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Comprehensive data understanding is a key success driver for data analytics projects. Knowing the characteristics of the data helps a lot in selecting the appropriate data analysis techniques. Especially in data-driven product planning, knowledge about the data is a necessary prerequisite because data of the use phase is very heterogeneous. However, companies often do not have the necessary know-how or time to build up solid data understanding in connection with data analysis. In this paper, we develop a methodology to organize and categorize and thus understand use phase data in a way that makes it accessible to general data analytics workflows, following a design science research approach. We first present a knowledge base that lists typical use phase data from a product planning view. Second, we develop a taxonomy based on standard literature and real data objects, which covers the diversity of the data considered. The taxonomy provides 8 dimensions that support classification of use phase data and allows to capture data characteristics from a data analytics view. Finally, we combine both views by clustering the objects of the knowledge base according to the taxonomy. Each of the resulting clusters covers a typical combination of analytics relevant characteristics occurring in practice. By abstracting from the diversity of use phase data into artifacts with manageable complexity, our approach provides guidance to choose appropriate data analysis and AI techniques. [ABSTRACT FROM AUTHOR]

Abstract Background Engagement with smartphone-based interventions stimulates adherence and improves the likelihood of gaining benefits from intervention content. Research often relies on system usage data to capture engagement. However, to what extent usage data reflect engagement is still an open empirical question. We studied how usage data relate to engagement, and how both relate to intervention outcomes. Methods We drew data from a randomized controlled trial (RCT) (N = 86) evaluating a smartphone-based intervention that aims to stimulate future self-identification (i.e., future self vividness, valence, relatedness). General app engagement and feature-specific engagement were retrospectively measured. Usage data (i.e., duration, number of logins, number of days used, exposure to intervention content) were unobtrusively registered. Results Engagement and usage data were not correlated. Multiple linear regression analyses revealed that general app engagement predicted future self vividness (p = .042) and relatedness (p = .004). Furthermore, engagement with several specific features also predicted aspects of future self-identification (p = .005 – .032). For usage data, the number of logins predicted future self vividness (p = .042) and exposure to intervention content predicted future self valence (p = .002). Conclusions Usage data did not reflect engagement and the latter was the better predictor of intervention outcomes. Thus, the relation between usage data and engagement is likely to be intervention-specific and the unqualified use of the former as an indicator of the latter may result in measurement error. We provide recommendations on how to capture engagement and app use in more valid ways.

The purpose of this study was to share the design and development case of Advising Virtual Assistant (AVA), a chatbot created to provide support in academic advising in higher education. By analyzing participants' usage data and chatbot performance, we attempted to understand how students engage with AVA to fulfill their advising needs. AVA effectively delegated repetitive and prescriptive advising services thus enabling efficient utilization of human resources. Students' usage data and chatbot performance data showed increased engagement with academic advising and faster, readily available access to academic advising resources. Additionally, this study presented insights gained from the challenges faced during the integration of the chatbot and suggested directions for future research. The findings of this study are expected to aid academic advisors and university leadership in developing more efficient communication channels via a chatbot that can support the university's goals. [ABSTRACT FROM AUTHOR]

Background: Engagement with smartphone-based interventions stimulates adherence and improves the likelihood of gaining benefits from intervention content. Research often relies on system usage data to capture engagement. However, to what extent usage data reflect engagement is still an open empirical question. We studied how usage data relate to engagement, and how both relate to intervention outcomes. Methods: We drew data from a randomized controlled trial (RCT) (N = 86) evaluating a smartphone-based intervention that aims to stimulate future self-identification (i.e., future self vividness, valence, relatedness). General app engagement and feature-specific engagement were retrospectively measured. Usage data (i.e., duration, number of logins, number of days used, exposure to intervention content) were unobtrusively registered. Results: Engagement and usage data were not correlated. Multiple linear regression analyses revealed that general app engagement predicted future self vividness (p =.042) and relatedness (p =.004). Furthermore, engagement with several specific features also predicted aspects of future self-identification (p =.005 –.032). For usage data, the number of logins predicted future self vividness (p =.042) and exposure to intervention content predicted future self valence (p =.002). Conclusions: Usage data did not reflect engagement and the latter was the better predictor of intervention outcomes. Thus, the relation between usage data and engagement is likely to be intervention-specific and the unqualified use of the former as an indicator of the latter may result in measurement error. We provide recommendations on how to capture engagement and app use in more valid ways. [ABSTRACT FROM AUTHOR]

In 2015, the University of California, Berkeley, launched EBSCO Discovery Service (EDS), a web-scale discovery tool, with a goal of improving visibility and usage of collections. This study applies linear regression analysis to usage data for ebooks, ejournals, and abstracts and indexing (A&I) databases before and after implementation of EDS in order to identify correlations between the discovery layer and usage of library electronic resources across platforms. Our findings diverge from conclusions drawn in the previous literature that indicate that resource use generally increases after a discovery tool is implemented. We examine data from a longer period of time than the previous literature had, looking for statistically significant changes in resource use. The discovery layer at UC Berkeley did not lead to equal increases across platforms, but rather to a complex array of increases and decreases in use according to a variety of factors.

Background: Based on the most common psychological difficulties of the evacuees from the 2016 Fort McMurray wildfires in Alberta, Canada, a therapist-guided cognitive behavioral self-treatment was developed. This study aimed to explore how gender influences the usage and subjective appreciation of the RESILIENT online treatment. Methods: Our study included 81 English-speaking evacuees with significant posttraumatic symptoms, or with some posttraumatic symptoms accompanied by at least mild depression symptoms or subclinical insomnia, and who logged into the platform at least once. Various usage and subjective appreciation variables were analyzed, including number of completed sessions, number of logins, number of words per session, perceived efforts, perception of usefulness and intention to continue using the different strategies. Results: No difference was detected in most objective usage indicators. The number of words written in sessions 7 and 10 was significantly greater for women than for men. Regarding subjective appreciation, men had a greater perception of having put strong efforts in the cognitive restructuring strategy, while women reported in a greater proportion that they wanted to continue using physical exercise as a behavioral activation strategy. Conclusions: Our study offers a first look into how women and men use online treatments, and what their preferences are. [ABSTRACT FROM AUTHOR]

An increasing number of products are equipped with software and sensors. This suggests that, in order to deliver more customised performance, future products will be developed to accommodate systems that supply information on how these products are used. Today, information on the customer’s way of using a product is seldom factored into product design, but the opportunities for making use of it are increasing dramatically due to the amount of available data that can be logged. The proposed methodology is to formulate Customer Needs at a detailed level to be able to link customer satisfaction with a clear interface to the Design Requirements. These links are obtained by combining information acquired by means of surveys, among other methodologies, as well as usage data from customer products. The method is based on the planning House of Quality and also takes cost and risk into consideration. Risk is estimated using the Analytical Hierarchy Process, whereby a hierarchy of the most relevant customer information is constructed to make designers aware of how customer-focused the design process is. To validate the proposed methodology an illustrative example is presented. Results show that the method provides valuable information that enables the company to remain customer-focused during the whole process but also when strategic decisions on price and product launch are made. [ABSTRACT FROM PUBLISHER]

This study utilizes COUNTER 5 data from the University of Dayton (UD) to measure full-text retrievals of e-journal articles from five major academic journal publishers (Taylor & Francis, SAGE, Oxford, Wiley, and Springer). Usage data from these publishers' e-journals within EBSCO is compared to the same content when accessed from publisher platforms such as Wiley Online Library or SpringerLINK. Building on previous studies that have largely focused on links (or referrals) from the library discovery layer to publisher platforms, this study analyzes usage of full text-articles stored within EBSCO Discovery Service and EBSCOhost subject databases to consider how these full-text holdings within EBSCO might affect referrals to publisher platforms. The findings indicate that full-text article holdings within EBSCO are used more often than the same content in publisher platforms, suggesting that UD students and researchers rely heavily on—and likely often start with—EBSCO for their learning and research. [ABSTRACT FROM AUTHOR]

The value of literature resources can be reflected not only by the citing behavior of scholars, but also by other using behavior of readers, such as downloading, reading and sharing. In this study, the high-usage papers in the discipline of "economics and business" in Web of Science were selected for sciencetometric analysis. The general characteristics of high-usage papers were described, followed by the analysis of guarantee rate of East China Normal University and CASHL (China Academic Social Sciences and Humanities Library) for high-usage papers. It is found that the high-usage papers are generally the latest published or with high journal impact factors, and there is a significant positive correlation between the usage counts and the citations. East China Normal University's guarantee rate for the high-usage papers in the ~ discipline of "economics and business" is only 83. 9 %, but CASHL's guarantee rate in this discipline -J reaches 99.1 %, which greatly makes up for the lack of guarantee rate in a single university. Usage metrics If is expected to be a useful supplement to the study of literature guarantee rate based on citation analysis. [ABSTRACT FROM AUTHOR]

Donald A.B. Lindberg, M.D., brought with him when he joined NLM an inquisitive mind, tech savvy, and new ideas. He was an early advocate of both outreach and evaluation innovation at NLM. Dr. Lindberg initiated and supported multiple pilot test and implementation projects to strengthen NLM's health information outreach to healthcare providers, research scientists, health science and hospital librarians, and the general public, including minority and underserved populations. He helped steer NLM's transition to the Internet, and NLM's development of a robust framework for evaluating Internet and Web-based health information dissemination and outreach to its many audiences. Dr. Lindberg's leadership led to numerous landmark accomplishments, including the capacity-building "Measuring the Difference" outreach evaluation Guide, and a multidimensional approach to Internet and website evaluation that placed NLM at the forefront of federal agencies using these new and emerging technologies to support their missions. [ABSTRACT FROM AUTHOR]

In April 2020, the OAPEN Library moved to a new platform, based on DSpace 6. During the same period, IRUS-UK started working on the deployment of Release 5 of the COUNTER Code of Practice (R5). This is, therefore, a good moment to compare two widely used usage metrics – R5 and Google Analytics (GA). This article discusses the download data of close to 11,000 books and chapters from the OAPEN Library, from the period 15 April 2020 to 31 July 2020. When a book or chapter is downloaded, it is logged by GA and at the same time a signal is sent to IRUS-UK. This results in two datasets: the monthly downloads measured in GA and the usage reported by R5, also clustered by month. The number of downloads reported by GA is considerably larger than R5. The total number of downloads in GA for the period is over 3.6 million. In contrast, the amount reported by R5 is 1.5 million, around 400,000 downloads per month. Contrasting R5 and GA data on a country-by-country basis shows significant differences. GA lists more than five times the number of downloads for several countries, although the totals for other countries are about the same. When looking at individual tiles, of the 500 highest ranked titles in GA that are also part of the 1,000 highest ranked titles in R5, only 6% of the titles are relatively close together. The choice of metric service has considerable consequences on what is reported. Thus, drawing conclusions about the results should be done with care. One metric is not better than the other, but we should be open about the choices made. After all, open access book metrics are complicated, and we can only benefit from clarity.

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April 2020, the OAPEN Library moved to a new platform, based on DSpace 6. During the same period, IRUS-UK started working on the deployment of Release 5 of the COUNTER Code of Practice (R5). This is, therefore, a good moment to compare two widely used usage metrics -- R5 and Google Analytics (GA). This article discusses the download data of close to 11,000 books and chapters from the OAPEN Library, from the period 15 April 2020 to 31 July 2020. When a book or chapter is downloaded, it is logged by GA and at the same time a signal is sent to IRUS-UK. This results in two datasets: the monthly downloads measured in GA and the usage reported by R5, also clustered by month. The number of downloads reported by GA is considerably larger than R5. The total number of downloads in GA for the period is over 3.6 million. In contrast, the amount reported by R5 is 1.5 million, around 400,000 downloads per month. Contrasting R5 and GA data on a country-by-country basis shows significant differences. GA lists more than five times the number of downloads for several countries, although the totals for other countries are about the same. When looking at individual tiles, of the 500 highest ranked titles in GA that are also part of the 1,000 highest ranked titles in R5, only 6% of the titles are relatively close together. The choice of metric service has considerable consequences on what is reported. Thus, drawing conclusions about the results should be done with care. One metric is not better than the other, but we should be open about the choices made. After all, open access book metrics are complicated, and we can only benefit from clarity. [ABSTRACT FROM AUTHOR]

This paper examines the materials acquired during a consortial PDA pilot; which of the books are still being used, by which institutions, whether the size of a participating institution or the subject of a book have impact on the continued use of the books, and if there is a relationship between the schools that triggered a PDA purchase and the schools that have gone on to use a given title. Findings suggest that models involving some library mediation may be more effective in meeting the various needs of participating institutions. [ABSTRACT FROM AUTHOR]

Downloading a mobile health (m-health) app on your smartphone does not mean you will ever use it. Telling another person about an app does not mean you like it. Using an online intervention does not mean it has had an impact on your well-being. Yet we consistently rely on downloads, clicks, ‘likes’ and other usage and popularity metrics to measure m-health app engagement. Doing so misses the complexity of how people perceive and use m-health apps in everyday life to manage mental health conditions. This article questions commonly used behavioural metrics of engagement in mental health research and care, and proposes a more comprehensive approach to measuring in-app engagement.

Aim: This paper explores the real practices of users of a smart carpooling application that learns their mobility habits and predicts their future trips to propose relevant matches. Method: A combination of usage data and interviews analysis allows us to explore the commuter experience from registration to the first and the next shared rides. Findings: The results highlight the shortcomings associated with human factors in carpooling and with human-smart system interactions. They show that perceptions of practical constraints and poor counterparts are the major reasons for difficulty in incorporating carpooling into daily mobility. Psychosocial barriers take different forms at different steps of the carpooling experience (search for information or guarantees about other users, the necessity of conversing with others, much uncertainty about how to behave). The fact that the service is smart amplifies these problems and reduces the desire to carpool again because it creates new misunderstandings (i.e., the user does not understand what the system vs. the other users do) and discomfort in relation to other riders (no answer, too many refusals, necessity of refusing, negative carpool experience, or concern over proposing a bad carpool). Despite these difficulties, the users perceive carpooling as a good solution and a positive human experience when the matching is accurate. We propose some recommendations to overcome the identified difficulties. [ABSTRACT FROM AUTHOR]

With an abundance of digital library projects of all types and sizes, a common practice is for larger institutions to host digital collections for smaller ones. Many institutions use analytics data to inform their digital collections projects; have the larger host institutions established standards for sharing analytics data with smaller contributing institutions in multi-institution projects? This literature review of articles published between 2009 and 2019 (with selected earlier articles included) seeks to understand what data hosts provide and how contributors use this data, with an emphasis on promotion, metadata, and contributor engagement. The authors analyzed the articles against a framework of six research questions and concluded that there was little information on sharing analytics data. Further assessment of contributor needs and attitudes about analytics is warranted. [ABSTRACT FROM AUTHOR]

To address the challenges of aging societies, various information and communication technology (ICT)-based systems for older people have been developed in recent years. Currently, the evaluation of these so-called active and assisted living (AAL) systems usually focuses on the analyses of usability and acceptance, while some also assess their impact. Little is known about the actual take-up of these assistive technologies. This paper presents a framework for measuring the take-up by analyzing the actual usage of AAL systems. This evaluation framework covers detailed information regarding the entire process including usage data logging, data preparation, and usage data analysis. We applied the framework on the AAL prototype CARIMO for measuring its take-up during an eight-month field trial in Austria and Italy. The framework was designed to guide systematic, comparable, and reproducible usage data evaluation in the AAL field; however, the general applicability of the framework has yet to be validated. [ABSTRACT FROM AUTHOR]

It is nearly a given that most academic library directors feel the need to collaborate with other libraries to contain costs, develop new programs, and accomplish their missions; and historically, many have done so by participating in a library consortium, and sometimes in multiple consortia. In this article, the executive directors of The Pennsylvania Academic Library Consortium, Inc. (PALCI) and the Private Academic Library Network of Indiana (PALNI), two long-standing academic library consortia, share their insights and experiences as they have observed the necessity for broadening consortial collaboration through cross-consortial partnerships, moving from coordinated efforts toward deeper collaboration across consortium boundaries. The authors describe and reflect on several recent examples of cross-consortial initiatives in which their respective consortia are involved, examining why consortia are beginning to collaborate more deeply across boundaries and the challenges and opportunities associated with those collaborations. [ABSTRACT FROM AUTHOR]

Metrics usage are becoming more important since the main sources of scientific information facilitate them. In this work, exposure years, citations and usage counts of the Web of Science Core Collection (WoS) were collected for a sample of multidisciplinary documents with high citation rates on a specific topic: entrepreneurship. The h-classic methodology was applied to obtain a representative group of documents and the statistical relationships established between the three variables were studied. The results showed how the years of exposure imply a decrease in usage counts, but do not influence the citations received in the long term. Citations and usage counts have a clearly direct and positive correlation that allows to predict either of the two variables from the other. [ABSTRACT FROM AUTHOR]

In this paper, we take a closer theoretical and empirical look at the linking elements in German N1+N2 compounds which are identical to the plural marker of N1 (such as -er with umlaut, as in Häus-er-meer ‘sea of houses’). Various perspectives on the actual extent of plural interpretability of these pluralic linking elements are expressed in the literature. We aim to clarify this question by empirically examining to what extent there may be a relationship between plural form and meaning which informs in which sorts of compounds pluralic linking elements appear. Specifically, we investigate whether pluralic linking elements occur especially frequently in compounds where a plural meaning of the first constituent is induced either externally (through plural inflection of the entire compound) or internally (through a relation between the constituents such that N2 forces N1 to be conceptually plural, as in the example above). The results of a corpus study using the DECOW16A corpus and a split-100 experiment show that in the internal but not external plural meaning conditions, a pluralic linking element is preferred over a non-pluralic one, though there is considerable inter-speaker variability, and limitations imposed by other constraints on linking element distribution also play a role. However, we show the overall tendency that German language users do use pluralic linking elements as cues to the plural interpretation of N1+N2 compounds. Our interpretation does not reference a specific morphological framework. Instead, we view our data as strengthening the general approach of probabilistic morphology. [ABSTRACT FROM AUTHOR]

INTRODUCTION Academic librarians, especially in the field of scholarly communication, are often expected to understand and engage with research impact indicators. However, much of the current literature speculates about how academic librarians are using and implementing research impact indicators in their practice. METHODS This study analyzed the results from a 2015 survey administered to over 13,000 academic librarians at Carnegie-classified R1 institutions in the United States. The survey concentrated on academic librarians’ familiarity with and usage of research impact indicators. Results: This study uncovered findings related to academic librarians’ various levels of familiarity with research impact indicators and how they implement and use research impact indicators in their professional development and in their library job duties. DISCUSSION In general, academic librarians with regular scholarly communication support duties tend to have higher levels of familiarity of research impact indicators. In general, academic librarians are most familiar with the citation counts and usage statistics and least familiar with altmetrics. During consultations with faculty, the Journal Impact Factor (JIF) and citation counts are more likely to be addressed than the author h-index, altmetrics, qualitative measures, and expert peer reviews. The survey results also hint towards a growing interest in altmetrics among academic librarians for their professional advancement. CONCLUSION Academic librarians are continually challenged to keep pace with the changing landscape of research impact metrics and research assessment models. By keeping pace and implementing research impact indicators in their own practices, academic librarians can provide a crucial service to the wider academic community.