The Foundation of the U.S. Knowledge-Based Economy

Pool of Knowledge by Ian MuttooIn a recent academic journal article co-authored with Godwin Ariguzo and Angappa Gunasekaran, the ascension of the U.S. economy from service-based to knowledge-based is investigated and a foundational model offered. According to official U.S. sources reporting gross domestic product (GDP) data, the U.S. became a service-based economy (majority of Gross Domestic Product made up by services) at the end of 1958, beginning of 1959 ($211.2 billion GDP services, $200 billion GDP goods), much earlier than previously proposed. Today, services make up 70 percent of total U.S. GDP ($9.8 trillion out of $14.07 trillion).

Following the logic of total factor productivity, the argument can be made that the U.S. officially became a knowledge-based economy, simply measured as the point at which a majority of total service exports are made up of knowledge-based services, at the end of 1997, beginning of 1998. By the end of 1997, 50.74 percent of all U.S. service exports consisted of knowledge-based services. Today, knowledge-based service exports make up 64.6 percent of total U.S. service exports, accounting for $390.95 billion of $604.90 billion in annual service exports for 2011. The diffusion of the internet is thought to be the proximate trigger for the transition of the U.S. to a quaternary stage economy.

We attribute the establishment of the U.S. knowledge-based economy, based on the best available extent research, to the synergistic interaction of five components. The five components consist of a foundation of information and communications technology, plus open innovation, education, knowledge management and creativity. A visual representation of the foundational structure on which the U.S. knowledge-based economy is built is offered below.

Structural Components of the Knowledge-Based Economy

The proposed model is not exhaustive. Certainly other factors contribute to the ascension of the U.S. economy from primarily service-based to knowledge-based. However, while the five structural components offered above are well known and well researched individually, ours is one of the first manuscripts to suggest that the synergistic interaction of the five has provided the U.S. knowledge-based economy with its competitive advantage in the global market.

How long we will enjoy this competitive advantage remains to be seen and depends, in part, on the fate of our system of higher education and its ability to generate graduates with the knowledge and skills required to foster innovation.


R You Ready for DIY Statistics and Social Media Marketing Analytics?

The logo for the R statistical software package

This post originally appeared in Social Media Marketing Magazine, Issue 1, Number 6

Statistics. For many current and former students, just the sound of this word evokes nightmares. However, once you learn to master some basic tasks using statistical analysis software, the world becomes your data playground. Thus, the purpose of this column is to introduce you to one of the most powerful statistical analysis software packages (open source!) and to provide examples of how it can be used to build your social media marketing analytics capabilities.

R is one of the most popular statistical analysis software programs available. It is used by statisticians, financial analysts, marketing researchers and social media researchers. To download and install R, go to the Comprehensive R Archive Network (CRAN) site.

You’ll find versions of R built for Linux, MacOS X and Windows. In my opinion, the versions for Linux and Windows work best. Issues for Mac users related to installing and updating additional statistical analysis packages (3311 are currently available – and like R, all for free) are known to exist. Mac users experiencing problems are encouraged to consider installing Virtual Box in order to install and run the Linux version of the R statistical program. After selecting, downloading and installing the base program, visit the Quick-R site for examples of how to use R for statistical analysis.

Two recent additional (or add-on) social media statistical packages are available to be installed in R: RGoogleTrends and twitteR. The former is one of the most difficult R add-ons to install in R (it took me a couple of hours to make it work) and the latter performs many of the same functions of one of the earlier software packages recommended in this column, NodeXL.


RGoogleTrends is not available for download within the R “install packages” option. To install RGoogleTrends, one must visit this site to download the .tar.gz file (I use 7-zip for Windows to unzip the file). Hyunyoung Choi and Hal Varian provide a unique perspective of the power of RGoogleTrends in their 2009 white paper “Predicting the Present with Google Trends”. Joe Rothermich developed an interesting presentation (2011) that illustrates the power of using RGoogleTrends to measure market sentiment and events. Download and read both to get an idea of the power of R from a social media marketing perspective.


The easier of the two R add-on packages to download and install (can be done within the “install packages” option included in the R base package), twitteR provides users with access to the networks of businesses or people who are on Twitter. Using twitteR, one can perform network analysis tasks that include basic statistics. Jeffrey Breen recently presented (2011) an incredible example of how to use R and twitteR to mine Twitter for consumer attitudes. His presentation includes advanced R code for how to replicate/duplicate his research.

Admittedly, none of this is easy. But spending the time to master R, RGoogleTrends and twitteR will make you a better social media marketing researcher. R you up to the challenge?


Social Media Growth 2006 to 2011

Dell Social MediaIn a previous post, social media growth from 2006 through 2010 was documented. As with that effort, there are no clear or easy answers when investigating the growth of social media sites over the past five years. No reliable or audited data exists for social media sites. Therefore, the numbers presented in the table below represent an estimate of total registered users for each of the sites investigated. The numbers are not assumed to be accurate, valid or reliable – they are as presented: estimates based on the best available public information. Data was collected for three social media sites and three blog hosting sites: Facebook, Twitter, LinkedIn,, Tumblr and Posterous. Estimates for the latter three represent the number of blogs hosted on the sites (not the number of unique bloggers, a much lower number, as estimated in the original post). The result of the change in reporting for the three blogging platforms, necessitated by the lack of data regarding number of unique bloggers, explains some of the exceptional growth reported below. No data for self-hosted blogs using is presented. As of December 2011, all variations of WordPress version 3 have been downloaded in excess of 65 million times.

SMM Growth Table 2011

As in the original post, the Compound Annual Growth Rate (GAGR) is calculated for each using the free Investopedia Compound Annual Growth Rate calculator available on their website.

When examining the charts individually, the growth patterns look similar. Globally, the total number of people using social media continues to increase. Facebook, alone, reports 11.45 percent of the global population as registered users. The average CAGR for the six social media sites is 443.66 percent ranging from 75.97 percent to 1,145.73 percent. Again, multiple factors contribute to this exceptional growth rate as compared to the data reported in 2010, including the change in reporting for blogs (total number of blogs hosted, not unique users) and the rapid growth of both Twitter and Posterous. Growth charts are presented below for each of the social media sites included in this investigation.

Facebook Growth 2006-2011

Twitter Growth 2006-2011

LinkedIn Growth 2006-2011 Growth 2006-2011

Tumblr Growth 2006-2011

Posterous Growth 2006-2011

When charted together, the domination of Facebook’s growth and share of voice in the social media world remains apparent. The launch of Google+ as a viable alternative to Facebook, although well received by the social media community, has not gained much traction in the broader audience. Data from Google+ will be included in next year’s update.

Social Media Growth 2006-2011 The final chart presented is social media share of voice. The inner ring contains the data from 2006 and the outer ring presents the data from 2011.

Social Media Share of Voice 2006-2011

It is clear that in terms of diffusion of innovation, social media remains in the growth stage. Equally as clear, but not reported above, is that more people globally are accessing social media from mobile devices. For marketers, the implications are irrefutable: get social and become mobile or risk losing share of voice in the social/mobile marketing era.


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