Sociotechnology plan for Getting People Out to Vote

Abstract:

According to the US Census Bureau (2016), there are approximately 227 Million eligible voters in the U.S. However; the Bipartisan Policy Center stated that in 2012 the voter turnout was 57.5%. This helps establish a need for Getting Out to Vote (GOTV) efforts. Regardless of any party’s political views, ideologies, and platforms, each party should improve their Get Out to Vote (GOTV) initiatives, which help convert citizens into active voters on election day (Garecht, 2006). Fortunately, technology and big data could be used to leverage the GOTV strategies to allow for mass social contact that is tailored to the voter and yet still cheaper than door-to-door canvassing. The purpose of this sociotechnical plan for GOTV is to build a bi-partisan mobile application that serves the needs of the citizens and politicians to increase poll attendance rate and ensuring a more democratic process.

Introduction:

Democracy in any nation is at its best when everyone participates.  Regardless of any party’s political views, ideologies, and platforms, each party should improve their Get Out to Vote (GOTV) initiatives, which help convert citizens into active voters on election day (Garecht, 2006). GOTV initiatives are meant to get voter who doesn’t usually vote to get out and vote on election day or those who intend to vote to follow through (Bash, 2016; Stanford Business, 2012).  According to the Institution for Social and Policy Studies (ISPS) (2016), a large number of studies have found that personalized methods, like door-to-door canvassing for GOTV, is the best and most robust method currently out there. However, mass email, mailers, or robocalls isn’t, because it lacks dynamic and authentic interaction.  Nearing the last few days of the election, voters or would-be voter already have picked who they would vote for and which way to vote on certain initiatives (Bash, 2016).  So it is not a matter of convincing people but having a high voter turnout.

A good goal for any political party’s GOTV initiative is to obtain 10% of the voters needed to win the election (Garecht, 2006). Door-to-door canvassing is very cost prohibited, but they are cost efficient whereas mass social contact is not cost efficient even though it is cheaper (Gerber, Green, & Larimer, 2008; ISPS, 2016). Green et al. (2008) stated that door-to-door canvassing costs approximately 10x more than mass social contact per vote.  Even though the costs are huge when doing door-to-door canvassing, the Republican National Committee for 2016 projects to have knocked on 17 million doors for their efforts, compared to 11.5 million in 2012’s elections (Bash, 2016).  Fortunately, technology and big data could be used to leverage the GOTV strategies to allow for mass social contact that is tailored to the voter and yet still cheaper than door-to-door canvassing.  Currently, Social media, email, online ads, and websites are used for GOTV (Fuller, 2012).

Scope: 

The current and next generation voters will be highly social and technologically advance, leveraging social media and other digital tools to learn about the issues from each candidate and become social media influencers (Horizons Report, 2016c). Therefore, as a feature, social media could be used as a way to develop personal learning networks and personal learning on the issues and initiatives (Horizons Report, 2016c; Horizons Report, 2016e). Twitter has been used by students to discover information, publishing, and sharing ideas, while at the same time exploring different perspectives on the same topic to promote cultural and scientific progress (Horizons Report, 2016a).

Walk book, an app is being used by the Republican National Committee to aid in their GOTV efforts, which shows voter’s living location, their party affiliation, and how reliable they are as a voter (Bash, 2016). The walking book mobile app, also allows for door-to-door canvassing personnel to have dynamic discussions through dynamic scripting which handles a ton of different scenarios and responses to their questions.  Data is then collected and returned to the Republican National Committee for further analysis and future updates.

Another feature of Social media technologies, is that as these technologies continue to evolve beyond 2016, these tools can be used for crowdsourcing, establishing an identity, networking, etc. (Horizons Report, 2016b; Horizons Report, 2016e).  Establishing identities can work for the political campaign, but leveraging established voter social media identities could help create that more tailored response to their values and what is at stake in the election.  The limitation comes from joining all these data sources containing huge amounts of unstructured data into one system, to not only decipher a voter’s propensity to vote but their political leaning (Fuller, 2012).

Purpose: 

According to the US Census Bureau (2016), there are approximately 227 Million eligible voters in the U.S. However; the Bipartisan Policy Center stated that in 2012 the voter turnout was 57.5%. This helps establish a need for GOTV efforts. When more people go out to vote, their voices get heard.  Even, in states that are heavily democratic or republican, a higher turnout can increase the chance of the political candidate to be more centrist in their policies to remain elected in the future (ThinkTank, 2016; vlogbrothers, 2014). The lower the voter turnout, the higher the chance the actual voice of the people are not heard of.  Vlogbrother’s best said: “If you aren’t voting, no one hears your voice, so they have no reason to represent you!”. Also, elections are not usually about the top ticket vote, but all the down ballot stuff as well, like local city, county, and state level public offices and ballot initiatives (ThinkTank, 2016). The purpose of this sociotechnical plan for GOTV is to build a bi-partisan mobile application that serves the needs of the citizens and politicians to increase poll attendance rate and ensuring a more democratic process.

Supporting forces: 

Social: When using scripts that state that voter turnout will be high, helps increase voter turnout, because the people start identifying voting as something they must do as part of their identity because others are doing it as well (Stanford Business, 2012). Also, in today’s world, social media has become a way for people to be connected to their social network at all times, and there is a real fear of missing out (FOMO) if they are not as responsive to their social media tools (Horizons Report, 2016d). Other social aspects have been derived from behavioral science, like adding simple set of questions like

  • “What time are you voting?”
  • “Where are you voting?”
  • if there is early voting “What day will you be voting?”
  • “How will you get there?”
  • “Where would you be coming from?”

Has shown to double voter commitment and turnout, when focused on people who don’t organically think of these questions (Stanford Business, 2012). Helping, voters determine answers to these questions help with their personal logistics and show how easy it is for them to vote.  This was one of the key deciding factors between Barak Obama’s versus Hillary Clinton’s GOTV 2008 Democratic Primary campaign (Rigoglioso, 2012).  Also, if there is a way to have those logistic questions posted on their social media platform, they become social champions to vote but are also now socially held accountable to vote.  Finally, VotingBecause.com is a social platform for voters to share why they are voting in the election, making them more socially accountable to vote (Sutton, 2016).

Technological: Currently platforms like YouTube are using their resources and their particular platform to help their users get out to vote (Alcorn, 2016). The USA Today Network has worked together to launch a one stop shop, VotingBecause.com, which helps voters easily read about the issues and candidates in the election and even register to vote (Sutton, 2016).

Economical:  According to a Pew Survey (2015), 68% of US adults own a smartphone, while 86% of all young adults (ages 18-20) own a smartphone. In a different Pew Survey (2015a), showed that 65% of adults use social media, up from the 7% in 2005. Therefore, a huge voting block has a social media account and a smartphone (Levitan, 2015b) for which technology can be leveraged at a cheaper cost than door-to-door canvassing.

Challenging forces: 

Social: Unfortunately, this FOMO leads to people feeling burnt out or exhaustive. Therefore users of social media need to balance their time on it (Horizons Report, 2016d). Facts and rumors are both all posing as information in social media, and deciphering which is which is exhausting (Levitan, 2015). Therefore, for this innovation to become a reality, any information shared via social media should come from an independent and trusted source.

Legal: In most states, it is illegal to take a photograph of a polling place, which would make it hard for people to show their civic duty accomplishment on social platforms without getting into legal issues (Fallen, 2016). This may decrease people’s want to share and feel connected, which eventually could also impact the likelihood of decreasing personal and social accountability in voting.

Technological: Building a comprehensive database of the typical low propensity voter, so that a campaign can create personal messages and personal conversations with those voters (Fuller, 2012). A good database would include a Phone number, address, voting propensity, voting record, street address, email address, issues of importance, etc. (Fuller, 2012). Security is also an issue, the one stop shop mobile application solution must take into account each person’s right to access certain types of data, to still ensure anonymity of voting records of civilians.

Ethics: Collecting huge amounts of data from social media and tying that to personal yet public voting record could cause harm, given that political beliefs are of a private manner.  If the data is not used primarily for GOTV initiatives to get everyone’s voice heard in the political system, then it shouldn’t be collected.  Data could be used unethically by some to suppress the vote as well. Therefore this must be conducted by an independent (non-partisan) group.

Methods:

Social media is constantly evolving thus the use of Delphi Technique from a political think tank, political scientists, sociologist, social behaviorist, and actual GOTV managers would be needed on an ongoing basis (Horizons Report, 2016b; Horizons Report, 2016e; Stanford Business, 2012). Dalkey and Helmer (1963), described that the Delphi project was a way to use expert opinion, with the hopes of getting the strongest consensus of a group of experts.  Pulat (2014) states that ideas are listed, and prioritized by a weighted point system to help reduce the number of possible solutions with no communication between the experts or of the results during the process until the very end.  However, Dalkey and Helmer (1963) described the process as repeated interviewing or questioning individual experts while avoiding confrontation of other experts.  Experts must be drawn from different groups on the research spectrum: theoretical to the application as well as different academic fields to help build the best consensus on the methodology to leverage social media on GOTV efforts. Finally, one could consider conducting the Delphi Technique either in a one roof model where everyone gathers in one physical place to meet face-to-face or the without walls model where members only communicate through technological means (Whittenhauer, n.d.).

Models:

To build this socio-technical system to leverage unstructured social media data with voter registration data in GOTV efforts, one must consider the different levels in designing a socio-technical system as seen in Figure 1 (Sommerville, 2013).  Each of the levels plays an important role in facilitating the entire socio-technical plan and must be heavily detailed, with backup systems.  Backup systems and a disaster recovery plan are needed to avoid the same fate that Mitt Romney’s GOTV 2012 program ORCA suffered, where thousands of Romney volunteers were left with a data blackout (Haberman & Burns, 2012). But, it is important to note that a good socio-technical GOTV plan would include all the different levels because all these different levels in the socio-technical system feed into each other and overlap in certain domains (Sommerville, 2013).

2

Figure 1. The socio-technical Architecture with seven levels. (Source: Adapted from the Sommerville, 2013).

But, elections are bound at fixed points in time, and they must come to an end.  Thus, this allows for the socio-technical GOTV plan to have a work breakdown structure.  The resulting work breakdown structure could be multiplied or divided based on the lead time or the importance of the election candidacy or initiative (Garecht, 2002; Progressive Technology Project, 2006):

  • As soon as possible:
    • Create a GOTV plan, strategy, methodology, based on the methodologies created through the Delphi method, with a wide range of experts.
    • Assign one person as a chairman for GOTV.
    • Sign up volunteers for GOTV efforts, remembering that they are not there to convince anyone who to vote for, just to get them to vote.
  • 90-60 days before the election:
    • Gather all data from all the data sources and create one system that ties it together.
    • Identify phase: Have predictive data analytics begin running algorithms to decipher which voter has a lower than average propensity to vote on election day and allow the algorithm to triage voters.
      • Add people who attend political events, staff members, volunteers, and they should already have a higher propensity to vote.
    • When applicable have GOTV staff file absentee ballots.
  • 30 days before the election:
    • Begin implementation of the GOTV Plan.
    • Updating databases, registration information, voter addresses, etc.
    • Identify phase: Keep rerunning the predictive data analytics model.
    • Motivation Phase: Getting people to vote, by making it easy for them to vote, and establishing social accountability via their social media accounts.
    • When applicable have GOTV staff file absentee ballots.
  • Ten days to 1 week before the election:
    • New volunteers come in at this time, to help, and in the GOTV plan, there should be training and roles given to them so that they can be of most use.
    • Identification + Motivation Phase: Contact each person on that list to remind them of their civic duty, motivate them, and remind them where their polling place is and what time they stated would be best for them to vote.
    • Motivation Phase: Using social media advertising tools to drop ads on people located on these low propensity voter lists as derived from predictive data analytics. Even sending text and email reminders of their polling places and times of operation, would help make the voting process easier for these voters.
    • When applicable have GOTV staff file absentee ballots.
  • Election day:
    • Have voter log into a system to say that they have voted, or scan their social media to see who has voted to cross out their names from those who have yet to vote that day. The aim is to get 100% conversion rate of the inactive voter to active voter.

Understanding that this work breakdown structure deals with the intersection of technology and people is key to making it work effectively.

Analytical Plan:

The aim of this socio-technical GOTV plan is to have 100% conversion rates of inactive to active voters.  However, this is quite impossible for larger campaigns and big elections (Progressive Technology Project, 2006).  But, to analyze the effectiveness, of the socio-technical GOTV plan is to cross reference the data that the predictive data analytics has created for low propensity voters, to the voters reached through various technological or social media means, to the voters who voted and that match the GOTV list.

Another way to evaluate the effectiveness of the socio-technical GOTV plan is to see how closely did the real results matched to the milestones identified in the work breakdown structure.  Daily figures should be captured along with narratives to supplement the numerical data to create lessons learned, to eventually be fed back to the experts who devised the methodology to conduct further future developments.  The Delphi Technique can be reiterated with the new data to build a better socio-technical GOTV plan in the future.

Anticipated Results:

As voter turnout increases, no matter which political party wins, the views will be more centrist rather than polarizing, because each of the voter’s voices was heard (ThinkTank, 2016; vlogbrothers, 2014).  Another result from this socio-technical GOTV plan is that the voter is now empowered to make a data-driven decision from the national level and down ballot due to the information presented in these GOTV plans (Alcorn, 2016; Sutton, 2016). This in turn will help create a positive use of social media as a tool to enhance learning and develop personal learning network (Horizons Report, 2016c; Horizons Report, 2016e). Finally, an unintended social impact of this GOTV plan is creating more civically minded citizens that are active in politics at all levels who are willing to be influencers discovering, creating, publishing and sharing ideas (Horizons Report, 2016a, Horizons Report, 2016c, ThinkTank, 2016).

Conclusion:

According to the US Census Bureau (2016), there are approximately 227 Million eligible voters in the U.S. However; the Bipartisan Policy Center stated that in 2012 the voter turnout was 57.5%. This voter turnout rate is horrible, and the Vlogbrother’s said: “If you aren’t voting, no one hears your voice, so they have no reason to represent you!”. This helps establish a need for GOTV efforts. When more people go out to vote, their voices get heard.  Also, elections are not usually about the top ticket vote, but all the down ballot stuff as well, like local city, county, and state level public offices and ballot initiatives (ThinkTank, 2016).

According to a Pew Survey (2015), 68% of US adults own a smartphone, while 86% of all young adults (ages 18-20) own a smartphone. In a different Pew Survey (2015a), showed that 65% of adults use social media, up from the 7% in 2005. Therefore, a huge voting block has a social media account and a smartphone (Levitan, 2015b) for which technology can be leveraged at a cheaper cost than door-to-door canvassing. Green et al. (2008) stated that door-to-door canvassing costs approximately 10x more than mass social contact per vote.  Currently, Social media, email, online ads, and websites are used for GOTV (Fuller, 2012). Fortunately, technology and big data could be used to leverage the GOTV strategies to allow for mass social contact that is tailored to the voter and yet still cheaper than door-to-door canvassing.

The Diffusion of Innovation (DOI) theory 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 (Bass, 1969; Robertson, 1967; Rogers, 1962; Rogers 2010). Entities need to make a binary decision that can fluctuate in time between whether or not to adopt an innovation (Herrera, Armelini, & Salvaj, 2015). Rogers (1962) first proposed, that the timing of the adoption rates of innovation between entities follows a normal frequency distribution: innovators (2.5%), early adopters (13.5%), early majorities (34%), late majority (34%), and laggards (16%). The cumulative frequency distribution mimics an S-curve (Bass, 1969; Robertson, 1967; Rogers, 1962). However, Bass (1969) claimed that Rogers’ frequency distribution was arbitrarily assigned, and therefore he reclassified both innovators and early adopters as innovators and everyone else as imitators, to create a simplified numerical DoI model.

Imitators are more deliberate, conservative, and traditional learners, who learn from innovators, whereas innovators are more venturesome (Bass, 1969; Rogers, 2010). Innovators have a lower threshold of resistance to adopting an innovation than imitators since the innovator’s adoption rates are driven by the adopters’ perception of the innovation’s usefulness (Rogers, 2010; Sang-Gun, Trimi, & Kim, 2013).  Therefore, an innovator will adopt the implications of this socio-technological GOTV plan, record their findings and when a certain amount of data is collected, and this innovation has been implemented much time over, will it finally get adopted by the imitators.  Once a significant amount of imitators has adopted these measures, we can then begin to see U.S. voter turnout reach the 80% or higher mark.

Areas of Future Research:

For further the GOTV low propensity voter prediction and more accurate predictive data analytics algorithms are needed. Thus more research is needed.  Different types of predictive data analytics can produce different results, whether the algorithm is using supervised or unsupervised machine learning techniques. Other efforts needed in the future is preprocessing the unstructured social media data and connecting it to voter registration data.

References

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Sony Walkman and Scenario Planning

The Sony Walkman: Scenario-type planning

Sony didn’t do the proper scenario-type planning and only relied on standard forecasting, which is why it’s market share fell behind Apple’s.  A key requirement for scenario planning is for everyone in the planning session to understand that knowing the future is impossible and yet people want to know where the future could go (Wade, 2014).  However, it is important to note that scenarios are not predictions; scenarios only illuminate different ways the future may unfold (Wade, 2012)! Sony should have created a brainstorming session to identify as many of the driving force(s) or trend(s) that could have an impact the Sony Walkman (Wade, 2014)?  Thus, Sony should have thought of any trend or force (direct, indirect, or very indirect) that would have any effect in any way and any magnitude to the problem.

The Sony Walkman Story

Before the introduction of the Sony Walkman, cassette player technology existed in the 1960s, but households preferred to listen to vinyl records instead (Haire, 2009). The Walkman, a device that merged a light weight and portable cassette player with a light weight headphone, was introduced to the Japanese market in 1979 where it was sold out in three months at $150 per device (Adner, 2012; Franzen, 2014).  The device even had two headphone jacks so that two people can listen to the same music/recording at the same time (Haire, 2009). In the 1980s, the Walkman commanded about 50% of the market share in both Japan and the U.S. selling over 200 million devices over 30 years (Adner, 2012; Haire, 2009). The iPod made by Apple from 2001-2009 sold 160 million units (Haire, 2009).

Then, in 1990 CDs and digital music files like the mp3 came into existence (Adner, 2012; Franzen, 2014).  CDs and mp3s provided better quality and integrity than cassettes, which started to drive the cassette player’s market share towards zero.  Cassettes worked on the film, which tends to degrade with time as well, where the digital files didn’t.  The first mp3 player was from Saehan Information Systems, in 1998 (Adner, 2012).  Sony quickly adapted to these new formats as well and created the CD version of the Walkman and eventually the mp3 version of the Walkman, but it still stuck onto is proprietary music format the ATRAC (Franzen, 2014; Haire, 2009). Also, the industry saw this change from cassettes to CDs to mp3s has happened and was trying to figure out which mp3 player would eventually dominate the market like the Walkman did (Adner, 2012).

In 2001, the iPod came into the scene and took over the market, even when the market was already saturated with about 50 different types of mp3 players (Adner, 2012).  Steve Jobs learned that on its own, the iPod was useless, but with broadband download speeds and mp3 marketplace the market was ready for the easy to use the device at $399 and 5 GB of storage (Adner, 2012, Apple, n.d.). What made the iPod so successful was the analysis of the challenging forces that made mp3 players a hard market to sell and addressing them by providing seamless integration with an mp3, which was introduced the iTunes music management software in its first iteration in 2001 and re-imagined storefront called the iTunes Music store in 2003 (Adner, 2012).

By 2008, Apple had claimed 48% of the market share in mp3 devices which was similar levels of the Walkman in the 1980s (Adner, 2012). In 2010 the cassette version of the Walkman device line came to an end (Franzen, 2014). In 2015, the newest mp3 Walkman device the ZX2 is $1200 with 128 GB and expandable microSD card slot, which is now Sony’s aim for higher quality audio devices (Miller, 2015). Unfortunately, the ZX1 and ZX2 doesn’t have smartphone features like apps (Franzen, 2014; Miller, 2015).

Challenging forces to move from the Walkman to mp3 players:

Legally: In 1998-2001 there was no storefront to download mp3 music legally (Adner, 2012).

Technology: Even if music was obtained legally from CDs, people had to use a third party software to convert files, which in those computational computing days took hours to conduct (Adner, 2012).

Therefore, who cares if you are first to market (Saehan Information Systems) if there is no easy way to download mp3 music easily and legally.

Supporting forces to move from mp3 players to the iPod:

Legally: The iTunes Music Store, allowed for songs to be downloaded at a modest price and legally for $0.99, which Apple was able to get 10% commission from it (Adner, 2012).

Technology: The seamless integration of the iPod to the music storefront made the device easy to use, which helped increased its market share in the mp3 market. By 2009, over 8 Billion songs were sold, totally $800 Million in revenue (Adner, 2012).  This iTunes storefront, became a cash cow for Apple, while the iPod went under further innovation into the iPhone product line and the iPod touch product line (Apple, n.d.).

Example Scenario Planning four quadrants for the Sony Walkman case based on the forces listed above:

capture

Conclusion

Sony didn’t do proper scenario-type planning and only relied on standard forecasting, which is why it’s market share fell behind Apple’s.  However, the lesson learned from this case study is that a company doesn’t need to be the first mover to make it big in the market. A proper scenario planning could be the key to succeeding when entering a saturated market.  Apple was three years late to the party, yet it was their patience, learning about the supporting and challenging forces for mp3 player dominance, and letting the key market players align for their product, was the key to Apple’s success.

It is easy to do a scenario planning exercise on past events to today’s events (Wade, 2014). It is harder to do scenario planning moving into the future. Also, scenario planning events should never remain static.  The future is always evolving.  Thus, the scenario plan should change to reflect the new landscape, but the difference is that this planning allows for the mind to be more flexible and receptive to pivot quickly (Wade, 2014).  Scenario planning can take into account any force, not just the two listed above Political/Legal, Economical, Environmental, Societal, Technological, etc. (Wade 2012, Wade 2014).

References

Higgs Boson: Case Study on an infamous prediction that came true

Definitions:

  • Forecasting (business context): relies on empirical relationships that were created from observations, theory, and consistent patterns, which can have assumptions and limitations that are either known or unknown to give the future state of a certain event (Seeman, 2002). For instance forecasting, income from a simple income statement could help provide key data for how a company is operating, but the assumptions and limitations on using this method can wipe out a business (Garrett, 2013).
  • Predictions (business context): are a more general term in which, is a statement of a future state of a certain event, that can be based on empirical relationships, strategic foresight, or even scenario planning (Seeman, 2002; Ogilvy, 2015).
  • Scenarios: alternate futures that change with time as supportive and challenging forces unfold, usually containing enough data like the likelihood of success or failure, the story of the landscape, innovative opportunities, challenges to be faced, signals, etc. (Ogilvy, 2015; Wade, 2012).

Case Study: An infamous prediction that came true

The Higgs Boson helps tell the origin of mass in the universe (World Science Festival, 2013). Mass is the resistance of an object to be pushed and pulled by other objects or forces in the universe, and mass is made up of the constitute particles of that object (Greene, 2013; PBS Space-Time, 2015; World Science Festival, 2013).  The question is where does the mass of these particles that give an object its mass comes from?  The universe if filled with an invisible Higgs Field, in which these particles are swimming in and experiencing a form of resistance (when the particle speeds up or slows down), this resistance in the Higgs Field is the mass of the particles (Greene, 2013; World Science Festival, 2013).  Certain particles have mass (electrons), and others don’t (photons), this is because the certain particles interact with the invisible Higgs Field (PBS Space-Time, 2015). Scientist use the large Hadron Collider to speed up particles in such a way that when they collided in the correct way (1:1,000,000,000 chance), the particles’ collisions would be able to clump a bit of the Higgs Field to create a Higgs particle that lasted for a 10-22 second (Greene, 2013; PBS Space-Time, 2015; World Science Festival, 2013). Therefore, finding the Higgs particle is a direct link to proving that the existence of the Higgs field (PBS Space-Time, 2015).

The importance of proving this prediction correct (World Science Festival, 2013):

  • Understanding where mass comes from
  • The Higgs particle is a new form of particle that doesn’t spin
  • Shows that mathematics lead the way to discovering something about our reality

This was a prediction in the waiting to be confirmed through observation for over 50 years, which got its roots in the form of scientific and mathematical roots of quantum physics and by Higgs in 1964 (Greene, 2013; PBS Space-Time, 2015; World Science Festival, 2013).

Supporting Forces for the prediction:

  • Technological: the development of technology to study mathematics over the course of 50 years helped facilitate the discovery of this prediction (Greene, 2013; World Science Festival, 2013). The actual technology use is called the ATLAS detector attached to the Large Hadron Collider (Greene, 2013).
  • Financial: Through international collaboration from thousands of scientists and over a dozen of countries, they were able to amass the financial capital to build this $10 Billion Large Hadron Collider.

References:

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).

References

An Innovation that is possible 15-20 years from now

Innovation idea that is not possible today but will be in the next 15-20 years

Mobile technology is everywhere today, and their use is prolific among all the diverse populations in the U.S., even to segments of the populations that do not own a computer own a smartphone (Kumar, 2015).  Electronic transactions carrying trillions of dollars, sensitive flight data, etc. take place all the time (Kumar, 2015; Safian, 2015).  Safian (2015) is calling that mobile voting will be one of the many things that will occur in the next 20 years.

Thirty-three states offer online voter registration and that allowed for 6.5% of the electorate to register for 2014 up from 1.7% in 2010 (Election Assistance Commission [EAC], 2015; Jayakumar, 2015). About 19.2% of ballots in 2014 were rejected due to improper registration (EAC, 2015).  Eighty cities and towns in Canada have experimented with mobile voting since 2003, and Sweden, Latvia, and Switzerland have tested the idea (Gross, 2011).  Since 2005, Estonia with a mobile voting period that last about seven days and is available for all citizens had about 1/4 to 1/3 votes cast were online (Vabariigi Valimiskomisjon, 2016).

Mobile voting, can help reduce the cost of elections, reduce the need for polling places, encourage and engage disenfranchised voters, reduce the time it takes to cast a vote, reduce the need to travel to a polling place, facilitate fast results, more convenient way of collecting huge data about the voting population and their turnout, while finally allowing for easier voter registration (Jayakumar, 2015; Kumar, 2015). However, to make mobile voting a key innovation in the next 15-20 years, the main goals of mobile voting must be addressed: security, accessibility, anonymity, conveniency, and verifiable (Gross, 2011; Jayakumar, 2015; Kumar, 2015 Safian, 2015).

Forces that define the innovation that may facilitate or reduce its likelihood of success

Technological: Paper ballots allow for and provide anonymity, free from manipulation (Jayakumar, 2015). Even though, some ballots could be switched. Mobile voting devices currently have issues with security and verifiability (Jayakumar, 2015).  However, other countries are working on providing democracy to all through allowing both paper and electronic ballots as previously discussed.  However, mobile voting is not like other typical transactional data from a bank, where a user can correct errors (Jayakumar, 2015).  Technology must take this into account.  Such that, voting data is unalterable in transit from the mobile device to the main destination (Jayakumar, 2015).  However, in 2014, Zimmerman and Kiniry were able to show how Alaska’s PDF Ballots are insecure, as proof that the technology is currently not as reliable to ensure a tamper free election.

Ethical: Mobile voting can allow for the lowest income workers afraid to take time off from work to vote, or single parents with no daycare options, or people without cars in a remote rural area, increase turnout during midterm and off-season elections, e.g. runoff elections (Jayakumar, 2015; Kumar, 2015). It is suggested that voter intimidation may also be resolved through mobile voting, as people can vote in the privacy of the person’s home (Kumar, 2015).

Financial: Huge cost savings could be realized because, in 2014, 732K poll workers were hired for 114K polling locations, which amounts to 6.4 people per polling location (Election Assistance Commission [EAC], 2015).

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