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1 – 10 of over 5000Kalervo Järvelin and Pertti Vakkari
This paper analyses the research in Library and Information Science (LIS) and reports on (1) the status of LIS research in 2015 and (2) on the evolution of LIS research…
Abstract
Purpose
This paper analyses the research in Library and Information Science (LIS) and reports on (1) the status of LIS research in 2015 and (2) on the evolution of LIS research longitudinally from 1965 to 2015.
Design/methodology/approach
The study employs a quantitative intellectual content analysis of articles published in 30+ scholarly LIS journals, following the design by Tuomaala et al. (2014). In the content analysis, we classify articles along eight dimensions covering topical content and methodology.
Findings
The topical findings indicate that the earlier strong LIS emphasis on L&I services has declined notably, while scientific and professional communication has become the most popular topic. Information storage and retrieval has given up its earlier strong position towards the end of the years analyzed. Individuals are increasingly the units of observation. End-user's and developer's viewpoints have strengthened at the cost of intermediaries' viewpoint. LIS research is methodologically increasingly scattered since survey, scientometric methods, experiment, case studies and qualitative studies have all gained in popularity. Consequently, LIS may have become more versatile in the analysis of its research objects during the years analyzed.
Originality/value
Among quantitative intellectual content analyses of LIS research, the study is unique in its scope: length of analysis period (50 years), width (8 dimensions covering topical content and methodology) and depth (the annual batch of 30+ scholarly journals).
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Sergei O. Kuznetsov, Alexey Masyutin and Aleksandr Ageev
The purpose of this study is to show that closure-based classification and regression models provide both high accuracy and interpretability.
Abstract
Purpose
The purpose of this study is to show that closure-based classification and regression models provide both high accuracy and interpretability.
Design/methodology/approach
Pattern structures allow one to approach the knowledge extraction problem in case of partially ordered descriptions. They provide a way to apply techniques based on closed descriptions to non-binary data. To provide scalability of the approach, the author introduced a lazy (query-based) classification algorithm.
Findings
The experiments support the hypothesis that closure-based classification and regression allow one to both achieve higher accuracy in scoring models as compared to results obtained with classical banking models and retain interpretability of model results, whereas black-box methods grant better accuracy for the cost of losing interpretability.
Originality/value
This is an original research showing the advantage of closure-based classification and regression models in the banking sphere.
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Abstract
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Mu-Yen Chen, Chien-Hsiang Liao, Edwin David Lughofer and Erol Egrioglu
Nadja Fugleberg Damtoft, Dennis van Liempd and Rainer Lueg
Researchers and practitioners have recently been interested in corporate sustainability performance (CSP). However, knowledge on measuring CSP is limited. Many CSP-measurements…
Abstract
Purpose
Researchers and practitioners have recently been interested in corporate sustainability performance (CSP). However, knowledge on measuring CSP is limited. Many CSP-measurements are eclectic, without guidance for contextual applications. This paper aims to develop a conceptual framework that categorizes, explains and evaluates measurements based on their accuracy and precision and provides a guideline for their context-specific application.
Design/methodology/approach
The authors conducted a systematic literature review of an initial sample of 1,415 papers.
Findings
The final sample of 74 papers suggested four measurement categories: isolated indicators, indicator frameworks, Sustainability Balanced Scorecards (SBSC) and Sustainability Performance Measurement Systems (SPMS). The analysis reveals that isolated indicators are inaccurate and imprecise, limiting their application to organizations with delimited, specific measurements of parts of CSP due to the risk of a GIGO-effect (i.e. low-quality input will always produce low-quality output). CSP-indicator frameworks are imprecise but accurate, making them applicable to organizations that handle a more significant amount of CSP data. They have a risk of greensplashing, i.e. many indicators not connected to the industry, organization or strategy. In contrast, SBSCs are precise but inaccurate and valuable for organizations desiring a comprehensive strategic management tool with limited capacity to handle sustainability issues. They pose a risk of the streetlight effect, where organisations do not measure relevant indicators but what is easy to measure.
Originality/value
The ideal CSP-measurement was identified as SPMSs, which are both precise and accurate. SPMSs are useful for organizations with complex, comprehensive, connected and tailored indicators but are methodologically challenging.
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