With the rapid adoption of telehealth services, health measures are increasingly tied to internet access. However, there is limited literature on how internet connectivity correlates to health measures and health care utilization. We examined the links between willingness to pay (WTP) for internet services and health measures, namely, health insurance type, general health, physical health, mental health, health literacy, and cost/transportation barriers to health care access.
Methods:
We designed a custom survey and collected 5,200 responses in four U.S. states. We implemented a stratified conjoint analysis to estimate the WTP for internet speed and quality and its connections to health measures.
Results:
Among insurance plan types, Medicare beneficiaries had the lowest WTP—lower than the uninsured. Respondents with more days of poor mental health had significantly higher WTP. We found no links between health literacy and WTP. Those who delayed care due to transportation barriers had lower WTP. Respondents who did not disclose health information consistently had lower WTP.
Discussions:
We documented strong ties between health measures and WTP for internet services. At a time when health care utilization increasingly depends on internet connectivity, policymakers must identify and plan for technological factors that could hinder health care utilization or jeopardize population longevity and health.
LeeJ. Delayed or forgone care and virtual care in a public health crisis using nationally representative population data. Telemed J E Health, 2023; 29(2):222–234; doi: 10.1089/tmj.2022.0138
2.
CelebiMY, KıymetE, Elif BöncüoğluE, et al.Follow-up of cases with the telemedicine method during the COVID-19 pandemic: An alternative strategy for reduction of hospital workload and hospital-related transmissions. Telemed J E Health, 2023; 29(3):454–458; doi: 10.1089/tmj.2021.0613
3.
PatelH, HassellA, KenistonA, et al.Impact of remote patient monitoring on length of stay for patients with COVID-19. Telemed J E Health, 2023; 29(2):298–303; doi: 10.1089/tmj.2021.0510
4.
RivlinK, Brenner-LevoyJ, OdumT, et al.Provider mistrust and telemedicine abortion care preferences among patients in Ohio, West Virginia, and Kentucky. Telemed J E Health, 2023; 29(3):414–424; doi: 10.1089/tmj.2022.0101
5.
TalalAH, SofikitouEM, WangK, et al.High satisfaction with patient-centered telemedicine for hepatitis C virus delivered to substance users: A mixed-methods study. Telemed J E Health, 2023; 29(3):395–407; doi: 10.1089/tmj.2022.0189
6.
ThawaniSP, MinenMT, StainmanRS, et al.Neurologists’ evaluations of experience and effectiveness of teleneurology encounters. Telemed J E Health, 2023; 29(3):442–453; doi: 10.1089/tmj.2021.0551
7.
AdamsAM, WuH, ZhangFR, et al.Postpartum care in the time of COVID-19: The use of telemedicine for postpartum care. Telemed J E Health, 2023; 29(2):235–241; doi: 10.1089/tmj.2022.0065
8.
RabbaniM, BogulskiCA, AllisonK, et al.Telehealth and willingness to pay for internet services. Telemed J E Health, 2024; 30(7):1880–1891; doi: 10.1089/tmj.2023.0692
9.
BradfordWD, KleitA, Krousel-WoodMA, et al.Comparing willingness to pay for telemedicine across a chronic heart failure and hypertension population. Telemed J E Health, 2005; 11(4):430–438; doi: 10.1089/tmj.2005.11.430
10.
RabbaniM, BogulskiCA, EswaranH, et al.Willingness to pay for internet services: A stratified analysis by socioeconomic characteristics. SSRN, 2025; 5223917; doi: 10.2139/ssrn.5223917Doi
11.
RabbaniM. Internet price, speed, and disparity: The case of rural health care providers in the United States. Telecomm Pol, 2024; 48(2):102674; doi: 10.1016/j.telpol.2023.102674
12.
RabbaniM. Dollars and megabits: A comparative analysis of telecom and health care connect fund. Inform Econ Policy, 2024; 67:101082; doi: 10.1016/j.infoecopol.2024.101082
13.
RabbaniM, BogulskiCA, EswaranH, et al.Willingness to pay for internet speed and quality. Telematics Informatics, 2024; 93:102173; doi: 10.1016/j.tele.2024.102173
14.
DiamantAL, HaysRD, MoralesLS, et al.Delays and unmet need for health care among adult primary care patients in a restructured urban public health system. Am J Public Health, 2004; 94(5):783–789; doi: 10.2105/AJPH.94.5.783
15.
ChewLD, GriffinJM, PartinMR, et al.Validation of screening questions for limited health literacy in a large VA outpatient population. J Gen Intern Med, 2008; 23(5):561–566; doi: 10.1007/s11606-008-0520-5
BridgesJFP, HauberAB, MarshallD, et al.Conjoint analysis applications in health—a checklist: A report of the ISPOR good research practices for conjoint analysis task force. Val Health, 2011; 14(4):403–413; doi: 10.1016/j.jval.2010.11.013
20.
LeeHJ, WhitacreB. Estimating willingness-to-pay for broadband attributes among low-income consumers: Results from two FCC Lifeline pilot projects. Telecomm Pol, 2017; 41(9):769–780; doi: 10.1016/j.telpol.2017.04.001
21.
TayyaranMR, KhanAM, AndersonDA. Impact of telecommuting and intelligent transportation systems on residential location choice. Transport Planning Technol, 2003; 26(2):171–193; doi: 10.1080/715020598
22.
PratiktoFR. Uncapacitated pricing optimization for mobile broadband services. jti, 2018; 20(1):49–58; doi: 10.9744/jti.20.1.49-58
23.
DaudaSY, LeeJ. Technology adoption: A conjoint analysis of consumers’ preference on future online banking services. Inform Syst, 2015; 53:1–15; doi: 10.1016/j.is.2015.04.006
24.
BendellBL, SullivanDM, HanekKJ. Gender, technology and decision-making: Insights from an experimental conjoint analysis. IJEBR, 2020; 26(4):647–670; doi: 10.1108/IJEBR-04-2019-0232
25.
ShinH-C, CallowM, DadvarS, et al.User acceptance and willingness to pay for connected vehicle technologies: Adaptive choice-based conjoint analysis. Transport Res Rec, 2015; 2531(1):54–62; doi: 10.3141/2531-07
26.
BorgersA, SnellenD, PoelmanJ, et al.Preferences for car-restrained residential areas. J Urban Design, 2008; 13(2):257–267; doi: 10.1080/13574800801965734
27.
Van HeckeL, GhekiereA, CauwenbergJV, et al.Park characteristics preferred for adolescent park visitation and physical activity: A choice-based conjoint analysis using manipulated photographs. Landsc Urban Plann, 2018; 178:144–155; doi: 10.1016/j.landurbplan.2018.05.017
28.
WangD, LiS-M. Housing preferences in a transitional housing system: The case of Beijing, China. Environ Plan A, 2004; 36(1):69–87; doi: 10.1068/a35263
ClarkB, StewartGB, PanzoneLA, et al.Citizens, consumers and farm animal welfare: A meta-analysis of willingness-to-pay studies. Food Pol, 2017; 68:112–127; doi: 10.1016/j.foodpol.2017.01.006
31.
HainmuellerJ, HopkinsDJ. The hidden American immigration consensus: A conjoint analysis of attitudes toward immigrants. American J Political Sci, 2015; 59(3):529–548; doi: 10.1111/ajps.12138
32.
Guti′errez-RomeroR, LeBasA. Does electoral violence affect vote choice and willingness to vote? conjoint analysis of a vignette experiment. J Peace Res, 2020; 57(1):77–92; doi: 10.1177/0022343319892677
33.
LeeS, LeeSY, RyuMH. How much are sellers willing to pay for the features offered by their e-commerce platform? Telecomm Pol, 2019; 43(10):101832; doi: 10.1016/j.telpol.2019.101832
34.
Isabel SchäufeleI, HammU. Consumers’ perceptions, preferences and willingness-to-pay for wine with sustainability characteristics: A review. J Cleaner Product, 2017; 147:379–394; doi: 10.1016/j.jclepro.2017.01.118
35.
KrystallisA, FotopoulosC, ZotosY. Organic consumers’ profile and their willingness to pay (wtp) for selected organic food products in Greece. J Int Consumer Mark, 2006; 19(1):81–106; doi: 10.1300/J046v19n01_05
36.
KimJ, NamC, RyuMH. What do consumers prefer for music streaming services? A comparative study between Korea and US. Telecomm Pol, 2017; 41(4):263–272; doi: 10.1016/j.telpol.2017.01.008
37.
GutierrezJI, DubovA, AlticeF, et al.Measuring PrEP preferences among U.S. military men who have sex with men: Results of an adaptive choice based conjoint analysis study. Res Sq, 2021; doi: 10.21203/rs.3.rs-62569/v3
38.
SuryanegaraM, BukhariPA. Fixed mobile convergence (FMC) services in Indonesia: A conjoint analysis of customer preference. In: 2018 18th International Symposium on Communications and Information Technologies (ISCIT), Bangkok, Thailand. IEEE; 2018, pp. 195–198; doi: 10.1109/ISCIT.2018.8587995
39.
OgboE, BrownT, GantJ, et al.The impact of OTT on preferences for mobile services: A conjoint analysis of users in Nigeria. In: TPRC48: The 48th research conference on communication, information and internet policy, virtual conference, 2020. Available from: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3749749
40.
BolarinwaOA, AmehS, OchimanaC, et al.Willingness and ability to pay for healthcare insurance: A cross-sectional study of seven communities in East and West Africa (Sevencewa). PLOS Glob Public Health, 2021; 1(11):e0000057; doi: 10.1371/journal.pgph.0000057
RogersRD, MoellerFG, SwannAC, et al.Recent research on impulsivity in individuals with drug use and mental health disorders: Implications for alcoholism. Alcohol Clin Exp Res, 2010; 34(8):1319–1333; doi: 10.1111/j.1530-0277.2010.01216.x
43.
HunterLE, MeerEA, GillanCM, et al.Excessive deliberation in social anxiety. Nature Human Behav, 2021; 6(1):146; doi: 10.1038/s41562-021-01180-y
44.
TarafdarM, MaierC, LaumerS, et al.Explaining the link between technostress and technology addiction for social networking sites: A study of distraction as a coping behavior. Inform Syst J, 2020; 30(1):96–124; doi: 10.1111/isj.12253
45.
LebniJY, ToghroliR, AbbasJ, et al.A study of internet addiction and its effects on mental health: A study based on Iranian university students. J Educ Health Promot, 2020; 9(1):205; doi: 10.4103/jehp.jehp_148_20
46.
OtsukaY, KaneitaY, ItaniO, et al.Relationship between internet addiction and poor mental health among Japanese adolescents. Iran J Public Health, 2020; 49(11):2069–2077; doi: 10.18502/ijph.v49i11.4722
47.
ErgunN, ÖzkanZ, GriffithsMD. Social media addiction and poor mental health: Examining the mediating roles of internet addiction and phubbing. Psychol Rep, 2025; 128(2):723–743; doi: 10.1177/00332941231166609
48.
FosterS, O’MealeyM. Socioeconomic status and mental illness stigma: The impact of mental illness controllability attributions and personal responsibility judgments. J Ment Health, 2022; 31(1):58–65; doi: 10.1080/09638237.2021.1875416
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
