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.

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

The Academy of Management Annual Meeting is happening next week. We are organizing a presenter symposium on evaluating novel technologies with some of the most known scholars in this area.

In the meantime, let me share some interesting papers in this area that I’ve read.

Specialists, Generalists, or Both? Founders’ Multidimensional Breadth of Experience and Entrepreneurial Ventures’ Fundraising at IPO – a mixed quantitative and experimental study. The researchers find that “while generalist founders might be beneficial for starting up a venture, it appears that public investors have less trust in such multifaceted founders at the later stage of IPO. Indeed, for these later-stage entrepreneurial ventures, investors trust “consistent” specialists to scale the business.“

Will the startup succeed in your eyes? Venture evaluation of resource providers during entrepreneurs’ informational signaling – a study comparing evaluation outcomes between investors with/without founding experience. The interesting part is also considering gaining support from founders without investing experience. The study suggests that ventures may be better off engaging with experienced founders as it may increase the likelihood of receiving positive venture evaluation, which can then lead to higher chances of receiving resource support. While the study finds no difference between founders and investors in their willingness to invest financial resources, those with founding experience tend to be more open to providing social support to new ventures.

Evaluating Ventures Fast and Slow: Sensemaking, Intuition, and Deliberation in Entrepreneurial Resource Provision Decisions – inspired by the famous book by Daniel Kahneman, the article provides a framework on how investors make decisions through a combination of sensemaking, intuition and deliberation.

A Game Theoretic Approach to the Selection, Mentorship, and Investment Decisions of Start-Up Accelerators – proposes a model using game theory on the value provided by accelerators. This study was a bit difficult for me to follow given my lack of background in these math-heavy formal models. Nonetheless, what was interesting was their implication that the most important role of an accelerator is its screening, that is, before mentorship, and seed investment.

The effects of exposure to others’ ideas and their ratings on online crowdsourcing platforms on the quantity and novelty of subsequently generated ideas – analyzes ideas posted in two crowdsourcing platforms by Starbucks and Lego, combined with two experiments. They find that exposure to different kinds of ideas leads to different subsequent ideas depending on the novelty and quality of the original idea.

The last few months gave me some things to think about with respect to which research directions I would like to pursue. To be productive about these reflections, I decided to step back a bit and see what are the emerging topics in technology and innovation management. I wanted to check what conversations are happening currently across the literature and see which conversations I can potentially join.

To do this, I downloaded articles published from 2020 to the present in the Web of Science. I used the keywords “technolog*” and “innovat*”. I narrowed down the articles to the top journals in general management namely: academy of management review, academy of management journal, administrative science quarterly, organization science, journal of business venturing, entrepreneurship theory and practice, strategic management journal, strategic entrepreneurship journal, journal of management, organization studies, journal of management studies. As seen this excludes the top/traditional TIM journals like research policy and technovation as I wanted to see which conversations are diffusing outside of my smaller circle.

I analyzed about 549 articles and did the typical bibliometric analysis. I came up with the following bibliometric coupling map, which shows the different themes.

I quickly browsed through each of the clusters to identify the research themes. In the following table, I describe my findings.

Cluster (Number of Articles)	Top Keywords	Theme	Sample paper
Red (156)	work, organization, organizational, technology, theory, practice, process, digital, actor, social	How the nature of work is changing due to digital technologies	Behavioral Visibility: A new paradigm for organization studies in the age of digitization, digitalization, and datafication
Green (146)	firm, innovation, patent, knowledge, resource, technological, industry, performance, technology, search	How can firms manage innovation in uncertain environments?	Exploring Uncharted Territory: Knowledge Search Processes in the Origination of Outlier Innovation
Blue (127)	venture, entrepreneurship, entrepreneur, entrepreneurial, firm, startup, performance, founder, opportunity, innovation	How does a venture’s business model emerge and evolve over time?	Parallel Play: Startups, Nascent Markets, and Effective Business-model Design
Yellow (52)	platform, ecosystem, design, product, firm, digital platform, business model, network effect, strategy, performance	How can platforms be effectively governed to maximize value for different actors?	From proprietary to collective governance: How do platform participation strategies evolve?
Violet (48)	team, network, creativity, employee, creative, idea, innovation, diversity, individual, project	How can teams and networks be harnessed for creativity?	Networks, Creativity, and Time: Staying Creative through Brokerage and Network Rejuvenation
Light blue (20)	family, family firm, innovation, succession, family business, transgenerational, firm, ownership, woman, role	How can family firms be innovative?	Innovation Motives in Family Firms: A Transgenerational View

Conducting this quick analysis has made me realize that the things I have been working on have been quite different from the interests of top scholars in the general management field. A lot more reflection I need to do then.

Ecosystem types: A systematic review on boundaries and goals – studies the relations between different types of ecosystems (business, innovation, entrepreneurship and knowledge). It offers the following areas for future research:

• Role of external environment of the ecosystem
• Indicators and dimensions to measure performance
• New research methods and designs to study ecosystems
• Take other useful theories to apply in ecosystems

A framework and databases for measuring entrepreneurial ecosystems – explores different databases and how they can provide insights into the various roles of the government in ecosystems. Namely, these four roles are: catalyst (develops human capital and promotes tech venture creation), coordinator (drives and boosts outputs of ventures), certifier (validates the technical and commercial merits of ventures’ outputs) and customer (procures the outputs from ventures).

Ecosystems transformation through disruptive innovation: A definition, framework and outline for future research – explores the processes through which ecosystems transform. They highlight four interlinking phases: (1) the presence of transformational forces such as technology development and changes in customer behaviors, (2) strategic opportunity identification from the different actors, (3) value alignment across actors including providers, creators and users, and (4) ecosystem revitalization through both capability enhancing and destroying.

The emergence of entrepreneurial ecosystems based on enabling technologies: Evidence from synthetic biology – explores the synbio space, looking at the emerging sub-ecosystems within: pharmaceuticals, hardware, smart factories, smart cities, waste management, foodstuffs, and consumer goods. It also identifies the barriers that must be overcome by ecosystems based on such enabling technologies: how to manage IP considering the complex interactions across actors, how to manage the different clock speeds across quintuple helix actors and how to manage the ethical challenges in such a fast-moving technology.

Last week, we organized at ESADE the socioeconomic impact projects for ATTRACT. The main message from the workshop is the need for researchers to experiment and try other methods for impact evaluation, beyond traditional measures.

In line with this, let me go through some interesting articles I have read in the past weeks.

Entrepreneurial experimentation: a key function in systems of innovation – during the ATTRACT workshop, one of the core propositions is that we can systematize serendipity to turn ideas into impactful solutions. Digging deeper, the perception of serendipity arising can just be the side effect of extensive experimentation by entrepreneurs. The successful experiments are the ones that seem like serendipity.

Why the agile mindset matters – agile is one of those terms that are thrown around everywhere. Agile mindset was characterized by the following: learning spirit, collaborative exchange, empowered self-guidance and customer co-creation.

Reconceptualizing Imitation: Implications for Dynamic Capabilities, Innovation, and Competitive Advantage – questioning the current assumptions on imitation:

• imitation is easy (vs. imitation is hard)
• weak firms imitate (vs. strong firms imitate)
• uncertainty promotes imitation (vs. uncertainty hinders imitation)
• there is only one imitation strategy (vs. many imitation strategies)

Structuring the Start-up: How Coordination Emerges in Start-ups through Learning Sequencing – by following five startups over 2 years, the study finds how coordination emerges through the following learning sequence: anticipatory learning (anticipate early coordination problems), vicarious learning (copying ideas and practices from other startups), experimental learning (rolling out new mechanisms and scaling them up when successful), and trial-and-error learning (combine different coordination mechanisms and simplifying them).

Tradition as a resource: Robust and radical interpretations of operatic tradition in the Italian opera industry, 1989–2011 – a cool study of how reinterpretations of Italian opera affect ticket sales. The authors distinguish between different forms of interpretations. Robust interpretation involves preserving the most familiar aspects of a tradition but changing features like time/location. On the other hand, radiccal interpretation means modifying the core features such as altering the dramatic composition. The study finds that season-ticket holders are more likely to enjoy robust interpretations, while single-ticket holders are more likely to enjoy radical interpretations.

Although I read through the new publications every week, I have not posted on my blog for quite a while. Nonetheless, hope to resume again this week.

Let me restart by focusing on research impacts. Two weeks ago, I was lucky to visit Bologna and attend a workshop on entrepreneurship with some of the top scholars, especially in the sub-field of academic entrepreneurship – one of the ways by which research creates impacts.

Here are some of the nice articles I’ve read recently:

Becoming an academic entrepreneur: how scientists develop an entrepreneurial identity – introduces the idea of liminal venturing to explore how individuals who have established a strong identity as academic scientists develop their identities as entrepreneurs.

Managing individual research productivity in academic organizations: A review of the evidence and a path forward – a review of levers impacting research productivity including resource allocation, structural choices, organizational culture and task environment

Incentivizing Effort Allocation Through Resource Allocation: Evidence from Scientists’ Response to Changes in Funding Policy – studies the unintended impacts of policy change on research. They specifically examined how stem cell scientists in the US responded to funding restrictions in human embryonic stem cell (hESC) research. They find that this reduced these scientists’ output even in non-hESC areas, with the top scientists being most affected.

Reaching for the stars: When does basic research collaboration between firms and academic star scientists benefit firm invention performance? – explores academic stars collaborating with pharmaceutical firms. They find that the outcomes of collaborations as measured by patent citations only superior if various conditions are met – if the star scientist is dedicated to the collaboration by not having other simultaneous engagements with other firms or when the collaboration extends beyond basic research toward translational research.