This web map visualizes the prevalence of households in a given geography that do not own a computer, smartphone, or tablet. Data are shown by tract, county, and state boundaries -- zoom out to see data visualized for larger geographies. The map also displays the boundary lines for the jurisdiction of Rochester, NY (visible when viewing the tract level data), as this map was created for a Rochester audience.This web map draws from an Esri Demographics service that is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2014-2018ACS Table(s): B28001, B28002 (Not all lines of ACS table B28002 are available in this feature layer)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 19, 2019National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -555555...) have been set to null. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small. NOTE: any calculated percentages or counts that contain estimates that have null margins of error yield null margins of error for the calculated fields.

In 2021, the global annual cellular data usage is projected to reach roughly *** thousand petabytes (PB), with approximately *** thousand petabytes coming from the use of mobile handsets, in other words, mobile phones. Tablets and cellular IoT devices currently do not compare to mobile phones in terms of data usage, but they are expected to grow in the upcoming years.

SELECTED SOCIAL CHARACTERISTICS IN THE UNITED STATES COMPUTERS AND INTERNET USE - DP02 Universe - Total households Survey-Program - American Community Survey 5-year estimates Years - 2020, 2021, 2022 The 2008 Broadband Improvement Act mandated the collection of data about computer and internet use. As a result, three questions were added to the 2013 American Community Survey (ACS) to measure these topics. The computer use question asked if anyone in the household owned or used a computer and included four response categories for a desktop or laptop, a smartphone, a tablet or other portable wireless computer, and some other type of computer. Respondents selected a checkbox for “Yes” or “No” for each response category. Respondents could select all categories that applied. Question asked if any member of the household has access to the internet. “Access” refers to whether or not someone in the household uses or can connect to the internet, regardless of whether or not they pay for the service. If a respondent answers “Yes, by paying a cell phone company or Internet service provider”, they are asked to select the type of internet service.

Ziel dieser Studie war es, den Einfluss verschiedener Anreizsysteme auf die Bereitschaft zur Teilnahme an der passiven mobilen Datenerfassung unter deutschen Smartphone-Besitzern experimentell zu messen. Die Daten stammen aus einer Webumfrage unter deutschen Smartphone-Nutzern ab 18 Jahren, die aus einem deutschen, nicht wahrscheinlichen Online-Panel rekrutiert wurden. Im Dezember 2017 beantworteten 1.214 Teilnehmer einen Fragebogen zu den Themen Smartphone-Nutzung und -Fähigkeiten, Datenschutz und Sicherheit, allgemeine Einstellungen gegenüber der Umfrageforschung und Forschungseinrichtungen. Darüber hinaus enthielt der Fragebogen ein Experiment zur Bereitschaft, an der mobilen Datenerhebung unter verschiedenen Anreizbedingungen teilzunehmen. Themen: Besitz von Smartphone, Handy, Desktop- oder Laptop-Computer, Tablet-Computer und/oder E-Book-Reader; Art des Smartphones; Bereitschaft zur Teilnahme an der mobilen Datenerfassung unter verschiedenen Anreizbedingungen; Wahrscheinlichkeit des Herunterladens der App zur Teilnahme an dieser Forschungsstudie; Befragter möchte lieber an der Studie teilnehmen, wenn er 100 Euro erhalten könnte; Gesamtbetrag, den der Befragte für die Teilnahme an der Studie verdienen müsste (offene Antwort); Grund, warum der Befragte nicht an der Forschungsstudie teilnehmen würde; Bereitschaft zur Teilnahme an der Studie für einen Anreiz von insgesamt 60 Euro; Bereitschaft zur Aktivierung verschiedener Funktionen beim Herunterladen der App (Interaktionshistorie, Smartphone-Nutzung, Merkmale des sozialen Netzwerks, Netzqualitäts- und Standortinformationen, Aktivitätsdaten); vorherige Einladung zum Herunterladen der Forschungs-App; Herunterladen der Forschungs-App; Häufigkeit der Nutzung des Smartphones; Smartphone-Aktivitäten (Browsen, E-Mails, Fotografieren, Anzeigen/Post-Social-Media-Inhalte, Einkaufen, Online-Banking, Installieren von Apps, Verwenden von GPS-fähigen Apps, Verbinden über Bluethooth, Spielen, Streaming von Musik/Videos); Selbsteinschätzung der Kompetenz im Umgang mit dem Smartphone; Einstellung zu Umfragen und Teilnahme an Forschungsstudien (persönliches Interesse, Zeitverlust, Verkaufsgespräch, interessante Erfahrung, nützlich); Vertrauen in Institutionen zum Datenschutz (Marktforschungsunternehmen, Universitätsforscher, Regierungsbehörden wie das Statistische Bundesamt, Mobilfunkanbieter, App-Unternehmen, Kreditkartenunternehmen, Online-Händler und Social-Media-Plattformen); allgemeine Datenschutzbedenken; Gefühl der Datenschutzverletzung durch Banken und Kreditkartenunternehmen, Steuerbehörden, Regierungsbehörden, Marktforschung, soziale Netzwerke, Apps und Internetbrowser; Bedenken zur Datensicherheit bei Smartphone-Aktivitäten für Forschungszwecke (Online-Umfrage, Umfrage-Apps, Forschungs-Apps, SMS-Umfrage, Kamera, Aktivitätsdaten, GPS-Ortung, Bluetooth). Demographie: Geschlecht, Alter; Bundesland; höchster Schulabschluss; höchstes berufliches Bildungsniveau. Zusätzlich verkodet wurden: laufende Nummer; Dauer (Reaktionszeit in Sekunden); Gerätetyp, mit dem der Fragebogen ausgefüllt wurde. The goal of this study was to experimentally measure the influence of different incentive schemes on the willingness to participate in passive mobile data collection among German smartphone owners. The data come from a web survey among German smartphone users 18 years and older who were recruited from a German nonprobability online panel. In December 2017, 1,214 respondents completed a questionnaire on smartphone use and skills, privacy and security concerns, general attitudes towards survey research and research institutions. In addition, the questionnaire included an experiment on the willingness to participate in mobile data collection under different incentive conditions. Topics: Ownership of smartphone, cell phone, desktop or laptop computer, tablet computer, and/or e-book reader; type of smartphone; willingness to participate in mobile data collection under different incentive conditions; likelihood of downloading the app to particiapte in this research study; respondent would rather participate in the study if he could receive 100 euros; total amount to be earned for the respondent ot participate in the study (open answer); reason why the respondent wouldn´t participate in the research study; willlingness to participate in the study for an incentive of 60 euros in total; willingness to activate different functions when downloading the app (interaction history, smartphone usage, charateristics of the social network, network quality and location information, activity data); previous invitation for research app download; research app download; frequency of smartphone use; smartphone activities (browsing, e-mails, taking pictures, view/ post social media content, shopping, online banking, installing apps, using GPS-enabled apps, connecting via Bluethooth, playing games, stream music/ videos); self-assessment of smartphone skills; attitude towards surveys and participaton at research studies (personal interest, waste of time, sales pitch, interesting experience, useful); trust in institutions regarding data privacy (market research companies, university researchers, government authorities such as the Federal Statistical Office, mobile service provider, app companies, credit card companies, online retailer, and social media platforms); general privacy concern; feeling of privacy violation by banks and credit card companies, tax authorities, government agencies, market research, social networks, apps, and internet browsers; concern regarding data security with smartphone activities for research purposes (online survey, survey apps, research apps, SMS survey, camera, activity data, GPS location, Bluetooth). Demography: sex, age; federal state; highest level of school education; highest level of vocational education. Additionally coded was: running number; duration (response time in seconds); device type used to fill out the questionnaire.

Malaysia Internet Usage: Device Vendor Market Share: Tablet: General Mobile data was reported at 0.000 % in 02 Jul 2024. This stayed constant from the previous number of 0.000 % for 01 Jul 2024. Malaysia Internet Usage: Device Vendor Market Share: Tablet: General Mobile data is updated daily, averaging 0.000 % from Dec 2023 (Median) to 02 Jul 2024, with 199 observations. The data reached an all-time high of 0.850 % in 25 Mar 2024 and a record low of 0.000 % in 02 Jul 2024. Malaysia Internet Usage: Device Vendor Market Share: Tablet: General Mobile data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Malaysia – Table MY.SC.IU: Internet Usage: Device Vendor Market Share.

Expressto Mobile Tablet

## Overview

Expressto Mobile Tablet is a dataset for object detection tasks - it contains Blood Well Xmsx annotations for 310 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The energy consumption of Android devices, measured via data collection from features, is a recurring theme in the literature. To evaluate the performance of such devices, databases are generated by collecting data from features while using the Android operating system. This is a database generated using Tucandeira Data Collector from the daily use of smartphones and tablets while performing everyday tasks. The dataset contains 98 features and 10,331,114 records related to dynamic, background, list of applications, and static data. Device records were collected daily from ten distinct devices and stored in CSV files that were later organized to generate a database by cleaning and preprocessing the data that are publically available in the Mendeley Data Repository. The dataset formed an integral component of the SWPERFI RD&I Project, a research, development, and innovation initiative aimed at improving the performance and energy optimization of mobile devices. This project was undertaken at the Federal University of Amazonas.

Kenya Internet Usage: Device Vendor Market Share: Tablet: General Mobile data was reported at 0.000 % in 06 May 2025. This stayed constant from the previous number of 0.000 % for 05 May 2025. Kenya Internet Usage: Device Vendor Market Share: Tablet: General Mobile data is updated daily, averaging 0.000 % from Mar 2024 (Median) to 06 May 2025, with 18 observations. The data reached an all-time high of 0.290 % in 25 Mar 2024 and a record low of 0.000 % in 06 May 2025. Kenya Internet Usage: Device Vendor Market Share: Tablet: General Mobile data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Kenya – Table KE.SC.IU: Internet Usage: Device Vendor Market Share.

Snapshot img

Detachable Tablet Market Size 2025-2029

The detachable tablet market size is forecast to increase by USD 3.65 billion, at a CAGR of 4.6% between 2024 and 2029.

The market is experiencing significant growth, driven by the proliferation of affordable options and the increasing implementation of portable PCs in education institutions. The availability of low-cost detachable tablets is expanding the market's reach, making these devices accessible to a broader consumer base. Additionally, the adoption of convertible laptops, which offer the functionality of both a laptop and a tablet, is increasing as users seek versatile devices for both personal and professional use. However, the market faces challenges, including the high price point of premium detachable tablets and the growing competition from other mobile devices, such as smartphones and laptops.
Furthermore, the lack of standardization in detachable tablet design and compatibility with various software applications can hinder market growth. Companies looking to capitalize on market opportunities must focus on offering competitive pricing, ensuring compatibility with popular software, and providing innovative features to differentiate their products. Effective navigation of these challenges requires a deep understanding of consumer needs and preferences, as well as a commitment to continuous product innovation.

What will be the Size of the Detachable Tablet Market during the forecast period?

Request Free Sample

The market continues to evolve, driven by advancements in technology and shifting consumer preferences. Cloud storage solutions enable seamless access to digital content, while long battery life allows for uninterrupted use. The integration of detachable keyboards transforms these devices into productive 2-in-1 laptops, catering to the needs of professionals. Virtual reality applications expand the market's reach, offering immersive experiences in various sectors. Moreover, viewing angles and high-definition displays enhance the user experience, ensuring crisp visuals for multimedia consumption. The fusion of digital content and detachable tablets facilitates multitasking and flexibility, making these devices indispensable in mobile computing.

Battery life, a critical factor, is continuously improving, ensuring longer usage hours. Detachable keyboards, with their magnetic connectors, offer a sleek design and easy portability. Virtual reality applications, with their immersive capabilities, are revolutionizing industries such as education, healthcare, and entertainment. The ongoing development of detachable tablets is further fueled by advancements in digital content, virtual reality, and productivity suites. Machine learning and artificial intelligence enhance user experience, while capacitive touchscreens and digital pens cater to creative professionals. Fingerprint sensors and screen protectors ensure data protection, making these devices secure and reliable. In summary, the market is characterized by continuous innovation and evolving patterns, offering versatile solutions for various applications in mobile computing.

The integration of cloud storage, detachable keyboards, virtual reality, viewing angle, and digital content creates a dynamic market that caters to the ever-changing needs of consumers and businesses alike.

How is this Detachable Tablet Industry segmented?

The detachable tablet industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

OS

  Windows
  iPadOS
  Others


Type

  Below 8 inches
  8 inches
  Above 8 inches


Application

  Personal
  Professional


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    Australia
    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By OS Insights

The windows segment is estimated to witness significant growth during the forecast period.

In the dynamic world of technology, the market continues to evolve, integrating advanced features that cater to both personal and professional use. Microsoft's Windows, an operating system installed on the majority of electronic devices, powers many detachable tablets. Windows, with its long-standing history dating back to 1985, offers compatibility with a vast array of software applications. The latest addition to the Windows family, Windows 11, was released in October 2022, and since then, Microsoft has consistently delivered updates to improve user experience. One such update, version 23H2, was made available on October 31, 2023. Data protection is a priority in today's digital age, and detachable tablets are no exception.

Advanced security features like face recognition, fingerprint sensors, and machine learning algorithms

The Digital Education Monitor (Monitor Digitale Bildung) creates for the first time a comprehensive and representative empirical database on the state of digitized learning in the various educational sectors in Germany - schools, vocational training, higher education and advanced training. The focus of the study covers: Use of digital media at universities. Digital forms of learning. Use of Open Educational Resources for learning, digital media in courses and digital testing. Topics: 1. Technical equipment: media technology or hardware used for university or in leisure time (smartphone, cell phone, tablet, PC or notebook, digital camera, interactive whiteboard, beamer, other); allowed use of own devices such as smartphone or tablet in lectures and other courses; opinion on the use of own digital devices in courses (smartphones and tablets should be allowed for learning in a course, agreement on a ban of digital devices in the course due to distraction by WhatsApp or Facebook, conscious use of paper and pen for notes). 2. Use as digital learning forms: technologies and applications used for learning (e.g. chat services such as WhatsApp, digital presentation tools such as PowerPoint, etc.) and opportunities for use (use directly in events, use elsewhere for study, private use, no use). 3. Open educational resources - use for learning: free or paid use or non-use of internet resources for learning and testing (learning apps, learning management systems, e.g. Moodle or ILIAS, digital learning resources, e.g. e-books, learning videos, software, e.g. statistics and calculation programs, business games, literature management programs, e-assessment systems, examination systems). 4. Digital media in courses: attitude towards the use of digital media and applications in courses (good if lecturers use classical teaching aids, courses should only be conducted with digital media, motivation resp. more work by creating own learning videos or websites, like to learn for exams with learning apps or digital tests, learning apps or digital tests put students under pressure, thanks to digital media they can choose their own learning opportunities, they are overwhelmed by the range of digital media, it is better to receive anonymous feedback from a learning program than personal feedback from the lecturer, lecturers should try out new things with digital media more often, lecturers should focus on the subject matter, the media used do not matter, use of WhatsApp, Facebook, etc. only for private purposes). 5. Networking, communication: evaluation of digital applications in terms of their networking potential (digital applications such as Facebook, WhatsApp or Moodle improve the exchange with other students, with teachers, between the own university and partner universities or internship companies). 6. Digital forms of learning: evaluation of selected forms of learning with regard to one´s own motivation to learn (lecturer gives presentation with learning videos, presentation tools or uses whiteboard, lecturer uses pdf documents or e-books in his lectures, lecturer uses classic teaching and learning aids, such as blackboard or books, individual research on selected content, lecturer moderates discussions with the help of digital media, e.g. response systems, work with software independently, e.g. statistics or design programs, learning with a learning management system, using self-learning programs in the event, such as simulations, learning apps or learning games, preparing for the event with a video and deepening topics in the classroom, structuring the event in a blended learning format, a combination of face-to-face learning and e-learning, collaboratively creating presentations, web content or other projects with digital media. 7. Digital testing: participation during studies in an examination with computer assistance (examination or test as an entrance examination for a course, assignments and tests as a between course optimization examination, examination or test as a final examination of a course); type of digital procedure (visibility of the examination result only to the respondent or evaluation of the result by the instructor or by the computer). Demography: sex; age (grouped); type of institution of higher education; public, private, or confessional sponsorship of the institution; subject group of the course; intended degree; semester of study. Additionally coded were: respondent ID; college code; anonymous and voluntary survey noted; groups college size. Der Monitor Digitale Bildung schafft erstmals eine umfassende und repräsentative empirische Datenbasis zum Stand des digitalisierten Lernens in den verschiedenen Bildungssektoren in Deutschland – Schule, Ausbildung, Hochschule und Weiterbildung. Einsatz von digitalen Medien an der Hochschule. Digitale Lernformen. Einsatz von Open Educational Resources zum Lernen. Digitale Medien in Lehrveranstaltungen. Digitales Prüfen. Themen: 1. Technische Ausstattung: für die Hochschule oder in der Freizeit genutzte Medientechnik bzw. Hardware (Smartphone, Handy, Tablet, PC bzw. Notebook, digitale Kamera, interaktives Whiteboard, Beamer, Sonstiges); erlaubte Nutzung eigener Geräte wie Smartphone oder Tablet in den Vorlesungen und andren Lehrveranstaltungen; Meinung zur Nutzung eigener digitaler Geräte in Veranstaltungen (Smartphones und Tablets sollten zum Lernen in einer Veranstaltung erlaubt sein, Zustimmung zu einem Verbot digitaler Geräte in der Veranstaltung aufgrund der Ablenkung durch WhatsApp oder Facebook, bewusste Nutzung von Papier und Stift für Mitschriften). 2. Einsatz als digitale Lernformen: zum Lernen genutzte Technologien und Anwendungen (z.B. Chat-Dienste wie WhatsApp, digitale Präsentationstools wie PowerPoint, etc.) und Nutzungsgelegenheiten (Nutzung direkt in den Veranstaltungen, anderweitige Nutzung für das Studium, private Nutzung, keine Nutzung). 3. Open Educational Resources - Einsatz zum Lernen: Kostenlos oder kostenpflichtig genutzte bzw. nicht genutzte Internetangebote zum Lernen und Prüfen (Lern-Apps, Lernmanagementsysteme, z.B. Moodle oder ILIAS, digitale Lernressourcen, z.B. E-Books, Lernvideos, Software, z.B. Statistik- und Kalkulationsprogramme, Planspiele, Literaturverwaltungsprogramme, E-Assessmentsysteme, Prüfungssysteme). 4. Digitale Medien in Lehrveranstaltungen: Einstellung zum Einsatz von digitalen Medien und Anwendungen in Lehrveranstaltungen (gut, wenn Dozenten klassische Unterrichtsmittel einsetzen, Lehrveranstaltungen sollten nur mit digitalen Medien durchgeführt werden, Motivation bzw. mehr Arbeit durch das Erstellen eigener Lernvideos oder Webseiten, gern mit Lern-Apps oder digitalen Tests für Prüfungen lernen, Lern-Apps oder digitale Tests setzen unter Druck, dank digitaler Medien Lernangebote selbst aussuchen, Überforderung durch Angebot an digitalen Medien, besser eine anonyme Rückmeldung von einem Lernprogramm als eine persönliche Rückmeldung vom Dozenten, Dozenten sollten öfter Neues mit digitalen Medien ausprobieren, Dozenten sollten Fokus auf das Fachliche legen, eingesetzte Medien sind dabei egal, Nutzung von WhatsApp, Facebook etc. nur für private Zwecke). 5. Vernetzung, Kommunikation: Bewertung digitaler Anwendungen im Hinblick auf ihr Vernetzungspotential (digitale Anwendungen wie Facebook, WhatsApp oder Moodle verbessern den Austausch mit andern Studierenden, mit Lehrenden, zwischen der eigenen Hochschule und Partnerhochschulen oder Praktika-Unternehmen). 6. Digitale Lernformen: Bewertung ausgewählter Lernformen im Hinblick auf die eigene Lernmotivation (Dozent hält Vortrag mit Lernvideos, Präsentationstools oder setzt Whiteboard ein, Dozent nutzt pdf-Dokumente oder E-Books in seinen Veranstaltungen, Dozent nutzt klassische Lehr- und Lernmittel, wie Tafel oder Bücher, eigenständige Recherche zu bestimmten Inhalten, Dozent moderiert Diskussionen mithilfe digitaler Medien, z.B. Response Systeme, selbst mit Software arbeiten, z.B. Statistik- oder Konstruktionsprogramme, Lernen mit Lernmanagementsystem, Nutzen von Selbstlernprogrammen in der Veranstaltung, wie Simulationen, Lern-Apps oder Lernspiele, Vorbereitung auf die Veranstaltung mit einem Video und Themenvertiefung vor Ort, Strukturierung der Veranstaltung im Blended-Learning-Format, einer Kombination aus Präsenzlernen und E-Learning, gemeinsames Erstellen von Präsentationen, Webinhalten oder anderer Projekte mit digitalen Medien). 7. Digitales Prüfen: Teilnahme während des Studiums an einer Prüfung mit Unterstützung des Computers (Prüfung oder Test als Aufnahmeprüfung für eine Lehrveranstaltung, Aufgaben und Tests als Prüfung zwischendurch zur Optimierung des Kurses, Prüfung oder Test als Abschlussprüfung einer Lehrveranstaltung); Art des digitalen Verfahrens (Sichtbarkeit des Prüfungsergebnisses nur für den Befragten bzw. Bewertung des Ergebnisses vom Dozenten oder vom Computer). Demographie: Geschlecht; Alter (gruppiert); Hochschultyp; staatliche, private oder konfessionelle Trägerschaft der Hochschule; Fächergruppe des Studiengangs; angestrebter Studienabschluss; Fachsemester. Zusätzlich verkodet wurde: Befragten-ID; Hochschul-Code; anonyme und freiwillige Befragung zur Kenntnis genommen; Gruppen Hochschulgröße.

The goal of this study was to experimentally measure the influence of different incentive schemes on the willingness to participate in passive mobile data collection among German smartphone owners. The data come from a web survey among German smartphone users 18 years and older who were recruited from a German nonprobability online panel. In December 2017, 1,214 respondents completed a questionnaire on smartphone use and skills, privacy and security concerns, general attitudes towards survey research and research institutions. In addition, the questionnaire included an experiment on the willingness to participate in mobile data collection under different incentive conditions.

Topics: Ownership of smartphone, cell phone, desktop or laptop computer, tablet computer, and/or e-book reader; type of smartphone; willingness to participate in mobile data collection under different incentive conditions; likelihood of downloading the app to particiapte in this research study; respondent would rather participate in the study if he could receive 100 euros; total amount to be earned for the respondent ot participate in the study (open answer); reason why the respondent wouldn´t participate in the research study; willlingness to participate in the study for an incentive of 60 euros in total; willingness to activate different functions when downloading the app (interaction history, smartphone usage, charateristics of the social network, network quality and location information, activity data); previous invitation for research app download; research app download; frequency of smartphone use; smartphone activities (browsing, e-mails, taking pictures, view/ post social media content, shopping, online banking, installing apps, using GPS-enabled apps, connecting via Bluethooth, playing games, stream music/ videos); self-assessment of smartphone skills; attitude towards surveys and participaton at research studies (personal interest, waste of time, sales pitch, interesting experience, useful); trust in institutions regarding data privacy (market research companies, university researchers, government authorities such as the Federal Statistical Office, mobile service provider, app companies, credit card companies, online retailer, and social media platforms); general privacy concern; feeling of privacy violation by banks and credit card companies, tax authorities, government agencies, market research, social networks, apps, and internet browsers; concern regarding data security with smartphone activities for research purposes (online survey, survey apps, research apps, SMS survey, camera, activity data, GPS location, Bluetooth).

Demography: sex, age; federal state; highest level of school education; highest level of vocational education.

Additionally coded was: running number; duration (response time in seconds); device type used to fill out the questionnaire.

Once-daily oral HIV pre-exposure prophylaxis (PrEP) is an effective strategy to prevent HIV, but is highly dependent on adherence. Men who have sex with men (MSM) who use substances face unique challenges maintaining PrEP adherence. Digital pill systems (DPS) allow for real-time adherence measurement through ingestible sensors. Integration of DPS technology with other digital health tools, such as digital phenotyping, may improve understanding of nonadherence triggers and development of personalized adherence interventions based on ingestion behavior. This study explored the willingness of MSM with substance use to share digital phenotypic data and interact with ancillary systems in the context of DPS-measured PrEP adherence. Adult MSM on PrEP with substance use were recruited through a social networking app. Participants were introduced to DPS technology and completed an assessment to measure willingness to participate in DPS-based PrEP adherence research, contribute digital phenotyping data, and interact with ancillary systems in the context of DPS-based research. Medical mistrust, daily worry about PrEP adherence, and substance use were also assessed. Participants who identified as cisgender male and were willing to participate in DPS-based research (N = 131) were included in this subsample analysis. Most were White (76.3%) and non-Hispanic (77.9%). Participants who reported daily PrEP adherence worry had 3.7 times greater odds (95% CI: 1.03, 13.4) of willingness to share biometric data via a wearable device paired to the DPS. Participants with daily PrEP adherence worry were more likely to be willing to share smartphone data (p = 0.006) and receive text messages surrounding their daily activities (p = 0.003), compared to those with less worry. MSM with substance use disorder, who worried about PrEP adherence, were willing to use DPS technology and share data required for digital phenotyping in the context of PrEP adherence measurement. Efforts to address medical mistrust can increase advantages of this technology for HIV prevention.

The goal of this study was to measure the attitudes towards data sharing and data-collecting organizations before and after the introduction of the EU General Data Protection regulations (GDPR) among people in Germany. The data come from a three-wave split-panel web survey among people 18 years and older in Germany who were recruited from a German nonprobability online panel. In April 2018 (before the GDPR came into effect), 2,095 participants completed the Wave 1 questionnaire on device ownership, social media use, trust in different data collecting organizations, willingness to share data, general trust, awareness of and knowledge about the GDPR, and privacy concerns. In July and in October 2018 (after the GDPR came into effect), respondents from the earlier waves were invited to participate in a second and a third web survey that repeated most of the questions from the first wave. In addition to participants from the earlier waves, fresh respondents were also invited to Waves 2 and 3. A total of 2,046 (Wave 2) and 2,117 (Wave 3) respondents completed the questionnaire in the subsequent waves. 1,269 participated in all three waves.

Topics:

Wave 1

Possession of smartphone, mobile phone, PC, tablet and/or e-book reader; social media use: account with user name and password at selected providers (Google, Facebook, Twitter, LinkedIn, Xing); trust in institutions (Google, Facebook, Bundesamt für Statistik, Universitätsforscher) with regard to the protection of personal data and reasons for this assessment; probability scale with regard to the protection of personal data at the above-mentioned institutions and reasons for this assessment; agreement with the import of personal data of the social insurance institutions to the survey data; general personal trust; awareness of the EU General Data Protection regulations (GDPR) ; knowledge test: goals of the GDPR (open); feeling of invaded privacy by the following institutions: Google, Facebook, government agencies, university researchers; general privacy concerns.

Wave 2

Possession of smartphone, mobile phone, PC, tablet and/or e-book reader; social media use: account with user name and password with selected providers (Google, Facebook, Twitter, LinkedIn, Xing); trust in institutions (Google, Facebook, Federal Statistical Office, university researchers) with regard to the protection of personal data; general personal trust; awareness of the EU General Data Protection regulations (GDPR); knowledge test: goals of the GDPR (open); consent to the storage of various personal data by Facebook or Google (name, e-mail address, home address, date of birth, telephone number, income, marital status, number of children, current location, Internet browser history, account names from other social media and data received from third parties); feeling of invasion of privacy by the following institutions: Google, Facebook, government agencies, university researchers; general privacy concerns.

Wave 3

Possession of smartphone, mobile phone, PC, tablet and/or e-book reader; social media use: account with user name and password at selected providers (Google, Facebook, Twitter, LinkedIn, Xing); trust in institutions (Google, Facebook, Federal Statistical Office, university researchers) with regard to the protection of personal data; general personal trust; awareness of the EU General Data Protection regulations (GDPR); knowledge test: goals of the GDPR (open); concerns about privacy in general; comprehensibility of excerpts of the contents of the EU General Data Protection regulations (GDPR) (resp. on passenger rights in the event of denied boarding and flight delays); estimated popularity of smartphones (proportion of smartphone owners per 100 adult Germans); repetition of the question on trust data collecting organisations (Google, Facebook) with regard to the protection of personal data and general personal trust; readiness for data exchange by Google (or Facebook or the Federal Statistical Office) for research purposes (or for commercial purposes).

Demography: sex; age (year of birth); federal state; school education; professional qualification.

Additionally coded was: running number; respondent ID; experimental groups GDPR Info; duration (reaction time in seconds); used device type to complete the questionnaire.

The questionnaire also included two experiments, one on the effect of GDPR-related information on trust in data collecting organisations and one on the comfort of data shar...

Malawi Internet Usage: Device Vendor Market Share: Tablet: General Mobile data was reported at 0.000 % in 02 Jul 2024. This stayed constant from the previous number of 0.000 % for 01 Jul 2024. Malawi Internet Usage: Device Vendor Market Share: Tablet: General Mobile data is updated daily, averaging 0.000 % from Dec 2023 (Median) to 02 Jul 2024, with 199 observations. The data reached an all-time high of 12.540 % in 25 Mar 2024 and a record low of 0.000 % in 02 Jul 2024. Malawi Internet Usage: Device Vendor Market Share: Tablet: General Mobile data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Malawi – Table MW.SC.IU: Internet Usage: Device Vendor Market Share.

Egypt Internet Usage: Device Vendor Market Share: Tablet: Onn data was reported at 0.030 % in 27 Jan 2025. This records an increase from the previous number of 0.000 % for 26 Jan 2025. Egypt Internet Usage: Device Vendor Market Share: Tablet: Onn data is updated daily, averaging 0.000 % from Jul 2023 (Median) to 27 Jan 2025, with 64 observations. The data reached an all-time high of 0.090 % in 19 Dec 2023 and a record low of 0.000 % in 26 Jan 2025. Egypt Internet Usage: Device Vendor Market Share: Tablet: Onn data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Egypt – Table EG.SC.IU: Internet Usage: Device Vendor Market Share.

This survey charted Finnish citizens' as well as social and healthcare service professionals' attitudes and views concerning secondary use of health and social care data in research and development of services. The study contained two target groups: (1) persons who suffered or had a close relative or acquaintance who suffered from one or more chronic conditions, diseases or disorders, and (2) social and healthcare service professionals. First, the respondents' opinions on the reliability of a variety of authorities and organisations were examined (e.g. the police, Kela, register and statistics authorities, universities) as well as trust in appropriate handling of personal data. They were also asked which type of information they deemed personal or not (e.g. bank account number and balance, purchase history at a grocery store, web browsing history, patient records, genetic information, social security number, phone number). They were asked to evaluate which principles they considered important in handling personal health data (e.g. being able to access one's personal data and to have inaccurate data rectified, and being able to restrict data processing), and the study also surveyed how interested the respondents were in keeping track of the use of their health data, and how willing they would be to permit the use of anonymous health data and genetic information for a variety of purposes (e.g. medicine and treatment development, development of equipment and services, and operations of insurance companies). Next, it was examined whether the respondents kept track of their physical activity with a smartphone or a fitness tracker, for instance, and if they would be willing to permit the use of anonymous data concerning physical activity for a variety of purposes. In addition, the respondents' attitudes were charted with regard to developing medicine research by combining anonymous health data and patient records with other data on, for instance, physical activity, alcohol use, grocery store purchase history, web browsing history, and social media use. The study also examined the willingness to permit access to personal health data for social and healthcare service professionals in a service situation, as well as for social and healthcare authorities and other authorities outside of a service situation. Finally, it was charted how important the respondents deemed different factors relating to data collection (e.g. being able to decide for which purposes personal data, or even anonymous data, can be used, and increasing awareness on how health data can be utilised in scientific research). The reliability of a variety of authorities and organisations, such as social welfare/healthcare organisations, academic researchers and pharmaceutical companies, was also examined in terms of data security and purposes for using data. Background variables included, among others, mother tongue, marital status, household composition, housing tenure, socioeconomic class, political party preference, left-right political self-placement, gross income, economic activity and occupational status, and respondent group (citizen/healthcare service professional/social service professional).

Snapshot img

US Tablets Market Size 2025-2029

The US tablets market size is forecast to increase by USD 2.76 billion, at a CAGR of 4.5% between 2024 and 2029.

The market continues to shape the tablet market, with an increasing number of consumers and businesses adopting this portable computing device for various applications. Key trends include the integration of advanced technologies such as artificial intelligence and gesture recognition, which enhance user experience and expand functionality. Digital content, particularly video streaming and online education, is a major driver, with tablets offering a more convenient and portable alternative to laptops and computers. Battery life and 5G technology are crucial considerations for tablets, enabling seamless connectivity and extended usage. The emergence of innovative devices like smart glasses and holographic displays adds to the market's excitement.


In the healthcare sector, tablets are transforming medical education and telemedicine, with applications in remote patient monitoring and virtual reality. Organic light-emitting diodes (OLED) and virtual reality displays are gaining popularity due to their superior image quality and energy efficiency. However, challenges persist, such as supply chain disruptions and the ongoing competition with laptops and PCs. Gaming and battery life are critical factors for tablet users, with the former demanding high processing power and the latter requiring long-lasting batteries. The adoption of foldable and rollable tablets is another trend, offering a more versatile and compact design.

What will be the Size of the market During the Forecast Period?

Request Free Sample

The market continues to evolve, with portable electronic devices increasingly becoming essential tools for various industries and individuals. Tablets offer a versatile blend of functionality and portability, enabling users to stream video content, create digital art, and collaborate remotely. Hybrid devices, featuring both tablet and laptop capabilities, further expand the market's reach. Advancements in display technologies, such as higher screen resolutions and larger sizes, enhance the user experience. Integration of 5G connectivity and machine learning algorithms facilitates faster processing and improved functionality. Marketing strategies focusing on portable productivity, digital content consumption, and interactive presentations cater to diverse user needs.


Tablet designs continue to evolve, with lighter devices and advanced security features addressing user demands. Accessories like interactive whiteboards, bill payment apps, and tablet management software expand the tablet's utility. The integration of VR and AR technologies, gesture recognition, and mobile workforce solutions further broadens the tablet's applications. Tablets are increasingly used for digital content creation, graphic design, and mobile learning, making them indispensable tools for professionals and students alike. Remote collaboration, online learning platforms, and point-of-sale systems are additional areas where tablets are making a significant impact. The tablet market is poised for continued growth, driven by technological advancements and evolving user needs.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Distribution Channel

  Offline
  Online


Type

  Hybrid
  Convertible
  Slate
  Rugged


OS

  iOS
  Android
  Windows
  Others


Geography

  US

By Distribution Channel Insights

The offline segment is estimated to witness significant growth during the forecast period. The market caters to consumer requirements for portable computing devices, with offline distribution channels playing a significant role. Consumer electronics stores, brand-specific outlets, mass merchandisers, department stores, and office supply stores are key offline distribution channels. These channels offer a hands-on shopping experience, enabling customers to interact with tablets before purchase. Major retailers like Best Buy, Micro Center, and Fry Electronics provide expert guidance and in-store support. Brand-specific stores from Apple, Microsoft, and Samsung offer dedicated assistance for their respective devices. Tablets cater to various sectors, including education, healthcare, business automation, and entertainment consumption.
Technological advancements in display technologies, 5G connectivity, machine learning, and virtual reality are driving market growth. Portable devices with longer battery life, higher resolutions, and efficient graphics chips are in demand. Key trends include the emergence of hybrid tablets and convertible laptops, improved color ac

Haiti Internet Usage: Device Vendor Market Share: Tablet: General Mobile data was reported at 0.000 % in 21 Jun 2024. This stayed constant from the previous number of 0.000 % for 20 Jun 2024. Haiti Internet Usage: Device Vendor Market Share: Tablet: General Mobile data is updated daily, averaging 0.000 % from Jun 2024 (Median) to 21 Jun 2024, with 9 observations. The data reached an all-time high of 0.080 % in 17 Jun 2024 and a record low of 0.000 % in 21 Jun 2024. Haiti Internet Usage: Device Vendor Market Share: Tablet: General Mobile data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Haiti – Table HT.SC.IU: Internet Usage: Device Vendor Market Share.

This dataset consists of growth and yield data for each season when sunflower (Helianthus annuus L.) was grown for seed at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU) research weather station, Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). In each season, sunflower was grown on two large, precision weighing lysimeters, each in the center of a 4.44 ha square field. The square fields are themselves arranged in a larger square with four fields in four adjacent quadrants of the larger square. Fields and lysimeters within each field are thus designated northeast (NE), southeast (SE), northwest (NW), and southwest (SW). Sunflower was grown in the NE and SE fields. Irrigation was by linear move sprinkler system. Irrigation protocols described as full were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings made with a neutron probe to 2.4-m depth in the field. Irrigation protocols described as deficit typically involved irrigations to establish the crop early in the season, followed by reduced or absent irrigations later in the season (typically in the later winter and spring). The growth and yield data include plant population density, height, plant row width, leaf area index, growth stage, total above-ground biomass, leaf and stem biomass, head mass (when present), kernel number, and final yield. Data are from replicate samples in the field and non-destructive (except for final harvest) measurements on the weighing lysimeters. In most cases yield data are available from both manual sampling on replicate plots in each field and from machine harvest. These datasets originate from research aimed at determining crop water use (ET), crop coefficients for use in ET-based irrigation scheduling based on a reference ET, crop growth, yield, harvest index, and crop water productivity as affected by irrigation method, timing, amount (full or some degree of deficit), agronomic practices, cultivar, and weather. Prior publications have focused on sunflower ET, crop coefficients, and crop water productivity. Crop coefficients have been used by ET networks. The data have utility for testing simulation models of crop ET, growth, and yield and have been used for testing, and calibrating models of ET that use satellite and/or weather data. Resources in this dataset:Resource Title: 2009 Bushland, TX, east sunflower growth and yield data. File Name: 2009_East_Sunflower_Growth_and_Yield.xlsxResource Description: This dataset consists of growth and yield data the 2009 season when sunflower (Helianthus annuus L.) was grown at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU) research weather station, Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). Sunflower was grown on two large, precision weighing lysimeters, each in the center of a 4.44 ha square field. The two square fields were themselves arranged with one directly north of and contiguous with the other. Fields and lysimeters within each field were designated northeast (NE), and southeast (SE). Irrigation was by linear move sprinkler system. Irrigations were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings made with a neutron probe to 2.4-m depth in the field. Irrigation management resulted in the crop being well watered and meeting reference “tall crop” conditions during periods before harvests. The growth and yield data include plant height, plant row width, leaf area index, growth stage, total above-ground biomass, leaf and stem biomass, and final yield. Data are from replicate samples in the field and non-destructive (except for final harvest) measurements on the weighing lysimeters. In most cases yield data are available from both manual sampling on replicate plots in each field and from machine harvest. There is a single spreadsheet for the east (NE and SE) lysimeters and fields. The spreadsheet contains tabs for data and corresponding tabs for data dictionaries. There are separate data tabs and corresponding dictionaries for plant growth during the season, and manual harvest from replicate plots in each field and from lysimeter surfaces, and machine (combine) harvest, An Introduction tab explains the tab names and contents, lists the authors, explains conventions, and lists some relevant references.Resource Title: 2011 Bushland, TX, east sunflower growth and yield data. File Name: 2011_East_Sunflower_Growth_and_Yield.xlsxResource Description: This dataset consists of growth and yield data the 2011 season when sunflower (Helianthus annuus L.) was grown at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU) research weather station, Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). Sunflower was grown on two large, precision weighing lysimeters, each in the center of a 4.44 ha square field. The two square fields were themselves arranged with one directly north of and contiguous with the other. Fields and lysimeters within each field were designated northeast (NE), and southeast (SE). Irrigation was by linear move sprinkler system. Irrigations were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings made with a neutron probe to 2.4-m depth in the field. Irrigation management resulted in the crop being well watered and meeting reference “tall crop” conditions during periods before harvests. The growth and yield data include plant height, plant row width, leaf area index, growth stage, total above-ground biomass, leaf and stem biomass, and final yield. Data are from replicate samples in the field and non-destructive (except for final harvest) measurements on the weighing lysimeters. In most cases yield data are available from both manual sampling on replicate plots in each field and from machine harvest. There is a single spreadsheet for the east (NE and SE) lysimeters and fields. The spreadsheet contains tabs for data and corresponding tabs for data dictionaries. There are separate data tabs and corresponding dictionaries for plant growth during the season, and manual harvest from replicate plots in each field and from lysimeter surfaces, and machine (combine) harvest, An Introduction tab explains the tab names and contents, lists the authors, explains conventions, and lists some relevant references.

Annual series of tourist municipalities of the Canary Islands. Tourists aged 16 and over according to the use made to the smartphone or tablet in the Canary Islands and types of accommodation by tourist municipalities of the Canary Islands for years. Series since 2018.