## ABSTRACT

This study analyzes how the COVID-19 pandemic has altered individual perceptions of Internet service providers (ISPs) and Internet importance, reliability, and status as an essential public utility (EPU). The authors found that lower income, younger, women, and racial-ethnic minority participants had lower ISP and Internet reliability perceptions. The pandemic increased perception of Internet as an EPU by 15% and access to in-home Information and Communication technology was significantly related to perceptions of Internet importance and reliability. Significantly, women perceived higher importance of household Internet than men, specifically for education, employment, and telehealth. Additionally, racial-ethnic minorities relied on Internet for entertainment and education more than white participants. The authors provide recommendations for public utility models of Internet, Internet-reliant technology adoption campaigns, and policy that targets sociodemographic/geographic barriers to Internet access.

## Introduction

### Current Trends in Household Internet and Related Perceptions

In the United States, 85% of adults say they use the Internet daily,1 90.3% have a computer,2 and 72% have at least one social media account.3 Only 7% of Americans never use the Internet, and they tend to be older, live in rural areas, and have lower incomes.4 Internet consumption rates indicate relatively positive perceptions of the Internet. Studies have shown that people are reluctant to label the Internet as a negative medium due to its ability to help with academic, professional, health, and social functions.5 People were more aware of the social benefits of the Internet (e.g., communication, e-commerce) during the pandemic than before.6 For example, the COVID-19 pandemic caused many businesses and schools to switch to remote work and learning, which increased reliance on Internet services and made the Internet indispensable. Additionally, the U.S. Congress passed an act that allowed Medicare to be billed for telemedicine appointments during the pandemic,7 which opened access to telemedicine to many while reducing the chances of COVID-19 infection. However, privacy concerns or perceptions of the Internet as unsafe limit willingness to use the Internet for activities that include sensitive information,8 such as telemedicine,9 e-banking,10 and even adoption of smart home technology like smart meters.11

While the shifts to remote work and learning were difficult for all, they were especially hard for those with inadequate access to Internet and technology. In fact, a study in New Zealand found that the top barrier preventing people from working from home more often was not having fast enough Internet.12 This is assuming one’s job can be done from home, as some research estimates that only 37% of jobs in the United States can be done from home and are concentrated to higher income workers.13

There are other negative factors associated with Internet use as well, such as Internet addiction,14 fraud,15 and cyberbullying.16 Further, increased daily online duration, reasons for Internet use, types of applications used, and having a positive COVID-19 case within the household were all predictive of Internet addiction.17

### The Arguments for and Against Internet As an “Essential Public Utility”

A public utility is defined as the provision of public or private common-good services, such as public transportation, energy, and water.18 Citizens are guaranteed access to these services through either legislation or government funded service provision. Presently, Internet service providers (ISPs) are not regulated like a public utility, instead they are treated as private entities that subscribe to the rules set by the Federal Communications Commission. This means that while they must abide by certain federal and state regulations, such as spectrum licensing or utility pole attachment rules, they are under no obligation or duty to provide a service to the public. While specific regulations vary by state, in general public utilities are regulated by state regulatory commissions to ensure essential goods or services are provided to the public both generally and indiscriminately. In many instances this means that a public utility provider both has an obligation to provide a service as well as some regulation against arbitrary or unreasonable withdrawal of service (Cornell Law, 2020). For this study we utilize the term “Essential Public Utility (EPU)” which combines both the term “public utility” in combination with its contextual descriptor “essential,” which has been attached to this term as it refers to services deemed essential to modern daily life. Overall, 53% of Americans say the Internet has been essential during the pandemic, but many still do not believe that it should be the government’s responsibility to ensure connectivity.19 Access to reliable and affordable Internet services is becoming as important to households as utilities such as electricity and water.20 However, these utilities, as opposed to Internet services, are shared across customers equally at a fixed rate, while Internet is treated like a commodity, with prices determined by supply and demand and access determined by the ability to afford Internet services and infrastructure. Arguments primarily against Internet as an EPU focus on public utilities’ monopolistic power over the services they offer and how this power can lead to a lack of innovation and competition in a free market.21 Innovation is a key element due to the Internet’s ever-evolving nature, and many sources point to the outdated utility infrastructure in the United States as an example of what would happen to Internet if it became a public utility. Further, arguments against point to lack of competition driving the prices of Internet services up.22

However, utility services, like electricity, are “natural monopolies,” meaning they have high market entry costs that limit competitor investment in the business and result in small, publicly owned companies servicing small parts of the country, rather than major companies controlling larger parts of the country.23 Critics of the unregulated free-market system of Internet services claim that Internet-provision’s major companies have never truly participated in the free market, resulting in a relative monopoly.24 Currently, U.S. citizens pay twice as much for regular broadband services than other developed countries like France or Japan.25

Other arguments for Internet being an EPU highlight the lack of accessible, affordable, and reliable Internet services in the United States. For example, the Federal Communications Commission estimates that 14.5 million Americans do not have access to broadband at speeds of at least 25/3 Mbps,26 which does not include those with infrastructure access who cannot afford Internet services or have Internet services at different speeds that are still inadequate. More importantly, research has found that current broadband providers like AT&T often shirk their responsibilities in delivering broadband to rural, low-income, and minority areas, mainly due to a lack of fiscal incentives for companies to expand to rural and low-income areas.27 In general, majority black census tracts, low-income communities, and rural communities have lower broadband adoption rates.28 Given the socioeconomic stratification in Internet access and the impacts of digital access on physical, mental, economic, and social outcomes,29 the World Health Organization has determined Internet access to be a human right.30

## Rationale

Health measures put in place by both government entities and private businesses resulted in increased time at home during the COVID-19 pandemic. Increased time at home meant that a greater number of the public were reliant on home Internet for employment, education, healthcare, and other digital services. In addition, a greater emphasis was placed on in-home technologies to meet these needs.

This study identifies whether this increased time at home had an impact on the individual’s perceptions of their ISP, Internet as an EPU, reasons for Internet reliance, and perceptions of Internet reliability and importance. In addition, we examined how access to information and communication technology (ICT) in the home impacted these perceptions. The results of this study help to inform whether the COVID-19 pandemic and ICT availability have altered the way U.S. residents perceive at-home Internet access.

## Research Questions

RQ1. How do participants’ perceptions of their ISPs vary across demographics before and during the COVID-19 pandemic?

RQ2. How do participants’ perceptions of Internet as an EPU vary across demographics? Did the pandemic have a significant impact on perceptions of Internet as an EPU?

RQ3. In what ways did participants rely on Internet during the pandemic?

RQ4. How do participants’ perceptions of Internet reliability and importance vary across demographics?

RQ5. Does participants’ perceived Internet reliance, importance, and/or perception of Internet as an EPU impact access to in-home ICT?

## Methods

### Survey Design and Analysis

We distributed an online survey (n = 1,991) via Qualtrics to residents of California (CA; n = 997) and Florida (FL; n = 994) in 2021. Analyses were limited to participants who had Internet access in their home; 92 participants did not have home Internet access, making the sample total 1,847 participants. Our survey was designed with and administered through Qualtrics Paid Panel Service. The “opt-in” sample was collected online voluntarily, based on low-income and gender representation in CA and FL, meaning we sought evenness between men and women respondents but oversampled for low-income participants due to the nature of our research objectives. The response rate was about 30%. CA and FL were chosen as sites for this study due to each state’s distinct COVID-19 policies and diverse populations, as an original intent of survey distribution was to measure the impact of varying state-level COVID-19 restrictions on household energy and Internet. CA and FL enacted policies that were suitable for this comparison; for example, CA took a strict approach to contain the COVID-19 virus that included several restrictions early on (e.g., mask mandates, stay-at-home orders), whereas FL’s response was more lenient. CA and FL also have high concentrations of low-income populations. While this convenience sample with some representation of gender and income at the state level is a limiting factor, the results have implications for underserved communities in other geographic areas of both the continental United States and western nations. The survey consisted of multiple parts, including questions related to home Internet, Internet perceptions (i.e., reliability and importance), ownership of different ICTs (i.e., washer and dryer, electric vehicle, programmable thermostat, and smart meter), and demographic information, such as income, age, employment status, racial-ethnicity, and so on. A key variable in the present study is participants’ perceptions of Internet as an EPU both before and during the pandemic. Specifically, our survey asked participants: “Prior to the COVID-19 pandemic did you consider household Internet to be an ‘essential public utility’?” and “Do you now consider household internet an ‘essential public utility’?.”

Most measures, except for demographics and household information, were based on a five-point Likert scale, where a one indicates “strongly disagree,” “very unlikely,” “very unconcerned,” “much worse than before,” “much lower than average,” “easy,” “very unimportant,” “very affordable,” or “never,” and a five indicates “strongly agree,” “very likely,” “very concerned,” “much better than before,” “much higher than average,” “difficult,” “very important,” “very expensive,” or “very often.” Other questions could be answered with yes, no, and unsure/do not know. Finally, some questions presented different scenarios that participants could select if it applied to their Internet usage situation. While the survey collected the data from individuals who stated that they did not have home Internet access, we limited the analyses to participants with in-home Internet access (n = 1,847), and participants who indicated they were unsure of or did not know the answer to a question were excluded from analyses. This means those without Internet access were filtered out of the dataset for analysis.

### Participant Demographics

Participant demographics have been listed in Table 1.

TABLE 1

Participant Demographics

Demographics%Demographics%
Location CA Income
CA 50.1 $0–$20,999 20.1
FL 49.9 $21,000–$31,999 20.3
Home Internet type $32,000–$42,999 19.8
Broadband only 55.5 $43,000–$53,999 20.3
Cellular data only 9.1 $54,000–$64,999 2.1
Satellite only 4.7 $65,000–$75,999 3.1
Multiple sources 27.9 $76,000–$86,999 1.3
No home Internet 2.8 $87,000 or more 13.0 Gender FL Income Woman 49.9$0–$15,999 18.7 Man 49.3$16,000–$26,999 20.0 Nonbinary/no answer 0.8$27,000–$37,999 12.4 Age$38,000–$48,999 7.9 18 to 29 years old 14.2$49,000–$59,999 20.2 30 to 41 years old 22.9$60,000–$70,999 5.2 42 to 57 years old 22.0$71,000 or more 15.6
58 years old or older 40.9 Racial-ethnicity
Homeownership White or European American 68.3
Renter 45.1 Latinx or Hispanic/Mexican/Central American 9.6
Owner 54.9 Black or African American 8.4
Education Asian or Asian American 6.0
High school diploma or general education degree (GED) 40.4 Indigenous (Native Americans, Hawaiians, Pacific Islanders) 0.4
Associate’s degree 21.0 Multiracial/multiethnic 6.5
Bachelor’s degree 23.8 Other or unidentified 0.7
Master’s degree 9.1 Household size
Doctorate or professional equivalent 2.3 1 person 25.3
Employment 2 people 34.4
Full-time 29.0 3 people 16.9
Part-time 12.7 4 people 13.8
Unemployed 20.6 5 or more people 9.7
Retired 28.3
On disability 8.1
Demographics%Demographics%
Location CA Income
CA 50.1 $0–$20,999 20.1
FL 49.9 $21,000–$31,999 20.3
Home Internet type $32,000–$42,999 19.8
Broadband only 55.5 $43,000–$53,999 20.3
Cellular data only 9.1 $54,000–$64,999 2.1
Satellite only 4.7 $65,000–$75,999 3.1
Multiple sources 27.9 $76,000–$86,999 1.3
No home Internet 2.8 $87,000 or more 13.0 Gender FL Income Woman 49.9$0–$15,999 18.7 Man 49.3$16,000–$26,999 20.0 Nonbinary/no answer 0.8$27,000–$37,999 12.4 Age$38,000–$48,999 7.9 18 to 29 years old 14.2$49,000–$59,999 20.2 30 to 41 years old 22.9$60,000–$70,999 5.2 42 to 57 years old 22.0$71,000 or more 15.6
58 years old or older 40.9 Racial-ethnicity
Homeownership White or European American 68.3
Renter 45.1 Latinx or Hispanic/Mexican/Central American 9.6
Owner 54.9 Black or African American 8.4
Education Asian or Asian American 6.0
High school diploma or general education degree (GED) 40.4 Indigenous (Native Americans, Hawaiians, Pacific Islanders) 0.4
Associate’s degree 21.0 Multiracial/multiethnic 6.5
Bachelor’s degree 23.8 Other or unidentified 0.7
Master’s degree 9.1 Household size
Doctorate or professional equivalent 2.3 1 person 25.3
Employment 2 people 34.4
Full-time 29.0 3 people 16.9
Part-time 12.7 4 people 13.8
Unemployed 20.6 5 or more people 9.7
Retired 28.3
On disability 8.1

For further group comparison, we categorized income levels into four identifiable groups for both CA and FL separately due to their different income levels: low–low-, upper–low-, medium-, and high-income groups. The income groups were selected based on median income from each state reported in the 2020 U.S. census. Quartiles for each state were generated by SPSS. The lowest quartile was divided into two groups to develop the low–low and upper–low-income groups. In CA, low–low-income represents $0 to$20,999; upper–low-income represents $21,000 to$31,999; medium-income represents $32,000 to$86,999; and high-income is $87,000 or more. Similarly, in FL, low–low-income represents$0 to $15,999; upper–low-income represents$16,000 to $26,999; medium-income represents$27,000 to $70,999; and high-income is$71,000 or more. For simplicity, we also combined the different age levels into three groups: young (18–29), lower-middle aged (30–41), upper-middle aged (42–57), and old (58 or older).

## Results and Analysis

### Perceptions of ISPs Before and During the Pandemic

ISP perceptions before and during the COVID-19 pandemic were measured using two averaged variables derived from questions about whether participants’ ISPs were reliable and kept the customers’ best interests in mind before the pandemic (Cronbach’s alpha = 0.77), as well as perceptions that their ISP took protective steps for their customers and made plans to prevent scams or service disconnections during the pandemic (Cronbach’s alpha = 0.90). The combination of all four ISP perceptions was also found to be related (Cronbach’s alpha = 0.87). A series of ANOVAs with Tukey’s post hoc tests were conducted to examine how perceptions of ISPs vary across demographics (i.e., income, age, racial-ethnicity, and gender). In general, our survey respondents did not have particularly strong opinions of their ISPs, though analyses show differences between demographic groups. Additionally, perceptions were relatively lower during the pandemic than before, which may indicate significant limitations in how ISPs responded to the pandemic, especially in terms of protecting customers.

The relationships between ISP perceptions and CA income were significant before the COVID-19 pandemic, F (3, 957) = 2.999, p < .05, and during the pandemic, F (3, 957) = 6.564, p < .001 (Figure 1). Post hoc tests revealed that, before the pandemic, only low–low-income participants had significantly less favorable ISP perceptions than high-income participants, but during the pandemic, all income groups had significantly lower ISP perceptions than high-income participants. The relationships between ISP perceptions and FL income before, F (3, 939) = 1.263, p = .29, and during the pandemic, F (3, 939) = 1.175, p = .32, were insignificant. ISP perceptions and age were significant before, F (3, 1897) = 8.247, p < .001, and during the pandemic, F (3, 1897) = 3.387, p < .05. Before the pandemic, younger participants were found to have significantly lower ISP perceptions than all other groups. During the pandemic, only lower-middle age and young participants had significantly different perceptions, with young participants having more negative perceptions of their ISPs. ISP perceptions before, F (3, 1886) = 1.018, p = .41, and during the pandemic, F (3, 1886) = 1.086, p = .37, were not found to differ across racial-ethnic groups. ISP perceptions and gender were significantly related before the pandemic, F (1, 1883) = 6.500, p < .05, but not during the pandemic, F (3, 1883) = .011, p = .92. Before the pandemic, women had lower perceptions of their ISPs than men, while during, women and men and equal perceptions. In sum, low-income participants, younger people, and women tended to have lower perceptions of their ISPs than their counterparts.

FIGURE 1

Average Internet service provider perceptions before and during COVID-19 across (a) CA income, (b) FL income, (c) age, (d) racial-ethnic, and (f) gender groups.

FIGURE 1

Average Internet service provider perceptions before and during COVID-19 across (a) CA income, (b) FL income, (c) age, (d) racial-ethnic, and (f) gender groups.

Close modal

### Internet As an EPU Before and During the Pandemic

Our survey asked participants whether they considered household Internet to be an EPU before and during the pandemic. Overall, 76.7% of participants considered household Internet as an EPU before the pandemic. This consideration increased to 86.7% during the pandemic (Table 2). Interestingly, only 3% of the total sample thought Internet was an EPU before but not during the pandemic, while 13% went from thinking Internet was not an EPU before to being an EPU during the pandemic. Specifically, as evidenced in Table 2, 15.3% of participants who said that Internet was not an EPU before the pandemic then said that it was an EPU during the pandemic, while 24.5% of those who said Internet was an EPU before the pandemic, said it was not an EPU during the pandemic. Of the participants that perceived Internet to be an EPU before but not during the pandemic, most were medium income (46.7% in CA, 60.7% in FL), young (22.4%) or lower-middle aged (44.8%), men (63.8%), and white (60.3%).

TABLE 2

Crosstabulation of Participants’ Perceptions of Internet As an Essential Public Utility Before and During the COVID-19 Pandemic

Not an EPU During COVIDEPU During COVIDTotal
Both States – χ2 = 417.557***
Not an EPU before COVID 179a (75.5%) 236b (15.3%) 415 (23.3%)
EPU before COVID 58a (24.5%) 1309b (84.7%) 1367 (76.7%)
Total 237 1545 1782
CA – χ2 = 135.551***
Not an EPU before COVID 64a (68.1%) 127b (16.0%) 191 (21.5%)
EPU before COVID 30a (31.9%) 669b (84.0%) 699 (78.5%)
Total 94 796 890
FL – χ2 = 135.551***
Not an EPU before COVID 115a (80.4%) 109b (14.6%) 224 (25.1%)
EPU before COVID 28a (19.6%) 640b (85.4%) 668 (74.9%)
Total 143 749 892
Not an EPU During COVIDEPU During COVIDTotal
Both States – χ2 = 417.557***
Not an EPU before COVID 179a (75.5%) 236b (15.3%) 415 (23.3%)
EPU before COVID 58a (24.5%) 1309b (84.7%) 1367 (76.7%)
Total 237 1545 1782
CA – χ2 = 135.551***
Not an EPU before COVID 64a (68.1%) 127b (16.0%) 191 (21.5%)
EPU before COVID 30a (31.9%) 669b (84.0%) 699 (78.5%)
Total 94 796 890
FL – χ2 = 135.551***
Not an EPU before COVID 115a (80.4%) 109b (14.6%) 224 (25.1%)
EPU before COVID 28a (19.6%) 640b (85.4%) 668 (74.9%)
Total 143 749 892

Note: Each subscript letter denotes a subset of categories whose column proportions do not differ significantly from each other at the .05 level.

*p < .05, **p < .01, ***p < .001.

Additionally, we looked at how EPU perceptions varied in each state (Table 2). The chi-square results were significant in both states. However, there were more participants in Florida (25.1%) who thought Internet was not an EPU, though the majority of participants in both states indicated Internet was an EPU in general. Further, more participants in CA than FL perceived Internet as an EPU during the pandemic than before. The reasons for these findings are not explicitly understood, but may have to do with demographics and policy differences in each state.

We used chi-square tests of independence to understand how EPU perceptions varied across demographics. In CA, there were no significant differences between income groups and perceptions of Internet as an EPU both before, χ2 (3, N = 916) = 2.945, p = .40, and during the pandemic, χ2 (3, N = 923) = 3.236, p = .36. However, there were significant differences between FL income groups and perceptions of Internet as an EPU before, χ2 (3, N = 920) = 12.709, p < .01, and during the pandemic, χ2 (3, N = 932) = 11.552, p < .01. Low–low- (34.6%) and upper–low-income participants (30.2%) were more likely to say Internet was not an EPU before the pandemic than higher incomes. During the pandemic, only low–low-income participants (25.8%) thought Internet was not an EPU.

Age groups also significantly differed in their EPU perceptions before, χ2 (3, N = 1,836) = 31.221, p < .001, and during the pandemic, χ2 (3, N = 1,855) = 34.262, p < .001. Before the pandemic, more young people (36.5%) thought Internet was not an EPU than all other age groups. During the pandemic, surprisingly, 90.5% of old (58+ year old) participants thought Internet was an EPU, while young participants (24.5%) were more likely than other groups to think it was not an EPU. Lower-middle and upper-middle aged participants had similar perceptions during the pandemic. The relationship between racial-ethnic groups and EPU perceptions were significant before, χ2 (5, N = 1,823) = 24.756, p < .001, and during the pandemic, χ2 (5, N = 1,843) = 33.445, p < .001. Both before and during the pandemic, more black (32.6% and 26.5%, respectively) and Latinx participants (32.8% and 19.8%, respectively) thought Internet was not an EPU, while white (76.5% and), Asian (87.5% and), and Multiracial (81.0%) participants thought it was an EPU. It is worth noting that all Indigenous participants thought Internet was an EPU, regardless of the pandemic, but due to the relatively small sample size, there was not much effect on the model. Lastly, gender and EPU perceptions were significant before, χ2 (1, N = 1823) = 4.149, p < .05, and during the pandemic, χ2 (1, N = 1843) = 8.797, p < .01. Before the pandemic, more men (77.9%) thought Internet was an EPU than women (73.8%), but during the pandemic, more women (88.4%) thought Internet was an EPU than men (83.6%).

### Reasons for Internet Reliance

The reasons individuals use Internet can illuminate the ways the pandemic has impacted Internet use; therefore, using a series of ANOVAs with Tukey’s post hocs, we analyzed participants’ reasons for using the Internet, including employment, health, entertainment, education, information access, communication, e-commerce, and e-banking (Table 3). Post hocs showed that high-income, young, white, and Asian participants were more likely to rely on the Internet for employment than their counterparts. Interestingly, gender was the only significant relationship with Internet reliance for health, with women having more health-related reliance than men. Racial-ethnic group was the only significant relationship with reliance for entertainment; black, Latinx, and multiracial participants were more likely to use the Internet for entertainment than other racial-ethnic groups. Participants who were high-income, young, and women were more likely to rely on the Internet for education. Additionally, Black, Latinx, and multiracial participants relied on Internet for education more than white participants. White and Asian participants, men, and older participants were more likely to use the Internet for information access. High-income participants in FL and older, white, and Asian participants in the whole sample were more often relied on the Internet for communication. High-income participants in FL and men used Internet for e-commerce. Asian participants used Internet for e-commerce more than black participants, though post hocs showed no other differences. For e-banking reliance, only FL high-income was more likely than FL low–low, with no other income differences. Older, white, Asian, and multiracial participants were also more likely to rely on the Internet for e-banking than their counterparts.

TABLE 3

ANOVA Results for Reason for Internet Use During the COVID-19 Pandemic Across Demographics

Demographics
M (SD)
EmploymentHealthEntertainmentEducationInformation AccessCommunicationE-commerceE-banking
CA Income
F (3,940) = 23.282*** 0.286 1.082 6.229*** 0.223 0.919 1.982 1.069
Low–low 0.29 (0.456) 0.36 (0.483) 0.72 (0.451) 0.38 (0.486) 0.76 (0.427) 0.71 (0.456) 0.44 (.497) 0.53 (.500)
Upper–low 0.27 (0.445) 0.39 (0.489) 0.71 (0.453) 0.32 (0.467) 0.73 (0.445) 0.72 (0.450) 0.45 (.499) 0.59 (.493)
Medium 0.35 (0.477) 0.38 (0.487) 0.77 (0.420) 0.33 (0.471) 0.74 (0.437) 0.74 (0.440) 0.52 (.500) 0.58 (.494)
High 0.68 (0.468) 0.42 (0.495) 0.74 (0.438) 0.53 (0.501) 0.73 (0.447) 0.66 (0.474) 0.54 (.500) 0.51 (.502).
FL Income
F (3,905) = 9.982*** 2.095 1.948 2.599* 1.835 6.199*** 4.275** 3.106*
Low–low 0.31 (0.465) 0.25 (0.436) 0.65 (0.480) 0.29 (0.454) 0.65 (0.480) 0.52 (0.501) 0.32 (.468) 0.44 (.498)
Upper–low 0.20 (0.402) 0.26 (0.441) 0.74 (0.441) 0.28 (0.453) 0.74 (0.441) 0.68 (0.467) 0.37 (.484) 0.50 (.501)
Medium 0.37 (0.482) 0.31 (0.465) 0.75 (0.436) 0.33 (0.472) 0.71 (0.455) 0.70 (0.458) 0.42 (.495) 0.56 (.497)
High 0.48 (0.501) 0.37 (0.484) 0.73 (0.443) 0.41 (0.494) 0.76 (0.427) 0.71 (0.456) 0.51 (.502) 0.59 (.493)
Age
F (3,1846) = 81.502*** 1.335 0.673 52.715*** 23.066*** 11.541*** 1.076 25.603***
Young 0.51 (0.501) 0.34 (0.473) 0.75 (0.436) 0.51 (0.501) 0.58 (0.494) 0.60 (0.491) 0.41 (.494) 0.36 (.480)
Lower-middle 0.52 (0.500) 0.38 (0.486) 0.72 (0.449) 0.49 (0.500) 0.66 (0.474) 0.64 (0.481) 0.44 (.497) 0.49 (.500)
Upper-middle 0.39 (0.489) 0.33 (0.470) 0.76 (0.429) 0.32 (0.466) 0.75 (0.433) 0.70 (0.460) 0.45 (.498) 0.57 (.496)
Old 0.14 (0.349) 0.33 (0.472) 0.73 (0.445) 0.19 (0.395) 0.82 (0.385) 0.77 (0.422) 0.48 (.500) 0.65 (.476)
Racial-Ethnic
F (5,1835) = 5.722*** 1.446 3.620** 7.552*** 4.688*** 5.903*** 3.386** 11.226***
White 0.32 (0.466) 0.34 (0.475) 0.75 (0.434) 0.30 (0.459) 0.75 (0.432) 0.71 (0.452) 0.47 (.499) 0.58 (.494)
Black 0.43 (0.497) 0.32 (0.470) 0.62 (0.487) 0.44 (0.498) 0.63 (0.483) 0.57 (0.497) 0.34 (.477) 0.36 (.481)
Latinx 0.40 (0.492) 0.30 (0.458) 0.69 (0.463) 0.47 (0.501) 0.61 (0.489) 0.60 (0.492) 0.37 (.483) 0.38 (.487)
Asian 0.46 (0.500) 0.43 (0.497) 0.78 (0.413) 0.43 (0.497) 0.77 (0.424) 0.80 (0.400) 0.53 (.501) 0.66 (.474)
Indigenous 0.13 (0.354) 0.25 (0.463) 0.50 (0.535) 0.38 (0.518) 0.75 (0.463) 0.50 (0.535) 0.25 (.463) 0.38 (.518)
Multiracial 0.48 (0.502) 0.39 (0.490) 0.79 (0.410) 0.44 (0.499) 0.72 (0.451) 0.67 (0.472) 0.47 (.501) 0.62 (.488)
Gender
F (1,1833) = 2.285 7.017** 0.212 9.260** 11.278*** 0.296 8.138** 2.446
Women 0.37 (0.483) 0.37 (0.484) 0.73 (0.443) 0.38 (0.485) 0.69 (0.461) 0.70 (0.458) 0.42 (.493) 0.53 (.499)
Men 0.34 (0.473) 0.31 (0.465) 0.74 (0.440) 0.31 (0.463) 0.76 (0.425) 0.69 (0.463) 0.48 (.500) 0.57 (.496)
Demographics
M (SD)
EmploymentHealthEntertainmentEducationInformation AccessCommunicationE-commerceE-banking
CA Income
F (3,940) = 23.282*** 0.286 1.082 6.229*** 0.223 0.919 1.982 1.069
Low–low 0.29 (0.456) 0.36 (0.483) 0.72 (0.451) 0.38 (0.486) 0.76 (0.427) 0.71 (0.456) 0.44 (.497) 0.53 (.500)
Upper–low 0.27 (0.445) 0.39 (0.489) 0.71 (0.453) 0.32 (0.467) 0.73 (0.445) 0.72 (0.450) 0.45 (.499) 0.59 (.493)
Medium 0.35 (0.477) 0.38 (0.487) 0.77 (0.420) 0.33 (0.471) 0.74 (0.437) 0.74 (0.440) 0.52 (.500) 0.58 (.494)
High 0.68 (0.468) 0.42 (0.495) 0.74 (0.438) 0.53 (0.501) 0.73 (0.447) 0.66 (0.474) 0.54 (.500) 0.51 (.502).
FL Income
F (3,905) = 9.982*** 2.095 1.948 2.599* 1.835 6.199*** 4.275** 3.106*
Low–low 0.31 (0.465) 0.25 (0.436) 0.65 (0.480) 0.29 (0.454) 0.65 (0.480) 0.52 (0.501) 0.32 (.468) 0.44 (.498)
Upper–low 0.20 (0.402) 0.26 (0.441) 0.74 (0.441) 0.28 (0.453) 0.74 (0.441) 0.68 (0.467) 0.37 (.484) 0.50 (.501)
Medium 0.37 (0.482) 0.31 (0.465) 0.75 (0.436) 0.33 (0.472) 0.71 (0.455) 0.70 (0.458) 0.42 (.495) 0.56 (.497)
High 0.48 (0.501) 0.37 (0.484) 0.73 (0.443) 0.41 (0.494) 0.76 (0.427) 0.71 (0.456) 0.51 (.502) 0.59 (.493)
Age
F (3,1846) = 81.502*** 1.335 0.673 52.715*** 23.066*** 11.541*** 1.076 25.603***
Young 0.51 (0.501) 0.34 (0.473) 0.75 (0.436) 0.51 (0.501) 0.58 (0.494) 0.60 (0.491) 0.41 (.494) 0.36 (.480)
Lower-middle 0.52 (0.500) 0.38 (0.486) 0.72 (0.449) 0.49 (0.500) 0.66 (0.474) 0.64 (0.481) 0.44 (.497) 0.49 (.500)
Upper-middle 0.39 (0.489) 0.33 (0.470) 0.76 (0.429) 0.32 (0.466) 0.75 (0.433) 0.70 (0.460) 0.45 (.498) 0.57 (.496)
Old 0.14 (0.349) 0.33 (0.472) 0.73 (0.445) 0.19 (0.395) 0.82 (0.385) 0.77 (0.422) 0.48 (.500) 0.65 (.476)
Racial-Ethnic
F (5,1835) = 5.722*** 1.446 3.620** 7.552*** 4.688*** 5.903*** 3.386** 11.226***
White 0.32 (0.466) 0.34 (0.475) 0.75 (0.434) 0.30 (0.459) 0.75 (0.432) 0.71 (0.452) 0.47 (.499) 0.58 (.494)
Black 0.43 (0.497) 0.32 (0.470) 0.62 (0.487) 0.44 (0.498) 0.63 (0.483) 0.57 (0.497) 0.34 (.477) 0.36 (.481)
Latinx 0.40 (0.492) 0.30 (0.458) 0.69 (0.463) 0.47 (0.501) 0.61 (0.489) 0.60 (0.492) 0.37 (.483) 0.38 (.487)
Asian 0.46 (0.500) 0.43 (0.497) 0.78 (0.413) 0.43 (0.497) 0.77 (0.424) 0.80 (0.400) 0.53 (.501) 0.66 (.474)
Indigenous 0.13 (0.354) 0.25 (0.463) 0.50 (0.535) 0.38 (0.518) 0.75 (0.463) 0.50 (0.535) 0.25 (.463) 0.38 (.518)
Multiracial 0.48 (0.502) 0.39 (0.490) 0.79 (0.410) 0.44 (0.499) 0.72 (0.451) 0.67 (0.472) 0.47 (.501) 0.62 (.488)
Gender
F (1,1833) = 2.285 7.017** 0.212 9.260** 11.278*** 0.296 8.138** 2.446
Women 0.37 (0.483) 0.37 (0.484) 0.73 (0.443) 0.38 (0.485) 0.69 (0.461) 0.70 (0.458) 0.42 (.493) 0.53 (.499)
Men 0.34 (0.473) 0.31 (0.465) 0.74 (0.440) 0.31 (0.463) 0.76 (0.425) 0.69 (0.463) 0.48 (.500) 0.57 (.496)

Bold indicates significance; *p < .05, **p < .01, ***p < .001.

### Perceptions of Internet Reliability and Importance During the Pandemic

We used another series of ANOVAs with Tukey’s post hocs to understand if perceptions of how reliable and important Internet was during the pandemic varied across demographics. Internet importance differs from perceptions of Internet as a public utility, as individuals can believe Internet to be important but shouldn’t be regulated like a utility. In general, participants felt their Internet was reliable and important (Table 4).

TABLE 4

ANOVA Results for Participant Perceptions of Internet Reliability and Importance During the COVID-19 Pandemic

ReliabilityM (SD)FImportanceM (SD)F
CA Income
Low–low 4.16 (0.864) (3, 940) = 4.215** Low–low 4.53 (0.862) (3, 995) = 0.782
Upper–low 4.32 (0.666)  Upper–low 4.57 (0.827)
Medium 4.33 (0.733)  Medium 4.62 (0.765)
High 4.46 (0.690)  High 4.62 (0.719)
FL Income
Low–low 4.21 (0.832) (3, 905) = 3.941** Low–low 4.26 (1.034) (3, 993) = 10.167***
Upper–low 4.32 (0.707)  Upper–low 4.55 (0.795)
Medium 4.41 (0.725)  Medium 4.61 (0.734)
High 4.47 (0.612)  High 4.68 (0.720)
Age Groups
Young 4.02 (0.878) (3, 1846) = 36.257*** Young 4.31 (1.010) (3, 1989) = 12.208***
Lower-middle 4.23 (0.760)  Lower-middle 4.56 (0.839)
Upper-middle 4.35 (0.725)  Upper-middle 4.62 (0.747)
Old 4.54 (0.600)  Old 4.64 (0.694)
Racial-Ethnic Groups
White 4.36 (0.718) (5, 1835) = 1.209 White 4.60 (0.762) (5, 1976) = 3.822**
Black 4.32 (0.841)  Black 4.35 (0.982)
Latinx 4.30 (0.746)  Latinx 4.46 (0.943)
Asian 4.32 (0.693)  Asian 4.66 (0.716)
Indigenous 3.88 (0.354)  Indigenous 4.75 (0.463)
Multiracial 4.26 (0.852)  Multiracial 4.57 (0.825)
Gender
Women 4.40 (0.710) (1, 1833) = 9.536** Women 4.57 (0.745) (1, 1975) = 0.061
Men 4.29 (0.760)  Men 4.56 (0.859)
ReliabilityM (SD)FImportanceM (SD)F
CA Income
Low–low 4.16 (0.864) (3, 940) = 4.215** Low–low 4.53 (0.862) (3, 995) = 0.782
Upper–low 4.32 (0.666)  Upper–low 4.57 (0.827)
Medium 4.33 (0.733)  Medium 4.62 (0.765)
High 4.46 (0.690)  High 4.62 (0.719)
FL Income
Low–low 4.21 (0.832) (3, 905) = 3.941** Low–low 4.26 (1.034) (3, 993) = 10.167***
Upper–low 4.32 (0.707)  Upper–low 4.55 (0.795)
Medium 4.41 (0.725)  Medium 4.61 (0.734)
High 4.47 (0.612)  High 4.68 (0.720)
Age Groups
Young 4.02 (0.878) (3, 1846) = 36.257*** Young 4.31 (1.010) (3, 1989) = 12.208***
Lower-middle 4.23 (0.760)  Lower-middle 4.56 (0.839)
Upper-middle 4.35 (0.725)  Upper-middle 4.62 (0.747)
Old 4.54 (0.600)  Old 4.64 (0.694)
Racial-Ethnic Groups
White 4.36 (0.718) (5, 1835) = 1.209 White 4.60 (0.762) (5, 1976) = 3.822**
Black 4.32 (0.841)  Black 4.35 (0.982)
Latinx 4.30 (0.746)  Latinx 4.46 (0.943)
Asian 4.32 (0.693)  Asian 4.66 (0.716)
Indigenous 3.88 (0.354)  Indigenous 4.75 (0.463)
Multiracial 4.26 (0.852)  Multiracial 4.57 (0.825)
Gender
Women 4.40 (0.710) (1, 1833) = 9.536** Women 4.57 (0.745) (1, 1975) = 0.061
Men 4.29 (0.760)  Men 4.56 (0.859)

*p < .05, **p < .01, ***p < .001.

Low–low-income participants in CA had significantly lower perceptions of reliability than medium- and high-income participants, with no other group differences; the same relationship exists between FL income groups, where low–low-income participants had significantly lower perceptions of reliability than medium- and high-income participants. Old participants had significantly higher perceptions of reliability than all other age groups, who were all significantly different from each other. Women also thought their Internet was more reliable than men. There were no statistical differences between racial-ethnic groups.

The relationship between CA income and Internet importance was insignificant. However, in FL, low–low-income participants had significantly lower perceptions of Internet importance than all other income groups. Younger participants had significantly lower perceptions of importance than lower-middle aged, upper-middle aged, and older participants, who were not statistically different from each other. Black participants had significantly lower perceptions of Internet importance than white and Asian participants, while all other groups were statistically similar. There were no statistical differences between genders and perceptions of Internet importance.

### Demographic Predictors of Internet Perceptions

We ran several multiple linear regressions to test if income, age, gender, and racial-ethnicity significantly predicted levels of Internet reliance and perceptions of Internet reliability and importance (Table 5), as well as perceptions of Internet as an EPU before and during the pandemic (Table 6). Level of Internet reliance captured the number of reasons participants used the Internet based on the reasons for reliance in section “Reasons for Internet Reliance,” with a maximum of eight reasons. Racial-ethnicity was recoded so 0 = white participants and 1 = all participants of color (i.e., Black, Latinx, Asian, Indigenous, and Multiracial), and the variable was renamed to POC. The r-square for every model was low (0.015–0.081) but significant, except for perceptions of Internet importance in CA. This indicates that other factors may be involved in explaining Internet perceptions. For example, other research suggests unique Internet issues for renters,31 people with energy-reliant medical needs (e.g., medical devices that require electricity),32 and rural residents.33 These factors could be explored in future research.

The overall regression was statistically significant for level of Internet reliance in CA and FL (Table 5). In the CA model, it was found that income and age significantly predicted Internet reliance levels, while gender and POC did not. For every one unit increase in income, the level of Internet reliance increased, while it decreased for every one unit increase in age, meaning higher incomes and younger age were associated with higher levels of Internet reliance. Age had the most influence on the model. In the FL model, only income was significant, such that an increase in income corresponded to an increase in level of Internet reliance. The CA and FL regression models for perceptions of Internet reliability were significant. In the CA model, only income and age were significant predictors of reliability perceptions; as income and age increased, so did perceptions that their Internet was reliable. In the FL model, income, age, and POC were significant; participants with higher incomes, who are older and POC had higher perceptions of Internet reliability. Age was the most influential predictor of Internet reliance in the CA and FL models. Lastly, the models for perceptions of Internet importance were significant in the FL model only. Income and age were significant predictors of Internet importance, with increases in income and age corresponding to greater perceived Internet importance. Income was a slightly stronger predictor of Internet importance than age.

TABLE 5

Linear Regression Results for Level of Internet Reliance, Internet Reliability, and Internet Importance Across Income, Age, Gender, and POC Groups

VariablesUnstandardized BStandardized βStandard ErrorR2 and ANOVA
Level of Internet Reliance
CA income  .171*  .081 .070 R2 = .015
F (4, 945) = 3.617**
Age −.159* −.084 .064
Gender  .001  .000 .133
POC −.244 −.059 .141
FL income  .372***  .179 .068 R2 = .034
F (4, 926) = 8.040***
Age  .035  .019 .067
Gender −.016 −.004 .130
POC  .001  .000 .163
Internet Reliability
CA income  .080**  .102 .025 R2 = .070
F (4, 945) = 17.669***
Age  .166***  .233 .023
Gender −.092 −.061 .048
POC  .063  .041 .051
FL income  .081**  .102 .026 R2 = .081
F (4, 926) = 21.338***
Age  .191***  .265 .025
Gender −.054 −.036 .049
POC  .122*  .067 .061
Internet Importance
CA income  .001  .001 .027 R2 = .003
F (4, 928) = .729
Age  .015  .022 .024
Gender −.038 −.025 .050
POC −.058 −.037 .053
FL income  .085**  .110 .026 R2 = .026
F (4, 893) = 6.048***
Age  .073**  .106 .025
Gender  .074  .051 .049
POC  .045  .026 .061
VariablesUnstandardized BStandardized βStandard ErrorR2 and ANOVA
Level of Internet Reliance
CA income  .171*  .081 .070 R2 = .015
F (4, 945) = 3.617**
Age −.159* −.084 .064
Gender  .001  .000 .133
POC −.244 −.059 .141
FL income  .372***  .179 .068 R2 = .034
F (4, 926) = 8.040***
Age  .035  .019 .067
Gender −.016 −.004 .130
POC  .001  .000 .163
Internet Reliability
CA income  .080**  .102 .025 R2 = .070
F (4, 945) = 17.669***
Age  .166***  .233 .023
Gender −.092 −.061 .048
POC  .063  .041 .051
FL income  .081**  .102 .026 R2 = .081
F (4, 926) = 21.338***
Age  .191***  .265 .025
Gender −.054 −.036 .049
POC  .122*  .067 .061
Internet Importance
CA income  .001  .001 .027 R2 = .003
F (4, 928) = .729
Age  .015  .022 .024
Gender −.038 −.025 .050
POC −.058 −.037 .053
FL income  .085**  .110 .026 R2 = .026
F (4, 893) = 6.048***
Age  .073**  .106 .025
Gender  .074  .051 .049
POC  .045  .026 .061

*p< .05, **p < .01, ***p < .001.

The overall regression models were statistically significant for perceptions of Internet as an EPU both before and during COVID-19 in the CA and FL models (Table 6), though the r-squared for each model was relatively low, suggesting other factors may explain perceptions of Internet as an EPU. In the CA models, age and gender significantly predicted EPU perceptions, while income and POC did not. Before the pandemic, increases in age led to increases in EPU perceptions while increases in gender led to decreases in EPU perceptions; this means older participants and men were more likely to perceive Internet as an EPU before the pandemic than their counterparts. During the pandemic, increases in age and gender led to increases in EPU perceptions, indicating older participants and women more likely perceived Internet as an EPU during the pandemic than their counterparts. Age was the most important predictor in both CA models. In the FL models, income and age were significant predictors for EPU perceptions before the pandemic while income and gender were significant during the pandemic. Before the pandemic, as income and age increased, so did EPU perceptions. Income was the most influential predictor in this model. During the pandemic, as income and gender increased, so did EPU perceptions, such that participants with higher incomes and women had higher EPU perceptions in FL. Gender had a slightly stronger effect on EPU perceptions during the pandemic than income.

TABLE 6

Linear Regression Results for Perceptions of Internet As an Essential Public Utility Before and During the Pandemic Across Income, Age, Gender, and POC Groups

VariablesUnstandardized BStandardized βStandard ErrorR2 and ANOVA
EPU Before
CA income  .003  .006 .015 R2 = .018
F (4, 903) = 4.213**
Age  .042**  .107 .014
Gender −.067* −.080 .028
POC  .034  .040 .030
FL income  .041**  .090 .015 R2 = .018
F (4, 905) = 4.189**
Age  .032*  .076 .015
Gender  .004  .005 .030
POC −.026 −.025 .037
EPU During
CA income  .004  .012 .011 R2 = .033
F (4, 910) = 7.615***
Age  .050***  .172 .010
Gender  .047*  .076 .021
POC  .004  .006 .022
FL income  .032*  .081 .013 R2 = .024
F (4, 919) = 5.735***
Age  .022  .061 .013
Gender  .062*  .083 .025
POC −.053 −.059 .031
VariablesUnstandardized BStandardized βStandard ErrorR2 and ANOVA
EPU Before
CA income  .003  .006 .015 R2 = .018
F (4, 903) = 4.213**
Age  .042**  .107 .014
Gender −.067* −.080 .028
POC  .034  .040 .030
FL income  .041**  .090 .015 R2 = .018
F (4, 905) = 4.189**
Age  .032*  .076 .015
Gender  .004  .005 .030
POC −.026 −.025 .037
EPU During
CA income  .004  .012 .011 R2 = .033
F (4, 910) = 7.615***
Age  .050***  .172 .010
Gender  .047*  .076 .021
POC  .004  .006 .022
FL income  .032*  .081 .013 R2 = .024
F (4, 919) = 5.735***
Age  .022  .061 .013
Gender  .062*  .083 .025
POC −.053 −.059 .031

*p< .05, **p < .01, ***p < .001.

### Impact of Internet Perceptions on In-Home Device Access

We used ANOVA tests with Tukey’s HSD post hoc tests and multiple linear regression models to understand the connections between perceptions of Internet reliability, importance, and as an EPU and in-home device access. Here, home technologies include computers, Wi-Fi, TV, smart thermostats, solar water heaters, clothing washers and dryers, dishwashers, cellphones, programmable thermostats, and smart meters (scale of 0–12). Overall, device ownership was high; the mean was 7.38 devices (SD = 2.179), and the median was 8.00.

The relationships between perceptions of Internet reliability, F (4, 1846) = 6.263, p < .001, and importance, F (4, 1846) = 4256, p < .01, to device ownership were significant. For reliability, post hoc tests showed that those who perceived their Internet to be reliable some (M = 6.82, SD = 2.25) or most of the time (M = 7.25, SD = 2.13) had significantly fewer devices than those whose Internet was reliable all the time (M = 7.61, SD = 2.17). However, there were no differences in device ownership between those with unreliable Internet and those with reliable Internet. For importance, participants who had neutral perceptions of Internet importance (M = 6.63, SD = 2.45) had significantly fewer devices than those who found Internet important (M = 7.52, SD = 2.19) or very important (M = 7.41, SD = 2.13). There were no other differences according to post hoc tests. In another series of ANOVAs, we determined the relationship between perceptions of Internet as an EPU before and during the pandemic and device ownership. The relationship between Internet as an EPU before the pandemic and device ownership was significant, F (1, 1712) = 5.371, p < .05, but the relationship between EPU during the pandemic and device ownership was not significant, F (1, 1728) = 3.139, p = .08. Before the pandemic, those who thought Internet was an EPU (M = 7.46, SD = 2.21) had more devices than those who did not think it was an EPU (M = 7.16, SD = 2.12).

In a multiple linear regression model, we analyzed how Internet perceptions (i.e., reliability, importance, EPU before, and EPU during the pandemic) predicted device ownership (i.e., number of ICTs owned). Histograms, scatterplots, and residual scatterplots were examined to test the assumptions of linearity, normality, and homoscedasticity. No assumptions were violated. Specifically, all VIF scores were below 2.0, indicating the assumption of multicollinearity had not been violated.34 The adjusted r-square was low (.013) but significant, F (4, 1666) = 6.433, p < .001. Reliability was the only significant predictor (B = .323, SE = .075, p < .001) of device ownership. As reliability goes up, so does device ownership. Therefore, reliability predicts device ownership, but as displayed below device ownership also predicts both reliability and importance. It is important to note that there may be endogeneity in this relationship resulting in significant predictability in both directions. There may be other compounding variables (perhaps socioeconomic score) that are playing a role, which can only be revealed through higher-level modelling in a future study. However, the present model is still robust and valid given that we do not violate the assumption of multicollinearity.

We sought to further determine which specific devices may be influencing Internet perceptions. As such, we used four multiple linear regression models to compare the impacts of each device on reliability, importance, and EPU perceptions, respectively and report the standardized coefficients (Table 7). The model for Internet importance had the best fit (Adjusted R2 = 0.055). The models show that ownership of computers, TVs, water heaters, and dishwashers had a significantly positive effect on perceptions of Internet reliability, while computer, Wi-Fi (e.g., as opposed to Internet access via cellular data), and cellphone ownership were significantly predictive of positive perceptions of Internet importance. Computers, Wi-Fi, and cellphones positively impacted perceptions of Internet as an EPU both before and during the pandemic, and smart meters had positive impacts on EPU perceptions during the pandemic but not before. Interestingly, some devices, such as owning an electric vehicle or a clothes dryer, lowered perceptions of Internet reliability, importance, and EPU, but not on a statistically significant level, which warrants further research.

TABLE 7

Multiple Linear Regression Results for the Impact of In-Home Technologies on Participant Perceptions of Internet Reliability, Importance, and Internet As an Essential Public Utility Before and During the COVID-19 Pandemic

BStandard Errort-ValueBStandard Errort-Value
Reliability – Adjusted R2 = .031 Importance – Adjusted R2 = .055
Computer  0.059 0.066 2.160* Computer  0.104 0.065 3.898***
Wi-Fi/Internet  0.022 0.084 0.819 Wi-Fi/Internet  0.153 0.080 5.674***
TV or Cable  0.114 0.045 4.117*** TV or Cable  0.003 0.046 0.094
Smart Thermostat  0.004 0.056 0.118 Smart Thermostat −0.021 0.058 −0.705
Water Heater  0.061 0.061 2.052* Water Heater −0.028 0.062 −0.981
Electric Vehicle −0.003 0.081 −0.101 Electric Vehicle −0.011 0.083 −0.376
Clothes Washer  0.031 0.094 0.631 Clothes Washer  0.048 0.097 1.022
Clothes Dryer −0.036 0.089 −0.738 Clothes Dryer −0.049 0.091 −1.029
Dish Washer  0.069 0.046 2.313* Dish Washer  0.025 0.047 0.879
Cellphone  0.042 0.111 1.574 Cellphone  0.102 0.107 3.863***
Programmable Thermostat  0.001 0.047 0.033 Programmable Thermostat −0.042 0.049 −1.441
Smart Meter −0.007 0.046 −0.251 Smart Meter  0.031 0.047 1.119
EPU Before – Adjusted R2 = .043 EPU During – Adjusted R2 = .048
Computer  0.115 0.036 4.162*** Computer  0.067 0.029 2.455*
Wi-Fi/Internet  0.117 0.044 4.171*** Wi-Fi/Internet  0.152 0.036 5.444***
TV or Cable −0.048 0.025 −1.717 TV or Cable −0.021 0.020 −0.753
Smart Thermostat  0.025 0.032 0.798 Smart Thermostat −0.021 0.025 −0.665
Water Heater  0.000 0.035 0.013 Water Heater −0.010 0.028 −0.331
Electric Vehicle  0.048 0.045 1.625 Electric Vehicle −0.004 0.036 −0.138
Clothes Washer −0.005 0.053 −0.091 Clothes Washer  0.063 0.042 1.295
Clothes Dryer  0.008 0.051 0.153 Clothes Dryer −0.005 0.040 −1.138
Dish Washer  0.022 0.026 0.731 Dish Washer  0.006 0.021 0.205
Cellphone  0.069 0.058 2.503* Cellphone  0.112 0.048 4.139***
Programmable Thermostat −0.055 0.027 −1.800 Programmable Thermostat −0.025 0.022 −0.821
Smart Meter  0.053 0.026 1.860 Smart Meter  0.054 0.021 1.931*
BStandard Errort-ValueBStandard Errort-Value
Reliability – Adjusted R2 = .031 Importance – Adjusted R2 = .055
Computer  0.059 0.066 2.160* Computer  0.104 0.065 3.898***
Wi-Fi/Internet  0.022 0.084 0.819 Wi-Fi/Internet  0.153 0.080 5.674***
TV or Cable  0.114 0.045 4.117*** TV or Cable  0.003 0.046 0.094
Smart Thermostat  0.004 0.056 0.118 Smart Thermostat −0.021 0.058 −0.705
Water Heater  0.061 0.061 2.052* Water Heater −0.028 0.062 −0.981
Electric Vehicle −0.003 0.081 −0.101 Electric Vehicle −0.011 0.083 −0.376
Clothes Washer  0.031 0.094 0.631 Clothes Washer  0.048 0.097 1.022
Clothes Dryer −0.036 0.089 −0.738 Clothes Dryer −0.049 0.091 −1.029
Dish Washer  0.069 0.046 2.313* Dish Washer  0.025 0.047 0.879
Cellphone  0.042 0.111 1.574 Cellphone  0.102 0.107 3.863***
Programmable Thermostat  0.001 0.047 0.033 Programmable Thermostat −0.042 0.049 −1.441
Smart Meter −0.007 0.046 −0.251 Smart Meter  0.031 0.047 1.119
EPU Before – Adjusted R2 = .043 EPU During – Adjusted R2 = .048
Computer  0.115 0.036 4.162*** Computer  0.067 0.029 2.455*
Wi-Fi/Internet  0.117 0.044 4.171*** Wi-Fi/Internet  0.152 0.036 5.444***
TV or Cable −0.048 0.025 −1.717 TV or Cable −0.021 0.020 −0.753
Smart Thermostat  0.025 0.032 0.798 Smart Thermostat −0.021 0.025 −0.665
Water Heater  0.000 0.035 0.013 Water Heater −0.010 0.028 −0.331
Electric Vehicle  0.048 0.045 1.625 Electric Vehicle −0.004 0.036 −0.138
Clothes Washer −0.005 0.053 −0.091 Clothes Washer  0.063 0.042 1.295
Clothes Dryer  0.008 0.051 0.153 Clothes Dryer −0.005 0.040 −1.138
Dish Washer  0.022 0.026 0.731 Dish Washer  0.006 0.021 0.205
Cellphone  0.069 0.058 2.503* Cellphone  0.112 0.048 4.139***
Programmable Thermostat −0.055 0.027 −1.800 Programmable Thermostat −0.025 0.022 −0.821
Smart Meter  0.053 0.026 1.860 Smart Meter  0.054 0.021 1.931*

*p < .05, **p < .01, ***p < .001.

## Limitations and Future Research

There are several limitations to the present study that could inspire future research. First, lower income households tend to have less home Internet access and are more likely to use smartphones to access the Internet than higher income households.35 However, this study controlled for those with Internet access in their homes already. Second, we focus on the states of CA and FL, but residents of other states or areas may have different perceptions and reasons for Internet use; therefore, research expanding into other regions of the country or a different country would further elucidate how energy and Internet insecurity can hinder different social groups. Third, this study focuses on the COVID-19 pandemic, while future research can focus on other disasters’ impacts, such as hurricanes or wildfires. Fourth, this study sticks to the gender binary and only considers the perceptions of men and women, largely due to the small sample of nonbinary respondents or other gender identities in our study. Similarly, there was a small sample size of Indigenous respondents; therefore, a concerted effort should be made to address Internet access and perceptions of Indigenous populations and non-gender conforming individuals. Lastly, more information is needed on how services become utilities, the implications of designating a utility as an EPU, how Internet as an EPU can improve broadband access and digital equity, and the normative implications of the study’s findings. This may include future research analyzing other Internet-related perceptions and variables, such as time of Internet use, privacy concerns, social and personal norms, and so on.

## Discussion

This study analyzed the Internet perceptions of CA and FL residents during the COVID-19 pandemic to greater understand the impacts of pandemics and extreme events on Internet perceptions. Overall, perceptions of Internet and ISPs were neutral to positive, but there were many group differences, especially related to income, age, and gender. We found that participants with lower incomes and those who are younger and female tended to have lower perceptions of their ISPs and Internet, including perceptions of Internet as an EPU before the pandemic and Internet reliability and importance, than their counterparts. Multiple linear regression models confirmed that individuals with higher incomes and who were older tended to rely on the Internet more, had higher perceptions of Internet reliability, and perceived their Internet to be more important. A potential explanation for these findings is that the demographics with the lowest perception levels tend to own the fewest ICTs, meaning they are exposed to the benefits (and possible risks) of the Internet less often than the demographics with higher perceptions, especially of Internet reliability. This lack of access to and connection with the Internet and ICTs may be negatively impacting lower income, younger, and female households’ perceptions. Additionally, these demographics tend to have fewer financial and social resources (e.g., knowledge, manpower, etc.) to adopt and use Internet and new technologies, indicating a distributional justice issue that can be addressed through policy.

Interestingly, multiple linear regression models found that men were more likely to perceive Internet as an EPU before the pandemic than women; however, women had higher perceptions of Internet as an EPU during the pandemic than men. One possible explanation for this finding is that women were more likely to lose work outside of the house36 and perform more unpaid care work than men,37 thereby putting women in closer proximity to the benefits of Internet for education, employment, entertainment, and healthcare than men. Being more involved in the domestic sphere may have increased women’s perceptions of Internet as an EPU, but this can be explained through future research. Further justification for this theory is that most of the participants who said Internet was an EPU before the pandemic but wasn’t during were medium income, younger, male, and white. These groups tended to have lower Internet perceptions, meaning these participants likely saw less value in the Internet being deemed an EPU. The reasons why 24.5% of respondents changed their mind about Internet as an EPU needs further exploration. Despite this, 86.7% of participants perceived Internet to be an EPU during the pandemic, which is a 13% increase from before the pandemic. Perceptions were especially positive in Californian, higher income, older, white, and Asian households; these groups often indicated using the Internet for things like education and employment, which may have increased their perceptions of Internet as an EPU. Given that California responded to the pandemic with stricter public-health policy decisions than Florida,38 these results indicate that specific state policy had an impact on Internet perceptions. Overall, it appears that changes in behavior related to utilizing households for education and employment had a direct impact on participants Internet perceptions, with those who utilized household technology having the strongest perceptions of importance, reliability, and perception as an EPU.

The reasons individuals relied on the Internet differed vastly between demographics. High-income participants in FL indicated relying on the Internet for more reasons than lower incomes, and we found few significant differences between Internet reliance in CA income groups, indicating greater socioeconomic stratification in Internet access and use in FL than CA. We found that high-income, young, white, and Asian participants relied on Internet for employment and that high-income, young, women, and racial-ethnic minority participants relied on Internet for education. Additionally, women relied on Internet for health needs, such as telehealth appointments, more than men, and black, Latinx, and multiracial groups relied on Internet for entertainment more than white and Asian participants. These findings are in line with other studies that find demographic differences in different types of Internet reliance.39 Reasons for Internet reliance, therefore, may not have changed much during the pandemic from pre-pandemic conditions.

Lastly, we found that the more ICTs participants owned, the more positive their perceptions of the Internet were; participants who perceived their Internet to be more reliable and important and an EPU owned more ICTs than their counterparts. This shows that access to reliable Internet influences willingness to adopt more ICTs, and that the function and use of ICTs depends on access to fast and reliable Internet. Furthermore, we found that owning computers, TVs, water heaters, and dishwashers positively influenced perceptions of reliability. Access to computers, Wi-Fi, and cellphones increased perceptions of importance and Internet as an EPU. Interestingly, owning an electric vehicle and clothes dryer was related to slightly negative perceptions of reliability, importance, and Internet as EPU, though not at a statistically significant level; therefore, more research could elucidate the reasons for these findings.

## Conclusions and Policy Implications

This study has demonstrated the impacts of the COVID-19 pandemic on Internet perceptions among residents from California and Florida. We found that perceptions were lowest among those with the least amount of access to the Internet, particularly in the state of Florida. Consistently, higher income, white, and Asian participants had positive perceptions of their Internet and ISPs and were more likely to report using the Internet for employment, communication, e-banking, and e-commerce, indicating that these groups use the Internet to connect and interact with the outside world. On the other hand, black, Latinx, women, and younger participants often indicated using the Internet for education and entertainment purposes, suggesting these groups use the Internet for personal growth and experiences.

Our findings have several implications for policy. First, it is significant that the majority of participants (though more in CA than FL) considered household Internet as an EPU. This shows that the COVID-19 pandemic has highlighted how essential access to household Internet is to the daily functions of life, much like other essential utilities such as water, gas, and electricity.40 One major distinction between Internet and other household utilities is the differences in regulation. Currently, utilities are regulated by both federal and state bodies to ensure that households have equal access at fixed rates. For example, the Public Utilities Regulatory Policy Act of 1978 (PURPA), which requires utility companies in states such as North Carolina and California to purchase their electricity from “qualified facilities” at the price the company would pay to generate the same amount of power themselves.41 PURPA was updated in 2020 to meet the changing demands of the energy market, such as offering greater flexibility in rate costs and allowing entities to self-classify as a “qualified facility.”42 PURPA has been cited as one of the leading factors of North Carolina’s installed solar capacity, which is second highest in the country.43 These policies exemplify how federal and state-level regulations can protect customers while still leaving room for innovation and change in the policies themselves and related technologies (i.e., Internet, ICT).

While there are many federal regulations on the Internet, especially through the FCC, there is much less regulation of Internet than there is for electricity and water, and many policies in place have hindered equitable access. For example, the FCC repealed their net neutrality act in 2017 and eliminated protections against discriminatory Internet connection practices (i.e., offering different rates based on location, content, website, etc.).44 Scholars suggest that, at minimum, net neutrality policies should be reinstated to help limit discrimination in the Internet market.45 Individual states are already taking action to fill this regulatory gap, such as California and Washington,46 and more states could adopt similar policies in lieu of stringent federal regulations. As the importance of Internet in daily life grows and the public perceive it to be an EPU, the question can be asked whether access should be required to be protected by law, thereby guaranteeing Internet access to all in a service area at a consistent rate. The results of this study signify the impact of COVID-19 on public perceptions of Internet importance. The idea of Internet as a public utility has important implications for policymakers to consider. While this study did not collect data that assesses these implications, the basis of these considerations should be examined in future research.

Despite flaws in current regulations, such as the net neutrality repeal, the federal government is actively making strides to address Internet access barriers through programs such as the Infrastructure Investment and Jobs Act which created the \$14 billion Affordable Connectivity Program (ACP), administered by the FCC, which provides discounts on Internet services and device purchases for low-income households.47 Ten million households were enrolled in the Affordable Connectivity Program by February 2022.48 In February 2022, the FCC also banned Internet providers from joining revenue-sharing arrangements with landlords, which will help prevent tenants from paying higher prices for lower quality service.49 While these policies are working toward socially just solutions to Internet access, guaranteeing access under law is not the only solution, as many access issues stem from lack of providers. In many areas in the United States, there is only one provider and, unlike electricity, with no regulations to ensure quality of service or cap prices, there are no regulations for how these individual providers can operate. Although our survey did not ask whether multiple providers were available to respondents, it is not too much of a stretch to assume that in a model where “competition,” and not government regulation, is designed to be the regulator of cost and quality, households suffer as a result of lack of choice.

To achieve the goal of expanding access and choice, existing infrastructure must be upgraded to meet the country’s ever increasing Internet demands, and new infrastructure must be built to expand access to underserved areas, especially in rural locations. Investments by state and federal governments, such as state-level broadband expansion efforts (e.g., § 65-25-134 in Tennessee, AB 14-658 and SB 743 in CA, HB 2384 in Illinois50) and the Infrastructure Investment and Jobs Act, are steps toward reducing the digital divide, but more can be done to reduce the cost of Internet access, especially for low income and rural communities, such as continuing and strengthening the Affordable Connectivity Program,51 collaborating with public organizations like libraries,52 and investing in Internet and digital skills,53 which the federal government is currently addressing through the Digital Equity Act.54 Holding ISPs accountable is also an essential policy step, as research has shown ISPs like AT&T often neglect delivering broadband to rural, low-income, and minority areas due to a lack of fiscal incentive to expand into such areas.55

Second, our results show that there is a direct link between more positive Internet perceptions and increased access to technology. The connection between Internet access and technology use is not particularly surprising but does point to an important aspect of technology adoption that cannot be ignored in an increasingly digital society; those with “better” Internet services tend to be more resource rich and adopt more ICTs, allowing those with more resources to greater participate in society, culture, economics, and so on. The Affordable Connectivity Program does address this issue by providing financial assistance with technology purchases and Internet costs, but local solutions are vital to the efficiency of broadband expansion programs.56 Education on the use, benefits, and risks of Internet access and education is necessary and can be achieved by partnering with public facilities.57 Additionally, discussions on Internet access at the policy level needs to emphasize the benefits of various technologies. For example, studies show that smart energy technology, such as smart meters and thermostats, improve household efficiency and decrease energy use.58 Policy that improves knowledge of, and access to, in-home technology will not only benefit households but could also increase understanding of how Internet limitations impact daily life.

Lastly, policy should focus on social barriers other than rurality and income to address Internet access and use in a broader population. Our results highlight how Internet and ISP perceptions vary across gender, income, and age, signaling unique characteristics and barriers to various, and intersecting, groups. Specifically, age, income, and gender emerged as significant predictors of Internet perceptions. Individuals with more resources (e.g., money, knowledge), such as those with higher income, older participants, and men, tended to have higher perceptions of their Internet and ISPs. Further, policy needs to address the social and demographic barriers to technology adoption in addition to Internet access and use barriers. Energy justice policy59 may be a fruitful starting place to develop Internet policy that is socially just given the similarities between the two services. Many federal and state broadband policies are working toward this goal with the Internet (i.e., Digital Equity Act, Affordable Connectivity Program); however, policy tends to ignore the role gender and age play on vulnerability to address income. The present study shows that women and younger adults are also excluded from the digital world more than their counterparts, which is an important distributional and procedural justice issue.60

## FOOTNOTES

1.

Perrin and Atske, “About Three-in-Ten U.S. Adults Say They Are ‘almost Constantly’ Online.”

2.

U.S. Census Bureau, “Computer and Internet Use in the United States: 2018.”

3.

Auxier and Anderson, “Social Media Use in 2021.”

4.

Marshall, Shannon, and Love, “How Teachers Experienced the COVID-19 Transition to Remote Instruction”; Reddick et al., “Determinants of Broadband Access and Affordability: An Analysis of a Community Survey on the Digital Divide”; Lai and Widmar, “Revisiting the Digital Divide in the COVID-19 Era.”

5.

Ghani and Khan, “The Internet as We See It: Gendered Perceptions from Pakistan.”

6.

Colmar Brunton, “Perceptions of the Internet 2020.”

7.

Holtz, “Patients Perceptions of Telemedicine Visits before and after the Coronavirus Disease 2019 Pandemic.”

8.

Kaleta and Mahadevan, “Examining Differences in Perceptions of Trust, Privacy and Risk in Home and Public Wi-Fi Internet Channels.”

9.

Hale and Kvedar, “Privacy and Security Concerns in Telehealth”; Shachar, Engel, and Elwyn, “Implications for Telehealth in a Postpandemic Future: Regulatory and Privacy Issues.”

10.

Mekovec and Hutinski, “The Role of Perceived Privacy and Perceived Security in Online Market”; Omariba, Masese, and Wanyembi, “Security and Privacy of Electronic Banking.”

11.

Horne et al., “Privacy, Technology, and Norms: The Case of Smart Meters”; McKenna, Richardson, and Thomson, “Smart Meter Data: Balancing Consumer Privacy Concerns with Legitimate Applications.”

12.

Colmar Brunton, “Perceptions of the Internet 2020.”

13.

Dingel and Neiman, “How Many Jobs Can Be Done at Home?”

14.

Atalay and Tekdemir Yurtdaş, “The Study of the Perceptions of Internet and Social Media among Adolescents and Problematic Use of Internet”; Siste et al., “The Impact of Physical Distancing and Associated Factors Towards Internet Addiction Among Adults in Indonesia During COVID-19 Pandemic: A Nationwide Web-Based Study.”

15.

Kaleta and Mahadevan, “Examining Differences in Perceptions of Trust, Privacy and Risk in Home and Public Wi-Fi Internet Channels.”

16.

Katzer, Fetchenhauer, and Belschak, “Cyberbullying: Who Are the Victims?”

17.

Siste et al., “The Impact of Physical Distancing and Associated Factors Towards Internet Addiction Among Adults in Indonesia During COVID-19 Pandemic: A Nationwide Web-Based Study.”

18.

McNabb, “Chapter 1: Public Utilities: Essential Services, Critical Infrastructure.”

19.

Vogels et al., “53% of Americans Say the Internet Has Been Essential During the COVID-19 Outbreak.”

20.

Vogels et al.

21.

Zittrain, The Future of the Internet—And How to Stop It; Thierer, “‘Net Neutrality’ Digital Discrimination or Regulatory Gamesmanship in Cyberspace?”; Downes, “Why the Public Utility Model Is the Wrong Approach for Internet Regulation.”

22.

Downes, “Why the Public Utility Model Is the Wrong Approach for Internet Regulation.”

23.

Paulas, “Why American Internet Should Be a Public Utility.”

24.

Plaiss, “From Natural Monopoly to Public Utility: Technological Determinism and the Political Economy of Infrastructure in Progressive-Era America.”

25.

Stewart, “America’s Monopoly Problem, Explained by Your Internet Bill.”

26.

Federal Communications Commission, “Fourteenth Broadband Deployment Report.”

27.

Callahan, “AT&T’s Digital Redlining of Cleveland.”

28.

Tomer et al., “Digital Prosperity: How Broadband Can Deliver Health and Equity to All Communities.”

29.

Tomer et al.; Vogels et al., “53% of Americans Say the Internet Has Been Essential During the COVID-19 Outbreak.”

30.

U.N. Human Rights Council, “The Promotion, Protection and Enjoyment of Human Rights on the Internet.”

31.

Chen et al., “When Disadvantage Collides: The Concentrated Disadvantage Effects on Energy Insecurity and Internet Burdens in the U.S.”; Conference, “The Connectivity Gap : The Internet Is Still Out of Reach for Many Low-Income Renters.”

32.

Chen et al., “When Disadvantage Collides: The Concentrated Disadvantage Effects on Energy Insecurity and Internet Burdens in the U.S.”

33.

Reddick et al., “Determinants of Broadband Access and Affordability: An Analysis of a Community Survey on the Digital Divide”; Lai and Widmar, “Revisiting the Digital Divide in the COVID-19 Era.”

34.

Field, Discovering Statistics Using SPSS.

35.

Anderson and Kumar, “Digital Divide Persists Even as Lower-Income Americans Make Gains in Tech Adoption”; Apptegy, “Equity and Technology: Why Mobile Communication Matters for Low Income Families.”

36.

Petts, Carlson, and Pepin, “A Gendered Pandemic: Childcare, Homeschooling, and Parents’ Employment during COVID-19.”

37.

Xue and McMunn, “Gender Differences in Unpaid Care Work and Psychological Distress in the UK Covid-19 Lockdown.”

38.

Hale, Angrist, Goldszmidt, Kira, Petherick, Phillips, Webster, Cameron-Blake, Hallas, Majumdar, and Tatlow. (2021). “A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker).” Nature Human Behaviour. https://doi.org/10.1038/s41562-021-01079-8

39.

Chiou and Tucker, “Social Distancing, Internet Access and Inequality”; Gould and Shierholz, “Not Everybody Can Work from Home: Black and Hispanic Workers Are Much Less Likely to Be Able to Telework”; Reed et al., “Patient Characteristics Associated With Choosing a Telemedicine Visit vs Office Visit With the Same Primary Care Clinicians.”

40.

Vogels et al., “53% of Americans Say the Internet Has Been Essential During the COVID-19 Outbreak.”

41.

Monast, “Electricity Competition and the Public Good: Rethinking Markets and Monopolies.”

42.

National Regulatory Research Institute, “PURPA Tracker.”

43.

U.S. Energy Information Administration, “North Carolina Has More PURPA-Qualifying Solar Facilities than Any Other State.”

44.

Legal Information Institute, “Internet Service Provider (ISP).”

45.

Rahman, “Infrastructural Regulation and the New Utilities”; Sohn, “Social Justice or Inequality: The Heart of the Net Neutrality Debate.”

46.

Morton, “Net Neutrality 2021 Legislation”; Linebaugh, “Net Neutrality Law: An Overview.”

47.

Federal Communications Commission, “Affordable Connectivity Program.”

48.

Perez, “Over 10 Million Households Enroll in Affordable Connectivity Program.”

49.

50.

Tenn. Code § 65-25-134; Assembly Bill No. 14 Chapter 658; Bradford, SB 743; Buckner, HB 2384.

51.

Federal Communications Commission, “Affordable Connectivity Program.”

52.

The White House, “Fact Sheet: President Biden and Vice President Harris Reduce High-Speed Internet Costs for Millions of Americans”; Powell, Bryne, and Dailey, “The Essential Internet: Digital Exclusion in Low-Income American Communities”; Jaeger et al., “The Intersection of Public Policy and Public Access: Digital Divides, Digital Literacy, Digital Inclusion, and Public Libraries.”

53.

Powell, Bryne, and Dailey, “The Essential Internet: Digital Exclusion in Low-Income American Communities”; Jaeger et al., “The Intersection of Public Policy and Public Access: Digital Divides, Digital Literacy, Digital Inclusion, and Public Libraries.”

54.

National Digital Inclusion Alliance, “Digital Equity Act.”

55.

Callahan, “AT&T’s Digital Redlining of Cleveland.”

56.

Monast, “Electricity Competition and the Public Good: Rethinking Markets and Monopolies”; Schmit and Severson, “Exploring the Feasibility of Rural Broadband Cooperatives in the United States: The New New Deal?”; Jaeger et al., “The Intersection of Public Policy and Public Access: Digital Divides, Digital Literacy, Digital Inclusion, and Public Libraries.”

57.

Jaeger et al., “The Intersection of Public Policy and Public Access: Digital Divides, Digital Literacy, Digital Inclusion, and Public Libraries”; Powell, Bryne, and Dailey, “The Essential Internet: Digital Exclusion in Low-Income American Communities.”

58.

Chou et al., “Cross-Country Review of Smart Grid Adoption in Residential Buildings.”

59.

Baker, Devar, and Prakesh, “The Energy Justice Workbook”; Sovacool and Dworkin, “Energy Justice: Conceptual Insights and Practical Applications.”

60.

Jenkins et al., “Energy Justice: A Conceptual Review”; Simcock, Frankowski, and Bouzarovski, “Rendered Invisible: Institutional Misrecognition and the Reproduction of Energy Poverty”; Chen et al., “Localized Energy Burden, Concentrated Disadvantage, and the Feminization of Energy Poverty.”

## BIBLIOGRAPHY

“Digital Divide Persists Even as Lower-Income Americans Make Gains in Tech Adoption.”
Pew Research Center Fact Tank
2019
Apptegy
.
“Equity and Technology: Why Mobile Communication Matters for Low Income Families.”
2021
. https://www.apptegy.com/guides/the-importance-of-mobile-for-low-income-families/.
Assembly Bill No. 14 Chapter 658
.
2021
. https://leginfo.legislature.ca.gov/faces/billNavClient.xhtml.
Atalay, Mercan, and Göklem Tekdemir Yurtdaş.
“The Study of the Perceptions of Internet and Social Media among Adolescents and Problematic Use of Internet.”
Journal of Human Sciences
17
, no.
1
(
2020
):
65
78
. https://doi.org/10.14687/jhs.v17i1.5674.
Auxier, Brooke, and Monica Anderson.
“Social Media Use in 2021.”
Pew Research Center
2021
. https://www.pewresearch.org/internet/2021/04/07/social-media-use-in-2021/.
Baker, Shalanda, Subin Devar, and Shiva Prakesh.
“The Energy Justice Workbook.”
2019
Callahan, Bill.
“AT&T’s Digital Redlining of Cleveland.”
2017
. https://www.digitalinclusion.org/blog/2017/03/10/atts-digital-redlining-of-cleveland/.
Chen, Chien-fei, Jimmy Feng, Nikki Luke, Cheng-Pin Kuo, and Joshua S. Fu.
“Localized Energy Burden, Concentrated Disadvantage, and the Feminization of Energy Poverty.”
IScience
25
, no.
4
(
April
2022
):
104139
. https://doi.org/10.1016/j.isci.2022.104139.
Chiou, Lesley, and Catherine Tucker.
“Social Distancing, Internet Access and Inequality.”
Cambridge, MA
,
2020
. https://doi.org/10.3386/w26982.
Chou, Jui-Sheng, Changwan Kim, Thanh-Khiet Ung, I Gusti Ayu Novi Yutami, Guo-Tai Lin, and Hyojoo Son.
“Cross-Country Review of Smart Grid Adoption in Residential Buildings.”
Renewable and Sustainable Energy Reviews
48
(
2015
):
192
213
. https://doi.org/10.1016/j.rser.2015.03.055.
Colmar, Brunton.
“Perceptions of the Internet 2020.”
2020
Cornell Law. Legal Information Institute: Public Utility
.
2020
. https://www.law.cornell.edu/wex/public_utility.
Dingel, Jonathan I., and Brent Neiman.
“How Many Jobs Can Be Done at Home?”
Journal of Public Economics
189
, no.
June
(
2020
). https://doi.org/10.1016/j.jpubeco.2020.104235.
Downes, Larry.
“Why the Public Utility Model Is the Wrong Approach for Internet Regulation.”
2014
. https://hbr.org/2014/11/why-the-public-utility-model-is-the-wrong-approach-for-internet-regulation.
Federal Communications Commission
.
“Affordable Connectivity Program.”
2022
. https://www.fcc.gov/acp.
________.
2022
Field, Andy.
Discovering Statistics Using SPSS
,
London, England
:
SAGE Publications
,
2009
.
ISBN: 978184879073
.
“The Internet as We See It: Gendered Perceptions from Pakistan.”
2020
. https://www.apc.org/en/pubs/internet-we-see-it-gendered-perceptions-pakistan.
Gould, Elise, and Heidi Shierholz.
“Not Everybody Can Work from Home: Black and Hispanic Workers Are Much Less Likely to Be Able to Telework.”
Economic Policy Institute
2020
. https://www.epi.org/blog/black-and-hispanic-workers-are-much-less-likely-to-be-able-to-work-from-home/.
Hale, Timothy M., and Joseph C. Kvedar.
“Privacy and Security Concerns in Telehealth.”
Virtual Mentor
16
, no.
12
(
2014
):
981
85
. https://doi.org/10.1001/virtualmentor.2014.16.12.jdsc1-1412.
Hale, Thomas, Noam Angrist, Rafael Goldszmidt, Beatriz Kira, Anna Petherick, Toby Phillips, Samuel Webster, Emily Cameron-Blake, Laura Hallas, Majumdar Saptarshi, and Tatlow Helen.
“A Global Panel Database of Pandemic Policies (Oxford COVID-19 Government Response Tracker.”
Nature Human Behaviour
(
2022
). https://doi.org/10.1038/s41562-021-01079-8.
Holtz, Bree E.
“Patients Perceptions of Telemedicine Visits before and after the Coronavirus Disease 2019 Pandemic.”
Telemedicine and E-Health
27
, no.
1
(
2021
). https://doi.org/10.1089/tmj.2020.0168.
Horne, Christine, Brice Darras, Elyse Bean, Anurag Srivastava, and Scott Frickel.
“Privacy, Technology, and Norms: The Case of Smart Meters.”
Social Science Research
51
(
2015
):
64
76
. https://doi.org/10.1016/j.ssresearch.2014.12.003.
Jaeger, Paul T, John Carlo Bertot, Kim M. Thompson, Sarah M. Katz, and Elizabeth J. Decoster.
“The Intersection of Public Policy and Public Access: Digital Divides, Digital Literacy, Digital Inclusion, and Public Libraries.”
Public Library Quarterly
31
, no.
1
(
2012
):
1
20
. https://doi.org/10.1080/01616846.2012.654728.
Jenkins, Kirsten, Darren McCauley, Raphael Heffron, Hannes Stephan, and Robert Rehner.
“Energy Justice: A Conceptual Review.”
Energy Research and Social Science
11
(
2016
):
174
82
Judken, Joel.
“Potential Impacts of Privatizing the Tennessee Valley Authority.”
Economic Policy Institute, Briefing Paper #402
June
4
,
2015
. https://files.epi.org/pdf/86240.pdf.
Kaleta, Jeffrey P., and Lakshman Mahadevan.
“Examining Differences in Perceptions of Trust, Privacy and Risk in Home and Public Wi-Fi Internet Channels.”
Journal of Systems and Information Technology
12
, no.
3
(
2020
):
265
87
. https://doi.org/10.1108/JSIT-04-2019-0075.
Katzer, Catarina, Detlef Fetchenhauer, and Frank Belschak.
“Cyberbullying: Who Are the Victims?”
Journal of Media Psychology
21
, no.
1
(
January
1
,
2009
):
25
36
. https://doi.org/10.1027/1864-1105.21.1.25.
Lai, John, and Nicole O Widmar.
“Revisiting the Digital Divide in the COVID-19 Era.”
Applied Economic Perspectives and Policy
.
43
, no.
1
(
October
3
,
2020
):
458
64
. https://doi.org/10.1002/aepp.13104.
Legal Information Institute
.
“Internet Service Provider (ISP).”
2020
. https://www.law.cornell.edu/wex/internet_service_provider_(isp).
Linebaugh, Chris D.
“Net Neutrality Law: An Overview.”
2021
. https://crsreports.congress.gov/product/pdf/R/R46973.
Marshall, David T., David M. Shannon, and Savanna M. Love.
“How Teachers Experienced the COVID-19 Transition to Remote Instruction.”
Phi Delta Kappan
102
, no.
3
(
October
26
,
2020
):
46
50
. https://doi.org/10.1177/0031721720970702.
McKenna, Eoghan, Ian Richardson, and Murray Thomson.
“Smart Meter Data: Balancing Consumer Privacy Concerns with Legitimate Applications.”
Energy Policy
41
(
2012
):
807
14
. https://doi.org/10.1016/j.enpol.2011.11.049.
McNabb, David E.
Chapter 1: Public Utilities: Essential Services, Critical Infrastructure
.
Cheltenham, UK
:
Edward Elgar Publishing
,
2016
. https://doi.org/10.4337/9781785365539.00007.
Mekovec, R., and Hutinski Ž. “The Role of Perceived Privacy and Perceived Security in Online Market.”
2012 Proceedings of the 35th International Convention MIPRO
2012
,
1549
54
. https://ieeexplore.ieee.org/document/6240857.
Monast, Jonas J.
“Electricity Competition and the Public Good: Rethinking Markets and Monopolies.”
90
, no.
3
(
2019
):
667
729
. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3275084.
Morton, Heather.
“Net Neutrality 2021 Legislation.”
National Conference of State Legislatures
2022
. https://www.ncsl.org/research/telecommunications-and-information-technology/net-neutrality-2021-legislation.aspx.
National Digital Inclusion Alliance
.
“Digital Equity Act.”
2021
. https://www.digitalequityact.org/.
National Regulatory Research Institute
.
“PURPA Tracker.”
2020
. https://www.naruc.org/nrri/nrri-activities/purpa-tracker/.
Omariba, Zachary B., Masese Nelson B., and G. Wanyembi.
“Security and Privacy of Electronic Banking.”
International Journal of Computer Science Issues
9
, no.
4
(
2012
). https://www.proquest.com/docview/1055223465.
Paulas, Rick.
“Why American Internet Should Be a Public Utility.”
Pacific Standard
2017
. https://psmag.com/news/why-american-internet-should-be-a-public-utility.
Perez, Paloma.
“Over 10 Million Households Enroll in Affordable Connectivity Program.”
2022
. https://www.fcc.gov/document/over-10-million-households-enrolled-affordable-connectivity-program.
Perrin, Andrew, and Sara Atske.
Pew Research Center
2021
Petts, Richard J., Daniel L. Carlson, and Joanna R. Pepin.
“A Gendered Pandemic: Childcare, Homeschooling, and Parents’ Employment during COVID-19.”
Gender, Work & Organization
28
, no.
S2
(
July
1
,
2021
):
515
34
. https://doi.org/10.1111/gwao.12614.
“From Natural Monopoly to Public Utility: Technological Determinism and the Political Economy of Infrastructure in Progressive-Era America.”
Technology and Culture
57
, no.
4
(
2016
):
806
30
.
Powell, Alison, Amelia Bryne, and Dharma Dailey.
“The Essential Internet: Digital Exclusion in Low-Income American Communities.”
Policy & Internet
2
, no.
2
(
2010
):
159
90
. https://doi.org/10.2202/1944-2866.1058.
Rahman, K. Sabeel.
“Infrastructural Regulation and the New Utilities.”
Yale Journal on Regulation
35
(
2018
):
911
39
. https://brooklynworks.brooklaw.edu/cgi/viewcontent.cgi?article=2031&context=faculty.
Reddick, Christopher G., Roger Enriquez, Richard J. Harris, and Bonita Sharma.
“Determinants of Broadband Access and Affordability: An Analysis of a Community Survey on the Digital Divide.”
Cities
106
(
2020
). 102904. https://www.sciencedirect.com/science/article/pii/S026427512031252X.
Reed, Mary E., Jie Huang, Ilana Graetz, Catherine Lee, Emilie Muelly, Chris Kennedy, and Eileen Kim.
“Patient Characteristics Associated with Choosing a Telemedicine Visit vs Office Visit with the Same Primary Care Clinicians.”
JAMA Network Open
3
, no.
6
(
June
17
,
2020
):
e205873
e205873
. https://doi.org/10.1001/jamanetworkopen.2020.5873.
Schmit, Todd M., and Roberta M. Severson.
“Exploring the Feasibility of Rural Broadband Cooperatives in the United States: The New New Deal?”
Telecommunications Policy
45
, no.
4
(
2021
):
102114
. https://doi.org/10.1016/j.telpol.2021.102114.
Shachar, Carmel, Jaclyn Engel, and Glyn Elwyn.
“Implications for Telehealth in a Postpandemic Future: Regulatory and Privacy Issues.”
JAMA
323
, no.
23
(
June
16
,
2020
):
2375
76
. https://doi.org/10.1001/jama.2020.7943.
Simcock, Neil, Jan Frankowski, and Stefan Bouzarovski.
“Rendered Invisible: Institutional Misrecognition and the Reproduction of Energy Poverty.”
Geoforum
124
, no.
April
(
2021
):
1
9
. https://doi.org/10.1016/j.geoforum.2021.05.005.
Siste, Kristiana, Enjeline Hanafi, Lee Thung Sen, Hans Christian, Adrian, Levina Putri Siswidiani, Albert Prabowo Limawan, Belinda Julivia Murtani, and Christiany Suwartono.
“The Impact of Physical Distancing and Associated Factors Towards Internet Addiction Among Adults in Indonesia During COVID-19 Pandemic: A Nationwide Web-Based Study.”
Frontiers in Psychiatry
11
, no.
September
(
2020
):
1
11
. https://doi.org/10.3389/fpsyt.2020.580977.
Sohn, Gigi B. “Social Justice or Inequality: The Heart of the Net Neutrality Debate.”
University of Pittsburgh Law Review 80th Publishing Anniversary Symposium: The Net Without Neutrality: Economic, Regulatory and Informational Access Impacts
Pittsburgh, PA
,
2019
. https://www.benton.org/blog/social-justice-or-inequality-heart-net-neutrality-debate.
Sovacool, Benjamin K, and Michael H. Dworkin.
“Energy Justice: Conceptual Insights and Practical Applications.”
Applied Energy
(
2015
). https://doi.org/10.1016/j.apenergy.2015.01.002.
Stewart, Emily.
“America’s Monopoly Problem, Explained by Your Internet Bill.”
Vox Media
,
2020
The White House
.
“Fact Sheet: President Biden and Vice President Harris Reduce High-Speed Internet Costs for Millions of Americans.”
2022
. https://www.whitehouse.gov/briefing-room/statements-releases/2022/05/09/fact-sheet-president-biden-and-vice-president-harris-reduce-high-speed-internet-costs-for-millions-of-americans/.
“‘Net Neutrality’ Digital Discrimination or Regulatory Gamesmanship in Cyberspace?”
Cato Policy Analysis Series
no.
507
(
2004
):
28
. https://www.cato.org/sites/cato.org/files/pubs/pdf/pa507.pdf.
Tomer, Adie, Lara Fishbane, Angela Siefer, and Bill Callahan.
“Digital Prosperity: How Broadband Can Deliver Health and Equity to All Communities.”
Brookings
2020
U.N. Human Rights Council
.
“The Promotion, Protection and Enjoyment of Human Rights on the Internet.”
2016
. https://digitallibrary.un.org/record/845728.
U.S. Census Bureau
.
“Computer and Internet Use in the United States: 2018.”
2021
. https://www.census.gov/newsroom/press-releases/2021/computer-internet-use.html.
.
“North Carolina Has More PURPA-Qualifying Solar Facilities than Any Other State.”
2016
. https://www.eia.gov/todayinenergy/detail.php?id=27632.
________.
“State Electricity Profiles.”
2021
. https://www.eia.gov/electricity/state/.
Veigle, Anne.
“FCC Adopts Rules to Give Tenants in Apartments and Office Buildings More Transparency, Competition and Choice for Broadband Service.”
2022
. https://docs.fcc.gov/public/attachments/DOC-380316A1.pdf.
Vogels, Emily A, Andrew Perrin, Lee Rainie, and Monica Anderson.
“53% of Americans Say the Internet Has Been Essential During the COVID-19 Outbreak.”
Pew Research Center
2020
. https://www.pewresearch.org/internet/2020/04/30/53-of-americans-say-the-internet-has-been-essential-during-the-covid-19-outbreak/.
Xue, Baowen, and Anne McMunn.
“Gender Differences in Unpaid Care Work and Psychological Distress in the UK Covid-19 Lockdown.”
PLOS ONE
16
, no.
3
(
March
4
,
2021
):
e0247959
. https://doi.org/10.1371/journal.pone.0247959.
Zittrain, Jonathan L.
“The Future of the Internet—And How to Stop It.”
2009
.
This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.