This statistic shows the results of a survey in which respondents were asked what types of reviews influence them when they are grocery shopping online in the United Kingdom in 2016. A majority of 41 percent are influenced by other comments from shoppers, followed by 40 percent being influenced by the products' average rating from other shoppers.

A November 2021 survey of online users in the United States found that 81 percent of respondents had used Google as a tool to evaluate local businesses in the past 12 months. Yelp was ranked second with over half of respondents using the review platform for such purpose.

This polygon files contains 2015-2016 school-year data delineating school attendance boundaries. These data were collected and processed as part of the School Attendance Boundary Survey (SABS) project which was funded by NCES to create geography delineating school attendance boundaries. Original source information that was used to create these boundary files were collected were collected over a web-based self-reporting system, through e-mail, and mailed paper maps. The web application provided instructions and assistance to users via a user guide, a frequently asked questions document, and instructional videos. Boundaries supplied outside of the online reporting system typically fell into one of six categories: a digital geographic file, such as a shapefile or KML file; digital image files, such as jpegs and pdfs; narrative descriptions; an interactive web map; Excel or pdf address lists; and paper maps. 2015 TIGER/line features (that consist of streets, hydrography, railways, etc.) were used to digitize school attendance boundaries and was the primary source of information used to digitize analog information. This practice works well as most school attendance boundaries align with streets, railways, water bodies and similar line features included in the 2015 TIGER/line "edges" files. In those few cases in which a portion of a school attendance boundary serves both sides of a street contractor staff used Esri’s Imagery base map to estimate the property lines of parcels. The data digitized from analog maps and verbal descriptions do not conform to cadastral data (and many of the original GIS files created by school districts do not conform with cadastral or parcel data).The SABS 2015-2016 file uses the WGS 1984 Web Mercator Auxiliary Sphere coordinate system.Additional information about SABS can be found on the EDGE website.The SABS dataset is intended for research purposes only and reflects a single snapshot in time. School boundaries frequently change from year to year. To verify legal descriptions of boundaries, users must contact the school district directly.All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

Abstract The growth of the Internet has enabled consumer-to-consumer interactions through online platforms where users share content and influence the purchase decisions of other consumers. The objective of this research is to identify the effect of perceived usefulness of online reviews on hotel booking intentions. The approach is quantitative, using a questionnaire to collect data from consumers who use online reviews before booking a hotel. The data were analyzed using structural equation modeling. The results showed the direct influence of perceived information usefulness on purchase intention, and the antecedent constructs— needs of information, information credibility, and information quality—had a positive and significant impact on perceived usefulness of online reviews. Comparing these results with research by Erkan and Evans (2016) conducted with UK consumers that use social media to decide about their purchases, in this study information credibility was more relevant than information quality, suggesting a more skeptical behavior of Brazilian consumers. These findings have implications for practitioners that manage the digital marketing of organizations inserted in this environment, mainly regarding the impact of credibility and quality of online reviews on hotel booking intentions, being this a practical contribution of the research.

In 2024, 77.3 percent of women and 76.8 percent of men used internet banking in Czechia. During the period under review, more men than women used online banking. However, that changed in 2024 when the number of women surpassed that of men. Overall, the number of Czechs using internet banking has been increasing.

The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated point locations (latitude and longitude) for public elementary and secondary schools included in the NCES Common Core of Data (CCD). The CCD is an annual collection of basic administrative characteristics that includes the physical address for all public schools, school districts, and state education agencies in the United States. The NCES EDGE program collaborates with the U.S. Census Bureau’s Education Demographic, Geographic, and Economic Statistics (EDGE) Branch to develop point locations for schools and school district administrative offices based on these addresses. The point locations in this data layer were developed from the 2016-2017 CCD collection. For more information about NCES school point data, see: https://nces.ed.gov/programs/edge/Geographic/SchoolLocations.All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated point locations (latitude and longitude) for postsecondary institutions included in the NCES Integrated Postsecondary Education Data System (IPEDS). The IPEDS program annually collects information about enrollments, program completions, graduation rates, faculty and staff, finances, institutional prices, and student financial aid from every college, university, and technical and vocational institution that participates in federal student financial aid programs under the Higher Education Act of 1965 (as amended). The NCES EDGE program uses address information reported in the annually updated IPEDS directory file and collaborates with the U.S. Census Bureau’s Education Demographic, Geographic, and Economic Statistics (EDGE) Branch to develop point locations for all institutions reported in IPEDS. The point locations in this data layer were developed from the 2016-2017 IPEDS collection. For more information about NCES school point data, see: https://nces.ed.gov/programs/edge/Geographic/SchoolLocations. All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

This data set contains:

• Full data files of a 2016 survey of attitudes to Open Peer Review in xls and csv formats
• Survey questions (pdf)
• Readme file (txt)

Between 8 September and 7 October 2016, OpenAIRE held a survey designed to aid the development of appropriate OPR approaches by providing evidence about the attitudes of authors, editors and reviewers towards OPR, their reservations and needs, as well as to gauging current levels of experience and reservation with different types of OPR. A supplementary aim was to collect feedback on a provisional definition of OPR as created during another strand of work. The survey aimed to aid the development of appropriate OPR approaches by providing evidence about the attitudes of authors, editors and reviewers towards OPR, their reservations and needs, as well as to gauge current levels of experience and reservations with different types of OPR. The survey was conducted via an openly accessible online questionnaire (using the scientific survey platform SoSci, www.soscisurvey.de). It received a total of 3062 complete responses (a further 635 responses were discarded as incomplete). The survey was open to all wishing to take part and distributed via social media, scholarly communications mailing lists, publisher newsletters and, in one case, a publisher internal mailing list (Copernicus Publications).

Acknowledgement: This work is funded by the European Commission H2020 project OpenAIRE2020 (Grant agreement: 643410, Call: H2020-EINFRA-2014-1)

Contact: Dr Tony Ross-Hellauer, University of Göttingen, State and University Library, ross-hellauer@sub.uni-goettingen.de

Global anthropogenic CO2 emissions for 2007 based on EDGARv4.3, fuel type and category specific emissions provided by Greet Janssens-Maenhout (EU-JRC), BP statistics 2016 (http://www.bp.com/content/dam/bp/excel/energy-economics/statistical-review-2016/bp-statistical-review-of-world-energy-2016-workbook.xlsx), temporal variations based on MACC-TNO (https://gmes-atmosphere.eu/documents/deliverables/d-emis/MACC_TNO_del_1_3_v2.pdf), temporal extrapolation and disaggregation described in COFFEE (Steinbach et al. 2011).

This dataset contains Social Security Administration's annual, national level Pre-effectuation Review of Disability Determinations data for fiscal year (FY) 2012-2016.

Additional information reported in lieu of inclusion in the annual report. Read the complete annual report https://www.mhrt.qld.gov.au/?page_id=70

The Australian Resource Reviews are periodic national assessments of individual mineral commodities. The reviews include evaluations of short-term and long-term trends for each mineral resource, world rankings, production data, significant exploration results and an overview of mining industry developments.

This table contains detailed data on consumer spending broken down into categories of goods and services. In this table, total final consumption expenditure is made up of actual individual consumption, final consumption expenditure by households, including non-profit institutions serving households, and final consumption expenditure by government broken down into individual consumption and collective consumption. Data available from 1969 to 2016. Status of the figures: The data from 1969 to 2015 are final. The data for 2016 is provisional. Since this table has been discontinued, the data is no longer finalized. Changes as of June 22, 2018 None, this table has been discontinued. Statistics Netherlands recently carried out a revision of the national accounts. New statistical sources and estimation methods are used for this. This review data table has been replaced by the Consumption table; categories of goods and services, national accounts. For additional information, see section 3. When will new figures be released? Not applicable anymore.

The 2015-2016 School Neighborhood Poverty Estimates are based on school locations from the 2015-2016 Common Core of Data (CCD) school file and income data from families with children ages 5 to 17 in the U.S. Census Bureau’s 2012-2016 American Community Survey (ACS) 5-year collection. The ACS is a continuous household survey that collects social, demographic, economic, and housing information from the population in the United States each month. The Census Bureau calculates the income-to-poverty ratio (IPR) based on money income reported for families relative to the poverty thresholds, which are determined based on the family size and structure. Noncash benefits (such as food stamps and housing subsidies) are excluded, as are capital gains and losses. The IPR is the percentage of family income that is above or below the federal poverty level. The IPR indicator ranges from 0 to a top-coded value of 999. A family with income at the poverty threshold has an IPR value of 100. The estimates in this file reflect the IPR for the neighborhoods around schools which may be different from the neighborhood conditions of students enrolled in schools. All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

In 2024, 69 percent of Czechs stated there were too many ads on the internet. The same opinion was shared by 68 percent of Czechs regarding social networks. In the period under review, the number of Czechs oversaturated with online advertising on social media continuously increased.

This data layer produced by the National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimates (EDGE) program provides a geographic locale framework that classifies all U.S. territory into twelve categories ranging from Large Cities to Remote Rural areas. NCES uses this framework to describe the type of geographic area where schools and school districts are located. The criteria for these classifications are defined by NCES, but they rely on standard geographic areas developed and maintained by the U.S. Census Bureau. The 2016 NCES Locale boundaries are based on geographic areas represented in Census TIGER/Line 2016. The NCES Education Demographic and Geographic Estimate (EDGE) program collaborates with the U.S. Census Bureau’s Education Demographic, Geographic, and Economic Statistics (EDGE) Branch to annually update the locale boundaries. For more information about the NCES locale framework, and to download the data, see: https://nces.ed.gov/programs/edge/Geographic/LocaleBoundaries. The classifications include:Large City (11): Territory inside an Urbanized Area and inside a Principal City with population of 250,000 or more.Midsize City (12): Territory inside an Urbanized Area and inside a Principal City with population less than 250,000 and greater than or equal to 100,000.Small City (13): Territory inside an Urbanized Area and inside a Principal City with population less than 100,000.Suburb – Large (21): Territory outside a Principal City and inside an Urbanized Area with population of 250,000 or more.Suburb - Midsize (22): Territory outside a Principal City and inside an Urbanized Area with population less than 250,000 and greater than or equal to 100,000.Suburb - Small (23): Territory outside a Principal City and inside an Urbanized Area with population less than 100,000.Town - Fringe (31): Territory inside an Urban Cluster that is less than or equal to 10 miles from an Urbanized Area.Town - Distant (32): Territory inside an Urban Cluster that is more than 10 miles and less than or equal to 35 miles from an Urbanized Area.Town - Remote (33): Territory inside an Urban Cluster that is more than 35 miles of an Urbanized Area.Rural - Fringe (41): Census-defined rural territory that is less than or equal to 5 miles from an Urbanized Area, as well as rural territory that is less than or equal to 2.5 miles from an Urban Cluster.Rural - Distant (42): Census-defined rural territory that is more than 5 miles but less than or equal to 25 miles from an Urbanized Area, as well as rural territory that is more than 2.5 miles but less than or equal to 10 miles from an Urban Cluster.Rural - Remote (43): Census-defined rural territory that is more than 25 miles from an Urbanized Area and is also more than 10 miles from an Urban Cluster.All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

Statistical coal mining data produced for the Queensland mining industry 2016–17

This statistic shows the number of memberships on Angie's List from 2012 to 2016. In the fiscal year 2016, Angie's List had approximately 5.09 million members.

The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimates (EDGE) program develops annually updated point locations (latitude and longitude) for postsecondary institutions included in the NCES Integrated Postsecondary Education Data System (IPEDS). The IPEDS program annually collects information about enrollments, program completions, graduation rates, faculty and staff, finances, institutional prices, and student financial aid from colleges, universities, and technical and vocational institutions that participate in federal student financial aid programs under the Higher Education Act of 1965 (as amended). The NCES EDGE program uses address information reported in the annually updated IPEDS directory file to develop point locations for all institutions reported in IPEDS. The point locations in this data layer represent the most current IPEDS collection available. For more information about NCES school point data, see: https://nces.ed.gov/programs/edge/Geographic/SchoolLocations. Collections are available for the following years: 2022-23 2021-22 2020-21 2019-20 2018-19 2017-18 2016-17 2015-16 All information contained in this file is in the public domain. Data users are ad vised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

The timeline shows the number of unique mobile visitors to recommendation platform Yelp from 2016 to 2021, per quarter. The local search and review site's mobile visitor numbers have displayed a steady growth, reaching 31 million unique mobile app devices in the first quarter of 2021.