This statistic depicts the share of employees in the United States who expect feedback on their performance in 2018, broken down by frequency. During the survey, 32 percent of respondents stated they expect feedback on their performance once a year.

[207+ Pages Report] The global Employee Engagement and Feedback Software market size is expected to grow from USD 928.4 million to USD 1774.38 million by 2028, at a CAGR of 11.40% from 2022-2028

In 2021, Google's share of online reviews increased to 71 percent, up from 67 percent in 2020, indicating a rise in willingness from consumers to share their experiences and opinions online. Overall, Google is the platform and search engine on which most consumers leave reviews for local businesses.

Based on a 2023 survey across 18 countries, it was found that approximately 28 percent of respondents used product reviews on social media platforms, specifically related to food, to make informed choices. Following closely, reviews related to clothing or shoes were relied upon by 27 percent of consumers.

Enterprise Feedback Management (EFM) Platform Market size was valued at USD 2.1 Billion in 2023 and is projected to reach USD 9.1 Billion by 2030, growing at a CAGR of 24.1 % during the forecast period 2024-2030.

Global Enterprise Feedback Management (EFM) Platform Market Drivers

The market drivers for the Enterprise Feedback Management (EFM) Platform Market can be influenced by various factors. These may include:

Improved consumer Experience: Real-time consumer feedback collection and analysis made possible by EFM platforms helps businesses provide better goods, services, and general customer happiness.

Business Process Optimisation: Organisations can discover areas for improvement, streamline operations, and increase efficiency by integrating EFM technologies into their business processes.

Data-Driven Decision Making: By offering insightful information gleaned from client input, EFM platforms enable organisations to make well-informed choices and tactical modifications.

Brand Reputation Management: By handling consumer feedback well, companies can keep an eye on sentiment surrounding their brand, respond quickly to problems, and preserve a favourable perception of their company.

Product Innovation: By identifying market trends, consumer preferences, and unmet needs through ongoing feedback gathering, organisations can more easily design novel goods and services.

Employee Engagement: By collecting employee feedback, EFM platforms help companies develop a culture of engagement, cooperation, and ongoing improvement.

Competitive Advantage: By providing better client experiences and fostering enduring loyalty, businesses may set themselves apart from rivals by utilising EFM solutions.

Regulatory Compliance: By guaranteeing data protection, recording consumer feedback procedures, and upholding industry norms, EFM platforms assist businesses in meeting regulatory obligations.

Scalability and Flexibility: The scalability and flexibility of cloud-based EFM systems allow businesses to develop, adjust to changing needs, and enhance their feedback management skills.

Return on Investment (ROI): Businesses can get a big return on their investment in feedback management solutions by using EFM platforms to increase revenue, customer loyalty, and retention.

This dataset comprises user feedback data collected from 15 globally acclaimed mobile applications, spanning diverse categories. The included applications are among the most downloaded worldwide, providing a rich and varied source for analysis. The dataset is particularly suitable for Natural Language Processing (NLP) applications, such as text classification and topic modeling. List of Included Applications:

TikTok Instagram Facebook WhatsApp Telegram Zoom Snapchat Facebook Messenger Capcut Spotify YouTube HBO Max Cash App Subway Surfers Roblox Data Columns and Descriptions: Data Columns and Descriptions:

review_id: Unique identifiers for each user feedback/application review. content: User-generated feedback/review in text format. score: Rating or star given by the user. TU_count: Number of likes/thumbs up (TU) received for the review. app_id: Unique identifier for each application. app_name: Name of the application. RC_ver: Version of the app when the review was created (RC). Terms of Use: This dataset is open access for scientific research and non-commercial purposes. Users are required to acknowledge the authors' work and, in the case of scientific publication, cite the most appropriate reference: M. H. Asnawi, A. A. Pravitasari, T. Herawan, and T. Hendrawati, "The Combination of Contextualized Topic Model and MPNet for User Feedback Topic Modeling," in IEEE Access, vol. 11, pp. 130272-130286, 2023, doi: 10.1109/ACCESS.2023.3332644.

Researchers and analysts are encouraged to explore this dataset for insights into user sentiments, preferences, and trends across these top mobile applications. If you have any questions or need further information, feel free to contact the dataset authors.

There are 193 drawings made by New Zealand primary, middle, and high school students when asked to draw a picture of feedback. This study builds on previous studies in which we asked students to draw pictures of assessment. These files were analysed and described in a published article:Harris, L. R., Brown, G. T. L., & Harnett, J. (2014). Understanding classroom feedback practices: A study of New Zealand student experiences, perceptions, and emotional responses. Educational Assessment, Evaluation and Accountability, 26(2), 107-133. doi:10.1007/s11092-013-9187-5An earlier version was presented as:Harris, L. R., Brown, G. T. L., & Harnett, J. (2012, April). Student pictures of feedback: Feedback is for learning and from teachers. Paper presented at the 2012 AERA Annual Meeting of the American Educational Research Association, Vancouver, BC.

The global feedback management system market is anticipated to reach a remarkable $437.3 million by 2033, expanding at a CAGR of 10.0% from 2025 to 2033. The increasing adoption of customer-centric strategies and the growing importance of online reviews and feedback are key drivers fueling market growth. Additionally, advancements in technology and the emergence of AI-powered feedback analysis tools are creating new opportunities for market players. The market is segmented by type into social media feedback, prototype feedback, free-form response analysis, and others. By application, it is divided into cell phones, computers, and others. Regionally, North America is the largest market, followed by Europe and Asia Pacific. Zendesk, Qualtrics, and HubSpot are among the leading companies in the feedback management system market. Key trends include the growing popularity of SaaS-based feedback solutions, the integration of feedback systems with CRM and other business applications, and the increasing use of feedback data for product development and customer experience optimization.

In 2019, Polish respondents most often received approval from team mates - 31 percent, and from their supervisor - 26 percent.

Data Description: This data set contains a record of all Citizen Service Requests (CSRs) feedback survey responses. When CSRs are closed out by the City, customers who provide an email address are automatically sent a notification that their work has been completed, as well as a link to a customer service satisfaction survey. Customers are able to provide feedback on work completion, satisfaction level, and any additional information. No identifying personal customer/citizen information (name, contact information, or additional comments) is included in this data.

Data Creation: Data generated when CSR feedback surveys are submitted

Data Created By: DPS

Refresh Frequency:

CincyInsights: The City of Cincinnati maintains an interactive dashboard portal, CincyInsights in addition to our Open Data in an effort to increase access and usage of city data. This data set has an associated dashboard available here: https://insights.cincinnati-oh.gov/stories/s/Customer-Service-CSR-Satisfaction/ks8a-xggj/

Data Dictionary: A data dictionary providing definitions of columns and attributes is available as an attachment to this dataset.

Processing: The City of Cincinnati is committed to providing the most granular and accurate data possible. In that pursuit the Office of Performance and Data Analytics facilitates standard processing to most raw data prior to publication. Processing includes but is not limited: address verification, geocoding, decoding attributes, and addition of administrative areas (i.e. Census, neighborhoods, police districts, etc.).

Data Usage: For directions on downloading and using open data please visit our How-to Guide: https://data.cincinnati-oh.gov/dataset/Open-Data-How-To-Guide/gdr9-g3ad

Annual review of statistics and its application Abstract & Indexing - ResearchHelpDesk - The Annual Review of Statistics and Its Application informs statisticians, and users of statistics about major methodological advances and the computational tools that allow for their implementation. The Annual Review of Statistics and Its Application debuted in the 2016 Release of the Journal Citation Report (JCR) with an Impact Factor of 3.045.

Survey of innovation and business strategy, frequency of review of key production performance indicators by top or middle managers, by North American Industry Classification System (NAICS) and enterprise size for Canada and regions from 2009 to today.

• Don't worry about solving CAPTCHAs, rotating proxies or installing headless browsers
• No need to update scrapers with every minor or major website layout or design change

• Simple pricing, pay per successful result only. Say goodbye to being charged for failed requests.

• Filter results by number of reviews, date

• Review data includes meta data about customers such as avatar, location, profile url, etc.

• Get page meta data like product price information, rating distribution, etc.

This dataset provides SPSS data files supporting the analysis of student perceptions of assessment, feedback, self-efficacy, task value, achievement goals, strategies, and self-reported achievement. Data are obtained from surveys in PRC and NZ with 3 samples (PRC undergraduates, NZ domestic undergraduates, and PRC international undergraduates in NZ universities). Code for analysis of the NZ data and invariance testing is provided for R software. The data were documented in structural equation models reported in Tracy's doctoral thesis at the University of Auckland. The data are anonymised and were collected with ethics approval from the UoA Human Participants Ethics Committee. This research was supervised by Gavin Brown and Richard Hamilton.

Abstract

In Brazil, policy makers in the state of Ceara are looking at how providing information to schools about best teaching practices, as well as offering peer learning opportunities, can help boost the performance of less effective teachers. The state government wants to stimulate more interaction among teachers at the school level that will lead to faster and cheaper diffusion of good practices within schools. The Secretariat requested World Bank assistance with the design and implementation of the random assignment experiment during the 2015 school year to measure cost-effectiveness of this approach.

The endline data collection was carried out in October - November 2015. Researchers used "Stallings Classroom Snapshot" instrument to gather information about teachers' use of time, materials and interactive pedagogical practices. The observations were made in 3,121 classrooms of 10th, 11th and 12th grade in randomly selected 300 schools.

The schools were randomly assigned to a treatment group (156 schools) or a control group (136 schools). The treatment was launched in March 2015. The treatment group received detailed feedback on the school's results from the classroom observations, information on teacher performance, self-help materials that included a book, videos and exercises about effective teaching strategies, and a log book and classroom observation templates to record teachers' observations of one other. The control schools received neither feedback nor information. After the treatment, researchers assessed the results of student test scores to determine whether the campaign helped improve classroom learning.

The baseline survey was conducted in November 2014.

Geographic coverage

The state of Ceará

Analysis unit

Classrooms; Schools

Kind of data

Observation data/ratings [obs]

Sampling procedure

Ceará has 573 secondary schools that offer the complete three-year cycle. Of these, a sample of 400 schools was stratified by size, geographic area and quartile of learning results. Researchers randomly assigned the 400 schools into 4 groups, with the first 175 assigned to the treatment group, a second group of 25 assigned to a no-observation group, the next 175 schools assigned to the control group, and the last 25 schools also assigned to the no-observation group.

A late start to the baseline round of classroom observations and a limited budget led to a reduction in the sample to 350 schools (175 treatments and 175 controls), which were selected through simple randomization to keep the sample balance. The team did not observe any classroom in the group of 50 schools that were randomly assigned to a no-observation group of the study, but was able to analyze the students' assessments results afterwards.

Sampling deviation

Out of the 350 schools of the randomization, with 175 each planned for treatment and controls, 292 schools were observed in November 2014 and in November 2015. The full initial sample could not be observed due to disruptions in the school calendar in November 2014 (standardized tests and holidays) and a shortage of observers in the Fortaleza district. The 292-school final sample included 156 schools in the treatment group and 136 in the control group. This difference in the attrition of treatment and control schools was due to the data collection firm focusing their efforts on making up for the schools of the treatment group that would benefit from the classroom observation and the intervention. As a result, because the loss of schools from the treatment and control groups was uneven, the research team conducted a series of balance checks to test the randomization.

In the treatment sample, the 19 schools that were not observed could not receive the information treatment (benchmarked classroom observation feedback for the teachers in their school). But these schools were given access to the other three components of the program – self-help materials, face-to-face training and coaching, and were observed again at endline. The same schools were observed to obtain the endline data. Matched repeat observations were made in 2,399 classrooms, 75% of those observed at baseline. Variations in the school calendar and logistical issues resulted in 25% of the 2015 observations being conducted in grades and subjects in the school that had not been observed at baseline.

Mode of data collection

Face-to-face [f2f]

Research instrument

1) The Stallings Classroom Snapshot Coding Sheet: The classroom snapshot records the participants, their activities, and the materials being used in the classroom, at ten separate instances throughout a class period.

2) School Principals Questionnaire: The questionnaire gathers information about teachers' engagement in the school, level of teacher training activities, and Principals' and supervisors' assessments of the value and impact of the treatment.

3) Classroom Demographic Sheet: The instrument is used for identification of the school and classroom, and the basic information related to the observed classroom, such as the number of student and the start time of the class.

A public and anonymous survey aimed towards soliciting direct feedback from users of DEQ's Envionmental Data Hub and related geospatial online offerings.

This dataset includes monthly customer feedback performance metric data for NYCT. For Subways, Buses, Access-A-Ride, MetroCard, and Other NYCT, there is data on the total number of complaints and commendations, and the number of complaints and commendations per 100,000 riders, along with monthly ridership.

Feedback And Reviews Management Software Market size was valued at USD 10.11 Billion in 2024 and is projected to reach USD 28.04 Billion by 2031, growing at a CAGR of 13.60% from 2024 to 2031.

Global Feedback And Reviews Management Software Market Drivers

client-Centric Business Models: In order to stand out in competitive marketplaces and foster client loyalty, businesses are placing a greater emphasis on customer pleasure. As a result of this change, there is an increasing need for systems that can efficiently gather and handle client input.

Real-Time Feedback: Businesses understand how critical it is to get real-time feedback in order to solve problems quickly and raise the calibre of their services. Real-time feedback collecting and analysis are made possible by feedback and reviews management software, which yields insights that may be put to use.

E-Commerce Expansion: As a result of the e-commerce platforms’ rapid expansion, there are now more online reviews and comments than ever before. Using advanced software solutions is necessary to manage this massive amount of data.

Influence on Purchase Decisions: Consumer purchase decisions are greatly influenced by online reviews. Adoption of specialised software is fueled by the necessity for businesses to actively manage and respond to reviews in order to maintain a favourable online reputation.

This is a dataset used for a multimodal poster presentation experiment and data analysis performed at the Division of Speech, Music and Hearing (TMH) at KTH Royal Institute of Technology in Stockholm Sweden in 2019 and 2020. The compressed elan_files.zip file contains raw ELAN annotated data, and the various .csv files contain formatted data for training for statistical learning models. Used in an upcoming publication.