bearer) of a docx encoding (another non-material-bearer) of a news article (nonmaterial object).

Digital reframing: The design thinking of redesigning traditional products into innovative digital products – explores how a traditional movie theater was redesigned using digital technologies for immersive 3D experiences. The role of the digital evolved from being a context, to being a component to being the offering.

Digital First: The Ontological Reversal and New Challenges for Information Systems Research – previously, the view is that the digital world is shaped by human experiences in the physical world. However, this is now an obsolete view. Referred to by the authors as ontological reversal, they emphasize that now, the digital world is the one shaping our reality. Examples include 3D printing a design first conceptualized on a computer and Google maps creating a navigation plan that then gets realized in the real world.

From Representation to Mediation: A New Agenda for Conceptual Modeling Research in a Digital World – with the digital further entangled with the physical, we need new tools to make sense of this. This is where conceptual models can play a bigger role:

• Represent physical reality in digital reality (e.g. databases containing data about product stock)
• Execute digital reality within physical reality (e.g. 3D printing)
• Translate between digital realities (e.g. smart contracts)
• Change physical reality (e.g. health intervention apps)

When does AI pay off? AI-adoption intensity, complementary investments, and R&D strategy – a study of high-tech ventures in South Korea. The study finds that AI is associated with higher revenue growth only at higher levels of adoption. Since AI is ultimately dependent on data, this growth is much more evident especially when companies leverage complementary technologies such as cloud computing.

The digitalisation paradox of everyday scientific labour: How mundane knowledge work is amplified and diversified in the biosciences – fascinating ethnographic research on a synthetic biology research group. The authors argue that:

Contrary to expectations of the removal of mundane work by automation and digitalisation, we suggest the emergence of a digitalisation paradox in knowledge-intensive, creative professions such as scientific work. We argue that while robotics and advanced data analytics in scientific work aim at simplifying work processes so as to increase productivity, they can also contribute to increasing the complexity, number, and diversity of tasks, and that this happens unevenly across the scientific hierarchy. 

Artificial intelligence in science: An emerging general method of invention – a study of the adoption of deep learning in the sciences. Particularly, they explored the impact of neural networks in the health sciences through both the citations of the studies and also recombinational novelty (measured through the distance of cited journals). In terms of citation impact, they find high variation across studies adopting deep learning. In terms of novelty, they find a negative association. The authors then suggest that deep learning has been used primarily to manage the increasing amount of data within a field, instead of using it to synthesize knowledge across domains.

Artificial intelligence as an enabler for innovation: A review and future research agenda – a special issue from Technological Forecasting and Social Change exploring how AI can enable innovation across idea generation, screening, experimentation, development and commercialization.

Machines augmenting entrepreneurs: Opportunities (and threats) at the Nexus of artificial intelligence and entrepreneurship – explores how entrepreneurship researchers can engage with the advances in AI. They highlight the following changes that entrepreneurs need to be ready to capitalize:

• As society transitions toward a “feeling economy,” entrepreneurs can leverage AI to aid in recognizing, communicating and responding to emotions
• With AI transforming jobs and creating new jobs, entrepreneurs need to adapt as occupational skills get redistributed across the economy.
• Entrepreneurs would need to be active in developing new governance mechanisms to ensure that AI does not harm society.
• Entrepreneurs would have to conceptualize what would be the role of humans in the decision process as AI becomes more prevalent.
• Entrepreneurs have to expand the role of humans in developing AI systems.
• As a tool, AI should be directed by society towards the good.

Gaining Organizational Adoption: Strategically Pacing the Deployment of Digital Innovations – study of how entrepreneurs promoted the adoption of their new technologies, conducted by a researcher embedded in a digital health accelerator. Two approaches were found: an embedded approach where entrepreneurs engage deeply with customers to identify and develop use cases and market-centric approach where entrepreneurs systematically study the market before engaging with customers. The most successful entrepreneurs used strategic pacing which meant (1) concealing functionalities that may threaten stakeholders in an adopting organization, (2) restraining claims that a use case could displace or substitute for the work conducted by some members of an adopting organization and (3) adjusting the speed of introduction by customer organization.

The Evolution of Technology – explores four different perspectives that drive the variation, selection and retention of technologies.

• Technology Realist – technical factors such as performance are the main drivers
• Economic Realist – economic factors including R&D investment and scale are the main drivers
• Cognitive Interpretivist – in contrast to technology realism which assumes that cognitive representations of a technology aligns with the artifact itself, it assumes that there are different cognitive interpretations of a technology which then drive evolution.
• Social Constructionist – social factors such as power and networks are the drivers

Shaping Nascent Industries: Innovation Strategy and Regulatory Uncertainty in Personal Genomics – explores how new ventures in personal genomics managed regulatory uncertainties. It introduces the idea of regulatory co-creation which refers to iterative engagement with
regulators to shape standards.

Breakthrough invention and problem complexity: Evidence from a quasi-experiment – explores how the Google breakthrough AlphaGo has affected how technologists formulate questions in Stack Overflow. The study claims that the questions posed are of higher complexity after such a breakthrough invention. One implication of this is that although breakthroughs can be leveraged to create innovations, it may not be straightforward given the coordination required to manage such emerging complexities.

A Knowledge Recombination Perspective of Innovation: Review and New Research Directions – a nice review of knowledge recombination that takes into account the following:

• the features of an individual knowledge components (e.g. newness, context specificity)
• the interactions among a set of knowledge components (e.g. breadth vs. depth, modularity, networks)
• the architecture design from their recombination
• the outcomes of the recombination process in terms of novelty and usefulness

Revisiting innovation typology: A systemic approach – disentangles the various terms used to describe innovation such as radical, discontinuous, breakthrough and disruptive innovation