Analog Twin

Analog Twin

by with No Comment ESR TravelogPhdLife

During our recent ENST4INE workshop in Denmark, we had an insightful tour at Universal Robot’s (UR). Part of the tour was an introduction to the programming of their robots in their educational facilities. While brief, it was amazing to get the robot to do some simple stacking tasks. Previously I had been working with a virtual version of the UR robot as part of a paper with my professor at the University of Southern Denmark (SDU), where we investigated different flexible production scenarios using simulation (see Figure below). It was surprising, how much of the knowledge I acquired during this project was directly transferable to programming the robot in real life. While the simulation clearly does not suffice to fully teach how to program a robot, many processes and concepts were almost the same. After this workshop I am convinced, that the simulation work I had been doing before can aid me in understanding real world applications and is suitable to evaluate a possible set up in a production line.

Analog Twin
Picture from: Ribeiro da Silva, E., Schou, C., Hjorth, S., Tryggvason, F., & Sørensen, M. S. (2022). Plug & Produce robot assistants as shared resources: A simulation approach. Journal of Manufacturing Systems, 63(March), 107–117. https://doi.org/10.1016/j.jmsy.2022.03.004
Analog Twin
Programming the robot (Picture taken by Parul).
My first secondment with AMPLYFI

My first secondment with AMPLYFI

by with No Comment PhdLifeSecondment

So, one step further in my PhD journey: my first secondment with EINST4INE is in the books. I spenT some time with one of my industry partners: AMPLYFI in Cardiff. Here, I summarise a few useful learnings that might be helpful for your research as well!

AMPLYFI

My first secondment with AMPLYFI
Cardiff Bay (picture taken by Constanze Leeb).

The company I visited during my first secondment is part of the EINST4INE consortium. AMPLYFI is a company providing a research platform which uses AI to analyse content and uncover previously hidden trends and opportunities. For this, I moved to Cardiff, Wales (so once across the U.K. from East to West) where the company is headquartered  and spent a few weeks with AMPLYFI. If you have never been – Cardiff is a great city, with an amazing castle, lots of nice arcades and it’s next to the sea (and if you time it right and are lucky, like me – you can maybe see the King)! Hint: bring a waterproof jacket (and backpack) and no umbrella – it can get quite rainy and windy.

Real fieldwork – A typical day at AMPLYFI

My days at AMPLYFI were always interesting and fun. First, the team is really nice and the atmosphere very motivating. Second, I was able to participate in many things during my time there. I had a desk in a shared working space, could come to the office every day, met many of the team, and was able to participate in most things, which enabled me to really experience the company and their activities. I talked to many colleagues, sat-in on meetings and calls, participated in workshops and was able to get hands-on experience with AMPLYFI’s tools.

Industry vs. Academia

You have all heard this before, but it really is true: the pace is very different in the industry. Everything happens faster – especially at a young company like AMPLYFI! A colleague has an idea, a small team gets together to look into it, and a few weeks later you can see the results. It is incredible how quickly AMPLYFI’s tools develop, I am sure there will be something new when I return for my second placement with them!

My first secondment with AMPLYFI
AMPLYFI’s Board Room (picture taken by Constanze Leeb).

What impressed me as well is how well organised AMPLYFI is. They are working closely across teams, keep each other up to date on their work, and have detailed documentation of their tasks performed – something that academia can benefit from as well!

Lastly, I have always studied intelligence work from a distance – reading about it and hearing others talk about it. But I have never had to perform it myself. During my time at AMPLYFI I was able to do a little research project on a topic of my choice using AMPLYFI tools, which was an invaluable practical experience. What I definitely learned from it is that intelligence work is just as hard and complex as I thought it is and my respect for all people performing intelligence work has increased even more.

Learnings for and from fieldwork

My first experience in fieldwork was great, but I did realise that many of the things that made it great came from my Sociology background and me taking time before the secondment to visualise what it would be like. Therefore, I am summarising some of my learnings here:

  • Be well organised!
    When you enter the field, it can be overwhelming. So, it is best if you already thought about a few things and prepared them. For example:

    • Know where you want to take which kind of notes. Especially, if your fieldwork will entail data that is not being recorded on audio or video!
    • Prepare folders beforehand for your data, e.g.: interviews with employees, interviews with customers, etc.
    • Think about a naming system for your data. Are you going to number your interview partners? Are you going to name the files after the interview partners, the day of the interview, etc.?
      My first secondment with AMPLYFI
      View from AMPLYFI’s Board Room.
  • Be part of everything you can!
    Often you learn the most interesting things in situations least expected. So, if you have dedicated time for fieldwork – use it and participate in everything you can.

    • You will not only learn a lot by participating in meetings, events, social activities etc. – you will also meet people! And this in turn can give you access to new interview partners or new events.
    • When you hear about an interesting event. – just ask if you can participate. The answer will likely be yes, as you will have some form of NDA/CDA in place anyways. Remember: others don’t know what you need or are specifically interested in! And also: Worst case – the answer is no, but at least you asked!
    • If you feel like you have no idea of what is happening – just participate anyways, you will figure it out on the way. Or not – and then you can ask about it afterwards.
  • Just ask!
    We all know the fear of asking questions from school or university. Of course, also back then the lesson was: just ask! This is also true here, especially as you are entering the field from outside – nobody expects you to know everything!

    • If you don’t understand something – ask about it. Usually, people are very happy to help and to explain something. Even if it is basic for them, they understand that it isn’t basic for you. Also – this is what research is sometimes about right? Asking the obvious questions to understand deeper underlying concepts.
    • Ask for access to internal documentation and access to internal communication! This will give you invaluable insights and help you understand your field a lot better! Just ask and explain that it’s insightful for a comprehensive understanding – if they are not happy to share, they will tell you so.

If you are interested to hear more about my time at AMPLYFI: I also wrote a blog post for the AMPLYFI website! You can check it out here, next to other interesting blogs on the AMPLYFI website.

Image 1 - Virtual Reality in Architecture © www.autodesk.com

Try it before you buy it: a case study on VR potential for building design

by with One Comment Perspectives

Introduction

The abilities to represent and simulate have always given humankind terrific support for taking decisions. The representation of space, for instance, has been one of the most important inventions of human history. Maps still allow humans to explain and navigate the world they live in.

Architecture is one of the fields that gained more from those abilities given the complexity and costs involved in the matter, and the related critical decisions. The capacity to represent space has evolved along with technological advances. The dawn of computer graphics replaced handmade scale models and graphical representations with digital tools that allow a more accurate, fast, and flexible representation and simulation. Rapid-growing technologies like augmented reality (AR) and virtual reality (VR) have huge potential in the representation of spaces and in informing the decisions related to them.

Figure 1. Architect Norman Foster checking out a project in VR. Source: Instagram, officialnormanfoster.

The case study

A case study conducted by Bianconi et al. (2019) explored the potential of VR technology for decision-making in construction from the double perspective of the designers and the occupants of the space.

In the study, an office building was reproduced in a virtual space and made explorable using VR headsets (Figure 2). A series of experiments involved several participants which were asked to perform orientation tasks in the virtual building. The tasks required to find and reach specific rooms and to estimate their position within the facility. Collected data included the orientation success rates but also tracking of the movements of participants in the building (Figure 3) and their gaze point (where the user was looking). This information has been used to understand where the occupants got lost and where they were looking for orientation cues to reach the objectives. The data allowed to spot critical design issues and failures in the wayfinding system.

A renovation proposal has been developed and modeled in virtual space addressing the weak points that emerged from the first round of experiments. The refurbished virtual facility has then been tested in VR using the same protocol and collecting the same type of data (Figure 4). Results revealed an increased success rate of orientation tasks and less bewilderment for the participants.

Figure 2. From the left: (1) the real building’s atrium, (2) its virtual reconstruction and (3) the heatmap representing the eye-tracking data. Source: Nicola Felicini.

 

Figure 3. Tracking of participants’ movements in the reconstructed building (left) and its proposed renovation (right). Source: Nicola Felicini.

What have we learned?

Even in this case, the representation and simulation capacities proved to be efficient tools in informing and taking decisions. The advantages of representation, enhanced by the digital nature of this one, allowed data collection in a controlled and controllable environment.

Considering the inconveniences of testing inside an operating building or the costs of building a mock-up or the proposed renovation, the amount of work appeared negligible compared to ‘real’ test options. Virtual reality has been confirmed to be a cost-effective way of running complex spatial simulations.

Another relevant aspect of the case study was the interaction between the human and the digital environment. The value of the case was not in the representation and simulation of the building but in the interaction between it and the occupants. Wearable digital tools like VR allowed a closer look at what decisions occupants took (movement tracking), which information they needed and where they looked for them (eye-tracking). As this case exemplifies, wearable technologies can constitute means of access for humans to the digital realm (and vice-versa), enabling new ways of interaction between the ecosphere and the infosphere.

Figure 4. The atrium of the renovation proposal and the eye-tracking heatmap obtained in the second round of tests. Source: Nicola Felicini

What’s next?

With the diffusion of digital tools, we are assisting in a progressive ‘democratization’ of them. At the time of the study, the simulation presented required intense modeling work and knowledge of specific software and techniques. Current trends in building design tools allow real-time immersive simulation as the building gets ‘drawn’. In opposition to the simplification ad diffusion of digital tools, we need to clarify their actual potential and when they are worth the investment. In the presented case, which design decisions have been taken following the architect’s experience and which are derived from the simulation in the digital environment? A comparative study with analog design techniques could elucidate these aspects.

Figure 5. In recent years immersive tools have been proposed as decision-making environments for construction. But their implementation still seems a big leap compared to current practices. Source: Meta.

 

 

 

 

 

 

 

 

 

 

 

 

The other question that emerged from the study, is how much reality there is in digital reality? In other words, how applicable are the results obtained in a virtual environment, in the real world? Would those occupants take the same decisions in the real building? How much does the level of immersivity affect the behaviour? Again, a comparative study could help identify the weight of these factors.

Investigating the potential of digital simulations and their relationship with the real world are promising research directions that will acquire importance as digital tools and virtual experiences progressively gain ground in our daily lives.

 

Source: Bianconi, F., Filippucci, M. and Felicini, N., 2019. IMMERSIVE WAYFINDING: VIRTUAL RECONSTRUCTION AND EYE-TRACKING FOR ORIENTATION STUDIES INSIDE COMPLEX ARCHITECTURE. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences.

michael-fousert-RvmTvJ6tSwA-unsplash

Socially responsible robotics: AI and robotics startups as part of a complex system.

by with 2 Comments PerspectivesPublication Summary

Startups are creating innovative robotics and AI solutions for the service sector such as an  AI-powered floor cleaning robot or a bartender robot.

Robotic bartender
Bartender robot. Photo by Michael Fousert on Unsplash

These technologies are triggering a job transformation for the service workforce that might compromise social and cultural sustainability. To frame startups’ role in the sustainable social development of the service sector we might need to look into other stakeholders’ actions. The responsibility of AI and robotics startups needs to be understood and acted upon in relation to other stakeholders. In other words, AI startups need to take responsibility internally, but they also need support externally from different actors to ensure that everybody is playing their role accordingly. This external support is based on collaborative efforts; however, sometimes it is difficult to collaborate across stakeholder borders because of the different vested interests and ensuing politics.

Here are some of the main stakeholders in the service ecosystem and how they can contribute to the sustainable social development of the service sector:

Investors: Include ethics consulting in investment decisions.

In the end, if venture capitalists or angel investors make ethics a key part of the startup funding cycle, AI and robotics startups have to pay attention. Investors should be motivated to fund ethical practices because it also protects them from risks that might damage the venture further down the line.

Academia: Promote university-led accelerator programs.

These programs can set a foundation for success and facilitate the continuous flow of information between businesses and academia. Startups can collaborate with experts in fields such as human resources and ethics to help them envision a sustainable way of using their products and services.

Social impact startups: Solve the “problem” created by AI and robotics startups.

Partnerships with social impact startups could work because the primary role of such startups is to innovate with solutions for unemployment, promoting social, economic, and cultural benefits. For example, these kinds of startups could develop tools that contribute to decent work by matching people to new jobs and finding creative ways to help people find a job. If AI startups are creating a “problem” by displacing some workers with robots, then other startups should take solving the problem as a new business opportunity.

Service providers and government: Create a balance with incentives.

Service providers should receive a public fund from the government to motivate them to hire companies with ethical solutions and for reskilling and upskilling the service workforce. Service providers’ role is also to redefine job profiles. Thus, service providers need to proactively balance task substitution and task enhancement to define new job positions.

Customers: Demand ethical practices.

Usually, AI startup founders will have no interest in ethics, but if the customer asks for an ethical approach, they might focus on it. Thus, if the main objective of a startup is to find product-market fit, and the market is asking for ethical practices, the startups have no choice but to follow the trend.

My key takeaway is that creating a more sustainable future with AI and robotics is a task we all have. It is not just about designers and developers, but also about how society understands and demands AI and robotics.

 

Source: Rojas, A., & Tuomi, A. (2022). Reimagining the sustainable social development of AI for the service sector: the role of startups. Journal of Ethics in Entrepreneurship and Technology, (ahead-of-print).

Photo by Ramon Salinero. Source: Unsplash

What is ecosystem orchestration?

by with One Comment Perspectives

Don’t ecosystems belong in nature? Isn’t orchestration to do with music? Yes and yes, but also, no 😊 as you will (hopefully) find out, these metaphors effectively provide meaning to practical and theoretical management concepts.

You might realize that academics like to use big words because of the need to convey complicated things in simple ways. Likewise, businesses like to use buzzwords to stay up-to-date with the latest talk and trends. Often, this results in terms being adopted and interpreted in many different ways where they risk losing meaning and understanding. While it is not necessarily wrong for terms to be applied in different ways, and there is beauty in this creativity, it is useful if there is an agreed consensus on what is meant.

Almost a year into my PhD on innovation ecosystem orchestration mechanisms, I still wonder myself what exactly that means. So, let’s go back to basics and unpack this. Please note: this is my perspective and developed understanding so far – subject to change and interpretation!

Starting with innovation, this essentially refers to the creation of change. The scale of this change can vary from incremental improvements to completely disrupting markets, as well as radical breakthrough change or more architectural improvements to a product/service. But, creating change for what? Innovation and innovative thinking span many contexts and provide tools suited to address challenges across the board including business, social, and environmental.

For innovation to materialise, there are clever strategies that bring together diverse set of actors (private companies, startups, government, etc.) to work together towards an aligned goal in which they can co-create shared value. This is what we call an ecosystem. This can be challenging as it relies on interdependencies – the dependence of two or more entities on each other – among these actors to make this happen.

A way to approach this is for a ‘hub firm’ to take the lead and orchestrate these activities. The role of an orchestrator is multifaceted whereby they can attract new actors to the ecosystem, facilitate resources and interactions between actors, resolve any tensions that may arise, find alignments across the ecosystem, and so on. What is unique about this management style is that it is intended to be non-hierarchical and instead focused on fostering cultures that create innovative thinking. I like to think of orchestrators as positive encouragers, empowering supporters, and uplifting influencers.

As for mechanisms to do this, I will leave you on a cliffhanger for a future blog post where I can share some results from my findings!

Photo by Michael Dziedzic on Unsplash

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

by with No Comment PhdLife

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 

Wessel, L., Baiyere, A., Ologeanu-Taddei, R., Cha, J., & Jensen, T. B. (2021). Unpacking the difference between digital transformation and IT-enabled organizational transformation. Journal of the Association for Information Systems, 22(1), 102–129. https://doi.org/10.17705/1jais.00655 

Zott, C., Amit, R., & Massa, L. (2011). The business model: Recent developments and future research. In Journal of Management (Vol. 37, Issue 4, pp. 1019–1042). https://doi.org/10.1177/0149206311406265 

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 

 

istock-1095801920-smart-city

Augmented Reality: The digital face of the future cities

by Perspectives

In 2007, when only a few had a smartphone in their hand, humanity stored a hundred times more information than twenty years before [1]. More impressive is that 90 percent of that data was generated in the two previous years [2]. The pace is only accelerating. By 2025 the figure is expected to reach 463 billion or GB generated every day! [3]. This avalanche of data comes from the digitalization of our societies. We started by making our phones smarter, now we are making cities smarter.

A smart city is a technologically modern urban area. It uses a large number of sensors to collect information about buildings, assets, citizens, and devices. In return, this data is used to manage resources and services more efficiently. The concept couples with the so-called Internet of Things (IoT). The same concept but applied to a smaller scale, typically to an electronic device or a group of them. With the progressive diffusion and capillarity of connectivity, the distinction between the two might fade, leaving space for a unique network of connected assets.

Digital models of buildings are generated during the design face. If after construction are connected in real-time with the real building they become ‘digital twins’. Source: Autodesk.

The tendency of coupling digital data with real entities is already present in specific sectors. Modern buildings are digitally built using accurate 3D models before the first brick gets leaned down. The same happens with complex industrial machinery like jet engines. Digital copies of patients are being studied for healthcare applications. These ‘digital twins’ are created to collect and store data: health changes of patients, performance statistics of an engine, and energy consumption of a building. The objective is always the same: to enhance knowledge, optimize resources, and prevent failures.

Some examples of connected assets aimed to make cities smarter can already be seen around. Navigation systems already suggest the shortest route given the status of traffic. The combination of this capability with autonomous driving and different ownership models (car sharing) seems a promising solution to traffic congestion. Barcelona, Manchester, Prague, and Melbourne are experimenting with connected bins to communicate how full they are and optimize trash collection. In the Netherlands, millions of smart meters have been installed to respond to surges or reductions in demand, increasing reliability and cutting costs. The states of New York and Norway are experimenting with intelligent public illumination that allows the lights to be adjusted based on real-time data.

However, smart city development is still an infant with different cities at different maturity. Digitization of cities is an impelling challenge given their importance for the future. According to the UN, by 2050 seven people out of ten will live in cities [4].

Connected bins and interactive meters are all examples of smart city application being tested around the globe. Source: Manchester Council, Times.

Facing this perspective of pervasive digitisation and massive urbanizaton, we need to re-imagine the way we interact with urban areas and information, to get the most out of these ‘data cities’.

For decades we relied only on screens and buttons. A first change happened with the graphical user interfaces (GUIs) and the commercialisation of the mouse in the 1970s. The advent of smartphones brought touchscreens that we can tap and swipe. Cutting edge technologies like computer vision for gesture recognition, ultrasounds for mid-air haptic and wearable sensors promise to make the touch unnecessary soon. While waiting for Elon Musk to directly connect our brain to computers, augmented reality (AR) is the step further to remove barriers between digital and physical worlds.

Augmented reality enhances the physical world by overlaying digital information in our field of vision. A recent study in the UK reports that only 11% of smartphone users are believed to have experienced AR [5]. This data is in contrast with the hundreds of millions of augmented pictures shared daily on social media [6] or the success of apps like Pokémon Go or Ikea Place (yes, they are all examples of AR). This unconsciousness highlights how AR is more diffused than we might think. Tim Cook, CEO of Apple compared the phenomena to the diffusion of apps. When the App Store went live in 2008 people were skeptical, saying: “mobile apps are not going to take off. And then, step by step, things start to move… and now you couldn’t imagine your life without apps. AR is like that. It will be that dramatic” [7].

Face filters are a diffused AR application used by millions of people every day. Source: Snapchat.

Even if awareness is currently low, people will progressively expect to access additional layers of content on the surfaces around them [8]. This will change our relationship with the built environment. Every object and building will become a potential trigger for digital content, which will be proactively suggested to the users at the right moment.

On top of the brick-and-mortar cities, we walk every day, we’ll see pop-up advertisements from brands, emergency alerts from authorities, service communications from institutions, and entertainment content during events. It will also work the other way around as AR will give “a face” to the smart city. Citizens will be able to upload information, report problems, and communicate with institutions from wherever they are. All things we get already, but done through the ‘diffused app’ the smart city will be; in a more frictionless, flexible, and natural way.

AR will unleash endless possibilities to add digital content in our daily experience. Consuming media on surfaces will become the norm as today is the use of a smartphone to access the internet. Source: Price Action Guide.

References:

[1] 2011, Hilmert, M & Lopéz, P. The World’s Technological Capacity to Store, Communicate, and Compute Information. Science.

[2] 2018, Bernard Marr. How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read. Forbes. Available at: https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/?sh=72c0153d60ba

[3] 2019, Desjardins Jeff. How much data is generated each day? World Economic Forum. Available at: https://www.weforum.org/agenda/2019/04/how-much-data-is-generated-each-day-cf4bddf29f/

[4] United Nations, Department of Economic and Social Affairs, Population Division (2018). The World’s Cities in 2018—Data Booklet (ST/ESA/ SER.A/417).

[5] Layered survey. April 2018, n=1000 UK.

[6] Snap Inc. Data Q3 2017.

[7] Griffin, A. (2017). Apple’s Tim Cook on iPhones, Augmented Reality, and How He Plans to Change Your World. Independent [online], 10 October 2017. Available at: https://www.independent.co.uk/life-style/gadgets-and-tech/features/apple-iphone-tim-cookinterview-features-new-augmented-reality-ar-arkit-a7993566.html (accessed 14 Aug 2022).

[8] Mindshare Futures (2018). Layered. Available at: https://d2j4z507ms5wl7.cloudfront.net/zappar_mindshare-layered-report.pdf.

patrick-perkins-ETRPjvb0KM0-unsplash

The importance of conjugating academic insights with industry engagement in management field

by with No Comment PhdLife

From the 4th to the 8th of July, my institute, RMIT Europe, hosted the first EINST4INE Summer School. Over the course of the week, numerous activities took place, enabling us, ESRs, to expand our academic knowledge, from one side, and understand the new horizons and frontiers in innovation and digital transformation, from the other.

On Tuesday, the 5th of July, we got the opportunity to participate in an industrial challenge involving two of our consortium partners: Innoget and Enel.

I took part in the challenge proposed by Innoget, which expected us to draw an applied technology roadmap for a clear understanding of potentially available digital solutions to improve the interactions among the different stakeholders.

Roadmaps are practical tools that help to clearly visualize plans for long-term objectives. During the summer school we attended a lecture by Dr. Rob Phaal who gave us a theoretical understanding of “roadmapping” and its potential applications in numerous fields, including our PhD journey.

During the Innoget challenge, people from my team and I firstly set a horizontal timeline on the top and a vertical column on the left, including the “why” (i.e., the objectives), the “what” (i.e., the elements we need to get the objectives) and the “how” (i.e., elements to build the “what”, in other words, the foundation of the plan).

Afterwards, we started to think about the goals, in particular those in the long-run, and then we progressively built the whole map accordingly. Building roadmaps is an iterative process, where many trials and errors take place. A first brainstorming session was essential for us to think outside the box and imagine big long-term goals for Innoget; we used sticky notes to collect and share ideas.

The picture was taken by Chiara Mariottini – from Innoget challenge – group presentation

This challenge was particularly useful because we got the chance to apply something theoretical from the management field to a practical industrial case, building a roadmap according to the expectations and the needs of an industrial partner.

Understanding the new horizons, needs, and frontiers in innovation and digital transformation is particularly important to identify hot topics and outline research projects that can practically solve specific issues or produce impactful results for companies operating in the market.

During the summer school we also got the chance to visit DFactory, a hub for the creation of an ecosystem to encourage the promotion and development of 4.0 industry. We saw robots, 3D printing, and other advanced Industry 4.0 technologies in action, understanding their functioning and their practical applications.

All these inputs were extremely valuable starting points to expand my practical knowledge about Digital Transformation, Industry 4.0, and innovation, and gave me new lenses for the exploration of the topics I am now addressing in my research projects.

Picture taken by Chiara Mariottini – DFactory – 08/07/2022
Picture taken by Chiara Mariottini – DFactory – 08/07/2022
Picture taken by Chiara Mariottini – DFactory – 08/07/2022

terren-hurst-blgOFmPIlr0-unsplash

My recent experience of presenting the research paper in a conference.

by with No Comment PhdLife

The majority of researchers hunt for a reputable conference to present their work at. As a master’s student, I presented several papers at various conferences, so I was accustomed to doing so and knew how to present my research, but as a doctoral student, this was my first conference in Italy. IEEE MeMeA 2022 is the name of the conference, which was held in Catania from June 21 to June 25, at the UNA hotel. The research papers of numerous researchers from various nations were presented. The opportunity to network with researchers from other scientific fields was a wonderful experience.

The title of my research paper on robotics is Tactile sensors for material recognition for social and collaborative robots. It was a review paper; it may be preferable to begin a research project with a review paper because it aids in a thorough understanding of the subject and stimulates reading more recent material pertinent to our investigation. After reading other people’s research on the same subject, writing a review paper might also give us fresh ideas. Anyone can read the paper I wrote by simply typing its title into IEEE Xplore, where it will be published.

The points I’m sharing for a good conference presentation are listed below:

  • Practice several times in front of a mirror.
  • Discuss your paper with your supervisor.
  • Be confident when presenting the paper.
  • Always put your presentation earlier in the conference room computer and check all slides one time to make sure all slides are working.
  • Don’t use new fonts in the presentation that are not supported in old versions of PowerPoint.
  • Use pptx or pdf format, take both formats with you.

A few researchers presented their posters on the first day of the three-day conference. Most conferences feature a poster session. During this time, researchers must display their posters on walls or on whiteboards. After that, many attendees approach the posters and ask questions about them. Poster sessions typically last for two hours or longer, during which time researchers must stand in front of their posters and answer the questions of visitors. My presentation was scheduled on the second day: I first went to see some research posters before going to the presentation room and delivering my talk. The presentation was fantastic. During my presentation, one professor asked me a few questions and said he would like to collaborate with my research.

In conclusion, I would like to address anyone who believes that if you present your study at a conference, it will be made public and someone else might steal your research concept. This is true in some cases; however, the longer portion of your work should be submitted to a reputable journal. I would suggest creating a plan whenever you begin a research project so that you can present a portion of it at a conference and later submit the entire thing to a reputable journal.

What is an ecosystem? Comparing industrial and academic perspectives

What is an ecosystem? Comparing industrial and academic perspectives

by with 4 Comments PhdLife

A terminology swamp

As I wrote in the last post, in June I participated in the first two live conferences of my PhD.
At ISPIM, I presented a preliminary study on Technology Social Ventures (TSVs), namely companies with a social mission and a tech-based strategy, taking an ecosystem perspective. Instead, at EURAM, I shared the findings of a work focusing on the entrepreneurial level, to understand how TSVs manage the social and technological component of their strategy.

Photo taken during my presentation at ISPIM 2022, Copenhagen
Photo taken during my presentation at ISPIM 2022, Copenhagen

 

Based on that, you may have realized that my main research interests lie at the intersection between technological innovation, societal challenges and ecosystems. Today, I would like to talk about this last topic.
Indeed, taking inspiration from the conversations I had at ISPIM conference, I realized:

  • First, how difficult it is explaining what an “ecosystem” is to someone not working in the field.
  • Second, scholars apply this concept in different ways and contexts, not always highlighting the differences.
  • Third, despite its wide adoption in the industry, practitioners usually refer to the classic conceptualization of business ecosystem, neglecting the other definitions identified in the literature.

Therefore, in this blog I would like try to address this mismatch providing a brief overview on what characterizes an ecosystem, according to the literature.

Why

But before that, why are we even talking about ecosystems in the management literature? To answer this question, we should go back to the early 90s, when J.F. Moore adopted a Darwinian perspective to describe market competition’s dynamics. He argued that:

“Successful businesses are those that evolve rapidly and effectively.”

That is, those who are best able to adapt to the environment. Yet innovative businesses can’t evolve in a vacuum.
In fact, drawing from other recent anthropological and biological discoveries, he suggests that a company can be viewed not as a member of a single industry but as part of a business ecosystem that crosses a variety of industries (Moore, 1993).

However, the diffusion of this concept remained latent for almost a decade, until Iansiti and Levien (2004) recalled it in their HBR Article titled “Strategy as Ecology”. Finally, it is a few years later, when Adner (2006) showed how most traditional companies fail to commercialize breakthrough innovations in isolation, that the ecosystem concept definitely took hold.

Most likely, one of the enabling factors of the increasing adoption of this term in the late 2000s is the advent of digital technologies. Accordingly, collaborations between organizations became easier and more frequent, making the proper management of interdependencies crucial, as predicted by Moore three decades ago.

What

That said, what is an ecosystem?

An ecosystem can be defined as “an interdependent network of self-interested actors jointly creating value” (Bogers et al., 2019). In other words, we can see an ecosystem as a set of organizations collaborating to offer a specific product or service, without having formal bonds or hierarchical relations.

However, ecosystems can take several forms, which makes our blog even more interesting (or complex, depending on the perspective 🙂 ). Here, I will briefly present four of them (Scaringella & Radziwon, 2018), starting from the most embedded in the geography literature to the most abstract one:

  • Entrepreneurial Ecosystem:
    • “a set of interdependent actors and factors coordinated in such a way that they enable productive entrepreneurship within a specific institutional context” (Stam, 2016);
      • Example: Typically these studies examine a high-technology cluster or the linkage between universities and local companies, such as Silicon Valley.
  • Knowledge Ecosystem:
    • “users and producers of knowledge that are organized around a joint knowledge search, and as such need to be located in close proximity” (Järvi et al., 2018; Van der Borgh et al., 2012).
      • Example: The High-Tech Campus Eindhoven (HTCE), in the Netherlands, served as the object of the study for Van der Borgh et al. (2012).
  • Business Ecosystem:
    • “a system in which companies coevolve capabilities around a new innovation, developed by a focal firm. They work cooperatively and competitively to support new products, satisfy customer needs, and eventually incorporate the next round of innovations” (Jacobides et al., 2018; Moore, 1993).
      • Example: Apple is the leader of an ecosystem that crosses at least four major industries: personal computers, consumer electronics, information, and communications (Moore, 1993).
  • Innovation Ecosystem:
    • “the alignment structure of a set of actors with varying degrees of multi-lateral, non-generic complementarities that are not fully hierarchically controlled, providing components and complements, in order for a focal value proposition to materialise” (Adner, 2017; Jacobides et al., 2018)
      • Example: Digital platforms such as the Apple Store can be described as innovation ecosystems. Instead, the Michelin’s PAX run-flat tire system presented by Adner (2017), represents an example of a non-platform based ecosystem, requiring an alignment between the actors to let the innovation materialize.
Description of the iOS ecosystem, taken from Shipilov and Gawer (2020)
Description of the Apple Store ecosystem, taken from Shipilov and Gawer (2020).

How

After having seen the definition, now I will report some of the main elements that help to identify an ecosystem from other concepts.

Among the most relevant factors, we can mention:

  • Complementarities
    • Three types of complementarities exist.
      • Unique: The first one means that A wouldn’t function without B (and, in case of co-specialization, viceversa), requiring coordination among the actors to achieve success.
      • Supermodular or “Edgeworth”: complementarities describe a relation between two objects, which can be two different products, assets, or activities, where more of A makes B more valuable.
      • Generic: even though a particular good or service may be needed for the production of a complex value proposition or innovation, that good or service may be generic (i.e., standardized) enough for firms to draw on it with little concern for governance structure or risks of misappropriation.

Ecosystems must be identified just with the first two typologies (Jacobides et al., 2018).

  • Interdependencies

While complementarities represent an economic relationship in terms of the potential for value creation, interdependencies represent a structural relationship between offers, in terms of how they are connected for the value to be created (Kapoor, 2018).
In a nutshell, this means that they are even more complex to manage, because they are not directly related to an economic exchange.
To be clearer, I will report an example related to Tesla, drawn from Kapoor (2018):

  • The structure of interdependencies between car producer and cell producer is distinct from the structure of interdependencies between car producer and charging infrastructure provider:
    • The first one has a direct relation;
    • The second one has a indirect relation, mediated by the user;
    • Moreover there are other interdependencies between cell producer and charging infrastructure provider or even between charging infrastructure and the electricity grid.

Therefore, the core concern for research grounded in an ecosystem perspective is to explain firms’ strategies and outcomes through the lens of such complementarities and interdependencies.
It follows that the co-evolution of actor’s strategies represents another key element, given the kind of relationships that exist between them (Ritala & Almpanopoulou, 2017), as well as the existance of a system-level outcome (Autio & Thomas, 2021).

Finally, we should highlight that these relations between different parties are usually multi-lateral and not hierarchically or formally controlled, compared to the dyadic relations that can be found in other structures, such as supply chains, markets and networks (Adner, 2017; Shipilov and & Gawer, 2020).

As you can see, talking about ecosystem means looking beyond the strict boundaries of an organization, something that now is more needed than never, given the complex societal challenges we are facing. And this is what I like the most about this concept, as it is an example of how and why managers should adopt wider lenses to define their strategies (Adner, 2012), aligning them with all the relevant stakeholders.

I hope that this post helped to clarify (even though in a non-exhaustive way) the ongoing conversation about ecosystems.

Now, it’s time for the second episode of my column!

 

Surfin’ Internet

  • Video:
    • If you haven’t had enough on ecosystems yet, this Debate from DRUID Conference 2019 won’t let you down.

 

  • Academic article
    • In this recent article introduced by Denny Gioia, Gabriela Rivera displays all the contradictions and mixed messages PhD Students have to deal with, while embarking in the first year of their Doctoral Program.
  • Tweet
    • Unless you have a natural talent, writing is never easy. And writing with an academic style can be even more painful. Here you can find a nice tip to improve your writing style.

 

Bibliography

Adner, R. (2006). Match your innovation strategy to your innovation ecosystem. Harvard business review, 84(4), 98.

Adner, R. (2012). The wide lens: A new strategy for innovation (Vol. 34, No. 9). Penguin Uk.

Adner, R. (2017). Ecosystem as structure: An actionable construct for strategy. Journal of management, 43(1), 39-58.

Autio, E., & Thomas, L. D. (2021). Researching ecosystems in innovation contexts. Innovation & Management Review.

Bogers, M., Sims, J., & West, J. (2019). What is an ecosystem? Incorporating 25 years of ecosystem research. Proceedings. https://doi.org/10.5465/AMBPP.2019.11080abstract

Iansiti, M., & Levien, R. (2004). The keystone advantage: what the new dynamics of business ecosystems mean for strategy, innovation, and sustainability. Harvard Business Press.

Jacobides, M. G., Cennamo, C., & Gawer, A. (2018). Towards a theory of ecosystems. Strategic management journal, 39(8), 2255-2276.

Järvi, K., Almpanopoulou, A., & Ritala, P. (2018). Organization of knowledge ecosystems: Prefigurative and partial forms. Research Policy, 47(8), 1523-1537.

Kapoor, R. (2018). Ecosystems: broadening the locus of value creation. Journal of Organization Design, 7(1), 1-16.

Moore, J. F. (1993). Predators and prey: a new ecology of competition. Harvard business review, 71(3), 75-86.

Ritala, P., & Almpanopoulou, A. (2017). In defense of ‘eco’in innovation ecosystem. Technovation, 60, 39-42.

Scaringella, L., & Radziwon, A. (2018). Innovation, entrepreneurial, knowledge, and business ecosystems: Old wine in new bottles?. Technological Forecasting and Social Change, 136, 59-87.

Shipilov, A., & Gawer, A. (2020). Integrating research on interorganizational networks and ecosystems. Academy of Management Annals, 14(1), 92-121.

Stam, F. C., & Spigel, B. (2016). Entrepreneurial ecosystems. USE Discussion paper series, 16(13).

Van der Borgh, M., Cloodt, M., & Romme, A. G. L. (2012). Value creation by knowledge‐based ecosystems: evidence from a field study. R&D Management, 42(2), 150-169.

Blockchain - Revolution or Buzzword?

Blockchain – Revolution or Buzzword?

by with 2 Comments PhdLife
Blockchain - Revolution or Buzzword?
Tweet by Elon Musk (19.06.2022)

Tweets like these are thought-provoking. It’s fascinating how far technologies like blockchain have come in our everyday lives. New cryptocurrencies are being created every day, and at the moment technological achievements seem to be expanding (check out Ali Syed Hassan’s blog post about NFTs).

This blog entry is dedicated to the technological awareness of blockchain technology. As we live in a rapidly changing world, it is important to keep up with the pace and understand how new technological innovations work.

So what is blockchain in the first place and what are the connections to distributed ledger technology (DLT), NFTs and cryptocrurrencies?

What is it all about?

Blockchain technology (BCT) is already established as an innovative component of society that continues to gain increasing relevance, especially for private financial usage. Key features of this distributed ledger technology ensure transparency between all parties, enhanced traceability and security and therefore provide a promising information technology system. BCT potential application reaches beyond digital currencies (such as Bitcoin and Dogecoin) and financial assets as its potential has been stated as “endless” with already established functions, for instance with financial transactions and blockchain enabled smart contracts (Abeyratne & Monfared, 2016).

Satoshi Nakamoto, the inventor of the first cryptocurrency, Bitcoin, developed a peer-to-peer network concept in 2008 which is the foundation of all cryptocurrencies (Nakamoto, 2008). Since many components of the Bitcoin blockchain are used for other virtual currencies, the focus in explaining the principle will be highlighted on the Bitcoin blockchain.

Peer-to-peer networks

A peer-to-peer network is a decentralized network in which every participant is treated equally. The users are linked with one another and have a large number of connections. Moreover, each participant is able to verify the legitimacy of the transactions carried out in the network by holding and forwarding a local copy of the decentralized database, the so-called blockchain register (Berentsen & Schär, 2017).

Types of network structures (Own representation based on Berentsen & Schär (2018)
Figure 1: Types of network structures (Own representation based on Berentsen & Schär, 2018)

Figure 1 illustrates the principle of the classic central network concept and the decentralized network concept. While a classic Internet application is divided into one service provider and many clients, the functionality in a decentralized network is provided by the cooperation of the existing participants. The decentralized infrastructure offers many advantages, such as its resistance to failures and attacks because it doesn’t rely on a central instance.

Network Participants

Cooperating computers in peer-to-peer networks have been referred to as participants. These participants are also called network nodes and facilitate three different functions: the verification function, the wallet function and mining (Sixt, 2017).

  • The verification function

This function describes all activities that are required for network participation. The task is fulfilled when nodes save local copies of the blockchain register and verify incoming transaction information, just before this information is stored and transmitted to other nodes.

  • The wallet function

This function covers the storage of public and private keys (cryptographic security units) of Bitcoin users. In
addition, a graphical user interface is usually integrated in order to simplify the receipt and dispatch of Bitcoin units.

  • The mining process

Nodes that perform a mining function are also called miners. They invest a great amount of computing power in order to participate in the generation process of new blocks which expand the blockchain register.

Executing the Transaction

Since there are no traditional banking accounts in the Bitcoin network, the coins are transferred to so-called Bitcoin addresses. A Bitcoin address is created with the wallet software by generating a cryptographic key pair, that are assigned to the users. The transfer plus an optional transaction fee is send to the network. The first node to receive the transaction executes the verification function by checking several factors. The transaction is considered as valid after this process, and the transaction is ready to be executed after feeding it into a new block.

Blockchain structure (Own representation based on Berentsen & Schär (2017))
Figure 2: Blockchain structure (Own representation based on Berentsen & Schär, 2017)

Expanding the Blockchain Register

The generation of a new block is known as mining. This mining activity processes all validated transactions in an irreversible way in the network. Each node that performs the mining function is able to create new block candidates by bundling unconfirmed transactions from their local transaction store (Sixt, 2017). In addition to transaction information, this block candidate contains a so-called block header, which contains descriptive information for the identification and localization of the block, as well as an identification number which references the previous block (Berentsen & Schär, 2017). Each block thus references the digital fingerprint of its respective predecessor block, which is why these blocks are firmly anchored in the structure and are dependent on one another (Berentsen & Schär, 2017). Figure 2 illustrates the dependency of the blocks.

Simplified transaction process (Own representation based on Berentsen & Schär (2017))
Figure 3: Simplified transaction process (Own representation based on Berentsen & Schär, 2017)

Proof-of-work and outview

The original blockchain consensus mechanism achieves consensus among the miners via a so-called proof-of-work scheme (Sixt, 2017). This scheme classifies a certain block of candidates as valid after a high level of computing power has been used. Each miner tries to solve a given problem that is extremely energy-intensive in order to be the first node to provide the proof-of-work and, ultimately, to chain the candidate block he has created in the blockchain register (Zohar, 2015).

The proof-of-work sets an increasing security factor for the register, since the chain is secured by computing power and strictly linked to costs. Figure 3 visualizes the complete transaction and mining process.

All in all it can be said that this system was brought to prominence due to cryptocurrencies. Nowadays, a variety of fields, including:

  • healthcare
  • real estate
  • government and
  • music

are finding applications for blockchain’s powerful architecture and secure way of storing, verifying, as well as encrypting data. As my research topic is also connected to implementation barriers of novel technologies and adoption behaviour, the evolution of blockchain that is applied to more and more sectors is an extremely interesting trend to follow for me.

For more information on blockchain and NFTs, check out the official website.

 

References

ABEYRATNE, S. & MONFARED, R. 2016. Blockchain Ready Manufacturing Supply
Chain Using Distributed Ledger. International Journal of Research in
Engineering and Technology, 05.

BERENTSEN, A. & SCHÄR, F. 2017. Bitcoin, Blockchain und Kryptoassets.

NAKAMOTO, S. 2008. Bitcoin: A Peer-to-Peer Electronic Cash System. Cryptography
Mailing list at https://metzdowd.com

SIXT, E. 2017. Bitcoins und andere dezentrale Transaktionssysteme: Blockchains als
Basis einer Kryptoökonomie.

ZOHAR, A. 2015. Bitcoin: Under the hood. Commun ACM Communications of the
ACM, 58, 104-113.

 

Exploring about meaningful work & mobile telepresence robots in healthcare

Exploring about meaningful work & mobile telepresence robots in healthcare

by with No Comment PhdLife

The data collection for my first research project about robotics and meaningful work continued in Seville. Collecting data means gathering information, in my case, by conducting interviews, observing in a real setting, and reading archival data. I started at a nursing home in Málaga, Spain where I had the chance to interview the staff and observe the dynamics of the nursing home called Vitalia Teatinos, where there are a couple of mobile telepresence robots (MTRs) being tested (GoBe and CLARC). You can read more about this stage of the data collection here. After the nursing home, the second and third settings I visited were two different hospitals in Seville. Each hospital has a GoBe robot.

Exploring about meaningful work & mobile telepresence robots in healthcare
Testing GoBe at the hospital facilities. Picture was taken by the author.

As I mentioned in my last blog post, I was able to collect data there thanks to Blue Ocean Robotics, EINST4INE’s industry partner, and thanks to the University of Málaga and the Andalusian Health Service, who are working together on a project called SUSTAIN. In this project, they are testing GoBe, a mobile telepresence robot with video calling features to facilitate doctor-patient and patient-family members interactions. The main objective of SUSTAIN project is to test these robots in healthcare settings to understand how safe and effective they can be, and also to evaluate the medical, patient, resident, and family satisfaction of such interactions.

Exploring about meaningful work & mobile telepresence robots in healthcare
Source: GoBe Robots

In my opinion, MTRs can be a great tool to support physical presence and enable telemedicine in ways that a tablet and a mobile phone cannot provide. I think that the embodiment of the robot can provoke different sensations compared to other devices, for example, GoBe’s height resembles a human height and that may provoke a different sensation than when video calling through a mobile phone. Also, the big screen allows you to have a clear view of the face, which is sometimes hard when you have a video call via mobile phone, and you cannot show your complete face because of the camera’s limited reach.

This is only my point of view from what I have seen, but some studies have explored the use of MTRs for telemedicine and found that it can be effective indeed but also involves implementation challenges (Beane & Orlikowski, 2015; Laigaard et al., 2022). Implementing robots at the workplace implies changing organizational dynamics, work practices, and occupations challenging socio-psychological factors. This is where my research inquiry starts: robots’ impact on healthcare workers’ social dynamics has received little attention, for example, the effects on meaningful work.

Meaningful work allows employees to fulfill the purpose of their life via daily work practices and that is linked to human well-being. In healthcare and caring practices, some studies have found that the personnel finds meaningful work within social relationships (Pavlish & Hunt, 2012; Pavlish et al., 2019). For example, building relationships with patients and family members and also when they are explicitly acknowledged for their work.

Therefore, my research project will explore meaningful work perceptions of healthcare staff when interactions are afforded by MTRs. Accessing empirical data and interviewing participants at their workplaces was a great opportunity. The different settings allowed me to obtain insights into the particular perceptions of a clinician that is working at a hospital compared to one that is working at a nursing home. I was also able to interview some independent clinicians that work at private clinics or at the patients’ homes, which made it even more interesting. It was very intriguing to see the different perspectives that each of them had depending on their occupation. The participants had different occupations such as physiotherapists and psychologists and it helped me to understand what their points of view are about robotics and meaningful work, daily practices, and what they think about using MTRs in their workplace.

Now it is time to analyze the data and write up!

 

References:

Beane, M., & Orlikowski, W. J. (2015). What Difference Does a Robot Make? The Material Enactment of Distributed Coordination. Organization Science, 26(6), 1553–1573. https://doi.org/10.1287/orsc.2015.1004

Laigaard, J., Fredskild, T. U., & Fojecki, G. L. (2022). Telepresence Robots at the Urology and Emergency Department: A Pilot Study Assessing Patients’ and Healthcare Workers’ Satisfaction. International Journal of Telemedicine and Applications, 2022, 1–6. https://doi.org/10.1155/2022/8787882

Pavlish, C., & Hunt, R. (2012). An Exploratory Study About Meaningful Work in Acute Care Nursing: An Exploratory Study About Meaningful Work in Acute Care Nursing. Nursing Forum, 47(2), 113–122. https://doi.org/10.1111/j.1744-6198.2012.00261.x

Pavlish, C. L., Hunt, R. J., Sato, H.-W., & Brown-Saltzman, K. (2019). Finding Meaning in the Work of Caring. In R. Yeoman, C. Bailey, A. Madden, & M. Thompson (Eds.), The Oxford Handbook of Meaningful Work (pp. 236–256). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780198788232.013.14