The Customer Survey Software market is experiencing robust growth, driven by the increasing need for businesses to understand customer preferences, improve products and services, and enhance customer experience. The market's expansion is fueled by several key factors, including the rising adoption of cloud-based solutions, the increasing use of advanced analytics for data interpretation, and the growing demand for personalized customer interactions. Businesses across diverse sectors, from retail and e-commerce to healthcare and finance, are leveraging survey software to gather valuable feedback, measure customer satisfaction (CSAT), and track Net Promoter Score (NPS). This allows for proactive improvements to processes, products and overall business strategies. The competitive landscape is dynamic, with established players like Qualtrics and SurveyMonkey competing against innovative startups offering specialized features and integrations. While the market faces certain restraints, such as data security concerns and the need for robust data analysis capabilities, the overall outlook remains positive, projecting continued expansion and market penetration in the coming years. We estimate a substantial market value based on available data and industry trends, and predict continued strong growth. The market segmentation reveals strong interest in various feature sets, with a focus on ease-of-use, data visualization, and advanced analytics. The integration of survey software with other business tools, such as CRM systems, is also driving adoption. Geographical distribution indicates a concentration in developed regions like North America and Europe, but emerging markets in Asia-Pacific and Latin America are rapidly catching up, presenting significant growth opportunities. The ongoing emphasis on customer-centricity across all business models further strengthens the market outlook. Furthermore, increasing adoption of mobile-first strategies and the rise of omnichannel customer experiences will further fuel the need for robust and versatile survey software. The continued development of AI-powered features for automated analysis and personalized recommendations will shape future market trends.

The Voice of the Customer (VoC) tools market is experiencing robust growth, projected to reach a market size of $1292.4 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 5.6% from 2025 to 2033. This expansion is fueled by several key drivers. Increasing customer expectations for personalized experiences necessitate businesses to actively listen and respond to feedback, driving adoption of VoC tools. The rise of e-commerce and omnichannel strategies necessitates efficient feedback mechanisms to manage customer interactions across multiple touchpoints. Businesses are realizing the importance of proactive customer service, using VoC data to identify and address potential issues before they escalate. The cloud-based deployment model is rapidly gaining traction, due to its scalability, cost-effectiveness, and ease of integration with existing systems. Retailers and e-commerce businesses are leading the adoption curve, followed by one-stop shops, demonstrating the critical role of customer feedback in optimizing operations and driving business outcomes. However, challenges such as data privacy concerns, integration complexities with legacy systems, and the need for skilled professionals to interpret and utilize the gathered data, pose restraints to faster market penetration. The market segmentation reveals a dynamic landscape. Cloud-based solutions dominate, reflecting the industry-wide shift towards cloud technologies. Among applications, retailers and e-commerce businesses represent significant market segments, highlighting the crucial role of customer feedback in the success of these industries. The geographic distribution reflects the mature markets in North America and Europe, with significant growth opportunities in the Asia-Pacific region driven by increasing digital adoption and e-commerce penetration. While precise figures for individual segments and regions are unavailable, the overall market trajectory clearly points towards substantial growth driven by improving technology, increasing customer expectations, and the demonstrable ROI of actionable customer insights. Future growth will likely be shaped by advancements in artificial intelligence (AI) and machine learning (ML) for automating feedback analysis and sentiment detection, further enhancing the efficiency and effectiveness of VoC tools.

The raw data, preprocessing script, preprocessed data file, and the main analyses for the project "Are new gender-neutral pronouns difficult to process in reading? The case of hen in Swedish". The psychopy data set includes measures of key press responses to comprehension questions recorded during the experiment. It also includes information on in what order the stimuli were displayed to the participant, and how much time they spent looking at the stimulus until they pressed a key to move on to the next stimulus.The questionnaire data set includes background information collected through the online questionnaire designing webpage Qualtrics (https://qualtrics.com) after the eye-tracking experiment. Data was collected between Sept 25th 2017 and March 8th 2018.The eye-tracking data set includes measures of reading behaviors such as fixation time on stimuli including gendered and gender-neutral pronouns. Instrument- or software-specific information needed to run the experiment and interpret the data:Psychopy (Peirce et al., 2019) for running the experiment (https://www.psychopy.org/). Python for opening and managing the related .py files (https://www.python.org).BeGaze (SensoMotoric Instruments, 2014) for opening and managing the .idf files. Requires software license. These files were created using SMI's software iView for recording eye movements with the eye-tracker.[https://gazeintelligence.com/smi-software-download]Rstudio used with R (R Core Team, 2017): https://rstudio.com/products/rstudio/ More information can be found in the README files, and on our OSF page.

The Brand Intelligence Software market is experiencing robust growth, driven by the increasing need for businesses to understand and manage their brand reputation in a rapidly evolving digital landscape. The market's expansion is fueled by several key factors: the proliferation of social media and online reviews, the rising importance of customer experience management, and the growing adoption of AI-powered analytics for deeper brand insights. Companies across various sectors, from large enterprises to SMEs, are leveraging brand intelligence solutions to monitor brand sentiment, track competitor activities, identify emerging trends, and proactively address potential brand crises. The cloud-based segment holds a significant market share, driven by its scalability, accessibility, and cost-effectiveness. We project a compound annual growth rate (CAGR) of 15% for the period 2025-2033, with the market valued at approximately $5 billion in 2025, based on current market trends and industry reports. North America and Europe currently represent the largest regional markets, but the Asia-Pacific region is expected to witness significant growth in the coming years due to increasing internet penetration and digital adoption. While the market presents lucrative opportunities, challenges remain. Data privacy concerns and the need for robust data security measures are critical considerations. The complexity of integrating brand intelligence solutions with existing marketing technologies and the need for skilled professionals to analyze and interpret the data can also hinder wider adoption. Nevertheless, ongoing technological advancements, particularly in AI and machine learning, are expected to further enhance the capabilities of brand intelligence software, making it more efficient and user-friendly. This will ultimately drive market growth and broaden its accessibility across diverse industries and company sizes, leading to wider integration within business strategies.

The global People & HR Analytics Software market is experiencing robust growth, driven by the increasing need for data-driven decision-making in human resource management. Businesses are increasingly recognizing the value of leveraging employee data to optimize workforce planning, improve recruitment strategies, enhance employee engagement, and ultimately boost overall productivity and profitability. The market's expansion is fueled by several key trends, including the rise of cloud-based solutions offering scalability and cost-effectiveness, the growing adoption of AI and machine learning for predictive analytics in HR, and the increasing focus on diversity, equity, and inclusion (DEI) initiatives, requiring sophisticated data analysis for effective monitoring and improvement. While the on-premises segment still holds a significant share, cloud-based solutions are witnessing rapid adoption due to their flexibility and accessibility. Large enterprises are currently the primary adopters, but the SME segment is exhibiting significant growth potential as awareness of the benefits of HR analytics increases and affordable solutions become more widely available. Geographic distribution shows strong performance in North America and Europe, driven by high technological adoption and established HR practices, with Asia-Pacific emerging as a region with significant growth potential. Market restraints include concerns surrounding data security and privacy, the complexity of implementing and integrating HR analytics solutions with existing HR systems, and the lack of skilled professionals capable of effectively analyzing and interpreting the generated data. Despite these challenges, the long-term outlook for the People & HR Analytics Software market remains positive. Continuous technological advancements, the increasing availability of user-friendly software, and the growing understanding of the ROI associated with HR analytics are poised to further propel market expansion. The competitive landscape is dynamic, with established players alongside innovative startups vying for market share. Strategic partnerships, acquisitions, and product innovations are key competitive strategies observed in this rapidly evolving market. We project a continued strong CAGR for the forecast period, with the market showing significant expansion across all segments and regions.

This data set contains the replication data for the article "Knowing and doing: The development of information literacy measures to assess knowledge and practice." This article was published in the Journal of Information Literacy, in June 2021. The data was collected as part of the contact author's PhD research on information literacy (IL). One goal of this study is to assess students' levels of IL using three measures: 1) a 21-item IL test for assessing students' knowledge of three aspects of IL: evaluating sources, using sources, and seeking information. The test is multiple choice, with four alternative answers for each item. This test is a "KNOW-measure," intended to measure what students know. 2) a source-evaluation measure to assess students' abilities to critically evaluate information sources in practice. This is a "DO-measure," intended to measure what students do in practice, in actual assignments. 3) a source-use measure to assess students' abilities to use sources correctly when writing. This is a "DO-measure," intended to measure what students do in practice, in actual assignments. The data set contains survey results from 626 Norwegian and international students at three levels of higher education: bachelor, master's and PhD. The data was collected in Qualtrics from fall 2019 to spring 2020. In addition to the data set and this README file, two other files are available here: 1) test questions in the survey, including answer alternatives (IL_knowledge_tests.txt) 2) details of the assignment-based measures for assessing source evaluation and source use (Assignment_based_measures_assessing_IL_skills.txt) Publication abstract: This study touches upon three major themes in the field of information literacy (IL): the assessment of IL, the association between IL knowledge and skills, and the dimensionality of the IL construct. Three quantitative measures were developed and tested with several samples of university students to assess knowledge and skills for core facets of IL. These measures are freely available, applicable across disciplines, and easy to administer. Results indicate they are likely to be reliable and support valid interpretations. By measuring both knowledge and practice, the tools indicated low to moderate correlations between what students know about IL, and what they actually do when evaluating and using sources in authentic, graded assignments. The study is unique in using actual coursework to compare knowing and doing regarding students’ evaluation and use of sources. It provides one of the most thorough documentations of the development and testing of IL assessment measures to date. Results also urge us to ask whether the source-focused components of IL – information seeking, source evaluation and source use – can be considered unidimensional constructs or sets of disparate and more loosely related components, and findings support their heterogeneity.

Background: Living near, recreating in, and feeling psychologically connected to nature are all associated with better overall mental health. This study aims to better understand people’s feelings towards different types of natural and built green space environments in the highly urbanized ‘garden city’ of Singapore. The key research question addresses the matter of what types of green space elicit positive (Eudemonic) or negative (Apprehensive) affective responses. Type of environment (natural and built), frequency of experience (high and low) and childhood location (urban, suburban, rural) were tested for effects of Eudemonia and Apprehension. 288 adults and university students residing in Singapore completed a survey that asked them to report affective states in response to images of 10 locally different environment types and to complete measures of nature connectedness, childhood location, frequency of visit to natural/built environments, and dispositional anxiety, as well as demographic items for age and gender.

This data record contains:

• Qualtrics survey data in SPSS (.spss), tab delimited (.dat) and open document (.ods) format.
• Supplementary material in PDF format (.pdf) containing the Mean (sd) ratings of Apprehension (A, anxious, isolated, lonely) and Eudemonia (E, alive, awe, connected, contemplative, empathy, freedom, fun, refreshed, relaxed, serene, talkative) for 10 types of environment.

The Qualtrics survey included the following:

• Participant demographics:
  • Age in years (continuous)
  • Gender (categorical: Male, Female, Nonbinary)
• Categorisation of urban green space in Singapore:
  • 20 photographs of urban green spaces in Singapore (stimuli).
  • 10 categories of urban green spaces consisted of: beach, forest, grassy field, heritage street, modern city street, rooftop garden, river, town park, wetland, and woodland.
  • Two photographs that were best suited to each category according to participant responses (i.e., highest frequency of category selection) were used as stimuli for the study, with a total of 20 photographs selected.
• Experiential feeling states (Eudemonia & Apprehension) (interval) (20 x 14 items).
  • “Imagine yourself in the environment shown above. To what extent would you feel the following?”
  • Responses were recorded on a 7-point scale ranging from not at all (1) to extremely (7).
• Frequency of experience in green space (interval) (20 x 1 item).
  • “On average, how often do you visit or experience the type of environment as the one shown above?” Responses were recorded on a 5-point scale ranging from never (1) to very often (5).
• Childhood location (categorical) (1 item). “In what sort of location did you spend the majority of your childhood?” Urban (Modernised city, city-centre, many buildings with few trees, high traffic), Suburban (More greenery than city-centre but still developed, outside the main city area, neighbourhood towns, moderate traffic), Rural (Mostly greenery, few facilities, low traffic, “kampung” environment).
• Nature Connectedness Index (NCI) (interval) (6 items). "The next items will help us understand how you feel about nature and natural environments. Remember, this is not a test so there are no 'right' or 'wrong' answers. We want to understand how you feel about nature." The six items draw on five pathways to nature connectedness: emotion, beauty, contact, meaning and compassion. Participants respond using a 7-point scale ranging from completely agree (1) to completely disagree (7). Raw scores were transformed using a weighted points index ranging from zero to 100.
• Brief State-Trait Anxiety Inventory (STAIT-5) (interval) (5 items). “A number of statements which people have used to describe themselves are given below. Read each statement and then select the number at the end of the statement that indicates how you generally feel.” Responses are recorded on a 4-point scale ranging from not at all (1) to very much so (4).

Software/equipment used to create/collect the data: Qualtrics Online Survey Software through JCU licence

Software/equipment used to manipulate/analyse the data: SPSS, Microsoft Excel

Abstract Background The collection of data on ‘infant feeding at hospital discharge’ is used to monitor breastfeeding outcomes, health service benchmarking, and research. While some Australian states have clear definitions of this data collection point, there is no operational definition of ‘infant feeding at hospital discharge’ in the Australian state of New South Wales. Little is known about how midwives interpret the term ‘infant feeding at hospital discharge’, in particular, the timeframe used to calculate these important indicators. The purpose of this study was to explore midwives’ and nurses’ practices of reporting ‘infant feeding at hospital discharge’ in the Australian state of New South Wales. Methods An online survey was distributed across public and private maternity hospitals in New South Wales, Australia. The survey asked midwives and nurses their practice of reporting ‘infant feeding at discharge’ from categories offered by the state Mothers and Babies report of either “full breastfeeding”, “any breastfeeding”, and “infant formula only”. The Qualtrics survey was available from December 2021 to May 2022. Results There were 319 completed surveys for analysis and all 15 NSW Health Districts were represented. Some participants reported using the timeframe ‘since birth’ as a reference (39%), however, the majority (54%, n = 173) referenced one of the feeding timeframes within the previous 24 h. Most midwives and nurses (83%, n = 265) recommended 24 h before discharge as the most relevant reference timeframe, and 65% (n = 207) were in favour of recording data on ‘exclusive breastfeeding’ since birth. Conclusion This study identified multiple practice inconsistencies within New South Wales reporting of ‘infant feeding at hospital discharge’. This has ramifications for key health statistics, state reporting, and national benchmarking. While the Baby Friendly Hospital Initiative accreditation requires hospitals to demonstrate and continuously monitor at least a 75% exclusive breastfeeding rate on discharge, only 11 New South Wales facilities have achieved this accreditation. We recommend introducing an option to collect ‘exclusive breastfeeding’ on discharge’ which is in line with participant recommendations and the Baby Friendly Hospital accreditation. Other important considerations are the updated World Health Organization indicators such as, “Ever breastfed”; “Early initiation of breastfeeding” (first hour); “Exclusively breastfed for the first two days after birth”.

The Feedback Analytics Software market is experiencing robust growth, driven by the increasing need for businesses of all sizes to understand customer sentiment and improve operational efficiency. The market, estimated at $5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $15 billion by 2033. This expansion is fueled by several key trends: the rising adoption of cloud-based solutions offering scalability and cost-effectiveness; the increasing sophistication of AI-powered analytics enabling deeper insights from customer feedback; and a growing emphasis on proactive customer experience management across diverse industries, including retail, e-commerce, and financial services. Large enterprises are currently the dominant segment, but the market is witnessing significant growth from SMEs seeking to leverage customer feedback for competitive advantage. The North American market currently holds the largest share, followed by Europe and Asia-Pacific, with growth potential across all regions due to increasing digitalization and technological advancements. However, market growth is not without its challenges. The high initial investment required for implementing sophisticated feedback analytics solutions can act as a barrier to entry for smaller businesses. Furthermore, the complexity of data integration and the need for skilled personnel to interpret and utilize the insights effectively can also impede widespread adoption. The competitive landscape is further characterized by a mix of established players offering comprehensive solutions and emerging niche players focusing on specific functionalities or industries. Successful players will need to differentiate themselves through innovative features, superior data security, and strong customer support to maintain a competitive edge in this rapidly evolving market.

With the growth of open pedagogy has come the growth of library support for open pedagogy. Likewise, more and more case studies are demonstrating how librarians use open pedagogy to support student growth in information literacy, specifically the Association of College & Research Libraries’ Framework for Information Literacy in Higher Education. However, little has been done to look at the broader picture of how librarians are supporting open pedagogy and how ready they feel to do so, especially in connecting open pedagogy to information literacy. This data comes from a survey of librarians in the United States and Canada who work in information literacy instruction and/or open education about their practice in open pedagogy. Methods The project used a survey, created in Qualtrics, to help answer the research questions. The survey was designed with four broad sections. The first focused on gathering background information about the participants and their institution, as well as their comfort level with open pedagogy, support provided at their institutions for open education, and then finally whether they have supported open pedagogy in a higher education course. Those who answered negatively to supporting open pedagogy were then directed to a second section available only to them about their interest in eventually supporting open pedagogy. Those who answered they had supported open pedagogy were directed to a third section that asked them about their experience with open pedagogy. Finally, all participants were directed to the fourth section, which asked them about barriers and needs to help support open pedagogy. The University of Nevada, Reno's Institutional Review Board granted the research project an exempt status. The survey was open to any active academic librarian in the United States or Canada who currently works in library instruction and/or open education. The authors opted to focus on these two areas of librarianship as the most likely areas to support open pedagogy. The survey was launched to seven listservs: ACRL Scholarly Communication, ACRL Instruction Section, ACRL Library Instruction Roundtable, ACRL Community and Junior College Libraries Section, SPARC’s LibOER, Creative Commons Open Education Platform, and the Medical Library Association’s MEDLIB-L. Reminder emails were sent on July 19 and August 7. Two hundred and eight respondents began the survey; 15 did not meet the inclusion criteria, and 48 did not complete the survey, leaving 145 respondents. No question required a response, however, meaning response totals for some questions might be less than 145. The data was cleaned and analyzed using RStudio version 2022.02.3+492. Open-text responses were removed from this dataset to help protect participants' privacy.

These data are associated with the publication "Food insecurity related to agricultural practices and household characteristics in rural communities of northeast Madagascar", accepted for publication at the journal Food Security, May 2021.The explanation of variables provided is given in a ReadMe file. Only de-identified and processed data are provided, in accordance with our International Review Board ethical protocol. Not all variables collected during the study can be provided publicly to ensure the confidentiality of our participants.Data were collected using the following methods:Study design: The survey team included RJY and NAFR as the primary enumerators, local Malagasy researchers from each village, and AO, CF, and MM. Data were collected during the months of June and August, in Mandena in 2018, and in Manantenina and Matsobe in 2019. In each of the three communities, we conducted social surveys for randomly selected households. In Mandena, a drone image of the village was overlaid with a grid system (100X100m cells), which was used to first randomly sample grids, and then randomly sample households within grids, in proportion to the number of houses in those grids. In Manantenina, a member of the research team who lived in the village provided a complete list of all village households, which was used to randomly sample households. In Matsobe, 2018-2019 census data were used to randomly select households. If no members of the randomly selected household could be found, that household was substituted for an available neighbor.All surveys were administered in the local dialect of Malagasy, and informed consent was given by all study participants prior to taking the survey. RJY or AFR and/or a local research team member, fluent in the local dialect, conducted the informed consent and survey with the study participant. The survey was conducted using Qualtrics software on Samsung tablets, and had an average duration of 60 minutes to complete. Study participants were compensated with 1,000 Ariary (MGA, approximately 0.30 USD) in mobile phone credit upon survey completion.Food insecurity: Questions about food insecurity were modified from a prior study of agrarian socioeconomics in Malawi (Ward et al. 2018). We asked respondents if they had times when there was not enough food to feed the family over the past three years. We note that in Malagasy culture, when referring to food security generally, the interpretation is whether there was enough rice for the household, since rice is the staple food. To address the causes of food insecurity, options on the survey included small land size, lack of money, the cost of food in the local markets, extreme natural events (i.e., cyclones, droughts, insect or rodent pest outbreaks), as well as allowing the respondent to give any other reason for food insecurity.Socioeconomic characteristics: Standard data on demographics of households were collected using a survey adapted from the Demographic and Health Survey instrument (ICF_International, 2012). These variables included the number of individuals in the household, their ages in years, gender, education level, and main activity (farming, wage labor, etc.), and whether farmers reported other wage-earning activities other than their subsistence farming. To assess material wealth, we also collected data on the ownership of common household assets, such as radio, television, telephone, generator, solar panels, and farming tools including shovels, axes, plows. We asked about the household materials used to build the walls, roof, and floor, including natural products that were collected such as bamboo, raffia, and Ravinala, and purchased materials including wood planks, aluminum sheets, or cement. To create composite asset indicators, we used principal components analysis (PCA) to summarize the data on household assets and household building materials into orthogonal axes that best captured the variance in the data. As an alternative measure, we summed the number of assets the respondent reported. Households were classified as having a single female head if the respondent was female, identified herself as the head of the household, and reported that she was either not married nor living with a partner, or was a widow.Agricultural practices: Questions about agricultural practices included the types of crops grown, how farmers grew rice (low-land flooded paddies, on hillsides, or both), and domestic animal ownership (the number of animals owned for livestock, poultry, and other animals, enumerated between 1-5 or more than 5 individual animals). The size of farm land was assessed by asking farmers about the input of rice that would be required to grow rice on their land, based on a conversion that approximately 15kg is used to farm one ha (pers. comm. with local stakeholders). Rice and vanilla harvests were calculated in kg.To calculate crop diversification, we enumerated the total number of crops the farmers reported growing in the last year, as well as the total number of cash crops (coffee, cloves, cacao, and vanilla). We also calculated the proportion of the top five crops grown by the respondent, based on the five most commonly grown crops across all respondents. We divided these proportions among the top five food and cash crops. Lastly, we used a PCA to summarize the crop data into two axes that best separated farmers according to those that grow similar crops. We quantified variation in domestic animal ownership as the sum of domestic animals owned, as well as the richness and diversity (Shannon diversity index) of all domestic animals owned (cattle, pigs, goats, poultry). We also conducted a PCA of domestic animals owned to use the first PC as a composite score of domestic animal ownership.

The submitted dataset was used in the Honours thesis, “Birthing in the Age of Social Media: The Influence of Social Media Content on Fear of Childbirth and Obstetric Preferences During Pregnancy.” The dataset contains three files, including the cleaned jamovi data file, jamovi output, and final manifest content analysis structure. All data was collected anonymously using Qualtrics. The jamovi data file contains the survey responses of 407 participants who were first-time (or nulliparous) Australian pregnant women. Respondents indicated their socio-demographic details, obstetric details and preferences, fear towards childbirth using the Fear of Birth Scale (FOBS), estimated social media use, and the five most frequently seen features within childbirth-related social media content. A total of 348 respondents were retained for the final analysis which was performed using jamovi. The jamovi output includes descriptive statistics, a two-tailed independent samples t-test, two chi-square tests of independence, and additional post-hoc analysis. The dataset also includes the final meaning units, codes, subcategories, and categories generated from the manifest content analysis performed on open-text responses. Open-text responses listed additional features seen within childbirth-related content that were not listed in the survey options.