I’m contributing to a class on data-driven digital transformation in the coming semester. For this then, I have to be updated with the latest research on data.

Data governance and digital innovation: A translational account of practitioner issues for IS research – discusses the main challenges of data governance and potential research themes for the IS community:

• Ensuring that data governance protects organizations without hindering innovation
• Finding the repertoires of mechanisms for data governance
• Apart from designing them, understanding the enactment of data governance
• Understanding how organizations can implement digital services even when the data assets are in flux

The role of data for AI startup growth – With the proliferation of generative AI like ChatGPT and Midjourney which have been built generally with public data, the primary way that new startups can differentiate themselves is by training their algorithms with proprietary data. This study verifies that, for AI startups, having proprietary training data is positively correlated with obtaining future VC funding.

Interoperability in the era of digital innovation: An information systems research agenda – Being involved in Open Science and FAIR data, one of the top issues is interoperability which refers to the ability to exchange and understand information from another system. The review provides an excellent overview of the challenges of interoperability and how to increase it (what unit of analysis, which actor is involved and whether it is at the level of IT, data or software). To guide researchers, the review also recommends areas to further study including its conceptualization, scoping and methodology.

The Digital Revolution, 3D Printing, and Innovation as Data – previously, firms “innovated from data” – acquiring and analyzing various consumer data to guide their innovation activities. However, “innovation as data” is reversing this where consumers use digital tools to transform data into innovative products.

Organizations Decentered: Data Objects, Technology and Knowledge – quite a difficult paper given my lack of knowledge in such tradition, It argues that data objects are becoming more prominent, increasingly influencing how organizations work especially in their processes of knowing. This then leads to the decentering of organizations where the relation of domain knowledge over data production becomes looser and internal versus external references are reordered.

Legitimating Illegitimate Practices: How Data Analysts Compromised Their Standards to Promote Quantification – fascinating study of how data analysts made previously illegitimate practices such as using hacky code, not holding out samples and using inaccurate data more acceptable. They did this by standardizing these practices, instantiating the practices in their technology platforms and educating collaborators in various meetings.

I have not blogged in a long time mostly because of my disillusion with AI. I felt like AI (with tools like ChatGPT and MidJourney) can do many of the things I do in my blog much better than I can. This made me forget that the main reason I made this blog is not really to create the most interesting content but mostly for myself – to help me learn things. So now, I’m planning to get back to the rhythm and post more frequently once again.

Soft but Strong: Software-based Innovation and Product Differentiation in the IT Hardware Industry – I like listening to the Decoder podcast where they interview CEOs from many consumer product companies. It is interesting to hear that many hardware companies have more engineers working on software than hardware. This study then validates why this might be the case. Researchers find that “investing in software-based innovation not only develop more new products but are also more likely to launch new products that are distinct from those of rivals as well as their own.”

The role of prototype fidelity in technology crowdfunding – Explores how much prototype fidelity, referring to how the prototype reflects the final product’s look and functionality, affects crowdfunding performance. The study finds that too much fidelity may not be ideal as it restricts the ability to co-create with the community. Moreover, the researchers found that materiality also plays a role in how such prototypes are judged (e.g. purely mobile apps vs. smart devices). There is an assumption that purely digital artifacts would be always updated and so ensuring fidelity of the prototype may not matter too much.

From theories to tools: Calling for research on technological innovation informed by design science – External observers of innovation studies would assume that we mostly spend our time evaluating various tools from design science such as the Business Model Canvas or coming up with new tools for design. But, this is not really the case, in fact, I would only know about these approaches recently as I began teaching more about innovation. This editorial from Technovation calls for much research in the area.

Transitioning additive manufacturing from rapid prototyping to high-volume production: A case study of complex final products – Explores how 3D printing can be scaled in an organization. While it can be easily assumed that technology characteristics such as slow printing speeds or limitations in capacity would be the main barrier, the study provides an alternate view. They find that misaligned technology development processes across subsystem units in an organization can hinder it more. To facilitate scale-up then, synchronization between these disparate teams is the priority.

Reconceptualising innovation failure – advances three dimensions of failure:

• Failure as experimentation – on-going testing towards new iterations of an idea
• Failure as judgment – often a strategy used by managers to reset towards a new direction
• Failure as event – refers to unexpected shocks that require recovery from the organization

From Bits to Atoms: Open Source Hardware at CERN – a new paper from my colleagues at ESADE theorizing about how open source can happen in hardware. Compared to open-sourcing in software, an important concept that they introduce is that of malleability – how much one can modify the artifact, taking into account embodiment (component’s physical or non-physical state), modularity (relationship across these components) and granularity (ability of an object to be decomposed into components). Based on these dimensions then, they speculate that open-sourcing or traditional hardware development may be more likely.