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.
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.
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.
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.
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.
Vision picking empowers users with access to real-time digital order information, while freeing them from handheld radio frequency devices. The smart glasses, as an example of vision picking enabler, provide visual and voice cues to guide order pickers. The glasses mostly also have installed navigation features that can sense the order picker’s position in the warehouse. This paper explores picking errors in vision systems with literature review and experimental work in laboratory environment. The results show the effectiveness of applying vision picking systems for the purposes of active error prevention, when they are compared to established methods, such as paper-picking and using cart mounted displays. A serious competitor to vision picking systems are pick-to-light systems.
The strong advantage of vision picking system is that most of the errors are detected early in the process and not at the customer’s site. The cost of fixing the error is thus minimal. Most errors consequently directly influence order picker’s productivity in negative sense. Nonetheless, the distinctive feature of the system is extremely efficient error detection.
Transportation no doubt remains a catalyst for all aspect of socio-economic and environmental development. Without its singular significance of mobility and accessibility for farmers, agricultural produce will rot on farms, while efforts in providing food would be fruitless. This paper assessed agricultural freight transportation in Saki area of Oyo State with a view of enhancing better product delivery mechanisms for farmers. It examined farmers’ socio-demographic; nature of farming and farm characteristics; and appraised the relationship between attributes of agricultural production and freight movement. Primary data employed consists of a questionnaire designed for farmers, structured interview for government officials complemented with personal field observations of agricultural freight transportation. 225 farmers were randomly selected for questionnaire administration. Major findings revealed that food crops, vegetables, fruits and poultry products are in persistent motion in the study area and that agricultural freight is a neglected sector with significant consequences on the access to cheap and affordable urban wellbeing. Findings also revealed that agricultural freight transportation within the study is very poor and uneconomical, as this depletes farmers’ profit-making. Regression analysis results show a significant relationship between attributes of agricultural freight and transport cost (F19205 11.916= P<0.05). The study recommends extensive road rehabilitation and constructions within the study area; provision of technological driven distribution and storage infrastructural facilities; creation of a databank for agricultural freight transport; reorganization and empowerment of farmers and improvement of rural infrastructure in Oyo state and Nigeria as a whole.
Supplier evaluation and selection is essential to any organization, and planning an effective and comprehensive approach to that end seems inevitable. Meanwhile, determining the requisite criteria for evaluating and selecting suppliers is probably one of the most important steps to be taken towards developing an evaluation and selection model in the organization. In this article, first a review of the literature on the criteria and the field of supplier evaluation and selection are provided. These criteria are then placed into proper categories. In order to formulate a supplier evaluation and selection framework for the manufacturing organization under study, the implemented categorization is applied where a list of fifteen attributes and performance criteria is created; where upon it is secured with the help of a designated panel (project team). These features are then screened using Lawshe’s method the “social attribute” is removed from the list of fifteen. The remaining 14 other criteria are configured within the SEAP (Suppliers Evaluation based on Attributes and Performances) framework. The framework follows the objective of continually evaluating suppliers, both potential and actual ones through incorporating their performances into their qualification ratings. Based on the proposed framework, suppliers are evaluated on the basis of two types of criteria, - feature (attribute) and performance.