The following is a mock research proposal completed at the behest of ICT600-201 at the University of Kentucky over a two day period. It should not be construed as a ready proposal.

Is political confirmation bias contributing to the digital divide?

Abstract

Empirically, the combination of social media and technology gives voice to otherwise disenfranchised parties. These groups include inner city minorities, non-traditional sexual and gender orientations, isolated Appalachian residents, disparate farmers, flyovers, and various ethnic groups, just to name a few. Ostensibly, all can communicate, organize, and participate in society on a level previously available only to privileged classes with greater means. However, as web-based society becomes more ubiquitous, digital divide is a growing concern. Furthermore, even as physical access gaps are filled, political and cultural silos among these same groups trend upward at an alarming rate (Nadeem, 2022). This suggests the possibility that the digital divide is not merely an issue of physical access and the ability to utilize technology but is also comprised of attitudinal barriers exacerbated by cognitive bias. In turn, cognitive bias may be driven by polarized political beliefs.

Digital Divide?

Digital divide is defined by the lack of broadband access to internet assets (Coleman & Atkinson, 2011). However, trends in the use of technology suggest the digital divide is more than a lack of physical access. Stringing wires into a house or putting a phone in someone’s hand does not impart the cognitive ability to acquire data or the information awareness to process it. This ancillary phenomenon is called the cognitive divide, and it is often cited as a greater challenge to resolve than the physical digital divide (Fonseca, 2010).

Increasing Division

Political, moral, and cultural divisions are nothing new in society. If we were to narrow a focus to the comparatively short history of the United States of America, the obvious and still festering scar of The American Civil War stands as a bloody reminder of the dangers in cultural bias. Narrow further to the Commonwealth of Kentucky, and you will find the infamous Hatfield and McCoy family feud, a conflict so ingrained in the public sphere it’s now a cliché reference for senseless conflict (and a tourist attraction). To go even more granular within the same state, this author’s own hometown of Morehead did its best to outshine our more famous brethren with a three-year conflict dubbed “The Rowan County War”. All but an open rebellion, its combatants engaged in mass murders, broad daylight shootouts, and even seizure of the local government. The chaos required full mobilization of the state militia on no less than three occasions and nearly resulted in dissolving the entire county.

The above conflicts all have one common factor – political strife and dehumanization brought about by escalating cognitive bias. Although the end results are arguably less violent in recent times, political division is perhaps more distinct than ever before. According to a November 2022 Pew Research Study (Nadeem, 2022) , over the last twenty years Americans identifying as either Republican or Democrat have become more antipathetic toward not only the opposing party, but toward people within an opposing party. The same study reports similar polarization among democracies worldwide. These statistics may indicate a developing cognitive bias as the timeline roughly coincides with the rise of mobile internet access and social media platforms.

Impact

Digital divide represents a social cost not only to those it directly affects, but all of society as well. For example, as more and more industries rush to cut costs and increase reach, even the simple act of applying for jobs is often relegated to an online only process. Should someone approach in person, they are redirected to a kiosk, thus marginalizing any parties not fully equipped to handle an online world and causing a loss of available labor. This in turn stifles industrialization and creates a cycle of dependency (Steele, 2018). It is therefore imperative to understand the root causes of the digital divide and not simply assume it is a matter of technological access. Cognitive and attitudinal barriers may play just as large of a role and may prove more difficult to resolve. Armed with a better understanding, we can institute more targeted, and ultimately more effective efforts.

Objectives

The long-term goal of this research is to develop targeted educational programs to counteract attitudinal and cognitive barriers that contribute to digital divide. The study will attempt to ascertain if a relationship exists between cognitive bias and attitudinal barriers that affect the digital divide. Cognitive bias is defined as the human tendency to simplify information processing through a filter of personal experience and preferences (Gillis & Bernstein, 2022). The results of this study may be useful to educational program coordinators, non-profit entities, and community leaders seeking to eliminate digital divide in their respective communities. Industry leaders may also find value when seeking labor from disconnected groups. Future research may find a starting point to begin focusing on granular aspects of attitudinal barriers to find locate counteractive measures.

Literary Review

Digital divide is a confirmed society issue explored via numerous anecdotal and peer reviewed outlets. However, review of previous research indicates there is little consensus on the root causes of digital divide. There is even some dispute on defining digital divide, although the following three examples are generally accepted.

1) Digital divide is the gap in access to computer hardware and the internet (Frederick, 2019). This approach is the simplest to address. Either technology is available, or it is not. The implications are that digital divide is primarily an issue of hardware. The complexities therefore lie in choosing the right hardware for environments. Once the last mile gap is bridged and everyone has a device, the divide is closed.

The weakness to this approach is that it does not explore the quality of access, if and how recipients can use the technology once in hand, nor the logistics and costs of maintenance.

2) Digital divide is the lack of broadband access to internet assets (Coleman & Atkinson, 2011). Unlike the more absolute definition of access, the addition of “broadband” introduces quality as an important caveat. This later distinction avoids the danger of overlooking areas that do have some level of access that would click a connected checkbox while being intermittent or functionally nonexistent.

Unfortunately, the term broadband is itself mercurial and lacks any standardized definition. Furthermore, this approach shares the same issues of being entirely hardware centrist and lacking a long-term strategy beyond the initial connection.

3) As a lack of ability to understand, learn, express, produce, share, collaborate, create, and innovate using technology (Fonseca, 2010). Fonseca does not provide a hard definition, rather they describe digital divide as a set of limitations imposed by a comprehensive set of results that go beyond physical access to technology and internet resources. Fonseca goes on to explore cognitive divide primarily as knowledge issue. The implication being that lack of education and existing acumen using technology leads to a snowball effect that increases the digital divide gap in non-industrialized nations of the world.

It is the third approach this research attempts to explore in further depth. Cognitive barriers (Fonseca, 2010), attitudinal barriers (Partridge, 2007), and cognitive bias (Gillis & Bernstein, 2022) are each addressed as separate issues in previous works. Fonseca primarily focused on education and found that cognitive barriers are an issue of socioeconomic status that can quickly produce a snowball effect. Lack of educational resources leads to inability to utilize technology, in turn hindering students’ ability to access educational resources in a vicious cycle. Proposed solutions included an emphasis on providing technology resources, and specific training in their use. An example of this approach is Costa Rica. Twenty-two years prior to Fonseca’s study the Costa Rican central government launched a program to combine digital technology and creative skills. At time of writing, Costa Rica had ascended to become an attractive investment target and technology hub for Latin America.

Conversely, attitudinal barriers affecting the digital divide were found by Partridge to be disconnected from socioeconomic status and educational levels. Instead, self-effigy and confidence were the primary factors, in turn, mainly tied to age. Partridge does note that attitudinal barriers when present will contribute to digital divide. This does suggest the possibility of digital divide being affected by political cognitive bias if the bias develops fully into an attitudinal barrier.

Thrane et al. noted age-related attitudinal barriers, with an additional caveat that as humans age, they become more resistant to new technology. This suggests that the theory of organic technology adoption as young generations come of age is invalid. Instead, current youth are just as apt to resist new technologies as their predecessors are to push back against the technologies of today. Meanwhile up and coming generations will adapt to the state of technology in their own formative years, repeating the cycle.

The possible relationship between cognitive barriers, attitudinal barriers, and cognitive bias due to political outlooks remains largely unexplored.

Methodology

The primary research method will be a Likert Scale survey collecting the following data:

• Political self-identity.
• Political passion levels.
• Information outlets.
• Feelings toward opposing political views and persons.
• Feelings toward unused informational outlets.
• Assessment of online access quality (if any).
• Importance of online access.
• Importance of technology resources in education.

The survey will be distributed to areas of known high connection levels and areas of known digital divide. An immediate known challenge is the delivery of surveys. Areas affected by digital divide and participants with a cognitive bias both present a challenge in their willingness and ability to complete a survey of any type. Possible solutions include setting up manned kiosk stations in areas of high foot traffic (i.e., lobbies of common sundry and grocery businesses, courthouses, and school drop-offs).

Responses from connected and disconnected areas will be analyzed utilizing the Proportional Odds Model to ascertain a possible relationship between political passion and cognitive bias. Firmly established cognitive biases in an area of digital divide combined with lack of cognitive bias in an area connected may indicate the presence of attitudinal barriers affecting technology adoption.

Weakness

Even distribution of surveys may prove difficult due to different habits between connected and disconnected parties. Simply ensuring even numbers of surveys from both areas may not be sufficient without a weighing formula to eliminate survey bias.

As the digital divide is a multi-faceted issue with multiple causes, there exists a severe built-in bias against individuals affected by lack of access. It would be a disservice and poor research to assume high political passions and living in a disconnected area means a cognitive or attitudinal barrier exists without further research. Even if a cognitive bias is firmly established, this does not necessarily mean an attitudinal barrier exists, nor is an attitudinal barrier necessarily a root cause for digital divide in the chosen area. Many of the factors contributing to the digital divide are completely outside the control of an individual. Rather, the presence of attitudinal barriers would be cause for further studies to ascertain the importance of other factors which may then more firmly indicate politically sourced attitudinal barriers are a significant contributor to localized digital divide.

References

Coleman, P. D., & Atkinson, J. K. (2011). The digital divide in Kentucky: Is rural online learning sustainable? Retrieved December 7, 2022, from http://www.jsedimensions.org/wordpress/wp-content/uploads/2011/03/Atkinson2011.pdf

Fonseca, C. (2010). The Digital Divide and the Cognitive Divide: Reflections on the Challenge of Human Development in the Digital Age. Retrieved December 5, 2022, from https://itidjournal.org/index.php/itid/article/download/618/618-1657-2-PB.pdf

Frederick, D. E. (2019, September 3). The Fourth Industrial Revolution and the digital divide. Library Hi Tech News. Retrieved December 9, 2022, from https://www.emerald.com/insight/content/doi/10.1108/LHTN-07-2019-0048/full/html

Gillis, A. S., & Bernstein, C. (2022, June 22). What is cognitive bias? Enterprise AI. Retrieved December 5, 2022, from https://www.techtarget.com/searchenterpriseai/definition/cognitive-bias

Nadeem, R. (2022, November 17). As partisan hostility grows, signs of frustration with the two-Party system. Pew Research Center – U.S. Politics & Policy. Retrieved December 7, 2022, from https://www.pewresearch.org/politics/2022/08/09/as-partisan-hostility-grows-signs-of-frustration-with-the-two-party-system/

Partridge, H. (2007). Redefining the digital divide: Attitudes do matter! Retrieved December 5, 2022, from
https://asistdl.onlinelibrary.wiley.com/doi/10.1002/meet.1450440251 Steele, C. (2018, December 17). The impacts of digital divide. Digital Divide Council. Retrieved December 8, 2022, from http://www.digitaldividecouncil.com/the-impacts-of-digital-divide/

Thrane, L. E., Shelley, M. C., Shulman, S. W., Beisser, S. R., & Larson, T. B. (2008). E-political empowerment – taylor & francis. Taylor & Francis Online. Retrieved December 5, 2022, from https://www.tandfonline.com/doi/abs/10.1300/J399v01n04_03