Small business owners’ external information-seeking behaviors: The role of perceived uncertainty and organizational identity complexity

This study examines how small business owners’ perceived uncertainty about their environment interacts with the complexity of their organization’s identity to explain their information seeking from external sources.  We hypothesize that perceived uncertainty is positively related to external information seeking, and, organizational complexity, in the form of different organizational identities, complicates this relationship and reduces the information seeking in certain conditions while increases in others. The results extend evidence to prior established relationships between perceived uncertainty and information seeking and also suggest that organizational complexity plays an equally important role as a critical moderator. Additionally, we propose a different classification scheme for the external sources and use this to test our hypotheses

Visual Analysis of Spatio-Temporal Event Predictions: Investigating the Spread Dynamics of Invasive Species

Invasive species are a major cause of ecological damage and commercial losses. A current problem spreading in North America and Europe is the vinegar fly Drosophila suzukii. Unlike other Drosophila, it infests non-rotting and healthy fruits and is therefore of concern to fruit growers, such as vintners. Consequently, large amounts of data about infestations have been collected in recent years. However, there is a lack of interactive methods to investigate this data. We employ ensemble-based classification to predict areas susceptible to infestation by D. suzukii and bring them into a spatio-temporal context using maps and glyph-based visualizations. Following the information-seeking mantra, we provide a visual analysis system Drosophigator for spatio-temporal event prediction, enabling the investigation of the spread dynamics of invasive species. We demonstrate the usefulness of this approach in two use cases

Empirical Evidence on the Use of Credit Scoring for Predicting Insurance Losses with Psycho-social and Biochemical Explanations

An important development in personal lines of insurance in the United States is the use of credit history data for insurance risk classification to predict losses. This research presents the results of collaboration with industry conducted by a university at the request of its state legislature. The purpose was to see the viability and validity of the use of credit scoring to predict insurance losses given its controversial nature and criticism as redundant of other predictive variables currently used. Working with industry and government, this study analyzed more than 175,000 policyholders’ information for the relationship between credit score and claims. Credit scores were significantly related to incurred losses, evidencing both statistical and practical significance. We investigate whether the revealed relationship between credit score and incurred losses was explainable by overlap with existing underwriting variables or whether the credit score adds new information about losses not contained in existing underwriting variables. The results show that credit scores contain significant information not already incorporated into other traditional rating variables (e.g., age, sex, driving history). We discuss how sensation seeking and self-control theory provide a partial explanation of why credit scoring works (the psycho-social perspective). This article also presents an overview of biological and chemical correlates of risk taking that helps explain why knowing risk-taking behavior in one realm (e.g., risky financial behavior and poor credit history) transits to predicting risk-taking behavior in other realms (e.g., automobile insurance incurred losses). Additional research is needed to advance new nontraditional loss prediction variables from social media consumer information to using information provided by technological advances. The evolving and dynamic nature of the insurance marketplace makes it imperative that professionals continue to evolve predictive variables and for academics to assist with understanding the whys of the relationships through theory development.IC2 Institut

Mining Behavior of Citizen Sensor Communities to Improve Cooperation with Organizational Actors

Web 2.0 (social media) provides a natural platform for dynamic emergence of citizen (as) sensor communities, where the citizens generate content for sharing information and engaging in discussions. Such a citizen sensor community (CSC) has stated or implied goals that are helpful in the work of formal organizations, such as an emergency management unit, for prioritizing their response needs. This research addresses questions related to design of a cooperative system of organizations and citizens in CSC. Prior research by social scientists in a limited offline and online environment has provided a foundation for research on cooperative behavior challenges, including \u27articulation\u27 and \u27awareness\u27, but Web 2.0 supported CSC offers new challenges as well as opportunities. A CSC presents information overload for the organizational actors, especially in finding reliable information providers (for awareness), and finding actionable information from the data generated by citizens (for articulation). Also, we note three data level challenges: ambiguity in interpreting unconstrained natural language text, sparsity of user behaviors, and diversity of user demographics. Interdisciplinary research involving social and computer sciences is essential to address these socio-technical issues. I present a novel web information-processing framework, called the Identify-Match- Engage (IME) framework. IME allows operationalizing computation in design problems of awareness and articulation of the cooperative system between citizens and organizations, by addressing data problems of group engagement modeling and intent mining. The IME framework includes: a.) Identification of cooperation-assistive intent (seeking-offering) from short, unstructured messages using a classification model with declarative, social and contrast pattern knowledge, b.) Facilitation of coordination modeling using bipartite matching of complementary intent (seeking-offering), and c.) Identification of user groups to prioritize for engagement by defining a content-driven measure of \u27group discussion divergence\u27. The use of prior knowledge and interplay of features of users, content, and network structures efficiently captures context for computing cooperation-assistive behavior (intent and engagement) from unstructured social data in the online socio-technical systems. Our evaluation of a use-case of the crisis response domain shows improvement in performance for both intent classification and group engagement prioritization. Real world applications of this work include use of the engagement interface tool during various recent crises including the 2014 Jammu and Kashmir floods, and intent classification as a service integrated by the crisis mapping pioneer Ushahidi\u27s CrisisNET project for broader impact

Purchase Decision Type Influences on Consumers’ Reliance: Brand-Related User-Generated Content

Consumers use brand-related user-generated content (UGC), such as online consumer reviews, for their pre-purchase information seeking. However, previous research on consumer information seeking has scarcely explored how purchase situations and product type influence consumers’ use of brand-related UGC. The purpose of this dissertation is to shed light on this area of research. In the first part of the study, Vaughn’s (1980; 1986) Foote, Cone, and Belding (FCB) grid, a popular product classification theory in advertising and consumer research, was updated based on a set of online surveys (N=1,104) that measured three purchase dimensions [i.e., purchase decision involvement (PDI), think/feel purchase, online/offline purchase context]. Multiple research hypotheses relevant to how purchase type influences one’s brand-related UGC seeking were explored, based on another set of online surveys (N=391) in the second part of the study. A Cronbach’s alpha test revealed that the think/feel purchase dimension of the FCB grid measured two purchase constructs, rather than measuring a single construct. The grid model now consists of 118 up-to-date product examples and 35 categories, and has improved usability for research in other fields, because the study altered the theory’s dichotomous-looking dimensions into non-dichotomous variables. To examine the hypotheses, a linear mixed effect model was utilized for analysis, and the results indicated that the four dimensions (PDI, think purchase, feel purchase, online/offline purchase context) are all positively associated with one’s reliance on brand-related UGC. Furthermore, the study found several more associations between demographic factors and consumers’ reliance on brand-related UGC. Age, gender, marital status, number of children in a household, and employment status showed significant associations, whereas education, household income, and ethnicity did not. The dissertation has several implications. First, ad practitioners may use the updated product grid to define overall themes of advertising (e.g., informative vs. emotional theme). Second, marketers can use the study results to determine their budgets for online brand promotions. Finally, the study may also provide implications to scholars whose research explores pre-purchase information-seeking, influences of product type on decision-making, consumer involvement, emotional/rational purchase decisions, and brand-related UGC

Difficulties of information seeking in using university library and solving ways -A field study at Tishreen University-

 The research aims at determining the degree of the difficulties of the information seeking by using university library in the areas of: ( sources group, technician procedures, time, place, dealing with employees), To achieve the objectives of the research, the researcher used descriptive approach, a questionnaire was done . consisting of (20) clause. It was distributed for (544) students in Tishreen University at 2015/2016. , and relying on appropriate statistical methods, results were as following: 1- This research resulted in a medium degree of the difficulties of the information seeking by using university library in the field of sources group because of lack of academic specialist sources. 2- This research resulted in a medium degree of the difficulties of the information seeking by using university library in the field of technician procedures because university library depends on classification in general not specialist one. 3- This research resulted in a medium degree of the difficulties of the information seeking by using university library in the field of time because time sometimes is suitable for students according to lectures table, sometimes is not. 4- This research resulted in a medium degree of the difficulties of the information seeking by using university library in the field of place because of some chaos. 5- This research resulted in a medium degree of the difficulties of the information seeking by using university library in the field of dealing with employees because of lace of librarian and specialist employees too. 6- the difficulties of information seeking increase throughout the coming years of studying because of student in high studies degree search for information more than student in license degree. 7- Students suggest some ways that decrease difficulties of information seeking in using university library: ( acquire specialist sources, depend specialist classification, increase hours that students can use the library, attention of library's furniture, appointment specialist employees at faculty field, increase copies of required books, design system of university library's data on internet

Customer churn prediction in telecom using machine learning and social network analysis in big data platform

Customer churn is a major problem and one of the most important concerns for large companies. Due to the direct effect on the revenues of the companies, especially in the telecom field, companies are seeking to develop means to predict potential customer to churn. Therefore, finding factors that increase customer churn is important to take necessary actions to reduce this churn. The main contribution of our work is to develop a churn prediction model which assists telecom operators to predict customers who are most likely subject to churn. The model developed in this work uses machine learning techniques on big data platform and builds a new way of features' engineering and selection. In order to measure the performance of the model, the Area Under Curve (AUC) standard measure is adopted, and the AUC value obtained is 93.3%. Another main contribution is to use customer social network in the prediction model by extracting Social Network Analysis (SNA) features. The use of SNA enhanced the performance of the model from 84 to 93.3% against AUC standard. The model was prepared and tested through Spark environment by working on a large dataset created by transforming big raw data provided by SyriaTel telecom company. The dataset contained all customers' information over 9 months, and was used to train, test, and evaluate the system at SyriaTel. The model experimented four algorithms: Decision Tree, Random Forest, Gradient Boosted Machine Tree "GBM" and Extreme Gradient Boosting "XGBOOST". However, the best results were obtained by applying XGBOOST algorithm. This algorithm was used for classification in this churn predictive model.Comment: 24 pages, 14 figures. PDF https://rdcu.be/budK

Data classification and forecasting using the Mahalanobis-Taguchi method

Classification and forecasting are useful concepts in the field of condition monitoring. Condition monitoring refers to the analysis and monitoring of system characteristics to understand and identify deviations from normal operating conditions. This can be performed for prediction, diagnosis, or prognosis or a combination of any these purposes. Fault identification and diagnosis are usually achieved through data classification, while forecasting methods are usually used to accomplish the prediction objective. Data gathered from monitoring systems often consists of multiple multivariate time series and is fed into a model for data analysis using various techniques. One of the data analysis techniques used is the Mahalanobis-Taguchi strategy (MTS) because of its suitability for multivariate data analysis. MTS provides a means of extracting information in a multidimensional system by integrating information from different variables into a single composite metric. MTS is used to conduct analysis on the measurement parameters and seeks a correlation with the result while also seeking to optimize the analysis by identifying variables of importance strongly correlated with a defect or fault occurrence. This research presents the application of a MTS based system for predicting faults in heavy duty vehicles and the application of MTS in a multiclass classification problem. The benefits and practicality of the methodology in industrial applications are demonstrated through the use of real world data and discussion of results. --Abstract, page iv

Manifesto for a European research network into Problematic Usage of the Internet

Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.The Internet is now all-pervasive across much of the globe. While it has positive uses (e.g. prompt access to information, rapid news dissemination), many individuals develop Problematic Use of the Internet (PUI), an umbrella term incorporating a range of repetitive impairing behaviours. The Internet can act as a conduit for, and may contribute to, functionally impairing behaviours including excessive and compulsive video gaming, compulsive sexual behaviour, buying, gambling, streaming or social networks use. There is growing public and National health authority concern about the health and societal costs of PUI across the lifespan. Gaming Disorder is being considered for inclusion as a mental disorder in diagnostic classification systems, and was listed in the ICD-11 version released for consideration by Member States (http://www.who.int/classifications/icd/revision/timeline/en/). More research is needed into disorder definitions, validation of clinical tools, prevalence, clinical parameters, brain-based biology, socio-health-economic impact, and empirically validated intervention and policy approaches. Potential cultural differences in the magnitudes and natures of types and patterns of PUI need to be better understood, to inform optimal health policy and service development. To this end, the EU under Horizon 2020 has launched a new four-year European Cooperation in Science and Technology (COST) Action Programme (CA 16207), bringing together scientists and clinicians from across the fields of impulsive, compulsive, and addictive disorders, to advance networked interdisciplinary research into PUI across Europe and beyond, ultimately seeking to inform regulatory policies and clinical practice. This paper describes nine critical and achievable research priorities identified by the Network, needed in order to advance understanding of PUI, with a view towards identifying vulnerable individuals for early intervention. The network shall enable collaborative research networks, shared multinational databases, multicentre studies and joint publications.Peer reviewe