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Abstract

As the availability of data is increasing everyday, the need to reflect on how to make these data meaningful and impactful becomes vital. Current data paradigms have provided data life cycles that often focus on data acumen and data stewardship approaches. In an effort to examine the convergence, tensions, and harmonies of these two approaches, a group of researchers participated in an interactive panel session at the Association of Information Science and Technology Annual meeting in 2019. The panel presenters described their various research activities in which they confront the challenges of the computational and social perspectives of the data continuum. This paper provides a summary of this interactive panel.

Abstract

Information behavior, as a part of human behavior, has many aspects, including a cognitive aspect. Cognitive biases, one of the important issues in psychology and cognitive science, can play a critical role in people’s behaviors and their information behavior. This article discusses the potential relationships between some concepts of human information behavior and cognitive biases. The qualitative research included semistructured face-to-face interviews with 25 postgraduate students who were at the writing-up stage of their research. The participants were selected using a purposeful sampling process. Interviews were analyzed using the coding technique of classic grounded theory. The research framework was the Eisenberg and Berkowitz information behavior model. The relationships that are discussed in this article include those between the principle of least effort on the one hand and availability bias and ambiguity aversion on the other; value-sensitive design and reactance; willingness to return and availability bias; library anxiety and ambiguity aversion, status quo bias, and stereotypical bias; information avoidance and selective perception, confirmation bias, stereotypical bias, and conservatism bias; information overload and information bias; and finally, filtering and attentional bias.

Abstract

E-commerce platforms generally provide various functions that can be adopted as signals for online sellers to convey implicit information to customers to promote sales. In this article, based on signaling theory and the stereotype content model, we categorize e-commerce signals into two types: signals of competence and signals of warmth. Signals of competence refer to the platform functions or mechanisms that can be leveraged by online sellers to indirectly convey information about their capabilities, such as promised delivery times and free return days. Signals of warmth refer to the platform functions or mechanisms that can be leveraged by online sellers to indirectly convey information about their kindness and care, such as the availability of online customer service agents. We explore the impacts of the two different types of signals on product sales for sellers with different credit rating levels. The empirical analysis is conducted on China's largest e-commerce platform, Taobao.com. The results show that online sellers with higher credit ratings should focus more on signals of warmth, while those with median and lower credit ratings should concentrate more on signals of competence. This study provides a theoretical framework that explains the effects of signaling on e-commerce platforms and may facilitate further exploration on signaling mechanisms. Our findings also provide implications for online sellers in terms of how to better utilize various signals as well as for e-commerce platforms on designing more effective supporting functions.

Abstract

As a scientific field, scientific mapping offers a set of standardized methods and tools which can be consistently adopted by researchers in different knowledge domains to answer their own research questions. This study examined the scientific articles that applied science mapping tools (SMT) to analyze scientific domains and the citations of these application articles. To understand the roles of these application articles in scholarly communication, we analyzed 496 application articles and their citations from 14 SMT by classifying them into library and information science (LIS) and other fields (non-LIS) in terms of both publication venues and analyzed domains. In our study, we found that science mapping, a topic that is deeply situated in the LIS field, has gained increasing attention from various non-LIS scientific fields over the last few years, especially since 2012. Science mapping application studies practically grew up in LIS domain and spread to other fields. The application articles within and outside of the LIS fields played different roles in advancing the application of science mapping and knowledge discovery. Especially, we have discovered the important role of articles, which studied non-LIS domains but published in LIS journals, in advancing the application of SMTs.

Abstract

This article aims to review the important roles of health knowledge organization systems (KOSs) during the COVID-19 pandemic. Different types of knowledge organization systems, including term lists, synonym rings, thesauri, subject heading systems, taxonomies, classification schemes, and ontologies are widely recognized and applied in both modern and traditional information systems. Apart from their usage in the management of data, information, and knowledge, KOSs are seen as valuable components for large information architecture, content management, findability improvement, and many other applications. After introducing the challenges of information overload and semantic conflicts, the article reviews the efforts of major health KOSs, illustrates various health coding schemes, explains their usages and implementations, and reveals their implications for health information exchange and communication during the COVID-19 pandemic. Some general examples of the applications, services, and analysis powered by KOSs are presented at the end. As revealed in this article, they have become even more critical to aid the frontline endeavors to overcome the obstacles due to information overload and semantic conflicts that can occur during devastating historic and worldwide events like the COVID-19 pandemic.

Abstract

Persona is a common human-computer interaction technique for increasing stakeholders’ understanding of audiences, customers, or users. Applied in many domains, such as e-commerce, health, marketing, software development, and system design, personas have remained relatively unchanged for several decades. However, with the increasing popularity of digital user data and data science algorithms, there are new opportunities to progressively shift personas from general representations of user segments to precise interactive tools for decision-making. In this vision, the persona profile functions as an interface to a fully functional analytics system. With this research, we conceptually investigate how data-driven personas can be leveraged as analytics tools for understanding users. We present a conceptual framework consisting of (a) persona benefits, (b) analytics benefits, and (c) decision-making outcomes. We apply this framework for an analysis of digital marketing use cases to demonstrate how data-driven personas can be leveraged in practical situations. We then present a functional overview of an actual data-driven persona system that relies on the concept of data aggregation in which the fundamental question defines the unit of analysis for decision-making. The system provides several functionalities for stakeholders within organizations to address this question.

Abstract

With the rapid growth of the smartphone and tablet market, mobile application (App) industry that provides a variety of functional devices is also growing at a striking speed. Product life cycle (PLC) theory, which has a long history, has been applied to a great number of industries and products and is widely used in the management domain. In this study, we apply classical PLC theory to mobile Apps on Apple smartphone and tablet devices (Apple App Store). Instead of trying to utilize often-unavailable sales or download volume data, we use open-access App daily download rankings as an indicator to characterize the normalized dynamic market popularity of an App. We also use this ranking information to generate an App life cycle model. By using this model, we compare paid and free Apps from 20 different categories. Our results show that Apps across various categories have different kinds of life cycles and exhibit various unique and unpredictable characteristics. Furthermore, as large-scale heterogeneous data (e.g., user App ratings, App hardware/software requirements, or App version updates) become available and are attached to each target App, an important contribution of this paper is that we perform in-depth studies to explore how such data correlate and affect the App life cycle. Using different regression techniques (i.e., logistic, ordinary least squares, and partial least squares), we built different models to investigate these relationships. The results indicate that some explicit and latent independent variables are more important than others for the characterization of App life cycle. In addition, we find that life cycle analysis for different App categories requires different tailored regression models, confirming that inner-category App life cycles are more predictable and comparable than App life cycles across different categories.

Abstract

Natural language processing (NLP) covers a large number of topics and tasks related to data and information management, leading to a complex and challenging teaching process. Meanwhile, problem-based learning is a teaching technique specifically designed to motivate students to learn efficiently, work collaboratively, and communicate effectively. With this aim, we developed a problem-based learning course for both undergraduate and graduate students to teach NLP. We provided student teams with big data sets, basic guidelines, cloud computing resources, and other aids to help different teams in summarizing two types of big collections: Web pages related to events, and electronic theses and dissertations (ETDs). Student teams then deployed different libraries, tools, methods, and algorithms to solve the task of big data text summarization. Summarization is an ideal problem to address learning NLP since it involves all levels of linguistics, as well as many of the tools and techniques used by NLP practitioners. The evaluation results showed that all teams generated coherent and readable summaries. Many summaries were of high quality and accurately described their corresponding events or ETD chapters, and the teams produced them along with NLP pipelines in a single semester. Further, both undergraduate and graduate students gave statistically significant positive feedback, relative to other courses in the Department of Computer Science. Accordingly, we encourage educators in the data and information management field to use our approach or similar methods in their teaching and hope that other researchers will also use our data sets and synergistic solutions to approach the new and challenging tasks we addressed.