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Although investing in new technologies often requires new business models and changes in organizational culture, firms frequently react in a static and rigid way in their attempt to change. This phenomenon is also referred to as inertia: “everything which remains in the stagnant situation or a constant movement unless an external force activates it“.

It seems that inertia against change is unavoidable in many firms. Inflexibility withholds firms to adapt to environmental changes which results in individual stagnancy and eventually inertia in the whole firm. Even for successful firms, resistance to change leads to difficulties in reconfiguring their business model. It is thus fundamental to overcome barriers to change in order to keep up with emerging technology trends (Moradi, Jafari, Doorbash  & Mirzaei, 2021).

Research shows that getting input for innovation processes from outside the firm (so-called innovation) is a way to overcome the negative impact of inertia by accelerating structural changes. Open innovation is defined as the “purposeful use of internal and external knowledge flows for increasing the speed of internal innovation and developing the market for external use of innovation.” (Chesbrough, 2003, p. 33-58). It’s thus a two-way stream to make innovation more effective and efficient. In fact, research shows that open innovation has a significant effect on business model innovation (Huang et al., 2013). They also found proof to suggest that open innovation has a significant positive effect on a firm’s performance (Huang et al., 2013).

A famous example of a firm that embraced open innovation is the business case of LEGO. After avoiding bankruptcy in 2003, LEGO decided to employ open innovation businesses on both the process and production levels to achieve various types of innovative output. Their strategy was based on three key pillars: 1) learning from external firms through interviewing, 2) learning from employees through feedback and interviewing, and 3) creating micro pilots to test LEGO’s capabilities and consumer needs (Lindegaard, 2014). Thanks to the successful implementation of open innovation, LEGO was able to create a pipeline of creative product ideas and a platform to keep their end customers engaged.

Sources:

Chesbrough, H. (2003). The Logic of Open Innovation: Managing Intellectual Property. California Management Review, 45(3), 33–58. https://doi.org/10.2307/41166175

Moradi, E., Jafari, S. M., Doorbash, Z. M. & Mirzaei, A. (2021). Impact of organizational inertia on business model innovation, open innovation and corporate performance. Asia Pacific Management Review, 26(4), 171–179. https://doi.org/10.1016/j.apmrv.2021.01.003

Huang, H.C., M.C. Lai, M.C. Lin, L.C. & Chen, C.T. (2013).  Overcoming organizational inertia to strengthen business model innovation: An open innovation perspective. Journal of Organizational Change Management, 26 (6) pp. 977-1002

Lindegaard, S. (2014) 3 Successful Open Innovation Cases: GE, Samsung and LEGO. [Online] Available from: https://www.linkedin.com/pulse/20141115202453- 46249-3-successful-open-innovation-cases-ge-samsung-andlego?trkInfo=VSRPsearchId%3A43274941430657186999%2CVSRPtargetId%3A 5939482912805122048%2CVSRPcmpt%3Aprimary&trk=vsrp_influencer_conten t_res_name.

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Emerging technologies and new business models influence the competitive landscape in an industry by redefining how value is created for customers and what they perceive as value. Technology and data are used to reorganize supply chains, to create new e-commerce channels, to leverage AI and predictive analytics. At the current rates of digitization in this fast-changing world, firms must invest in tech, processes, data, and people to become more flexible and faster, to make better decisions, and to meet new customer demands.

In his book “Adaptability: The Art of Winning in an Age of Uncertainty”, Max McKeown said, “All failure is failure to adapt, all success is successful adaptation” (McKeown, 2012). He explained that innovation, strategy, branding, marketing, and operations are useful but not enough. New business models are needed to seize an opportunity or to respond to competitors who already seized those opportunities. Adapting to customers’ changing value perceptions smarter and faster than the situation changes is fundamental to remaining competitive, to survive but above all to win.

However, the inertia to change turns out to be a substantial barrier for many incumbents. Many established firms are badly hit by changes. Firms fail to successfully exploit new opportunities not due to their inability to take action but to take appropriate action. As resistance to change is in our nature, they have a tendency to follow established patterns of behavior even in dramatic environmental shifts. Maintaining the status quo is a dangerous illusion. Although firms’ internal processes are hard to change and although investing in new (technological) resources implies tough trade-offs in resource allocation, firms need to overcome the everyday battle against inertia.

For example, since the strategic frames of the managers of Nokia were shaped by old ways of doing business which made them successful so far, they were not able to realize that the future of mobile phones was the touchscreen. They resisted the touchscreen revolution. It’s surprising that Nokia was quite innovative and adaptive at first. In fact, they were the first to build a prototype of a touchscreen. However, inertia led to Nokia’s downfall (Surowiecki, 2013).

Sources:

McKeown, M. (2012). Adaptability: The Art of Winning in an Age of Uncertainty (1ste edition). Kogan Page.

Surowiecki, J. (2013, September 3). Where Nokia Went Wrong. The New Yorker. https://www.newyorker.com/business/currency/where-nokia-went-wrong

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In the past decades, the internet of things, better known as IoT has developed rapidly. Since its first introduction in 1999 (Vision of Humanity, n.d.), the IoT market has reached a value of 478.36 billion dollars in 2022 and is expected to grow to 2465.26 billion dollars in 2029 (Fortune Business Insights, 2022). In a nutshell, IoT could be described as a network of connected devices and people (Clark, 2016). This connectivity brings multiple benefits, such as reducing waste, reducing fraud-related costs and increasing customer trust (Brous, et al., 2020). An example of how IoT could contribute to reducing waste is the smart thermostat (Bigirimana, 2018). With the smart thermostat, users can put the heating off through an app on their phone when they leave the house and put it on again when they are heading home.

However, this trend does not only bring benefits to its customers, there are also certain risks involved (Javaid, 2020). One of the risks is that not all the products are well protected against hackers (Wheelus, 2020). With the growing trend of IoT, the risks grow exponential (Deloitte, n.d.). But how bad is it to be hacked? This depends on the user, but it could lead to serious damage. Imagen, you have connected most of your security devices with your keys, to increase the convenience of opening and locking your home. However, the moment someone hacks your garage key, that person has also the chance to get into your alarm system (Deloitte, n.d.). Another vulnerable product related to IoT is the automotive car. Hackers were able to hack 1.4 million cars through the infotainment system of the Jeep Cherokee (Beale & Berris, n.d.). According to Beale and Berris (n.d.), hackers can even bring it to another level by hacking aeroplanes through USB ports at an airline seat.

All with all there can be concluded that IoT brings multiple benefits, but that those benefits are not without risks. The answer to the question: “Is the internet of things good or bad?” depends on who you ask the question. However, everyone who is using those products must be aware of the risks.

Beale, S. S. & Berris, P., n.d. Hacking the internet of things: vulnerabilities, dangers, and legal responses, s.l.: s.n.

Bigirimana, S., 2018. Managing Waste through the Internet of Things (IoT), Zimbabwe: Africa University.

Brous, P., Janssen, M. & Herder, P., 2020. The dual effects of the Internet of Things (IoT): A systematic review of the benefits and risks of IoT adoption by organizations. International Journal of Information Management, April, p. 3&4.

Clark, J., 2016. What is the Internet of Things (IoT)?. [Online]
Available at: https://www.ibm.com/blogs/internet-of-things/what-is-the-iot/
[Accessed 23 October 2022].

Deloitte, n.d. Cyber risk in an Internet of Things world. [Online]
Available at: https://www2.deloitte.com/us/en/pages/technology-media-and-telecommunications/articles/cyber-risk-in-an-internet-of-things-world-emerging-trends.html
[Accessed 24 October 2022].

Fortune Business Insights, 2022. With 26.4% CAGR, Internet of Things (IoT) Market Worth USD 2465.26 Billion by 2029. [Online]
Available at: https://www.globenewswire.com/news-release/2022/08/03/2491076/0/en/With-26-4-CAGR-Internet-of-Things-IoT-Market-Worth-USD-2465-26-Billion-by-2029.html#:~:text=Fortune%20Business%20Insights%E2%84%A2%20has,USD%20384.70%20billion%20in%202021.
[Accessed 23 October 2022].

Javaid, A., 2020. The Implications of IoT, Risks and Opportunities. [Online]
Available at: https://www.linkedin.com/pulse/implications-iot-risks-opportunities-ahmad-javaid-cisa-crisc/
[Accessed 25 October 2022].

Vision of Humanity, n.d. IoT Technologies Explained: History, Examples, Risks & Future. [Online]
Available at: https://www.visionofhumanity.org/what-is-the-internet-of-things/#:~:text=The%20term%20’Internet%20of%20Things,them%20through%20a%20supply%20chain.
[Accessed 24 October 2022].

Wheelus, C., 2020. IoT Network Security: Threats, Risks, and a Data-Driven Defense Framework, s.l.: Research Gate.

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Within the last few years, Ai has become an essential part of our daily life. A lot of our daily life activities have been more and more automated in the past decades (Western Governors University, 2022). Most people are already used to the idea of automatization and the fact that companies are collecting data from their customers. Nevertheless, the moment that our privacy is at risk, society realizes again how much power AI and Big Data have (Bruinsma, 2017). 

An example of this matter is the OV year card in the Netherlands. Since the introduction of the OV card, most users have seen its benefits. However, in 2017 research showed that the NS was able to track its customers through the OV card (Bruinsma, 2017) and people were, to say it mildly, not amused. Based on the article and the reaction of the NS, it seems that society is unwilling to hand in some parts of their privacy. However, data could be seen as the lifeline of AI (Wijn, 2020). Multiple data scientists use the credo “Garbage in, Garbage out”. In other words, bad data leads to bad AI (Hillman, 2022). Therefore, to provide the best possible solutions through AI, it is important to have the best possible data. However, to protect customer’s privacy, governments have introduced new regulations to protect the data and personal information of people (Pentland & Rahnama, 2022). According to Forbes, these regulations do not make it impossible to get the most out of the data, but it does make it more difficult (Schrock, 2018). In a way this makes sense, to secure privacy, parts of the data have been left outside which impacts the original data.

All in all, it is time to realize that AI has the capability to provide outstanding results, but that the privacy of people in this society is just more important. This does not have to be a bad thing, but it has to be straightened out that the concept of AI is working fine. Other aspects, such as privacy regulations make it just more difficult to meet all expectations. Maybe it is time to realize that society can’t have both and have to choose between, the best possible AI or the best possible privacy regulations.

Bruinsma, G., 2017. ‘Privacy wordt geschonden door OV-chipkaart met abonnement’. [Online]
Available at: https://www.ad.nl/economie/privacy-wordt-geschonden-door-ov-chipkaart-met-abonnement~a4405fa0/

Hillman, J., 2022. Data quality and AI safety: 4 ways bad data affects AI and how to avoid it. [Online]
Available at: https://www.prolific.co/blog/data-quality-and-ai-safety

Pentland , S. & Rahnama, H., 2022. The New Rules of Data Privacy. [Online]
Available at: https://hbr.org/2022/02/the-new-rules-of-data-privacy

Schrock, E., 2018. Regulations Won’t Kill AI – Bad Data Will. [Online]
Available at: https://www.forbes.com/sites/forbestechcouncil/2018/09/04/regulations-wont-kill-ai-bad-data-will/?sh=1eb64e043405

Western Governors University, 2022. All the Benefits of Artificial Intelligence. [Online]
Available at: https://www.wgu.edu/blog/benefits-artificial-intelligence2204.html#close

Wijn, M., 2020. Waarom Artificial Intelligence nergens is zonder data intelligence. [Online]
Available at: https://news.sap.com/netherlands/2020/01/waarom-artificial-intelligence-zonder-data-intelligence-niets-is/

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More and more companies, organizations and even government agencies are using artificial intelligence to predict and make decisions. Sometimes accurate predictions are not enough, but fairness, accountability and transparency must also be maintained. The model must be interpretable and explainable; the implicit ability of a model to explain its reasoning process using the components of the problem domain and offer a justification for its predictions. This post briefly explains some of the methods that provide explanations for the outcomes or behaviour of an artificial intelligence agent.

Post-hoc explanation methods

Some machine learning models are already intrinsically interpretable. Decision trees can be interpreted according to rules. Linear/logistic regression can be interpreted using a linear/logistic relationship. In addition, k-Nearest Neighbours can be interpreted as a result that resembles similar examples in the dataset. In addition, there are more complex machine learning methods. For these models you can use post-hoc explanation models (also possible for intrinsically interpretable models). These explanation models are additionally applied after the model has already been trained. Post-hoc methods can give explanations separated from the machine learning model (model-agnostic) or model-specific. It also may be for the whole model or for a single instance.

Model-agnostic post-hoc methods

• Partial Dependence Plot: This method sets one independent variable to one specific value for all instances. It shows the marginal effect of the independent variable on the dependent variable. The relationship can also be visualized.
• Individual Conditional Expectation: This method differs from the Partial Dependence Plot in that the marginal effect of the independent variables is calculated for each instance. This can uncover heterogeneous effects.
• Local surrogates: This method is used to explain individual predictions by using a surrogate linear regression model.
• Global surrogates: This method approaches the predictions of the complex model by using a simpler interpretable surrogate model.
• Shapley values: This computationally expensive method determines the contribution of the independent variables to the prediction for a given instance.

Explanation and choosing intrinsically interpretable models

Usually, there is a trade-off between the performance of the Machine Learning models and the complexity/interpretability. Depending on the situation, a decision has to be made about this. If the stakes are high, it might be a better way to look at interpretable models. It can be said that among the choice of different models, the model must be chosen with the simplest explanation as well as having all the necessary information/fit.

Reference:

Gevaert, C. M. (2022, augustus). Explainable AI for earth observation: A review including societal and regulatory perspectives. International Journal of Applied Earth Observation and Geoinformation, 112, 102869. https://doi.org/10.1016/j.jag.2022.102869

Molnar, C. (2020). Interpretable Machine Learning. Leanpub.

Ragini, R. (2021, 10 December). Principle of Parsimony – Ruhi Ragini. Medium. https://medium.com/@ruhi3929/principle-of-parsimony-d510356ca06a

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Virtual economies (VE) have been around since the emergence of video games. In essence they are no different from a real economy. Someone has a demand for a certain product, say an in-game cosmetic, and if the price is right another person will fulfil that demand and supply the buyer with the good. The only difference is that these economies solely trade virtual goods such as character skins, weapons, virtual property and more. A good representation of a VE is the MMORPG World of Warcraft (WoW) released in 2004. The game allowes players to, among others, ‘farm’ cosmetics and equipment that could be traded for virtual money or other goods. This resulted in an economy that trades over 21 billion gold (which is the in-game currency) a day (Royce, 2021). For those of you that don’t know what to do with that number, on the grey market 10 thousand gold is worth roughly 70 euros (MMOGA, n.d.), which amounts in transactions worth 1 million euros a day.  It’s considered a grey market since Activision Blizzard, the developers of WoW, do not permit the exchange of in game currency for real currency.

However not all VE are built the same. WoW’s developers allow users gain goods through for example killing foes and crafting, which results in developer defined and therefore limited creation of content (Sivan, 2016). Another approach would be that of online platforms such as Second Life. The game allows users to create a fully personalized characters (Avatar) and provides a 3D modelling tool to create fully customizable content. This result in a practically unlimited creation of content and in theory allows users to own and sell their creations as intellectual property (Sivan, 2016).

Games such as Eve Online have managed to take the concept of VE even more extreme, practically imitating real world economies. The game is a MMO based in space that allows for trade, mining, fighting and more. While the game functions on a limited creation of content concept, the developers practically do not interfere with the economy. In fact, the game allows players to create organizations that have become so big that they function as nation states. The largest of these is the Strategic Exploration and Development Corp, which has amassed over 11 thousand players (doltan, n.d.) from whom it collects taxes, sells protection and insurances, trades with, sells bonds to and more. In 2014, a clash between two of these organizations over resources and land resulted in the largest in-game battle ever recorded, with over 7 thousand players participating in a war that has been estimated to cost players roughly 300,000 USD (real-world) (How Money Works, 2021). The sheer number of transactions resulting from wars and other in game events has allowed players to launder money worth hundreds of thousands by selling it on the grey market and hiding it in player-based trades (How Money Works, 2021).

References

Dotlan. (n.d.). Top 100 Corporations (with alliance membership, sort by membercount). Available From <https://evemaps.dotlan.net/corp/top> [Accessed 18.10.2022]

How Money Works. (2021). How EVE Online’s Massive Virtual Wars Are Financed – How Money Works [Online]. Available From <https://www.youtube.com/watch?v=m6j_UsGJnkQ> [Accessed 18.10.2022]

MMOGA. (n.d.). WOW WOTLK CLASSIC GOLD. Available From <https://www.mmoga.com/World-of-Warcraft-WotLK-Classic/WoW-WotLK-Classic-Gold,EU,Auberdine-Horde/> [Accessed 17.10.2022]

Royce, B. (2021). World of Warcraft gamers move 21B gold through the player economy every day. Available From <https://massivelyop.com/2021/01/21/world-of-warcraft-players-move-21b-gold-through-the-economy-every-day/> [accessed 17.10.2022]

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Nowadays, most companies want to be data-driven. Every year, more and more money is poured into data gathering and data analytics departments to facilitate decision-making processes. Although this seems like a sensible strategy and something to strive for, what does being data-driven mean and is it something that we should want? More crucially, how is it possible that even though the investments in data-driven decision-making increase, most companies do not see an equal increase in their performance?

In data-driven decision-making, the data is at the core of the decision to be made. Data analysist and scientists are the most important employees in a data-driven company. After all, whatever the result of their analysis is, it will impact decisions. Even though data analysis is not a deterministic process and requires estimates, decisions, and interpretations, the results of data analysis in data-driven companies is seen as the truth.

However, this means that data is looking for a purpose. The reason that academic research starts with a literature review and hypothesizing before gathering additional data, is that it is very robust. It avoids explaining coincidental correlation as causation. More importantly, it avoids taking the data at face value and thereby misinterpreting the underlying relationship that may or may not exists.

Therefore, Stefano Puntoni, a former professor of marketing at the Rotterdam School of Management, Erasmus University, recommends turning it around. Instead of data-driven decision-making, companies should strive for decision-driven data analytics. The difference is that the decision is now the focal point of conducting the data analysis. Rather than empowering data analysts and scientists, this empowers managers whose job it is to make decisions.

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The concept of sustainability encompasses more than just environmental concerns. As this involves meeting present needs without compromising future needs, this also associates with social and economic concerns. Given this, it is without a doubt that sustainability is the only way forward and the use of emerging technologies is necessary to achieve this. 

A study conducted by Gartner emphasized that sustainability, particularly environmental and social factors, forms part of the top concerns of most organizational leaders in the world following profit or revenue. This, therefore, encourages organizations to implement sustainable technology that ultimately result in creating a positive impact. Given this trend, several companies have pledged to commit to the vision of net zero in the next years, including Amazon and Coca-Cola. Fund investing has also found their way to practice responsible investing. 

But how exactly is technology going to help in achieving sustainability? The use of artificial intelligence and robotics allow companies to improve efficiency in its operations, further resulting in a decrease in carbon footprint. In addition, metaverse—a new form of technology that combines the physical and digital world—paves the way for companies to pursue growth while limiting its adverse impacts on the environment. 

In addition to these types of technology, it is similarly important to cultivate an innovation culture. This should be at the core of its implementation process as it involves dealing with employees and ensuring that they are onboard with this vision. This can be done through the Kotter’s Transformation framework, with emphasis on creating an urgent need and forming a powerful guiding coalition. 

How about you? What do you think are important considerations in sustainable technology and how should organizations implement this?

References:

Why Is Sustainability Important?

https://www.gartner.com/en/newsroom/press-releases/2022-10-17-gartner-identifies-the-top-10-strategic-technology-trends-for-2023

https://www.accenture.com/fi-en/services/sustainability/sustainable-it-technology

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On the morning of February the 24^th Russian troops began the invasion of Ukraine which was stated by the Russians as a special mission for the denazification and demilitarization of Ukraine. This has been a horrible war and is still raging on. Technology plays a crucial role in modern warfare (not Call of Duty).

Communication is one of the five categories of the technology of war (Guilmartin, 2020). Elon Musk is the owner of SpaceX next to his most known company Tesla. SpaceX is aimed at designing, building, and launching (non-military) rockets and spacecrafts. Since this Spring Elon Musk and SpaceX have been helping Ukraine in their war against Russia. They have been donating and shipping around 20,000 Starlink satellites to Ukraine (Marquardt, 2022). Starlink satellites offers high-speed internet in rural and remote locations across the world. This has helped Ukraine keep up their military network of communications, which was previously destroyed by Russian forces.

But with these generous donations from SpaceX came financial problems. Elon musk ‘threatened’ to stop donating Starlink satellites to Ukraine (McIntyre, 2022). A statement was made from SpaceX that unless the United States government would help SpaceX financially they would stop delivering Starlink to Ukraine. The Pentagon has been considering aiding SpaceX but Elon musk has already withdrawn his request for financial aid (Theguardian.com, 2022).

It was recently shown how important the Starlink satellites are when Ukraine launched its counterattack and reclaimed some parts of Eastern Ukraine. There were multiple reports of outages of the satellites which were said to have affected the whole frontline in Ukraine (Marquardt, 2022). Elon Musk has a lot of power and responsibility as they are such a crucial provider for Ukrainian forces. Therefore, the EU is looking at a way of financing Starlink activities to make Ukraine less dependent on one supplier (Hofmans, 2022).

Bibliography

Guilmartin, J. F. (2020, November 4). military technology | Definition, History, Advances, &

Weapons. Encyclopedia Britannica. Retrieved October 18, 2022, from

https://www.britannica.com/technology/military-technology

Hofmans, T. (2022, October 18). Europese Unie onderzoekt mogelijke financiering van

Starlink in Oekraïne. HLN. Retrieved October 18, 2022, from

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Marquardt, A. (2022, October 15). Exclusive: Musk’s SpaceX says it can no longer pay for

critical satellite services in Ukraine, asks Pentagon to pick up the tab. CNN. Retrieved

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McIntyre, J. (2022, October 17). Elon Musk backs off his threat to stop providing free Starlink

internet service for Ukraine’s military. Washington Examiner. Retrieved October 18, 2022,

from https://www.washingtonexaminer.com/policy/defense-national-security/elon-musk

backs-off-his-threat-to-stop-providing-free-starlink-internet-service-for-ukraines-military

Theguardian.com. (2022, October 18). Pentagon considering paying for Musk’s Starlink

Network in Ukraine. The Guardian. Retrieved October 18, 2022, from

https://www.theguardian.com/world/2022/oct/17/pentagon-starlink-ukraine-musk funding