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. 

As a fan of Christopher Nolan and a researcher in the innovation management field, I couldn’t help but be excited by the release of “Oppenheimer”. As you may know, the movie describes the life of the American theoretical physicist J. Robert Oppenheimer, focusing on his time as a student, his leadership role in the Manhattan Project to develop the atomic bomb, and his downfall after World War II.

Atomic Bomb vs AI: similarities and differences

The movie has sparked many conversations in the public opinion about how the development of the atomic bomb resembles the dangers of an uncontrolled Artificial Intelligence (AI) use (Scientific American; Guardian; Wired). While these two innovations have some similarities, it is important to recognize their differences, too.
First, as also Nolan noted: “It’s reassuringly difficult to make nuclear weapons and so it’s relatively easy to spot when a country is doing that. I don’t believe any of that applies to AI.” Second, while AI is a broad term that covers a wide range of tools, nuclear fission is a specific chemical reaction with clear and known applications. Third, in this regard, if the atomic bomb detonation is the expected result of a process controlled by humans, most generative AI models are currently neither explainable nor predictable black boxes (repeating the same prompt may produce different results for which the machine was not even trained).

Thus, what can we learn from this movie that could still be relevant today and for future (revolutionary) innovations?

I believe that the most important takeaway is that the role of academia should not only be limited to develop innovations; rather, academics should also be actively involved in setting the rules and sensitize for a responsible use of the technology they contributed to being invented.
I will tell you more about it in the next paragraphs.

Academic activism

Nuclear activism

“This isn’t a new weapon, it’s a New World.”

After the two atomic bombs were dropped on Hiroshima and Nagasaki, Bohr’s prediction came true. The world changed forever. This is when Oppenheimer joined the already heated debate on using the bomb and began opposing the development of the H-Bomb. On this matter, he says in the movie that:

“Having played an active part in promoting a revolution in warfare, I needed to be as responsible as I could with regard to what came of this revolution.“

However, this position turned to be more problematic than expected. Several scientists that played a key role during the war were increasingly excluded from influential positions when it came to the future of atomic energy, due to their criticism (Bulletin of the Atomic Scientists). For Oppenheimer, this period reached its peak with the loss of his security clearance (Avalon Project). Nonetheless, academia managed to play a vital role in regulating atomic energy.

Particularly, Linus Pauling, an American chemist that received the Nobel Prize in Chemistry in 1954, had a huge influence on the U.S. public opinion in the disarming process and ban of nuclear weapons tests. In 1958, together with his spouse, he submitted a formal petition to the United Nations, urging the cessation of nuclear weapons testing. This document was signed by 11.021 scientists representing fifty countries. Moreover, Pauling also supported the research known as the “Baby Tooth Survey”, which provided legitimacy and a crucial boost to public opinion demands (Wikipedia).

Public pressure and the results of this research contributed to the signature of the moratorium on above-ground nuclear weapons testing, followed by the Partial Test Ban Treaty in 1963, between John F. Kennedy and Nikita Khrushchev. For his activism, Pauling received in that year the Nobel Peace Prize, which made Pauling the only person to have been awarded two unshared Nobel Prizes.

Other examples

Nevertheless, this is not an isolated case. The scientific community has played a pivotal role in regulating scientific innovations in other instances, too. The regulation of genetic engineering is another example (Krimsky, 2005).

The first publications describing the successful production and intracellular replication of recombinant DNA appeared in 1972 and 1973. Recognizing both the benefits and risks of this innovation, a group of experts (mainly biologists, but also including lawyers and physicians) met at Asilomar in 1975. During this conference, the experts discussed these concerns and a voluntary moratorium on recombinant DNA research was introduced for experiments that were considered particularly risky. These guidelines eventually informed the regulation set by the National Institutes of Health (USA) in the following years, which formally limited rDNA applications (Wikipedia).

Conclusion

The development of new technologies is leading us into an era of rapid transformation across various aspects of our lives. From Large Language Models to Blockchain, the pace of change is accelerating, while regulations often struggle to keep up (GZero).
In light of this, it is evident that academia’s role extends beyond just fostering the development and commercialization of new technologies. It also involves championing a responsible approach to innovation.

Engaged scholarship is a great starting point to increase the likelihood of advancing knowledge for science and practice (Van de Ven, 2007). However, as we have seen, sometimes the role of experts and citizens blur (Hoffman, 2016), making it necessary to reach another level of engagement: an engagement that leads to moving beyond the ivory tower of academia.

References

Hoffman, A. J. (2016). Reflections: academia’s emerging crisis of relevance and the consequent role of the engaged scholar. Journal of Change Management, 16(2), 77-96.

Krimsky, S. (2005). From Asilomar to industrial biotechnology: risks, reductionism and regulation. Science as Culture, 14 (4), 309-323.

Van de Ven, A. H. (2007). Engaged scholarship: A guide for organizational and social research. Oxford University Press, USA.

The EINST4INE network recently all met in Germany for our ‘Enabling Technologies Workshop’ hosted by the ENI team at the University of Stuttgart. As I reach the end of my second year of doctoral studies, I find myself mulling over career options post-PhD. The final session of this workshop gave us the opportunity to hear from highly successful academics (thank you to our energetic and passionate guest Christina Theodoraki for candidly sharing her career journey) and a safe space to ask many pending academic career questions to our experienced seniors. Here are some of my key takeaways and reflections:

A career in academia can be scary…

Firstly, I often already get asked “what do you actually do?” or “when you are getting a real job? Are you going to be a student forever?” to which I often laugh and even agree. If I can’t even explain what I do, how do I expect anyone to understand it? Perhaps I am on a path of being an eternal student, so what ‘value’ does that bring to the ‘real world’? This is difficult to justify. For some entertaining consolement on this issue, you can read De Vaujany’s (2016) expression of trying to answer what a management researcher does at a dinner party.

Secondly, even if you have figured out how to articulate what you do, you also have to believe it yourself. Many people in general suffer from imposter syndrome, but especially so in academia where we are trying to push the boundaries of knowledge, experiencing feelings of self-doubt regarding our intellect and abilities which is very challenging to overcome. Bothello and Roulet (2019) wrote a provoking essay in Journal of Management Studies, which outlines imposter syndrome in academic life, particularly for PhD students considering a career in management academia, that I recommend anyone in this position to read.

Thirdly, I am sure anyone reading this is familiar with the “publish or perish” culture. Particularly as a PhD student, even more so if publishing is a requirement of your PhD, this culture can feel overwhelming. Not only are we expected to publish, but to publish in highly ranked journals that can take years to develop, in order to advance one’s career. Even once you manage it, you’re then expected to do it more. Working in academia, you can easily fall victim to defining your worth through your h-index or publication output which seldom has positive outcomes, particularly for your mental health.

But it can also be exciting…

Firstly, as scientists, it is our job and our privilege to understand how the world works. Prompted by some wisdom from Calogero Oddo, us junior researchers realised that instead of talking about what we have done, we should perhaps focus on the how and the why. For a number of reasons, we tend to make a habit of reporting the quantitative metrics and measures e.g., I published in X journal, I attended Y conferences, as opposed to reporting on the science we are creating – those valuable insights that will help to push our field(s). I feel like sometimes I forget that I am working in such an exciting domain, and while I am not inventing any breakthrough concept, I am contributing knowledge and understanding to our collective discoveries.

Secondly, while I often get frustrated with the focus on pushing out publications rather than a focus on slow exploration and deep learning, reflecting with peers in industry and often those that completed a PhD themselves, it seems I can still be grateful that in academia we are spending time on questioning the why rather than pushing on with how and doing (sometimes before thinking). Both are vital, but it seems for those with curious minds then academia allows you to play and explore and question which is a wonderful thing.

Thirdly, since this line of work often includes international collaboration with a broad range of people (other scholars, organisations, institutions) we have so many exciting opportunities to explore the world to help understand it and ourselves. It might not be for everyone, and I am also acutely aware of the privileges in the ability to travel and attend conferences both personally and institutionally, but typically a career in academia opens you up to many experiences abroad whether it be through travel or through your colleagues and their diverse backgrounds.

Becoming a researcher or a scholar

A reflective moment I had as a result of some conversations during this workshop was that the terms “researcher” and “scholar” are often used interchangeably, but they can have slightly different connotations. A researcher is primarily focused on the systematic investigation of questions, problems, or phenomena to generate new knowledge, while a scholar is someone deeply engaged in the broader intellectual life of a field, which may include research, teaching, writing, and contributing to the academic community. While you can be both a researcher and a scholar, it is perhaps not often that you have the opportunity to be “scholarly” when you are working outside of academia.

Bottom line – you can make it your own and there are always choices.

There are many different pathways in an academic career, which even differentiates further depending on the country and institution you are based at. In light of this, in the final years of my PhD I will try to focus on:

• Engaging with different types of scholars and researchers to uncover the more ‘unknown’ or less obvious pathways,
• Understand what is meaningful to me without all the external pressures, topic hype, and noise,
• Determine what is realistic for me and which sacrifices I am willing to take or not take (which may look very different to others),
• Reflect on what my definition of success is,
• And consider what kind of impact I would like to make (on myself, colleagues, project partners, and even society more generally).

References:

Bothello, J., & Roulet, T. J. (2019). The imposter syndrome, or the mis-representation of self in academic life. Journal of Management Studies, 56(4), 854-861.

de Vaujany, F.-X. (2016). ‘The dinner: How can we explain management research just before dessert?’. Management, 19, 330–334.

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.  

Here, I refer to AI as any ‘intelligent’ system, mainly embodied but also virtual, that allows the automation of tasks with minimum or no human intervention in uncontrolled social environments. The type of AI that is or might be clearly displacing humans in some tasks at work. While some suggest that AI could stimulate economic development and create new jobs, others fear that it may devastate the economy [1]. Work is a very important part of our life, mainly because we spent most of our lifetime working but also because it is one of the main sources of meaning in life [2] and is considered a fundamental human need [3]. Through our day-to-day work, we can achieve things that will make us feel fulfilled and complete.

Then, what if AI takes that opportunity away from us?

For example, healthcare is a very vulnerable setting where AI can step in. Handing over activities that healthcare workers find meaningful to embodied AI technologies such as robots can lead to a loss of meaningfulness in their lives [2]. A nurse could find herself or himself asking “How to connect with patients if there is an intelligent robot taking care of them instead of me?” or “How am I going to be able to contribute to the patient’s well-being if this robot is doing what I used to do before?”. AI may represent a threat to meaningful work because tasks that were meaningful for them are not part of their routine anymore, however, there is another possibility: intelligent robots being an opportunity for meaningful work [4]. In that case, the nurse could be thinking “These robots are helping me with some tasks that I actually liked, but, in the end, it benefits the patients and that makes me feel good. Also, I have more time to focus on other meaningful activities”. As Sven Nyholm and Markus Rüther [2] put it, if AI systems take over activities that we find meaningful, then we have to find other meaningful activities or, otherwise, we will have a meaningfulness gap. According to the authors, the other option is that AI displaces humans only on mundane tasks. For this, it is necessary that AI technologies can take over these less meaningful activities without interfering with the activities that we consider meaningful [2].

Co-designing interactions for meaningful work with AI.

As we can see, there are many possible scenarios and “what if’s” for the many ways in which humans may feel about the automation of work. However, I believe that a design approach can help to give more certainty and direction to promote meaningful work. Design can be seen and defined from different perspectives; here, I talk about design as a process arrangement or rearrangement of elements in the best possible way to solve a problem. Thus, when I propose co-designing interactions, I refer to a collaborative organization and reorganization of the elements that form human-robot interactions. What are these elements? the features of the AI systems, the humans involved, the bystanders, the physical space, the role of the interactants, and the allocated tasks [5]. This list is not exhaustive, but it is a good start.

I think that interdisciplinarity may be key in navigating the ‘meaningful work with AI’ challenge. AI systems’ design, development, roles, and task allocation could be done by a group that involves or at least represents all stakeholders. Having a discussion on what tasks are meaningful, what tasks to automate, and how to balance the trade-offs can allow AI designers and developers to test their assumptions on what they thought could work in real life [1], and thus automate work in a dynamic and democratic way. The dynamism is based on the fact that many iterations and sessions with dialogue and negotiations have to be constant as part of a holistic process [6]. Meaningful work is a complex concept that most people may find difficult to define or understand if and how their own work is meaningful. Thinking about meaningful work invites self-reflection, so I think designing AI systems with meaningful work in mind is not an easy task, but it is relevant if we want a sustainable use of AI at work.

Is co-designing robotized interactions an ethical or an organizational endeavor?

We need mindful task allocation that considers the meaningfulness of work. But, who decides what tasks should be assigned to AI systems? Is it an ethical or an organizational discussion? I think it is both. Ethics can provide a list of the general criteria we should base our decisions on how to design human-robot interactions. For example, general criteria for good care involve a relevant amount of human contact [7]. Therefore, when assigning tasks between robots and healthcare workers we need to make sure that the workers have enough human contact, and for that, we need to consult them and not just assume that people from management know the answer.

Ethical discussions will allow us to design a more human work. We don’t need AI technologies to be the ones designing our work for us with systems that can predict and manage workflows for the sake of efficiency. As Kate Crawford [8] puts it, “Rather than representing a radical shift from established forms of work, the encroachment of AI into the workplace should properly be understood as a return to older practices of industrial labor exploitation established in the 1890s and early 20th century”. Indeed, research has shown that when work is designed only to improve efficiency, there is a decrease in satisfaction, but when both efficiency and satisfaction are considered, the tradeoffs are avoided [9]. This is why we need humans behind the redesign of work. Humans need to be engaged in the co-creation of meaning to feel that our lives are valuable [3].

References

[1] A. Rojas and A. Tuomi, “Reimagining the sustainable social development of AI for the service sector: the role of startups,” JEET, vol. 2, no. 1, pp. 39–54, Nov. 2022, doi: 10.1108/JEET-03-2022-0005.

[2] S. Nyholm and M. Rüther, “Meaning in Life in AI Ethics—Some Trends and Perspectives,” Philos. Technol., vol. 36, no. 2, p. 20, Jun. 2023, doi: 10.1007/s13347-023-00620-z.

[3] R. Yeoman, “Conceptualising Meaningful Work as a Fundamental Human Need,” J Bus Ethics, vol. 125, no. 2, pp. 235–251, Dec. 2014, doi: 10.1007/s10551-013-1894-9.

[4] J. Smids, S. Nyholm, and H. Berkers, “Robots in the Workplace: a Threat to—or Opportunity for—Meaningful Work?,” Philos. Technol., vol. 33, no. 3, pp. 503–522, Sep. 2020, doi: 10.1007/s13347-019-00377-4.

[5] K. Fischer et al., “Integrative Social Robotics Hands-on,” IS, vol. 21, no. 1, pp. 145–185, Jan. 2020, doi: 10.1075/is.18058.fis.

[6] R. V. Zicari et al., “Co-Design of a Trustworthy AI System in Healthcare: Deep Learning Based Skin Lesion Classifier,” Front. Hum. Dyn., vol. 3, p. 688152, Jul. 2021, doi: 10.3389/fhumd.2021.688152.

[7] M. Coeckelbergh, Robot ethics. Cambridge, Massachettes: The MIT Press, 2022.

[8] K. Crawford, Atlas of AI: power, politics, and the planetary costs of artificial intelligence. New Haven: Yale University Press, 2021.

[9] F. P. Morgeson and M. A. Campion, “Minimizing Tradeoffs When Redesigning Work: Evidence from a Longitudinal Quasi-Experiment,” Personnel Psychology, vol. 55, no. 3, pp. 589–612, Sep. 2002, doi: 10.1111/j.1744-6570.2002.tb00122.x.

Originally published at: https://www.alejandrarojasblog.com/codesigning-interactions-for-meaningful-work