All posts by : Vivian Marcelino

Aiming at being more effective and efficient in exploring digital innovations and new digital business ideas, incumbent firms have adopted separated structures such as innovation units, new business units, internal incubators and accelerators, digital labs, etc. While these structures have been successful in spurring new initiatives, they have not as effective in bringing these to scale. Part of the challenge is the need to transfer the new digital offerings, processes and models to core business and technology structures suitable for operationalising and/or commercialising these at scale – in other words, activities to explore new ideas in separated structures need to transition to become exploitative activities in core business and technology structures. This process, however, is far from simple as core business and technology structures are not naturally equipped with the needed capabilities and resources and its structures practices are often compatible with new digital solutions. 

In a recent publication, I aimed at tackling this challenge by embarking on a journey to dissect the transition to scale of eight digital service innovations developed in a large Asian incumbent bank. This exploration, detailed in the International Journal of Innovation Management, pivots around the intricate dance of transitioning digital innovations from their development in the Bank’s innovation unit to full-scale operationalisation and/or commercialisation in core business structures, with a focus on understanding the practices and challenges of enabling an aligned business-technology transition of digital innovations.  

Here, I present the essence of our findings and their implications for both academic research and industry practice: 

Key Highlights: 

• Introduction of a Dual Transition-to-Scale Model: We describe a nuanced framework that delineates the variegated intensities of integration, characterised by the flow of knowledge and resources between exploration and exploitation structures, required for digital innovations to seamlessly transition into scalable solutions. This model, described by innovation managers, focusses on manipulating the degree of integration to secure buy-in for the innovation, access core assets from business and technology peers, and embed new capabilities and resources in core structures to able the transfer of innovations to core business and technology actors.  

Illustration 1. Aligned business-technology transition of DI with integration and transfer 

• Diverse Project Scaling Trajectories: We outline three distinct scenarios of project scaling journeys, offering a granular view into the strategic integration and transition tactics pivotal for digital innovation scalability. In this, we describe how innovation managers deviate from the model that they describe by taking actions to cope with the challenges of pursuing digital innovation in an organisation still undergoing digital transformation. These coping actions, although aimed at creating the enabling conditions for innovations to transition, also created a disconnect between the business and technology transition of digital transformation, creating transition challenges at a later stage.  

Contributions to Academic Research: 

Our study enriches the academic investigation by presenting a model of how innovation managers expect to enable an aligned business-technology transition to scale and showing that integration and transfer decisions along the innovation process significantly impact the business-technology transition of digital innovations and, thus, need to be carefully managed to avoid misalignment.  

Contributions to Practice: 

For practitioners, this study offers actionable insights to orchestrate the scaling process effectively, ensuring that digital innovations are not only developed but also successfully operationalised and/or commercialised to achieve their full market potential. Innovation managers should be aware of their role in transition and of how their decisions around integration could affect transfer in the later stages. They should be careful not to create a disconnect between business and technology transition, which could have consequences not only for the scaling of the digital innovation but also for exploitation in the organisation. 

With this study, we aimed at expanding the scientific investigation into exploration-exploitation of digital innovations in the context of digital transformation. Yet, much remains to be understood and explored. In particular, how the orchestration of internal transition to scale might be effectively and efficiently aligned with go-to-market offers a promising area of investigation as innovation managers face the significant challenge of pursuing commercial success while needed to navigate the complex corporate environment. In short, companies need to be better in enabling internal transition and research has much to contribute.  

During my academic journey as a PhD student, I’ve come to recognise the crucial role of platforms that facilitate interaction between academia and industry. For students like me, these spaces are not just areas of collaboration—they are realms of revelation. They have been pivotal in shaping my research, thinking, and understanding of how innovation and digital transformation play out in the real world. 

Real-World Context to Theoretical Studies 

The world of academia often revolves around theories, models, and conceptual discussions. While this is essential for foundational knowledge, there’s a palpable gap when it comes to understanding how these theories apply to real-world scenarios. This is where industry-academia collaboration platforms come in. They provide PhD students like me with the real-world context, helping us align our research with genuine industrial needs and challenges. 

For instance, at the recent EINST4INE summer school at the Institute for Manufacturing (IfM) I had the incredible opportunity to engage with leading professionals from various sectors. These interactions brought home the stark realities and specific challenges they face; insights that can’t be gleaned from a textbook or a lecture. 

Enabling Impactful Research 

One of the essential goals of any research, especially at the PhD level, is to have a genuine impact. The industry offers a testing ground for our theoretical frameworks and models. By understanding the problems industry actors face, we can tailor our research to propose effective solutions. 

During my interactions at the summer school, discussions on innovation ecosystems and roadmaps as technology strategy tools were incredibly enlightening. This exchange allowed me to consider the application of theoretical constructs in diverse industrial settings, pushing me to think of research areas that truly matter. 

At the recent EINST4INE summer school, I also had the opportunity to discuss some of my research findings with industry experts and gather feedback on managerial tools that translate my research into practical applications. This opportunity is invaluable for PhD students who often lack the ability to directly engage with industry leaders.  

Preparing for a Seamless Transition 

Beyond research, these collaborative spaces prepare us for the eventual transition to the industry (if we choose that path). By understanding industry dynamics first-hand, we are better positioned to add value from day one. For someone like me, keen on ensuring that my research contributes to real-world impact, these platforms have been instrumental in aligning my studies with industry requirements. 

In Conclusion 

In the evolving landscape of innovation and digital transformation, the need for collaboration between academia and industry has never been more critical. Platforms and spaces that foster such collaboration are the bridges that connect theoretical knowledge with practical applications. As a PhD student, I can attest to the immense value they bring—not just in enriching our research but in moulding us to be better contributors to the world of industry and innovation. 

My quest to better understand the scaling challenge in digital transformation led to focus my research on the challenge of transitioning – the very needed – digital innovations (DIs), including processes, products, services and models, to operationalisation or commercialisation at scale in incumbent firms.  

According to recent industry estimates, around 75% of DIs still stall before reaching the desired scale (Martin 2018). This is a big issue; if companies continue to struggle to reap the benefits of their investment, we will have an endless cycle of search and failure.  

One of my areas of interest is the role of innovation managers in the transition to scale of digital innovations, especially when these innovations are developed in separated in dedicated structures (e.g., Digital Innovation Units, Innovation Labs, New Venture Units).  

Recently, I had the change to attend  ISPIM – International Society for Professional Innovation Management conference 2023, held in beautiful Ljubljana, and present some of my research. I was very happy to learn that my paper “Digital Innovation: Exploring Integration and Transition Modes in Scaling Success” was one of the three nominees for the Alex Goffman Award for the best student paper. 

The Role of Innovation Managers in the Transition to Scale of Digital Innovations: Key Takeaways 

The rise of digital technology is reshaping the landscape of businesses globally. In a comprehensive study, we delved into the critical role innovation managers play within large incumbent firms during their digital transformation journeys. Here’s what we found: 

1. The Orchestrating Role of Innovation Managers: Innovation managers are not just pioneers of innovative ideas. They are the very bridges that connect novel digital solutions to traditional business operations. They facilitate communication, handle resources, and manage complex interdependencies to ensure that innovations don’t just remain ideas but become scalable solutions. In this, innovation managers are the key enablers of the transition to scale of digital innovations by orchestrating the integration of new digital solutions and offerings with core business, ensuring these find a suitable home in the core of the organisation and managing the process through which these digital innovations migrate to the core structures of the organisation. 
2. The Standard Transition Model: Innovation managers have in mind a standard transition model for digital innovations. It’s thought that innovations will smoothly integrate with core business, become self-sufficient in the core of the organisation, and then scale. However, the reality of such transitions can be vastly different. 
3. Challenges in Digital Innovation: The process of transitioning digital innovations is riddled with challenges. From resource allocation hurdles to cumbersome task allocation in core businesses, innovation managers frequently face obstacles in turning a digital idea into a successful operation. 
4. Adapting to Challenges: One size does not fit all. Depending on the challenges faced, innovation managers might deviate from the standard transition model. These deviations are essential to ensure that digital innovations can continue to progress towards becoming scaled solutions, but they also come with important trade-offs.  

In conclusion, as the digital landscape rapidly evolves, the role of innovation managers becomes ever more pivotal. They stand at the crossroads of traditional business operations and groundbreaking digital solutions, ensuring not just the birth of innovative ideas but their successful journey to large-scale operations. 

Despite their crucial role, research has only recently started to investigate the actions of innovation managers beyond overseeing the innovation process. We need more research that brings insights into the role of innovation managers in the implementation of digital innovations, so that we can better understand how transition to scale challenges emerge, what strategies, actions or mechanics are used to cope with these challenges and how, in turn, these affect scaling.  

Completing a PhD is an intensive and demanding task. As a PhD student, we spend countless hours scouring through academic literature, collecting data, analysing information, etc.. The good news is that advanced technologies, such as ChatGPT, can help make this process much easier.

ChatGPT is an artificial intelligence (AI) chatbot developed by OpenAI designed to understand and generate human-like language. The advantage of ChatGPT is that it can help make the process of searching and analysing online information much faster.

Here are some ways ChatGPT can help PhD students do research:

Finding relevant literature

One of the most time-consuming tasks for PhD students is finding relevant literature for their research. ChatGPT can help by providing instant access to a vast collection of academic literature. With the help of natural language processing (NLP), ChatGPT can quickly search and retrieve relevant papers, books, and other academic sources, saving you valuable time and effort.

Generating research questions

ChatGPT can also help PhD students brainstorm research questions. By inputting keywords or phrases related to your research topic, ChatGPT can generate a list of potential research questions. This can be especially helpful when you’re stuck and need some inspiration to jump-start your research.

Data analysis

Data analysis is an essential aspect of many research projects. ChatGPT can be used to help with this by assisting in the analysis of complex data sets. With its NLP capabilities, ChatGPT can quickly identify patterns, trends, and correlations in data, providing valuable insights that can help support your research conclusions.

Writing assistance

Another critical aspect of the research process is writing. ChatGPT can help PhD students with their writing by providing suggestions for grammar, style, and tone. It can also generate summaries, abstracts, and even full paragraphs, which can be useful when you’re struggling to articulate your ideas.

Literature reviews

ChatGPT can be used to assist with writing literature reviews. It can help to summarize key findings and highlight important points from multiple sources, making the process of synthesizing and analysing academic literature much more manageable.

Of course, ChatGPT does not work miracles and should also not be trusted blindly. PhD students need to be attentive to potential biases, as an example. They will also need to make sure that the answers provided by ChatGPT are used as a brainstorming or supporting tool, and not as something that limits their thinking. Yet, as we move to a world increasingly dominated by advanced technologies such as AI, we would benefit from thinking about how we can use it to advance our work, making us more efficient and effective researchers.

In short, ChatGPT can be an incredibly valuable tool for PhD students, helping to streamline the research process and save time and effort.

Digital Transformation Success and Failure – Part II Insights from Academic Literature 

In my previous blog post – check it out if you have not seen it yet – I explored the industry and grey literature to find out what is known about the high failure rate of digital transformation initiatives, especially regarding the human and organisational factors that might contribute to the issue. 

This time, I am delving into the academic literature. Quick disclaimer, this is by no means a literature review. I have no intention of summarising the whole literature on the topic. Rather, you might approach this blog post as “scraping the surface” to get an initial general idea of what is said. 

Defining Success or Failure 

When looking at what defined success or failure in digital transformation, and how to measure it, I am sad to conclude that academic literature did not have great new insights. In general, my thoughts remain the same; more consensus is clearly needed on how to define and measure success and failure of digital transformation.  

That said, there are some interesting discussions regarding what it means to “digitally transform”. While in the industry literature there was little, if any, discussions regarding what defines digital transformation in the first place, the academic literature is very concerned with this matter.  

What is digital transformation?  

There are numerous definitions of digital transformation (DT) in the literature, but in general, DT is seen as process through which organizations leverage information, computing, communication, and connectivity technologies to trigger significant changes to its properties. Some authors see DT as a stage of transformation that follows the IT enabled transformation phenomenon in organisations and is particularly differentiated from digitalisation and digitisation for its transformation or redefinition of value creation paths. In general, academic literature points out to the disruptions and opportunities that digital technologies bring for business model transformation and the strategic renewal of firms.  

Along these lines, the understanding of success and failure in DT should be concerned with extent to which organisations are able to leverage digital technologies to redefine how the create and deliver value to customers.  

Challenges and Barriers in Digital Transformation 

Research and practice show that the pursuit of DT, and the related business model redefinition and transformation, is far from a simple and straightforward endeavour. In fact, the process is plagued by significant challenges and barriers.  

DT scholars have repeatedly stated that digital transformation is a huge and extremely difficult endeavour and that organisations attempting to digitally transform face significant challenges in making the change. 

First, at the organisational level, digital transformation scholars have paid particular attention to the issues of rigidity, change resistance and inertia. Further, they highlight that digital transformation requires organizational processes, structures, and capabilities that firms often lack. 

Firms also face the challenge of balancing the successful management of a healthy core businesses with the diverse development of multiple innovation efforts and the overall transformation process that entails substantial changes at all levels of the organisation. This is far from simple. In fact, trying to balance these efforts often lead to managerial paradoxes and tensions that are difficult, if not impossible to solve. 

Is leadership that important? 

Yes, but not alone. Aligned to the huge attention given to leadership in the grey literature, numerous digital transformations analysed the role of top management involvement and leadership in solving the challenges of innovation and transformation. In this regard, the literature agrees that yes, leadership is very important. However, studies also found that leadership involvement alone is not sufficient to solve the challenges of DT, with organisational design and competencies, being of key importance. 

Is scaling really a big issue? 

Yes, but it is seems like it still not as well understood as we would have expected. The academic literature seems to agree that firms often fail to bring DT initiatives to grow into a stage where they have transformational power. However, the academic literature tends to do the same as the grey literature; it focusses on the success factors as to demonstrate “how they succeed”, instead of “why they fail so much”.  

That said, so interesting insights do exist in the DT and aligned literature. Mostly, the academic literature highlights the same challenges as the grey literature but goes further in explaining why these challenges exist. 

Key Failure Factors 

In the scaling phase, projects face several uncertainties: 

• Technical uncertainties related to the underlying scientific knowledge, including technical feasibility, manufacturing, and maintainability.  
• Market uncertainties comprise to what extent customer needs are understood, transformed into products, and superior customer value is generated compared to competition.  
• Organizational uncertainties address the organizational and managerial conflict of fostering innovation while pursuing operational activities.  
• Resource uncertainties embrace all difficulties of internally and externally acquiring needed resources for innovation.  

Additionally, the academic literature points out that, especially regarding the new business models that are expected to come from digital transformation, the economic logic makes it hard for leaders and managers to prioritise these projects. That is to say, before scale, new business initiatives will never be as economically viable as the core businesses. Investing in the scale of these initiatives only makes sense if a future lens is applied. Because of these many uncertainties, innovation and transformation activities often get neglected in favour of day-to-day business needs.  

Further, multiple actors in the organisation will have different perspectives on the economic value of such new business and just the decision to scale is far from sufficient to guarantee scaling.  In fact, the academic literature highlights that scaling initiatives require significant changes for the core business units of the organisation. For digital transformation to succeed, the core of the organisation needs to migrate towards operating digital businesses.  

Thus, in additional to managing the successful scaling in the commercialisation of new digital businesses initiatives, companies need to manage the successful scaling in the transformational effect of these. Double the effort, double the trouble… 

What is next? 

Well, I just started to scrape the surface of the literature in digital transformation, so more insights will come soon. I will keep updating on the interesting discussions I find in the literature. Also, please do contribute! Any interesting insights into why digital transformation is so hard to scale? 

REFERENCES 

Appio, F. P., Frattini, F., Petruzzelli, A. M., & Neirotti, P. (2021). Digital Transformation and Innovation Management: A Synthesis of Existing Research and an Agenda for Future Studies. In Journal of Product Innovation Management (Vol. 38, Issue 1, pp. 4–20). Blackwell Publishing Ltd. https://doi.org/10.1111/jpim.12562 

Baculard, L.-P., Colombani, L., Flam, V., Lancry, O., & Spaulding, E. (2017). Orchestrating a Successful Digital Transformation. 

Bosler, M., Burr, W., & Ihring, L. (2021). Digital Innovation in Incumbent Firms: An Exploratory Analysis of Value Creation. International Journal of Innovation and Technology Management, 18(2). https://doi.org/10.1142/S0219877020400039 

Burgers, J. H., Jansen, J. J., van den Bosch, F. A., & Volberda, H. W. (2009). Structural Differentiation and Corporate Venturing: The Moderating Role of Formal and Informal Integration Mechanisms. Journal of Business Venturing,24(3), 206–220. 

Campbell, A., & Park, R. (2005). The Growth Gamble: When Leaders Should Bet Big on New Business and How They Can Avoid Expensive Failures. Nicholas Brealey International. 

Colarelli, O’Connor G., & Demartino, R. (2006). Organizing for Radical Innovation: An Exploratory Study of the Structural Aspects of RI Management Systems in Large Established Firms. Journal of Product Innovation Managemement , 23, 475–497. 

Correani, A., de Massis, A., Frattini, F., Petruzzelli, A. M., & Natalicchio, A. (2020). Implementing a Digital Strategy: Learning from the Experience of Three Digital Transformation Projects. California Management Review, 62(4), 37–56. https://doi.org/10.1177/0008125620934864 

Cozzolino, A., Verona, G., & Rothaermel, F. T. (2018). Unpacking the Disruption Process: New Technology, Business Models, and Incumbent Adaptation. Journal of Management Studies, 55(7), 1166–1202. https://doi.org/10.1111/joms.12352 

Gassmann, O., Widenmayer, B., & Zeschky, M. (2012). Implementing radical innovation in the business: the role of transition modes in large firms. 

Hanelt, A., Bohnsack, R., Marz, D., & Antunes Marante, C. (2021). A Systematic Review of the Literature on Digital Transformation: Insights and Implications for Strategy and Organizational Change. Journal of Management Studies, 58(5), 1159–1197. https://doi.org/10.1111/joms.12639 

Hill, S. A., & Georgoulas, S. (2016). Internal Corporate Venturing: A Review of (Al-most) Five Decades of Literature. In S. A. Zahra, J. Hayton, & D. O. Neubaum(Eds.),Handbook of corporate entrepreneurship(pp. 13–63). Cheltenham, UK:Edward Elgar. 

Hoonsopon, D., & Ruenrom, G. (2012). The Impact of Organizational Capabilities on the Development of Radical and Incremental Product Innovation and Product Innovation Performance. In Journal Of Managerial Issues: Vol. XXIV. 

Lanzolla, G., Lorenz, A., Miron-Spektor, E., Schilling, M., Solinas, G., & Tucci, C. L. (2020). Digital transformation: What is new if anything? Emerging patterns and management research. Academy of Management Discoveries , 341–350.  

Menz, M., Kunisch, S., Birkinshaw, J., Collis, D. J., Foss, N. J., Hoskisson, R. E., & Prescott, J. E. (2021). Corporate Strategy and the Theory of the Firm in the Digital Age. Journal of Management Studies, 58(7), 1695–1720. https://doi.org/10.1111/joms.12760 

Nadkarni, S., & Prügl, R. (2021). Digital transformation: a review, synthesis and opportunities for future research. Management Review Quarterly, 71(2), 233–341. https://doi.org/10.1007/s11301-020-00185-7 

Nambisan, S., Lyytinen, K., Majchrzak, A., & Song, M. (2017). Digital Innovation Management: Reinventing Innovation Management. Research in a Digital World. MIS Quarterly, 41(1), 223–238. https://doi.org/10.25300/MISQ/2017/41:1.03 

Raisch, S., & Tushman, M. L. (2016). Growing New Corporate Businesses: From Initiation to Graduation. Organization Science, 27 (5), 1237–1257 

Schneckenberg, D., Matzler, K., & Spieth, P. (2021). Theorizing business model innovation: an organizing framework of research dimensions and future perspectives. R&D Management, 2021, 10(13). 

Siachou, Evangelina, Vontris, Demetris and Trichina, Eleni, 2021. Can traditional organizations be digitally transformed by themselves? The moderating role of absorptive capacity and strategic interdependence.  Journal of Business Research, 124, pp. 408-421.  

Slater, S. F., Mohr, J. J., & Sengupta, S. (2014). Radical product innovation capability: Literature review, synthesis, and illustrative research propositions. Journal of Product Innovation Management, 31(3), 552–566. https://doi.org/10.1111/jpim.12113 

Smith, P., & Beretta, M. (2021). The Gordian Knot of Practicing Digital Transformation: Coping with Emergent Paradoxes in Ambidextrous Organizing Structures*. Journal of Product Innovation Management, 38(1), 166–191. https://doi.org/10.1111/jpim.12548  

Vial, G. (2019). Understanding digital transformation: A review and a research agenda. In Journal of Strategic Information Systems (Vol. 28, Issue 2, pp. 118–144). Elsevier B.V. https://doi.org/10.1016/j.jsis.2019.01.003 

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Zott, C., & Amit, R. (2015). Business model innovation: Toward a process perspective. In C. Shalley, M. A. Hitt, & J. Zhou (Eds.), The Oxford Handbook of Creativity, Innovation and Entrepreneurship(pp. 1–14). Oxford: Oxford University Press 

As I start my PhD journey, it becomes ever more clear just how important it is to exchange with and learn from other dedicated to topics similar to mine. That is, the human side of digital transformation and innovation.

I have been told that PhD journeys can be quite lonely at times, so I hope that this blog will help me get a bit closer to the research and practice community and maybe we can have some fun, interesting and potentially very geeky discussions along the way.

For this first post, we were asked to reflect a bit on why we chose this PhD. Well, what a great task given that I love to reminisce! I hope you bear with me as I delve into times past and, in the process, I hope I get to tell you a little bit about the focus on my research.

When I was a young professional in the United Nation, I had the amazing opportunity to be part of a very small team that was put in charge of creating and leading an internal Innovation Lab. Since those early days, I was already mostly concerned with what it would take from the people point of view to bring innovation into our organisation. This was such a concern that the mission of our Innovation Lab was defined as “to unleash the creative and innovative potential of our people.”

It was a conscious decision not to worry about the technology or the ROI, and instead to focus on supporting people to innovate in whatever it was that they were doing.

As I reflect on the path I that have followed previously to joining the EINST4INE program, I come to realize that it was probably these experiences that prompted me to focus on the human element of digital transformation for my PhD.

Over the years as a practitioner, I had the opportunity to approach innovation and digital transformation from different and diverse angles, levels, perspectives, etc. Nevertheless, I always end up asking myself “what does this mean for the people?”.

I strongly believe that there is no innovation or digital transformation without people. It is the humans – that go to work every day with their fears, needs, desires, troubles, happiness, sadness, etc. – that innovate. They are the ones that transform. It is through the aggregation of their daily actions and behaviours that things change.

Yet, I was always intrigued by the relatively little attention that is given to them in academia.

I have wondered whether it is because it is challenging to understand humans – maybe because we enter the realm of vulnerability and, as Brené Brown[1] has so well highlighted, we tend to hide from vulnerability as hard as we can?

If that is the case, it might be even more important to focus on the people in the context of innovation and digital transformation. As Brené Brown also reminded us, it is in the realm of vulnerability that the magic happens.

Thus, my PhD focus: understanding how intrinsically human factors affect efforts by traditional companies to achieve digital transformation.

I believe that by developing a better understanding of how people’s skills, competencies, beliefs, behaviours, etc. interact with organizational factors such as strategies, projects, resources, processes, structure, etc. in the context of digital transformation we can get much deeper insights into what goes right and what goes wrong.

I do not mean to be negative, but as we know up to 80% of digital transformation efforts fail. Can we pause a minute to imagine just how much time, resources, blood, sweat and tears go into these 80%?

When I look at this number, I cannot stop myself from thinking that maybe we are missing something important. I hope that this research will contribute at least a little to solving some of the problem. Perhaps by finding new and better ways to design and implement digital transformation and innovation?

As this blog post goes live, I am on my third week of my PhD journey. So far, I have been enjoying reading what all the great authors have been investigating and saying about the topic.

I have a very long and arduous road ahead, but I could not be more excited to be a part of this EINST4INE cohort. I chose to apply to EINSTAINE because of its strong industry connections and diverse and top-notch team of scholars. Having had the chance to interact with my pears and some of the great researchers that are part of this programme, I certainly have high hopes for the future.

Now that I have taken you through a bit of a personal journey, I am wondering, how about you? Do you think any of this makes any sense? Or do you think this topic is a total waste of time? What would you suggest me to look into? And if you had a wish list of things you would love to know about the human side of innovation and digital transformation, what would be in it?

I look forward to the interactions we will have as I move forward! And I hope you will find in this blog an enriching and safe space to exchange thoughts and ideas on helping humans innovate and transform their organisations.

[1]  Brené Brown is an American research professor, lecturer, author, and podcast host. Brown is known in particular for her research on shame, vulnerability, and leadership.