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There is no second-hand market for information goods. You cannot simply resell digital content online – or can you?

Compared to physical goods, reselling digital goods is tough. While there is a second hand market for old DVDs, music CDs and books, you are unlikely to be able to re-sell your eBook or iTunes song. But why is that?

When you are buying an e-book, you are not purchasing the book itself, but you are purchasing a license to that book instead (BookScouter 2016). While a normal book transfers to your ownership, for eBooks, the publisher owns the rights and can, depending on your contract, delete the eBook from your device (BookScouter 2016). In the United States, reselling digital products is striclty forbidden (Forbes 2019). In Europe though, all eyes are on a law suit of Tom Kabinet, who in 2014 created a platform where sellers could offer their “used” eBooks (Forbes 2019). After a purchase, the file got deleted from the sellers account to ensure that he or she could not keep on using the information good (Forbes 2019). After being pressured by the Dutch Association of Publishers, he had to turn his business model from being an intermediary to becoming a re-selle; Buying and reselling all e-books himself (Forbes 2019). So far, his case is still waiting for a ruling by the Court of Justice of the European Union (Forbes 2019).

So why would reselling eBooks be harmful?  Publishers found one fundamental problem: Old eBooks are perfect substitutes for new eBooks as they are not “wearing off” (Forbes 2019), meaning that the second-hand market would target the same type of consumers. This threatens both authors and publishers in the industry (Forbes 2019), as prices could be expected to decrease to a few cents per book (BookScouter 2016).

Nevertheless, also other companies seem to have identified the reselling of e-books as a potential digital business model. Amazon has announced to develope a system which allows eBook owners to resell their product on amazon.com in return for amazon account credit or another monetray reward. Nevertheless, publishers and authors will need to agree to their book being available for re-sale (Forbes 2019).

Therefore, re-selling digital content will no longer stay impossible. If publishers, authors and resellers find agreements on copyrights and usage, the possibility of reselling digital content might become a reality.

 

References

BookScouter (2016). ‘Reselling eBooks’. Accessed on 18 October 2019 on https://bookscouter.com/blog/2016/06/reselling-ebooks.

Forbes, Z. (2019). ‘A Marketplace for Second Hand Books’. Accessed on 18 October 2019 on http://www.bookbrunch.co.uk/page/free-article/a-marketplace-for-second-hand-ebooks/.

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If we think about technology, we mostly have recent developments such as Artificial Intelligence, Blockchain and Platforms coming to our mind. Nevertheless, many companies were able to generate large profits by reintroducing old technologies in their product range. Lately, technologies like polaroid cameras , record players and even the rotary telephone have returned to retail stores (TechTimes n.d.), but can every old technology be that successful again?

Is there a way to tell which technologies will be able to experience a successful comeback?

According to Lohr (2008), all technologies which returned to the market successfully have several features in common. Firstly, and also most importantly, the technology must have certain features and therefore a competitive advantage that the newer technologies do not offer (Lohr 2008). Nevertheless, the technology does not tend to return in its initial form but usually gets retooled and intergrated into a new business model to re-enter the market.

One reason for consumers to buy the so-called “Vintage Technologies” is that they are often more pragmatic and therefore focussed on their actual task (Tenner 2015). While especially older people might simply not want to keep up with new developments, the younger users may also find it beneficial to purchase simple technologies without unnecessary features.

Secondly, old technologies often get resold for their aesthetics and nostalgia. Polaroid cameras are a good example. Today, the cameras are available in many colors (Tenner 2015). On the other hand, polaroid cameras also maintained a competitive advantage over digital cameras, as they let you print a photo immediately and in a different style. Also, a smart business model has been built around the technology, marketing it as an elite vintage tool instead of as a pre-digital technology (Tenner 2015).

Lastly, technologies can be re-introduced as so-called rescue technologies, saving old formats such as cassettes and disks. Nevertheless, only little demand exists for saving old non-digitalized content.

Which technology do you think has a high chance of returning to the market? How could a successful marketing model around it look like?

References

Lohr, S. (2008). ‘Why old technologies are still kicking’. Accessed on 12 October 2019 on https://www.nytimes.com/2008/03/23/technology/23digi.html.

TechTimes (n.d.). ‘Obsolete and Outdated Technology that people still use today’. Accessed on 12 October 2019 on https://www.techtimes.com/photos/225444/20180420/obsolete-outdated-technology-people-still-use-today/4/.

Tenner, E. (2015). ‘Why People stick with outdated technology’. Accessed on 12 October 2019 on https://www.scientificamerican.com/article/why-people-stick-with-outdated-technology/.

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While many people are doing their ordinary day-to-day purchases online, replacing the auctions by big European auction houses like Artcurial or Villa Grisebach with an online experience seems unlikely to be successful. Many auctioneers enjoy the thrill of the live auction, the battling with a competitor and the raising of the famous paddle boards. Auctions can sometimes be more of an event and customer experience than an actual market.

So is there even a chance that an online business model of an auction house would work?

If we follow the recent announcements by the big European auction houses, then the answer can be expected to be yes. By today, four out of the ten biggest European auction houses have built an online platform for auctions, with two additional ones launching their platform this year.

Online auctions can, for example, solve logistical issues. Lately, many items have been offered that can be challenging to the logistics of an auction house and are difficult to present on stage. Furthermore, the catalogues of auction houses are also considered as too costly by now.

The big auction houses saw themselves threatened by platforms such as facebook, on which private people can offer and sell their products. Especially with the emergence of facebook groups specialized on auctioning a certain group of items, the big auction houses saw an upcoming problem of becoming an uneccesary imediator and being skipped in the value chain.

Nevertheless, it is highly unlikely that we will ever see a Banksy or another extremely famous and high-end product on one of the online platforms. With online auctions, the auction houses follow the objective of trying to attract people who are unlikely to ever attend a live auction and therefore broadening their audience and reach. Common products available for online bidding are wine bottles, jewelry, antique furniture and smaller paintings, which are still amongst the more “low priced” products of the auction houses.

While we will not expect online auctions to replace the traditional live auctions, as collectors will still like to take a closer look on their desired item and purchase it in a traditional manner, we can expect that auction houses without an online auction platform will soon loose sales on their minor products. So while your new favorite Banksy will not make it online, maybe your favorite collector wine will.

References

Guzman, N. (2018). ‘Is the future of auction houses online?’. Accessed on 07 October 2019 on https://www.bworldonline.com/is-the-future-of-auction-houses-online/.

Hickley, C. (2019). ‘Going, going, gone online: Europe’s Auction Houses go digital’. Accessed on 07 October 2019 on https://www.theartnewspaper.com/feature/going-going-gone-online.

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We all know at least one cryptocurrency: Bitcoin. Maybe there are even a few more coming to your mind. But while many of us might view cryptocurrencies as a good or bad type of investment, there is also one type that mainly follows a social goal:

Local Cryptocurrencies

Local cryptocurrencies are different from e.g. Bitcoin as they are not globally available but only locally (. They mainly aim at reviving the economy of a small town, city or region. One of the most famous examples is the app Colu which is already in use in London, Liverpool, Tel Aviv and Haifa. Together with the municipalities, the company issues the local coins such as the Belfast coin (which will be released later this year) and TLV coin (Colu, 2019). 

In East London, the local crytocurrency “Local Pound, East London” by Colu is quite a success. Locals who are interested in boosting the local economy and supporting smaller business can exchange their British Pounds in a one-to-one Ratio for the local cryptocurrency (Businessadvice, 2017). Businessadvice (2017) claims that every pound spent at a local business is worth 4 times as much to the community as a pound spent at a bigger retailer. The local businesses give back to the community by providing jobs and paying taxes. Next to providing the citizens with a digital wallet and therefore making transactions easier for customers and businesses, Colu also helps local businesses thrive by promoting their stores on their app. With a map function, small and often unknown or hidden stores nearby can be found. Furthermore, the app offers discounts at businesses to citizens who enroll for volunteering activities via the app and therefore also increases the sense of belonging in the community.

While many local currencies might follow these social goals, many local currencies are also just created for fun. The ekrona is exists as a currency for true northmen. Other currencies such as the Scotcoin and CatalonianCoin are used as a political Statement and to show the wish for Independence (FXEmpire, 2019).

References & Futher Readings

Colu (2019) Retrieved from:  https://www.colu.com/

FXEmpire (2019). Cryptocurrencies by the Region: Digital Money for Local Communities. Retrieved from: https://www.fxempire.com/education/article/cryptocurrencies-by-the-region-digital-money-for-local-communities-458625

Suberg, W. (2019). Belfast Launches Own Digital Currency to Boost Rockefeller Social Project Success. Retrieved from: (https://cointelegraph.com/news/belfast-launches-own-cryptocurrency-to-boost-rockefeller-social-project-success

East London Local Pound becomes latest currency taking the fight to corporate chains

Blockchain Comes to East London With Colu Local Currency Launch

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Machines can outperform humans

Everybody makes mistakes: Humans do, and machines do. Nevertheless, by now Artificial Intelligence (AI) is able to outperform humans in many fields, such as speech and image recognition. The authors Brynjolfsson and Mcaffee (2017) show that over the past 10 years image recognition through machine learning has improved significantly. The authors conducted a test where machines and humans had to identify a picture as a muffin or puppy picture. The human error rate stayed constant over their six year time frame of research (2010-2016). On average, people failed in 5% of all cases to label an image correctly. While in the year 2010 machines failed at roughly 27% of all cases, in 2015 they bet humans by undercutting their error rate. In 2016, the most recent test year, machines only failed to identify 3% of all images. So does that mean that we should trust machines more than humans with doing our work?

Opting for the lowest error rate?

While in the case of identifying a muffin or a puppy a failure is not that tragic, think of other scenarios where an error would be fatal. Common examples are driving a car or a medical treatment – if a mistake is being made the consequences can often be quite extreme and in some cases even deadly. Naturally we would conclude that the medium with the lower error rate should be used for a task so that as many tragedies as possible can be avoided.

Who do we trust more – Human or Machine?

Prahl & Van Swol (2016) from the University of Wisconsin have conducted a research identifying whether we trust computers or human advisors more when we need to make a decision. In their experiment they gave participants the task of making an estimate of the duration of an orthopedic surgery, a task that none of the participants had ever performed before or pre-knowledge about, with the help of either a computer or a human experienced in the field. Before the experiment started, participants were shown some recent hospital data to get an idea for a reasonable estimate. Each participant had to complete 14 rounds of making a prediction. In each round, they would first enter an estimate, then see the estimate of their advisor displayed and then be allowed to change their initial estimate. After each round they received feedback on how they performed. In the 6^th round, participants were given a bad advice by either the human advisor or the machine, depending on which group they were in. If they followed the advice, their accuracy decreased extremely much. The following rounds, the advice was back to very good estimates (Prahl & Van Swol, 2016).

Their results of Prahl & Van Swol (2016) show that in the beginning of the experiment, the trust in a human or machine advisor was almost the same. Nevertheless, after the human or machine advisor made a big mistake in the 6^th round, participants used the human and machine advice less frequently in the upcoming rounds. Surprisingly, the trust in machine advisors dropped significantly more than the trust in human advisors (see the graph below).

Source: Prahl, A., & Van Swol, L. (2017).

So this leaves us with the question: Why do we find machine errors “worse” than human errors?

The authors tried to find multiple explanations for this phenomenon. The authors state, that a “perfection schema” exists. Initially, people simply expect an application or machine to work perfectly and be run by an accurate algorithm; therefore they are losing more trust in a machine if it makes a mistake compared to a human (Madhavan & Wiegmann, 2007). For human advisors, participants could have had more empathy and might have kept a higher trust in them after their error because “every person can make a mistake”. Furthermore the authors suggest that participants might trust human advisors more than machines since they have more experiences with human advisors such as e.g. doctors or consultants as compared to machine advisors (Prahl & Van Swol, 2016).

What do you think? Would you trust a machine more than a human advisor? Can you think of further reasons why we are seeing an “algorithm aversion” in this or other examples?

References

Brynjolfsson, E., & Mcafee, A. (2017). The business of artificial intelligence. Harvard Business Review.

Madhavan, P., & Wiegmann, D. A. (2007). Similarities and differences between human–human and human–automation trust: An integrative review. Theoretical Issues in Ergonomics Science, 8(4), 277–301.

Prahl, A., & Van Swol, L. (2017). Understanding algorithm aversion: When is advice from automation discounted?. Journal of Forecasting, 36(6), 691-702.

Further/Related Readings

www.sciencedaily.com/releases/2016/05/160525132559.htm

https://onlinelibrary.wiley.com/doi/pdf/10.1002/for.2464?casa_token=NXRSJubpRYsAAAAA:SuffqBcDFVbrY5gv2QmVIGe6-78qkDp7ws881sZVEl801XPltNb8uzPeGm4oUdZpXFhFrdNFBJf-wfk

https://rmresults.com/blog/why-do-we-tolerate-human-over-machine-error

https://www.digitaltrends.com/cool-tech/the-challenges-of-driverless-shuttles-in-smart-cities/

https://phys.org/news/2016-05-humans-automated-advisor-bad-advice.html#jCp