Data Privacy and Academic Libraries: Non-PII,PII, and Librarians’ Reflections (Part 2)

Abstract

Using personally identifiable information (PII) (patron data) to make informed decisions in academic libraries through learning analytics programs has increasingly become more commonplace. In this column, we discuss how libraries around the world have used PII to make informed decisions about hours (gate count), electronic resources (collection usage and authentication), and research assistance (virtual reference). In addition, we discuss the use of learning analytics in library environments including the benefits and concerns associated with its use. Finally, we discuss how we have used PII at our institution’s library and the data metrics we plan utilize at some point in the future.     

Text mining of industry 4.0 job advertisements

Since changes in job characteristics in areas such as Industry 4.0 are rapid, fast tool for analysis of job advertisements is needed. Current knowledge about competencies required in Industry 4.0 is scarce. The goal of this paper is to develop a profile of Industry 4.0 job advertisements, using text mining on publicly available job advertisements, which are often used as a channel for collecting relevant information about the required knowledge and skills in rapid-changing industries. We searched website, which publishes job advertisements, related to Industry 4.0, and performed text mining analysis on the data collected from those job advertisements. Analysis of the job advertisements revealed that most of them were for full time entry; associate and mid-senior level management positions and mainly came from the United States and Germany. Text mining analysis resulted in two groups of job profiles. The first group of job profiles was focused solely on the knowledge related to Industry 4.0: cyberphysical systems and the Internet of things for robotized production; and smart production design and production control. The second group of job profiles was focused on more general knowledge areas, which are adapted to Industry 4.0: supply change management, customer satisfaction, and enterprise software. Topic mining was conducted on the extracted phrases generating various multidisciplinary job profiles. Higher educational institutions, human resources professionals, as well as experts that are already employed or aspire to be employed in Industry 4.0 organizations, would benefit from the results of our analysis.     

The role of big data analytics capabilities (BDAC) in understanding the challenges of service information and operations management in the sharing economy: Evidence of peer effects in libraries

Information and operations management in libraries presents a unique opportunity to provide insights for the sharing economy. Libraries correspond to a special type of sharing goods, named common-pool resources. Such resources have two characteristics: they are non-exclusive, but rival to each other. Service operations in libraries involve thousands of operations every year, making them a perfect context for the use of big data analytics capabilities (BDAC) to provide real-world evidence on the potential existing challenges in the sharing economy. Employing a novel dataset related to 723,798 library transactions, made by 16,232 individual users during a 10-year period (2006–2015), we estimate peer effects among users via regression analysis, considering the number of books each user borrows. Our main results suggest that a rise in the number of loans among a user’s peer group correlates with her own loans, an evidence of positive peer effects. However, a closer look at the data suggests a high degree of heterogeneity, in terms of behavioral patterns. First, we suggest that peer effects do not occur in the case of users who are not subject to monetary fines. Second, peer effects vary according to users’ category (student or non-student), and area of study (management, accounting, economics, and other courses). Third, there is evidence of different magnitudes of peer effects according to time in school, which suggests the existence of learning effects in a library setting. The results reported in this paper highlight the important role of big data analytics capabilities to uncover new challenges of the sharing economy, having important implications, both in theoretical and practical terms.     

An empirical study on business analytics affordances enhancing the management of cloud computing data security

The mechanism of business analytics affordances enhancing the management of cloud computing data security is a key antecedent in improving cloud computing security. Based on information value chain theory and IT affordances theory, a research model is built to investigate the underlying mechanism of business analytics affordances enhancing the management of cloud computing data security. The model includes business analytics affordances, decision-making affordances of cloud computing data security, decision-making rationality of cloud computing data security, and the management of cloud computing data security. Simultaneously, the model considers the role of data-driven culture and IT business process integration. It is empirically tested using data collected from 316 enterprises by Partial Least Squares-based structural equation model. Without data-driven culture and IT business process integration, the results suggest that there is a process from business analytics affordances to decision-making affordances of cloud computing data security, decision-making rationality of cloud computing data security, and to the management of cloud computing data security. Moreover, Data-driven culture and IT business process integration have a positive mediation effect on the relationship between business analytics affordances and decision-making affordances of cloud computing data security. The conclusions in this study provide useful references for the enterprise to strengthen the management of cloud computing data security using business analytics.     

大数据分析的方法及其在情报研究中的适用性初探

对基于数据或信息分析的情报研究来说,大数据分析方法带来了新机遇。文章在现有研究的基础上,先梳理了基于数据、流程及信息技术三种视角的大数据分析方法,并从中归纳出了面向统计、挖掘、发现、预测与集成等5种分析层次的17种相关研究方法。接着探讨了大数据分析方法在情报研究的适用性,分别找出10种可直接移植的方法、2种调整后可移植的方法、2种不适用的方法、3种需要继续研究或关注的方法。     

学习分析技术的发展和挑战——第四届学习分析与知识国际会议评析

第四届学习分析与知识国际会议于2014年3月24-28日在美国印第安纳州波利斯成功举行,会议以探讨学习分析研究、理论和实践的交叉点为主题,涵盖了学习分析技术在教育学、教育心理学、教育管理学、工程学中的运用,以及教育数据挖掘、计算机算法和数据可视化等方面的发展。文章首先说明了此次会议的背景,从研究、理论和实践三方面阐析学习分析主题之间的关系,简述了来自孟菲斯大学的格莱赛教授(Art Graesser)、香港大学的罗陆慧英(Nancy Law)教授和加州大学圣地亚哥分校的克莱默教授(Scott Klemmer)三位专家所作的主题报告;然后从学习分析与课程教学设计、教与学过程挖掘和评价、学习分析与学习资源、文本挖掘与语义分析、学习分析与数学教育、学习分析与教育一体化、学习分析多元化等七个方面对分论坛报告及会议进行系统综述;文章最后指出未来学习分析研究和发展的五个方向:逐步明晰学习分析系统概念与理论、研究通用性的算法和模型、研制学习分析技术标准、支撑数据驱动的学习和评估、融入教育信息化应用与实践、推进教育的深度发展和加快多元化进程,期望能够推动学习分析系统化研究和在教育中的深度应用。     

Proving Value and Improving Practice: Using System Data to Analyze User Behaviors

Today's library systems collect and supply a wealth of transaction data. The unobtrusive data collected by these systems can help us understand our users' search and information-retrieval behaviors. Analyzing user behaviors challenges or reinforces our practices, and the changes we make should not be simply consumer preferences but based upon the analysis of usage patterns and search behavior. Institutions of any size can use a cyclical grounded theory model to look at the unobtrusive data that is available from systems such as WorldCat Local, Google Analytics, and others to reveal our users' information-seeking behaviors.

The author's model shows how system-generated data can be a part of an assessment strategy for ongoing improvement that can be implemented by small academic libraries. By articulating inputs consisting of goals, users, and performance indicators, and by utilizing a grounded-theory approach, libraries can observe behaviors that can inform, as well as reveal, outputs.     

Beyond Description: Converting Web Site Usage Statistics into Concrete Site Improvement Ideas

Web site usage statistics are a widely used tool for Web site development, but libraries are still learning how to use them successfully. This case study summarizes how Morris Library at Southern Illinois University Carbondale implemented Google Analytics on its Web site and used the reports to inform a site redesign. As the main campus library at a research university with about 20,000 undergraduate and graduate students, the library included resources from multiple library departments on a single site. In planning the redesign, Morris Library's Virtual Library Group combined usage reports with information from other sources, such as usability tests and user comments. The Virtual Library Group faced barriers to interpreting and applying the usage statistics in the site redesign, including some that were specific to the library's implementation of the Google Analytics tool and some limitations inherent with Web usage statistics in general. Some key barriers in applying the usage statistics to a redesign included sifting through data that did not have implications for the site redesign, interpreting the implications of usage numbers for the site redesign, and balancing competing interests within the library. Nevertheless, the usage statistics enabled the Virtual Library Group to make better decisions by providing a source of factual information about the site's use rather than relying on staff members’ opinions and conjectures.     

Tracking User Behavior with Google Analytics Events on an Academic Library Web Site

ABSTRACT

The primary purpose of an academic library Web site is to serve as a portal to library-acquired content. Navigational design of a library Web site affects the user's ability to find and access content. At Albertsons Library, the goal of the navigational design of the Web site is to mimic user behavior on the Web site to help them access information and articles from over 300 different library vendors. Coordinating with different vendors makes tracking the navigational flow of user behavior difficult with the tool Google Analytics. Using the events feature in Google Analytics, the team responsible for Web design was able to track user flow, and was able to quantify how many users were actual “drop-offs” versus those that were clicks into library resources. Decisions made after acquiring these data resulted in a Web site with a 10 percent or less bounce rate, and decreased the number of clicks required for users accessing the library's content.     

Data quality management,data usage experience and acquisition intention of big data analytics

Big data analytics associated with database searching, mining, and analysis can be seen as an innovative IT capability that can improve firm performance. Even though some leading companies are actively adopting big data analytics to strengthen market competition and to open up new business opportunities, many firms are still in the early stage of the adoption curve due to lack of understanding of and experience with big data. Hence, it is interesting and timely to understand issues relevant to big data adoption. In this study, a research model is proposed to explain the acquisition intention of big data analytics mainly from the theoretical perspectives of data quality management and data usage experience. Our empirical investigation reveals that a firm's intention for big data analytics can be positively affected by its competence in maintaining the quality of corporate data. Moreover, a firm's favorable experience (i.e., benefit perceptions) in utilizing external source data could encourage future acquisition of big data analytics. Surprisingly, a firm's favorable experience (i.e., benefit perceptions) in utilizing internal source data could hamper its adoption intention for big data analytics.