Technological Adaptation and Long Run Growth

Most of us probably learned back in intermediate macroeconomics about long-run growth. Moreover, we also probably learned that innovation and technology is the true driver for long-run growth; yet, we also just learned the dynamics behind what drive the adaptation of technology in networks. What if the another portion of the driving force in long run economic growth isn’t merely the rate of technological innovation and advancement, but also the rate at which the majority of the population adapts new technologies?

We know that new technologies and innovations allows society to allocate and use scarce resources “better”; We can do more with less. By doing more with less, we make ourselves “richer” and hence better off. I believe it is a safe assumption that companies, the government & the military, and universities are always attempting to develop new technologies and innovate. It’s profitable to do so and it makes society as a whole better off. Thus, entities will continue to innovate.

However, these advances mean absolutely nothing unless they’re put to good use; they must be adopted by the majority of the masses, firms or individuals. Think about the technological advances in the past five years. In the past ten years. Think about how much “better off” we are because of these advances and how much more efficient we can be in the workplace. We have computers and software that cuts down complex tasks to mere minutes. Want to a triple integral? It takes most of us at least a few minutes to do. Wolfram Mathematica can do it a just a few seconds and it gives you a visual along with it.

But these revolutionary programs mean absolutely nothing unless we begin to use them. Moreover, as more people adopt these programs and technologies, the more we are productive and hence the better off we become. Therefore, if more individuals or firms adopt technologies at a higher rate, the better off we will be.

In conclusion, long run growth may not be just driven by technological advances, but rather a combination of the rate of technological advance and the rate by which we adapt these new technologies and innovations. Firms and entities will continue to innovate and build new technologies, but all this means nothing if it’s not being put to good use. Hence, the rate by which we adapt new technologies also influences long run growth.


How the HTC One M8 will put the pow in its pitch

In recent weeks there has been a new ad on television that markets HTC’s new phone.  The  HTC One M8 is introduced in a commercial that stars Gary Oldman, but it offers a very unique situation.  Instead of the commercial referencing the phone in design, capabilities, and uses of the phone in the commercial.  Oldman repeats the saying ‘blah blah blah’ and tells the viewer to go to the internet to find information about the phone and create a opinion based off of peer review in a sense.  HTC realized that it cannot directly compete with larger firms such as apple and Samsung, who can put billions of dollars into advertising their devices.

HTC has opted to use a informational cascade strategy in  marketing their device to the public.  When the company refers the viewer to ‘ask the internet’ they are relying on positive reviews on the device.  The positive reviews of the device will influence the decisions of others because they are connected by a network, the internet.  A simple herding experiment can then be drawn from this situation.  If the first reviewer of the new phone has a positive review then the second reviewer may be affected by the first reviewers decision.  The second reviewer has their own private information, but will also have the review of the other reviewer as a reference.  This strategy of reliance on the internet as a network and using a model of cascades for advertising may in the future be a key tool for smaller firms to compete with larger firms in the marketing category.

Rational Herding and the Housing Bubble

Rational Herding and the Housing Bubble

In class we talked about how informational cascades and the herding effect can either go well or completely disastrous just by the simple observation of another person’s decision. In this article, Robert Schiller explains how real estate housing bubbles are largely affected by the IC theory. 

People are influenced by the decisions and outcomes of other people. An example given in the article talks about investing in the housing market. If houses are of low value and a person decides to invest in an expensive house, this influences others to invest in houses too since then they will believe houses are a good investment. The next person is ignorant of the housing market and prices, but because of the first person’s decision, he makes a rational decision to buy a house as well. Now that two people have bought houses, other friends, family, acquaintances will begin purchasing houses at low prices and even very high prices. Their information about the housing market is restricted to the opinions of the housing investors. Thus, a housing bubble is created. People buy houses because they “think” it’s a good time to invest according other people, realtors end up selling many houses, and then a decline takes place when people begin to think the time frame for investing is “bad.” Following the cascade in investing in houses, a downfall cascade occurs when people stop investing in houses.

Furthermore, studies from Mr. Bikhchandani and his co-authors showed “that the probability of the cascade leading to an incorrect assumption is 37 percent. … Thus, we should expect to see cascades driving our thinking from time to time, even when everyone is absolutely rational and calculating.”


The Cascade of iOS and Android Software Updates

The majority of the U.S. population uses a smartphone as their main device for communication and the vast majority of those smart phones are running Apple’s iOS or Google’s Android.  These two main operating systems receive frequent updates to correct software bugs and fix security vulnerabilities but receive a major update approximately once per year.  While the majority of all smartphones run on one of these two operating systems the adoption rates could not be more different.  Apple’s iOS 7 was released in September of 2013 and has surpassed 87% adoption as of April 8th 2014 compared to Google’s Android Kit Kat 4.4 which was released in October of 2013 and claims 5.3%.  The version of Android that is installed on most user’s devices is 4.1, Jelly Bean, and was released in September of 2012. Considering this huge divide among smartphone operating system adoption rates it is clear that the probability of a user to adopt to the latest software is very high for Apple’s devices and much lower for devices running Google’s Android software.  Of course there are many factors that contribute to the much lower adoption rate on Android devices, the major one being that Google does not actually make any of the devices themselves, instead opting to release the software for device manufacturers to use and manipulate as they choose.  This simple ideology throws a major obstacle into the ability to update to the newest software and in many cases prevents someone from updating to the newest version.  iOS7 is compatible with the iPhone 4/4S, 5/5C/5S and was available to all of those users on the same day where Android must complete many different compatibility tests before it can be made officially available to devices running Android.  Some Android phones are still sold today running outdated software with no potential to ever get an update to the newest version where all Apple devices sold today are running iOS7 out of the box.

Software adoption is, in a way, very similar to information cascades and the clusters that prevent adoption from spreading across the entire population.  When it comes to Apple smartphones, the only devices that are excluded are the original iPhone and the iPhone 3G/3GS so the software is able to spread to the vast majority of all users and devices.  In the case of Google’s Android, many more clusters exist within the total network that prevent the software from spreading at the high rate of iOS.  As more devices become compatible the updates will be pushed out to qualified Android devices and those clusters that previously existed as an obstacle to the continuing cascade will join the main network.  The only problem is that the adoption rate for Android is so slow that by the time Google announces Android 4.5 most devices will (hopefully) just receiving the update to 4.4 and thus the fragmentation is guaranteed to continue.  Apple, on the other hand, will continue to see massive and quick adoption of it’s next iteration of software, likely named iOS8, and will continue to attract smartphone purchasers and application developers alike. 

Attached is an article briefly explaining the difference in adoption rates on Google’s Android and Apple’s iOS with graphic presentations that highlight the polar opposite that these two main operating systems represent.

Data Breaches: A New Way to Game Google

The problem of Google’s PageRank Algorithm has risen after the breach of Targer’s website. When Target’s website was attacked by hackers, the Pagerank of this retailor has shown slightly higher in search engines. One possible reason is that blogger and social media pointed back its website.

Since Targer is well-known company ON/OFFline, this is not helpful to increase the reputation of Target. However, other some unscrupulous businesses desperate to raise their profile on the crowded Web promoting a breach or even pretending one happen to elevate its PageRank.

This action would not work without media’s attention. The most publications are terrible at linking with situation. To use bad publicity to elevate the pagerank is creating problem. Criminal groups are already using computer hacking to manipulate search ranking which means it is possible to visit and infected virus on one’s computer.

“Google said that it has been fighting attempts to manipulate its search results for years and has sophisticated technologies in place to fight fraud.”

Diffusion Theory in Communications/Public Relations

I’m not an economics or business major. Much of the math we’ve been using has been lost, even simple algebraic principles, from not using it (it might have been a good idea to brush up before taking this class). Still, I’ve gained a better understanding of the underlying principles involved and have (somewhat) of an idea on how to apply and predict outcomes of diffusion in my field: communications/public relations/image management. Surprisingly, the content of the two classes has been somewhat hand in hand when discussing theory and process. Diffusion of Innovation, diffusion, diffusion theory–whatever you want to call it–is an important topic in PR; it explains how ideas are spread and can help to better plan marketing and public relations campaigns and the transfer of ideas/ideals/mission statements and public announcements.

The attached article is from the Journal of Consumer Research, so it is a bit lengthy, but it shows how what we’ve learned in class is applied to a business’ relations. One aspect of communications is that the practice has come to rely on the Two-Step Flow Model of influence. It applies the Triadic Closure Principle and gears campaigns, information (what-have-you), to the intervening and moderating publics. It is a better way to reach an audience, because if focuses on the stronger ties within the community, or network.

The article actually focuses on adoption a bit more than our text has and separates the different audiences; stating that those in influencing positions aren’t always the first to acquire the new technology, idea, etc. That “cascades triggered by hyperinfluentials are on average less successful than than those triggered in low-variance influential networks…” (but those in highly influential positions are still not to be discounted, because they can reach a major portion of the community in a shorter step).

Network Effects and the Implementation of Google Fiber

After learning about network effects, and how a new product or service must be adopted by certain nodes for it to spread across a network, I thought about Google Fiber.  Google Fiber is a new internet service that has greater features than the current internet services provided by cable companies.  Google Fiber is currently in a position where it is being implemented in certain cities.  Google Fiber is interesting because in order for it to be successful, the network effect will have to work in its favor over competitors like Verizon and Comcast.

Google Fiber already has a greater value to consumers than the services that are currently offered, because it runs at speeds 100 times faster than the broadband offered by others.  This means that as soon as Google Fiber is adopted by the right cities, it will spread to the surrounding cities and potentially take over the network.  Google already has implemented this service in Austin, Provo, and Kansas City, and has listed its potential future cites as Portland, San Jose, Salt Lake City, Phoenix, San Antonio, Nashville, Charlotte, and Atlanta.  When looking at a map of the United States, you can see that these cities are all spread evenly across the country.  This mostly like means that the people working at Google are taking the necessary steps to ensure that the network effect takes over.

It will be interesting to see how this plays out in the future.  As more and more cities adopt this service, it would make the most sense that it eventually takes over the entire network, unless the competing providers improve their services and match Google Fibers low price.  As a consumer, I can only hope that those at Google have done their homework on network effects and network theory, and chose the right cities to implement Fiber in, so that Google Fiber can eventually make its way across the network and be available to me in Philadelphia.