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1 – 10 of 190
Article
Publication date: 13 March 2009

Ranjit Bose

Advanced analytics‐driven data analyses allow enterprises to have a complete or “360 degrees” view of their operations and customers. The insight that they gain from such analyses…

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Abstract

Purpose

Advanced analytics‐driven data analyses allow enterprises to have a complete or “360 degrees” view of their operations and customers. The insight that they gain from such analyses is then used to direct, optimize, and automate their decision making to successfully achieve their organizational goals. Data, text, and web mining technologies are some of the key contributors to making advanced analytics possible. This paper aims to investigate these three mining technologies in terms of how they are used and the issues that are related to their effective implementation and management within the broader context of predictive or advanced analytics.

Design/methodology/approach

A range of recently published research literature on business intelligence (BI); predictive analytics; and data, text and web mining is reviewed to explore their current state, issues and challenges learned from their practice.

Findings

The findings are reported in two parts. The first part discusses a framework for BI using the data, text, and web mining technologies for advanced analytics; and the second part identifies and discusses the opportunities and challenges the business managers dealing with these technologies face for gaining competitive advantages for their businesses.

Originality/value

The study findings are intended to assist business managers to effectively understand the issues and emerging technologies behind advanced analytics implementation.

Details

Industrial Management & Data Systems, vol. 109 no. 2
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 4 May 2010

Qingyu Zhang and Richard S. Segall

The purpose of this paper is to review and compare selected software for data mining, text mining (TM), and web mining that are not available as free open‐source software.

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Abstract

Purpose

The purpose of this paper is to review and compare selected software for data mining, text mining (TM), and web mining that are not available as free open‐source software.

Design/methodology/approach

Selected softwares are compared with their common and unique features. The software for data mining are SAS® Enterprise Miner™, Megaputer PolyAnalyst® 5.0, NeuralWare Predict®, and BioDiscovery GeneSight®. The software for TM are CompareSuite, SAS® Text Miner, TextAnalyst, VisualText, Megaputer PolyAnalyst® 5.0, and WordStat. The software for web mining are Megaputer PolyAnalyst®, SPSS Clementine®, ClickTracks, and QL2.

Findings

This paper discusses and compares the existing features, characteristics, and algorithms of selected software for data mining, TM, and web mining, respectively. These softwares are also applied to available data sets.

Research limitations/implications

The limitations are the inclusion of selected software and datasets rather than considering the entire realm of these. This review could be used as a framework for comparing other data, text, and web mining software.

Practical implications

This paper can be helpful for an organization or individual when choosing proper software to meet their mining needs.

Originality/value

Each of the software selected for this research has its own unique characteristics, properties, and algorithms. No other paper compares these selected softwares both visually and descriptively for all the three types of data, text, and web mining.

Details

Kybernetes, vol. 39 no. 4
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 14 May 2018

Saurav Snehvrat, Amit Kumar, Rahul Kumar and Swarup Dutta

The academic discipline of ambidexterity is recognized as an established field within strategic management and organization theory. Extant works in the field recognize the recent…

Abstract

Purpose

The academic discipline of ambidexterity is recognized as an established field within strategic management and organization theory. Extant works in the field recognize the recent burgeoning academic emphasis on ambidexterity and suggest either an impending focus or a decline of the field. Although there have been attempts to review the field of ambidexterity, most of these reviews have not followed a systematic and scientific approach for extricating the key themes emerging within. The purpose of the study is to inductively and systematically capture the main elements constituting the current boundary of the field.

Design/methodology/approach

To achieve the stated purpose, this study uses data/text mining to analyze abstracts of scholarly ambidexterity papers published over the past two decades (1997-2016) in academic journals. Using text-mining, lexical analysis is performed to compute the frequency distribution of words. The frequently occurring words are studied in detail and their association with ambidexterity is also studied by means of co-relation. This analysis (used for generation of first order themes) is then complemented by a manual analysis of each first order theme to come up with trends and sub-themes lying within.

Findings

The analysis extracts eight distinct themes that indicate the current boundary of ambidexterity research. The findings highlight the potential areas for future academic attention such as networks, business models, leadership, dynamic capability and their inter-linkages with the field of ambidexterity. Overall, the field of ambidexterity is receiving heightened academic interest coupled with a dynamic proliferation across a host of related management fields. Apart from the required future consolidation, the field also needs new insights to enhance its explanatory power.

Research limitations/implications

This study analyzes abstracts of 504 works on ambidexterity (in the last two decades) to inductively indicate the current boundaries of the field using a data/text mining approach. Papers that do not explicitly mention ambidexterity in their abstracts, title or keywords are not included in the analysis.

Practical implications

The insights of the analysis will not only help researchers but also offer practitioners a good view point about the myriad of paths (not restricted to contextual, structural and temporal) through which ambidexterity can be promoted within and at the organization level.

Originality/value

The three-fold contribution of this study is a systematic and scientific approach adopted to define the current boundary of the field of ambidexterity, followed by an exploration of a set of eight distinct themes and finally the identification of ongoing debates, research gaps and future research questions in light of the analysis performed.

Details

International Journal of Organizational Analysis, vol. 26 no. 2
Type: Research Article
ISSN: 1934-8835

Keywords

Open Access
Article
Publication date: 23 December 2022

Patrick Ajibade and Ndakasharwa Muchaonyerwa

This study aims to promote the need for advanced skills acquisition within the LIS and academic libraries. This study focuses on the importance of library management systems and…

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Abstract

Purpose

This study aims to promote the need for advanced skills acquisition within the LIS and academic libraries. This study focuses on the importance of library management systems and the need for the graduates to be equipped with analytics skills. Combined with basic data, text mining and analytics, knowledge classification and information audit skills would benefit libraries and improve resource allocation. Agile institutional libraries in this big data era success hinge on the ability to perform depth analytics of both data and text to generate useful insight for information literacy training and information governance.

Design/methodology/approach

This paper adopted a living-lab methodology to use existing technology to conduct system analysis and LMS audit of an academic library of one of the highly ranked universities in the world. One of the benefits of this approach is the ability to apply technological innovation and tools to carry out research that is relevant to the context of LIS or other research fields such as management, education, humanities and social sciences. The techniques allow us to gain access to publicly available information because of system audits that were performed. The level of responsiveness of the online library was accessed, and basic information audits were conducted.

Findings

This study indicated skill gaps in the LIS training and the academic libraries in response to the fourth industrial technologies. This study argued that the role of skill acquisition and how it can foster data-driven library management operations. Hence, data mining, text mining and analytics are needed to probe into such massive, big data housed in the various libraries’ repositories. This study, however, indicated that without retraining of librarians or including this analytics programming in the LIS curriculum, the libraries would not be able to reap the benefits these techniques provided.

Research limitations/implications

This paper covered research within the general and academic libraries and the broader LIS fields. The same principle and concept is very important for both public and private libraries with substantial usage and patrons.

Practical implications

This paper indicated that librarianship training must fill the gaps within the LIS training. This can be done by including data mining, data analytics, text mining and processing in the curriculum. This skill will enable the news graduates to have skills to assist the library managers in making informed decisions based on user-generated content (UGC), LMS system audits and information audits. Thus, this paper provided practical insights and suggested solutions for academic libraries to improve the agility of information services.

Social implications

The academic librarian can improve institutional and LMS management through insights that are generated from the user. This study indicated that libraries' UGC could serve as robust insights into library management.

Originality/value

This paper argued that the librarian expertise transcends information literacy and knowledge classification and debated the interwoven of LMS and data analytics, text mining and analysis as a solution to improve efficient resources and training.

Details

Library Hi Tech News, vol. 40 no. 4
Type: Research Article
ISSN: 0741-9058

Keywords

Article
Publication date: 26 November 2021

Soohyung Joo, Jennifer Hootman and Marie Katsurai

This study aims to explore knowledge structure and research trends in the domain of digital humanities (DH) in the recent decade. The study identified prevailing topics and then…

Abstract

Purpose

This study aims to explore knowledge structure and research trends in the domain of digital humanities (DH) in the recent decade. The study identified prevailing topics and then, analyzed trends of such topics over time in the DH field.

Design/methodology/approach

Research bibliographic data in the area of DH were collected from scholarly databases. Multiple text mining techniques were used to identify prevailing research topics and trends, such as keyword co-occurrences, bigram analysis, structural topic models and bi-term topic models.

Findings

Term-level analysis revealed that cultural heritage, geographic information, semantic web, linked data and digital media were among the most popular topics in the recent decade. Structural topic models identified that linked open data, text mining, semantic web and ontology, text digitization and social network analysis received increased attention in the DH field.

Originality/value

This study applied existent text mining techniques to understand the research domain in DH. The study collected a large set of bibliographic text, representing the area of DH from multiple academic databases and explored research trends based on structural topic models.

Details

Journal of Documentation, vol. 78 no. 4
Type: Research Article
ISSN: 0022-0418

Keywords

Open Access
Article
Publication date: 2 February 2024

Sasadhar Bera and Subhajit Bhattacharya

This exploratory study examines and comprehends the relative importance of mobile app attributes from a consumer perspective. Both quantitative and qualitative analysis approaches…

Abstract

Purpose

This exploratory study examines and comprehends the relative importance of mobile app attributes from a consumer perspective. Both quantitative and qualitative analysis approaches explore users' behavior and attitudes toward the priorities of mobile app attributes and preferences, identifying correlations between attributes and aggregating individual attributes into groups.

Design/methodology/approach

Online convenience sampling and snowball sampling resulted in 417 valid responses. The numerical data are analyzed using the relative to an identified distribution (RIDIT) scoring system and gray relational analysis (GRA), and qualitative responses are investigated using text-mining techniques.

Findings

This study finds enhanced nuances of user preferences and provides data-driven insights that might help app developers and marketers create a distinct app that will add value to consumers. The latent semantic analysis indicates relationship structure among the attributes, and text-based cluster analysis determines the subsets of attributes that represent the unique functions of the mobile app.

Practical implications

This study reveals the essential components of mobile apps, paying particular attention to the consumer value component, which boosts user approval and encourages prolonged use. Overall, the results demonstrate that developers must concentrate on its functional, technical and esthetic features to make an app more exciting and practical for potential users.

Originality/value

Most scholarly research on apps has focused on their technological merits, aesthetics and usability from the user's perspective. A post-adoption multi-attribute app analysis using both structured and unstructured data is conducted in this study.

Details

IIM Ranchi Journal of Management Studies, vol. 3 no. 1
Type: Research Article
ISSN: 2754-0138

Keywords

Article
Publication date: 20 April 2020

Mehmet Ali Köseoglu, Fuad Mehraliyev, Mehmet Altin and Fevzi Okumus

This study aims to propose a competitor intelligence and analysis (CIA) model that can be used for the analysis of a firm’s competitors. Empirically, it investigates the…

1964

Abstract

Purpose

This study aims to propose a competitor intelligence and analysis (CIA) model that can be used for the analysis of a firm’s competitors. Empirically, it investigates the application of the CIA model on online reviews. This proposed model clarifies the confusion between terms such as competitive intelligence, competitor intelligence and competitor analysis and provides a more efficient process for managers.

Design/methodology/approach

The approach of the model integrates text mining techniques as a big data method with network analysis to form a competitor analysis. This study has considered two centrality metrics – degree centrality and betweenness centrality – to identify the functional associations among the resources elaborated by the customers of the hotels.

Findings

Findings show online reviews may be used as a solid source of intelligence. The intelligence maps visualized through the text-net technique is an efficient representation of tourist satisfaction and dissatisfaction with a tourism company and its competitors.

Practical implications

The proposed approach can be used in the hotel industry along with many others. The implications for scholars and managers and the possible directions for future research are also discussed in the study.

Originality/value

This study develops a new approach for competitive intelligence practices in the hotel industry and tests a new method for competitor analysis as a part of the competitive intelligence and analysis approach developed in this study.

Purpose

本研究提出了一个竞争对手情报与分析(CIA)模型, 可用于分析企业的竞争对手。对CIA模型在网络评论中的进行了实证研究。该模型厘清了竞争情报、竞争对手情报和竞争对手分析等术语之间的概念混淆, 为管理者提供了一个更有效的流程。

Design/methodology/approach

该模型将文本挖掘技术作为大数据方法与网络分析相结合, 来进行竞争对手分析。本研究考虑了两个中心性指标——度中心性和中介中心性——来识别酒店客户精心设计的资源之间的功能关联。

Findings

结果表明, 在线评论可被用作可靠的情报来源。情报地图通过文本网络技术可视化有效地展示了游客对旅游公司及其竞争对手的满意度和不满意度。

Practical implications

本文所提出的方法可用于酒店行业及许多其他行业。同时, 本文也探讨了本研究对学者与管理者的启示, 以及未来可能的研究方向。

Originality/value

本文提出了一种新的酒店行业竞争情报的实践方法, 并测试了一种新的竞争对手分析法, 作为竞争情报和分析方法的一部分。

Keywords

关键词 竞争情报, 竞争对手情报, 文本挖掘, 网络分析, 在线评论, 酒店

Objetivo

Este estudio propone un modelo de análisis de la inteligencia competitiva (CIA) que puede utilizarse para el análisis de los competidores de la empresa. Empíricamente, investiga la aplicación del modelo de la CIA a las reseñas on line. El modelo propuesto aclara la confusión entre términos como inteligencia competitiva, inteligencia de la competencia y análisis de la competencia y proporciona un procedimiento más eficiente para los gerentes.

Diseño/metodología/enfoque

El enfoque del modelo integra las técnicas de minería de textos, como método de Big Data, con el análisis de redes para realizar el análisis de la competencia. En este estudio se han considerado dos métricas de centralidad -centralidad de grado e intermediación- para identificar las asociaciones funcionales entre los recursos elaborados por los clientes de los hoteles.

Resultados

Los hallazgos muestran que las reseñas on line pueden ser utilizadas como una fuente sólida de inteligencia. Los mapas de inteligencia visualizados mediante la técnica de redes de texto son una representación eficiente de la satisfacción e insatisfacción de los turistas con la empresa turística y sus competidores.

Implicaciones prácticas

El enfoque propuesto puede utilizarse en la industria hotelera junto con otros muchos. En el estudio también se analizan las implicaciones para los investigadores y los gerentes y las posibles directrices para investigaciones futuras.

Originalidad/interés

Este artículo desarrolla un nuevo enfoque para la aplicación de la inteligencia competitiva en la industria hotelera y prueba un método nuevo para el análisis de la competencia como parte del enfoque del Análisis de la Inteligencia Competitiva desarrollado en este estudio.

Palabras clave Inteligencia competitiva, Inteligencia del competidor, Minería de textos, Análisis de redes, Reseñas on line, hoteles

Article
Publication date: 20 September 2018

Casey Daniel Hoeve

Despite its growing popularity, there is a noticeable absence of references to the inclusion of genealogy and family history studies within the field of digital humanities. New…

Abstract

Purpose

Despite its growing popularity, there is a noticeable absence of references to the inclusion of genealogy and family history studies within the field of digital humanities. New forms of inclusiveness, particularly in production-coding and cultural analysis, closely align genealogy and family history with the core tenants practiced among humanities computing and digital humanities. This paper aims to prove that genealogy as family history should be formally recognized within this cohort, as it can serve as a valuable and innovative partner for advocacy and technological advancement of the field.

Design/methodology/approach

By examining the literature, genealogy will be defined according to its use in the digital humanities, as well as its use in family history studies. The core tenants of humanities computing and digital humanities will be identified and compared against the research methodology and technological tools used in genealogy and family history research. The comparison will determine how closely the fields align, and if genealogy defined as family history should be used, and included within the field of digital humanities.

Findings

The progression of genealogy and family history from production to cultural analysis corresponds with the transition of production and coding (influenced by humanities computing) to the inclusion of experimental cultural research adopted by the digital humanities. Genealogy’s use of technological tools, such as databases, text encoding, data-text mining, graphic information systems and DNA mapping, demonstrates the use of coding and production. Cultural analysis through demographic study, crowdsourcing and establishing cultural connections illustrates new methods of scholarship, and connects coding and cultural criticism, serving as a bridge between digital humanities and the humanities at large. As genealogy continues to create new partnerships of a collaborative nature, it can, and will, continue to contribute to new areas of study within the field. As these practices continue to converge with the digital humanities, genealogy should be recognized as a partner and member in the digital humanities cohort.

Originality/value

Despite its growing popularity, there is a noticeable absence of references to the inclusion of genealogy and family history studies within the field of the digital humanities. The term genealogy resonates differently within the digital humanities, primarily articulating the history of the field over the study and research of family lineage. This study seeks to demonstrate how genealogy and family history can fit within the digital humanities, providing a new perspective that has not yet been articulated in the scholarly literature.

Details

Digital Library Perspectives, vol. 34 no. 3
Type: Research Article
ISSN: 2059-5816

Keywords

Article
Publication date: 12 September 2016

Asil Oztekin

The prediction of graduation rates of college students has become increasingly important to colleges and universities across the USA and the world. Graduation rates, also referred…

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Abstract

Purpose

The prediction of graduation rates of college students has become increasingly important to colleges and universities across the USA and the world. Graduation rates, also referred to as completion rates, directly impact university rankings and represent a measurement of institutional performance and student success. In recent years, there has been a concerted effort by federal and state governments to increase the transparency and accountability of institutions, making “graduation rates” an important and challenging university goal. In line with this, the main purpose of this paper is to propose a hybrid data analytic approach which can be flexibly implemented not only in the USA but also at various colleges across the world which would help predict the graduation status of undergraduate students due to its generic nature. It is also aimed at providing a means of determining and ranking the critical factors of graduation status.

Design/methodology/approach

This study focuses on developing a novel hybrid data analytic approach to predict the degree completion of undergraduate students at a four-year public university in the USA. Via the deployment of the proposed methodology, the data were analyzed using three popular data mining classifications methods (i.e. decision trees, artificial neural networks, and support vector machines) to develop predictive degree completion models. Finally, a sensitivity analysis is performed to identify the relative importance of each predictor factor driving the graduation.

Findings

The sensitivity analysis of the most critical factors in predicting graduation rates is determined to be fall-term grade-point average, housing status (on campus or commuter), and which high school the student attended. The least influential factors of graduation status are ethnicity, whether or not a student had work study, and whether or not a student applied for financial aid. All three data analytic models yielded high accuracies ranging from 71.56 to 77.61 percent, which validates the proposed model.

Originality/value

This study presents uniqueness in that it presents an unbiased means of determining the driving factors of college graduation status with a flexible and powerful hybrid methodology to be implemented at other similar decision-making settings.

Details

Industrial Management & Data Systems, vol. 116 no. 8
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 1 September 2022

Marijana Seočanac and Darko Dimitrovski

This study aims to understand the impact that the experience environment has on the nightlife experience, as well as to identify the factors from the nightlifescape that most…

Abstract

Purpose

This study aims to understand the impact that the experience environment has on the nightlife experience, as well as to identify the factors from the nightlifescape that most influence the tourists’ experience in Belgrade. Additionally, this study seeks to discover whether these factors changed after the outbreak of the COVID-19 pandemic.

Design/methodology/approach

To achieve the study’s objectives, 679 tourist reviews were collected from one of the most popular travel platforms, TripAdvisor, and analyzed using RapidMiner, the popular software for data/text mining.

Findings

The perception of the physical aspects of the experience environment, the presence of other tourists and the feeling of acceptance are identified as the key factors that influence tourists’ nightlife experience. This study also found that certain factors from the social and sensory environment, such as staff, the presence of other people, the atmosphere and music, had a positive impact on the tourist experience and their intention to recommend the nightlife experience in Belgrade. Moreover, it was discovered that the COVID-19 pandemic did not provoke changes in the main factors influencing tourists’ nightlife experience.

Originality/value

The perception of tourists about Belgrade during the night contributes to the growing body of tourism literature on destination image. Focusing solely on the perception of tourists about Belgrade during the night, this study adds a temporal determinant to the destination image, which can be considered as a valuable add on to the current knowledge in the field.

Details

Consumer Behavior in Tourism and Hospitality, vol. 17 no. 4
Type: Research Article
ISSN: 2752-6666

Keywords

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