Article Review: Knowledge Discovery through Text Analytics

The article “IT innovation adoption by enterprises: Knowledge Discovery through text analytics” was select as an article in the field of study. The path to get to this article is articulated below:

Science Direct (Elsevier) > Search Term: “Text Analytics”

This article was chosen due to my interest in text analytics of huge data sets, to help derive knowledge from this unstructured data set. This was a primary topic/interest for the dissertation, along with identifying another unconventional way to do literature reviews.


This study investigated two premises: (1) Is it possible to use text data mining techniques to conduct a more thorough and efficient literature review on any subject matter? (2) What are the drivers of IT Innovations? After having identified 472 quality articles spanning multiple fields in business administration and 30 years of knowledge.  The authors used a tool called Northernlight (, where they were able to answer both premises. 

The authors, state that current methods of most literature reviews are time-consuming, usually focus in the last five years and involve tons of attention. Most articles are scanned by title and abstracts before the researcher considers them to be read in their entirety. This method, as argued, is not useful. Thus, effective techniques consist of the use of “meaning extract” of a large set of documents (usually considered unstructured data sets) across various domains should help the researcher to obtain and discover knowledge efficiently. Therefore, the first premise deals with the utilization of text data mining techniques. These techniques shouldn’t just merely revolve around a core system of counting the identified keyphrases (or “themes”), but on automating “meaning extraction.” “Meaning extraction” measures the strength between keywords or phrases that are related to others. The end-user/researcher can apply rules to help enhance meaning extraction between sets of keywords. The authors conclude that these techniques are an excellent way to do a first-pass analysis. The first pass analysis can help generate more questions, which can lead to more future insights.

They prove the first premise by applying the Northernlight system towards IT innovation. The authors then used 472 data sets, in which IT innovation is mentioned in multiple disciplines across the field of business administration. By setting rules to identify keyword proximity to other keywords (or their equivalents) they were able to garner some insights into IT Innovation. Proximity could be measured as far as a sentence (~40 words) or a paragraph (~150 words). Thus, they determined that cost and complexity are the two most frequent IT innovation determinants (as well as complexity, compatibility, and relative advantage) based on an IT department’s perspective. However, on the enterprise level, perceived benefits, perceived usefulness, and ease of use, were determinants of IT innovation. Finally, organization size and top management support positively correlated with IT innovation with cost being negative towards IT innovation.


The obstacle that came in here was that some articles with really creative titles and were recently published came at a price. So the article that was chosen was still a good read, but one does wonder how good those papers are that have been priced/paywall. Is having a paywall from publicly funded sources be hidden behind a paywall. We paid for it through our taxes, why should we have to pay for it once the results are out.


How to do a podcast

In this post, you will get a behind the scenes look as you learn what it takes to produce your very own podcast. An opportunity to increase your presentation skill potential through this new landscape. This interactive session will teach attendees to learn how to plan, prepare and produce their very own podcast. You’ll get a bird’s eye view on the ins and outs of what it takes to become a successful podcaster. As an added bonus, the attendees of this seminar interacted and became a part of an official District 58 Podcast recording.

Below is the PowerPoint presentation I gave:


The raw audio file can be found below:

Whereas the production quality audio file can be found below:

Links to the products I used can be found:

A Kodak Moment

Kodak- good plan but something went wrong because of circumstances beyond their control

In 1884 patents for the photographic film were produced, and eight years later the company to be known as Kodak was founded by a high school dropout George Eastman (Elliott, 2012; Sparks, 2012).  In 1900, the “Brownie” camera was introduced at $1 each and film at $0.15, and it allowed for the general public to have access to a camera (Anthony, 2011; Elliott, 2012; Sparks, 2012). Quality was its major differentiator (Cohan, 2011). With time cameras got better, easier to use, and smaller to make the end-user experience as simple and welcoming as possible (Elliott, 2012). In the 1930s, Kodak had an IPO on the Dow Jones and in 1969 the film used to capture the Apollo 11 missions was from Kodak (Elliott, 2012; Sparks, 2012). In 1975 Kodak introduced the digital camera where pictures can be stored on cassette tapes (Sparks, 2012).

By 2004-2009, Kodak stopped selling film cameras and tape recorders to meet the new shift in the market for digital cameras (Elliot, 2012; Sparks, 2012).  As this new shift was occurring, 13 manufacturing plants and 130 processing labs closed, cutting over 47K jobs starting in 2003 (Strydom, 2012).  Therefore, Kodak started and kept losing their market share in 2011 and filed chapter 11 on 2012 as an attempt to transform the company into a pioneer in digital cameras (Sparks, 2012; Strydom, 2012). Today, they struggle to keep pensions and other benefits for their retired workers, while they are leaning out their processes and restructuring their costs (Strydom, 2012). Kodak to try to stay afloat, has sued Apple and Blackberry for stealing their patented technology (Anthony, 2011). The only thing that Kodak can do now sells its >1000 patents in cameras and video tapes (Anthony, 2011, Elliott, 2012).

Forces that adversely affected Kodak

Competition: From the 1900s, Kodak dominated the market shares with consumer photography (Elliott, 2012). However, competition is fierce. Sony, Canon, Apple, HP, and Fuji all began to specialize and develop faster than Kodak to chip away at its market share or create new markets as first movers (Anthony, 2011; Cohan, 2011; Elliott, 2012; Sparks, 2012).

Economical: Fuji specialized in film and was able to out price Kodak. Therefore Fuji film was able to quickly take away a cash cow product from Kodak, such that Kodak had to lay off 20K jobs just to offset (Cohan, 2011).  Fuji essentially was able to gain control of the market share in the film segment.

Ethical: In 1948, Polaroid came out with instant photography and Kodak copied that technology, lost a suit of $909M for stealing the technology from 1976-1986 (Cohan, 2011). Kodak to try to stay afloat, has fruitlessly sued Apple and Blackberry for stealing their patented technology (Anthony, 2011).

Technological: As Kodak moved away from the film, it tried to take some dominance in the digital camera market, but they couldn’t penetrate that market enough to be sustainable (Elliott, 2012). As it tried to gain market share in the digital camera realm, they were late.  The timing of Kodak entering into this market had allowed Sony and Canon to become the first movers and establish market control (Anthony, 2011). Kodak tried to work on digital camera personal printers, but HP was the first mover in that market and had a strong hold on the market share (Cohan, 2011).

Why it is relevant

There are many big companies out there today that has become comfortable and is now are seeing an introduction of competition, which is threatening to take away their market dominance and reduce the market share.  Though competition is great for consumers, which allows for pricing wars to exist, it is terrible for a company that is trying to drive down costs enough remain competitive while still turning over profit.

The story of Kodak is not different from other companies, and it does show that empires do fall.  It starts off with competition.  Fuji specialized in film and was able to out price Kodak (Cohan, 2011). Then it came with Apple that started to try to produce digital cameras, but it was then Canon and Sony that specialized in this new area and developed the technology much faster than Kodak that Kodak just couldn’t catch up (Cohan, 2011; Elliott, 2012; Sparks, 2012). Finally, when Kodak tried to dominate a space where they could gain a reasonable amount of market share by working on tangential technology, the digital camera printers, they were too late, and HP became the first mover in that technology space and held onto its dominance in the market (Cohan, 2011). Therefore, the story of Kodak should provide a cautionary tale to other companies.


Play-Doh: An innovation that came from error or accidents

The mixture of flour, water, salt, boric acid and mineral oil was first originally used as a reusable soup product to help clean wallpaper as part of the Kutol company (Biddle, 2012; Hiskey, 2015; Wonderopolis, n.d.). Hiskey (2015), chronicles that in 1933 coal was used to heat a home in a chimney, but came at the cost of causing sooty wallpapers, which established the need for the product, and there was the added dimension of the problem that wallpaper couldn’t get wet.  Noah McVicker and Cleo McVicker were able to create a component to clean wallpaper without getting it wet and partnered with Kroger groceries to be their distributor (Hiskey, 2015).  When coal fireplaces were being replaced with oil and gas and a new type of wallpaper that can be cleaned with water and soap was introduced, sales plummeted (Hiskey, 2015).  However, the lack of toxic chemicals made it an ideal not only as a cleaning product but to become the toy it is today eventually (Hiskey, 2015; Wonderopolis, n.d.).  The transition occurred when teachers began to use this wallpaper cleaner in an innovative way, for a molding compound to make art for craft projects in school (Hiskey, 2015; The Strong, n.d.; Wonderopolis, n.d.).  When, the inventor’s nephew, Joe McVicker, eventually came into the Kutol Company and noticed this secondary use of their product, and though it would be good to rename the product “Play-Doh” and market it to schools (Biddle, 2012; The Strong, n.d.; Wonderopolis, n.d.). In 1956, the nephew devoted his time to creating Play-Doh as part of a company called Rainbow Crafts Company and sold to both Macy’s and Marshall Fields, and in one year made $3 million just by selling Play-Doh in the primary colors (Hiskey, 2015; The Strong, n.d.; Wonderopolis, n.d.).  In the 1980s, the color pallet was expanded to 8 colors, with future versions glowing in the dark, containing glitter, and smell like shaving cream (The Strong, n.d.) The recipe has been perfected over time and has remained a trade secret; Play-Doh is now part of the Hasbro Company (Wonderopolis, n.d.). Under the wallpaper utility of this product, it sold for 34 cents per can, but under the toy utility of this product the company was able to sell it at $1.50 per can (Hiskey, 2015).  In 2003, Play-Doh was added to the “Century of Toys List,” as it has hit 100 years of existence (Wonderopolis, n.d.) 700 million pounds of Play-Doh have been sold and played with (The Strong, n.d.).In 2016, a Play-Doh Super Color pack with 20 different colors goes for $14.99, and a Play-Doh Rainbow Starter Pack with eight colors goes for $4.99 (Hasbro, n.d.). However, the amount of Play-Doh per mini color tub is small compared to homemade versions.  There are many ways to make your version of Play-Doh.  One version of this non-toxic homemade version of Play-Doh, as stated by Nicko’s Kids DIY (2012): (1) mix 2 cups of flour, 2 cups of water, 1 cup of salt, 2 tbsp. of vegetable oil, and 1 tbsp. Of cream of tartar over low heat in a pan until it becomes a dough; (2) while it is still warm, knead the dough and don’t add any more flour to it; (3) finally poke a hole to the center of the dough and drop in a few drops of food coloring and work in the color.

Forces that supported it

  • Commercial: Besides selling it in one-gallon tubs to schools, sales skyrocketed when it got a national platform to the kids show Captain Kangaroo, who was promised to get 2% of the sales as long as the product was featured (Hiskey, 2011; Hiskey, 2015). Play-Doh, after leaving Kutol and joining Rainbow Crafts Company, was sold to General Mills, which sold it to Hasbro who still owns the right and intellectual property of Play-Doh (Hiskey, 2011).
  • Technological: It’s non-toxic everyday household product chemical mixture allowed it to be safely used by children (Biddle, 2012; Hiskey, 2015; The Strong, n.d.; Wonderopolis, n.d.). However, the formula was reinvented in 1955 to make it last longer and not dry out so quickly by chemist Dr. Tien Liu (Hiskey, 2011).
  • Financial: Under the wallpaper utility of this product, it sold for 34 cents per can, but under the toy utility of this product the company was able to sell it at $1.50 per can (Hiskey, 2015).


Innovation: Technology and Trends in museums

Definition of Museum: term applied to zoos, historical sites, botanical gardens, aquariums, planetariums, children’s museums, and science and technology centers (US DoJ, 2009)

Museum Edition: Key trend: Short-term trend: Driving Ed Tech adoption in museums for the next one to two years

With the introduction of mobile technology, increasing in processing speed every year and the introduction of Artificial Reality (AR) through Pokémon Go, there is a huge opportunity to create discoverable museums displays serviceable through mobile devices (CNET, 2016; New Horizons, 2016; Bonnington, 2015). The AR technology uses the mobile device camera and interlaces pocket monster’s called Pokémon in real time through some creative coding, therefore through a mobile device these Pokémon are made visible, even though they do not exist (CNET, 2016). Mobile devices are not just for gaming they have become the primary computing device for most people across the globe as well as a primary way to access information (Bonnington, 2015; New Horizons, 2016).  Adding in, Pokémon Go’s added benefit, which promotes end users to walk to key areas have been designated to be either Pokestops (for getting key items for game play) or Pokémon Gym (to either build up a team’s gym or take it down) therefore enhancing the experience (CNET, 2016).  It is projected that in the next 5-years mobile devices could have enough processing power to handle 4K streaming, immersive virtual reality gaming, and seamless multi-tasking (Bonnington, 2015).  Therefore, creating a new museum experience using an AR system similar to Pokémon Go, with interactive museum displays similar to Pokestops or Pokémon Gyms could become a reality, enhance exploration, interpretation, and sharing.  This would essentially be a more interactive self-guided virtual tour, similar to what has been implemented in the Broad Museum in Los Angeles and is a prioritized strategy for San Francisco’s Museum of Modern Art (New Horizons, 2016).  If we can centralize/core up all of the museums into one interface similar to what Israel is doing with their museums (so far they have represented 60 museums), we could see bigger adoption rates (Museums in Israel, n.d.). According to New Horizons (2016), hyper zoom features on particular displays, gamification, location-based services, AR, ad social networking integration can increase patron’s experiences.  This area all aspects that Pokémon Go is trying to promote through their mobile device game.

Forces that impact the trend

  • Technological: There is a need to update the WiFi Infrastructure in museums to handle the increase in demand, which is a key force negatively impacting this technology (New Horizons, 2016; Government of Canada, n.d.). Though, computer codes and infrastructure designs are becoming more open source which is a force of positive impact.
  • Safety: There is added need to improve design and flow of a museum to accommodate distracted patrons using this new AR system.
  • Cultural: Museums at one point use to ban cameras, but now with many mobile devices and the proposed AR system above, it would be hard to enforce now (New Horizons, 2016). Also, given the fact that museums are wanting to increase participation.

Museum Edition: Technology: Improving Accessibility for Disabled populations

One in 10 people lives with a disability or approximately 0.65 Billion people (Disabled World, n.d.).  It is imperative and ethical that museums create exhibits for all their patrons. Deviations from societal norms have caused people with disabilities in the past to be considered as signs of divine disapproval, with the end thoughts and actions stating that they need to be fixed (Grandin, 2016), when there is nothing wrong with them, to begin with.  A few of the many areas for improvements with technology are:

  • Websites and online programming: making them more accessible and eliminating barriers through the incorporation of universally good design (New Horizons, 2016; Grandin, 2016).
  • Addressing Article 30 of the UN Disability Convention: Implementing technology to allow enjoyed access to performances, exhibits, or services (UN, 2006). This would allow, encourage, and promote all people to participate to the fullest extent possible (New Horizons, 2016; UN, 2006).
  • Use of software to create alternative formats for printed brochures: Braille, CDs, large print (US DoJ, 2009). Also, using that same software to create Braille exhibit guides (New Horizons, 2016).
  • Using closed captions for video displays (New Horizons, 2016).

An excellent way to test universally good design is for museums to partner with disabled students to test their design’s usability and provided meaningful feedback (New Horizons, 2016). Essentially, one way to approach universally good design is to ask the three questions (Wyman, Timpson, Gillam, & Bahram, 2016):

  1. “Where am I?”
  2. “Where can I go from here?”
  3. “How can I get there?” or “How can I make that happen?”


Forces that impact the technology

  • Educational: There is a lack of disability responsiveness training by the staff of a museum, which is leading to a lack of knowledge of best practices, how best to serve the disable population, etc. (New Horizons, 2016).
  • Financial: Lack of resources to design or even implement new programs for people with disabilities is a key force negatively impacting this technology (New Horizons, 2016; Grandin, 2016). However, the best designs are simple, intuitive, flexible, and equitable, therefore making accessible design a universally good design (Grandin, 2016; Wyman et al., 2016). How do museums know about universally good design? Museums are able to accomplish this by working with the disable community and advocacy organizations (New Horizons, 2016). So, as museums begin making their updates on exhibits, or to their building, they should take into account accessible design. For people with disabilities, a universally good design is one where there is no additional modifications are needed for them (Grandin, 2016).



One could define Innovation as an idea, value, service, technology, method, or thing that is new to an individual, a family, a firm, a field, an industry, or a country (Jeryaraj & Sabhewal, 2014; Rogers, 1962; Rogers, 2010; Sáenz-Royo, Gracia-Lázaro, & Moreno, 2015). Based on this definition above an invention can be seen as an innovation, but not all innovations are inventions (Robertson, 1967).  Also, even though something may not be considered as an innovation by one entity, it can still be considered as innovative if adopted by a completely different entity (Newby, Nguyen, & Waring, 2014).

Innovation moving from one entity to another can be considered as Diffusion of innovation.  Diffusion of Innovation is a theory that is concerned with the why, what, how, and rate of innovation dissemination and adoption between entities, which are carried out through different communication channels over a period of time (Ahmed, Lakhani, Rafi, Rajkumar, & Ahmed, 2014; Bass, 1969; Robertson, 1967; Rohani & Hussin, 2015; Rogers, 1967; Rogers 2010).  However, there are possible forces that can act on an innovation that can influence the likelihood of the innovation success, for example financial, technological, cultural, economical, legal, ethical, temporal, social, global, national, local, etc.  Therefore, when viewing a new technology or innovation for the future, one must think critically about it and evaluate it from different forces/lenses.


  • Ahmed, S., Lakhani, N. A., Rafi, S. K., Rajkumar, & Ahmed, S. (2014). Diffusion of innovation model of new services offerings in universities of karachi.International Journal of Technology and Research, 2(2), 75-80.
  • Bass, F. M. (1969). A new product growth for model consumer durables. Management science15(5), 215-227.
  • Jeyaraj, A., & Sabherwal, R. (2014). The bass model of diffusion: Recommendations for use in information systems research and practice.JITTA : Journal of Information Technology Theory and Application, 15(1), 5-30.
  • Newby, M., Nguyen, T.,H., & Waring, T.,S. (2014). Understanding customer relationship management technology adoption in small and medium-sized enterprises. Journalof Enterprise Information Management, 27(5), 541.
  • Robertson, T. S. (1967). The process of innovation and the diffusion of innovation. The Journal of Marketing, 14-19.
  • Rogers, E. M. (1962). Diffusion of innovations. (1st ed.). New York: Simon and Schuster.
  • Rogers, E. M. (2010). Diffusion of innovations. (4st ed.). New York: Simon and Schuster.
  • Rohani, M. B., & Hussin, A. R. C. (2015). An integrated theoretical framework for cloud computing adoption by universities technology transfer offices (TTOs).Journal of Theoretical and Applied Information Technology,79(3), 415-430.
  • Sáenz-Royo, C., Gracia-Lázaro, C., & Moreno, Y. (2015). The role of the organization structure in the diffusion of innovations.PLoS One, 10(5). doi:

Big Data Analytics: Future Predictions?

Big data analytics and stifling future innovation?

One way to make a prediction about the future is to understand the current challenges faced in certain parts of a particular field.  In the case of big data analytics, machine learning analyzes data from the past to make a prediction or understanding of the future (Ahlemeyer-Stubbe & Coleman, 2014).  Ahlemeyer-Stubbe and Coleman (2014), argued that learning from the past can hinder innovation.  Although Basole, Seuss, and Rouse (2013), studied past popular IT journal articles to see how the field of IT is evolving, and in Yang, Klose, Lippy,  Barcelon-Yang, and Zhang, (2014) they conclude that analyzing current patent information can lead to discovering trends, and help provide companies actionable items to guide and build future business strategies around a patent trend.  The danger of stifling innovation per Ahlemeyer-Stubbe and Coleman (2014), comes from when we consider a situation of only relying on past data and experiences and not allowing for experiencing or trying anything new.  An example is like trying to optimize a horse-drawn carriage; then the automobile will never have been invented (Ahlemeyer-Stubbe & Coleman, 2014).   This example is a very bad analogy.  We should not focus on only collecting data on one item, but its tangential items as well.  We should focus on collecting a wide range of data from different fields and different sources, to allow for new patterns to form, connections to be made, which can promote innovation (Basole et al. 2013).

Future of Health Analytics:

Another way to analyze the future is to dream big or from a movie (Carter, Farmer, and Siegel, 2014). What if we could analyze our blood daily to aid in tracking our overall health, besides the daily blood sugar levels data that most diabetics are accustom to?  The information generated from here can aid in generating a healthier lifestyle.  Currently, doctors aid patients in their care with their diet and monitor their overall health.  When you are going home, this care disappears.  But, constant monitoring may help outpatient care and daily living.  Alerts could be sent to your doctor or to other family members if certain biomarkers get to a critical threshold.  This could aid in better care, allowing people’s social network to help them keep accountable in making healthy life and lifestyle choices, and possibly lessen the time between symptom detection to emergency care in severe cases (Carter, Farmer, and Siegel, 2014).

Generating revenue from analyzing consumers:

Soon, it is not enough to conduct item affinity analysis (market basket analysis).  Item affinity (market basket analysis) uses rules-based analytics to understand what items frequently co-occur during transactions (Snowplow Analytics, 2016). Item affinity is similar to the current method to drive more sales through getting their customers to consume more.  However, what if we started to look at what a consumer intends to buy (Minelli, Chambers, and Dhiraj, 2013)? Analyzing data from consumer product awareness, brand awareness, opinion (sentiment analysis), consideration, preferences, and purchases from a consumer’s multiple social media platforms account in real time can allow marketers to create the perfect advertisement (Minelli et al., 2013).  Establishing the perfect advertisement will allow companies to gain a bigger market share, or to lure customers to their product and/or services from their competitors.  According to Minelli et al. (2013) predicted that companies in the future should be moving towards:

  • Data that can be refreshed every second
  • Data validation exists in real time and alerts sent if something is wrong before data is published in aiding data driven decisions
  • Executives will receive daily data briefs from their internal processes and from their competitors to allow them to make data-driven decisions to increase revenue
  • Questions that were raised in staff meetings or other organizational meetings can be answered in minutes to hours, not weeks
  • A cultural change in companies where data is easily available and the phrase “let me show you the facts” can be easily heard amongst colleagues

Big data analytics can affect many other areas as well, and there is a whole new world opening up to this.  More and more companies and government agencies are hiring data scientists, because they don’t just see the current value that these scientists bring, but they see their potential value 10-15 years from now.  Thus, the field is expected to change as more and more talent is being recruited into the field of big data analytics.


Ahlemeyer-Stubbe, A., & Coleman, S.  (2014). A Practical Guide to Data Mining for Business and Industry. Wiley-Blackwell. VitalBook file.

Basole, R. C., Seuss, D. C., & Rouse, W. B. (2013). IT innovation adoption by enterpirses: knowledge discovery through text analyztics. Decision Support Systems V(54). 1044-1054.

Carter, K.  B., Farmer, D., Siegel, C. (2014). Actionable Intelligence: A Guide to Delivering Business Results with Big Data Fast!. John Wiley & Sons P&T. VitalBook file.

Minelli, M., Chambers, M., Dhiraj, A. (2013). Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today’s Businesses. John Wiley & Sons P&T. VitalBook file.

Snowplow Analytics (2016). Market basket analysis: identifying products and content that go well together. Retrieved from

Yang, Y. Y., Klose, T., Lippy, J., Barcelon-Yang, C. S. & Zhang, L. (2014). Leveraging text analytics in patent analysis to empower business decisions – a competitive differentiation of kinase assay technology platforms by I2E text mining software. World Patent Information V(39). 24-34.