Search results

1 – 10 of over 193000
To view the access options for this content please click here
Article
Publication date: 6 June 2016

Wei Lu, Xinghu Yue, Qikai Cheng and Rui Meng

The purpose of this paper is to explore the use of inverse local context analysis (ILCA) to obtain data from limited accessible data sources.

Abstract

Purpose

The purpose of this paper is to explore the use of inverse local context analysis (ILCA) to obtain data from limited accessible data sources.

Design/methodology/approach

The experimental results show that the method the authors proposed can obtain all retrieved documents from the limited accessible data source using the least number of queries.

Findings

The experimental results show that the method we proposed can obtain all retrieved documents from the limited accessible data source using the least number of queries.

Originality/value

To the best of the authors’ knowledge, this paper provides the first attempt to gather all the retrieved documents from limited accessible data source, and the efficiency and ease of implementation of the proposed solution make it feasible for practical applications. The method the authors proposed can also benefit the construction of web corpus.

Details

The Electronic Library, vol. 34 no. 3
Type: Research Article
ISSN: 0264-0473

Keywords

To view the access options for this content please click here
Article
Publication date: 4 February 2014

Rakesh Niraj and S. Siddarth

Grocery retailers have access to detailed data on consumer purchases within their own chains. Previous research has used across-chain scanner panel data to develop optimal…

Abstract

Purpose

Grocery retailers have access to detailed data on consumer purchases within their own chains. Previous research has used across-chain scanner panel data to develop optimal price cuts targeted to individual households but whether such a targeting strategy will work with only within-chain data is unknown. The purpose of this research is to address this specific question.

Design/methodology/approach

The authors use scanner panel data from multiple categories to create across-chain and within-chain purchase histories for the same consumers. They then estimate models of purchase decisions on the two datasets and compare their performance.

Findings

Within-chain data fares significantly worse on both fit and prediction criteria. Retailers' upside to customizing is minimal compared to those reported for manufacturers. Finally, customized prices based on the within-chain model significantly underperform the promise of across-chain data.

Research limitations/implications

Store choice is not modelled. Research also needs to be replicated in other contexts. The authors conclude that limited purchase histories may not yield accurate enough estimates of marketing mix responsiveness, and that across-chain purchase histories are essential for effective targeted price cuts.

Practical implications

Loyalty card data may be useful for other purposes, like experimenting with segment-specific discounts, but its value in customizing prices at individual level is limited without adding other sources of information.

Originality/value

Previous research on price customization has been based almost exclusively on across-store data. However, retailers only have access to their own chain-specific data. This is the first study to comprehensively compare price customization based on within- and across-chain purchase data and show that the upside potential for price customization based on the former information set is quite limited.

Details

European Journal of Marketing, vol. 48 no. 1/2
Type: Research Article
ISSN: 0309-0566

Keywords

To view the access options for this content please click here
Article
Publication date: 11 May 2021

Elizaveta Gavrikova, Irina Volkova and Yegor Burda

The purpose of this paper is to design a framework for asset data management in power companies. The authors consider asset data management from a strategic perspective…

Abstract

Purpose

The purpose of this paper is to design a framework for asset data management in power companies. The authors consider asset data management from a strategic perspective, linking operational-level data with corporate strategy and taking into account the organizational context and stakeholder expectations.

Design/methodology/approach

The authors conducted a multiple case study based on a literature review and three series of in-depth interviews with experts from three Russian electric power companies.

Findings

The main challenge in asset data management for electric power companies is the increasing amount and complexity of asset data, which is frequently incomplete or inaccurately collected, hard to translate to managerial language, focused primarily on the operational level. Such fragmented approach negatively affects strategic decision-making. The proposed framework introduces a holistic approach, provides context and accountability for decision-making and attributes data flows, roles and responsibilities to different management levels.

Research limitations/implications

The limitations of our study lie in the exploratory nature of case study research and limited generalization of the observed cases. However, the authors used multiple sources of evidence to ensure validity and generalization of the results. This article is a first step toward further understanding of the issues of transformation in power companies and other asset intensive businesses.

Originality/value

The novelty of the framework lies in the scope, focus and detailed treatment of asset data management in electric power companies.

Details

International Journal of Quality & Reliability Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0265-671X

Keywords

To view the access options for this content please click here
Article
Publication date: 26 November 2020

Murtaza Ashiq, Muhammad Haroon Usmani and Muhammad Naeem

Research data management (RDM) has been called a “ground-breaking” area for research libraries and it is among the top future trends for academic libraries. Hence, this…

Abstract

Purpose

Research data management (RDM) has been called a “ground-breaking” area for research libraries and it is among the top future trends for academic libraries. Hence, this study aims to systematically review RDM practices and services primarily focusing on the challenges, services and skills along with motivational factors associated with it.

Design/methodology/approach

A systematic literature review method was used focusing on literature produced between 2016–2020 to understand the latest trends. An extensive research strategy was framed and 15,206 results appeared. Finally, 19 studies have fulfilled the criteria to be included in the study following preferred reporting items for systematic reviews and meta-analysis.

Findings

RDM is gradually gaining importance among researchers and academic libraries; however, it is still poorly practiced by researchers and academic libraries. Albeit, it is better observed in developed countries over developing countries, however, there are lots of challenges associated with RDM practices by researchers and services by libraries. These challenges demand certain sets of skills to be developed for better practices and services. An active collaboration is required among stakeholders and university services departments to figure out the challenges and issues.

Research limitations/implications

The implications of policy and practical point-of-view present how research data can be better managed in the future by researchers and library professionals. The expected/desired role of key stockholders in this regard is also highlighted.

Originality/value

RDM is an important and emerging area. Researchers and Library and Information Science professionals are not comprehensively managing research data as it involves complex cooperation among various stakeholders. A combination of measures is required to better manage research data that would ultimately move forward for open access publishing.

Details

Global Knowledge, Memory and Communication, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2514-9342

Keywords

Content available
Article
Publication date: 25 October 2019

Ning Yan and Oliver Tat-Sheung Au

The purpose of this paper is to make a correlation analysis between students’ online learning behavior features and course grade, and to attempt to build some effective…

Abstract

Purpose

The purpose of this paper is to make a correlation analysis between students’ online learning behavior features and course grade, and to attempt to build some effective prediction model based on limited data.

Design/methodology/approach

The prediction label in this paper is the course grade of students, and the eigenvalues available are student age, student gender, connection time, hits count and days of access. The machine learning model used in this paper is the classical three-layer feedforward neural networks, and the scaled conjugate gradient algorithm is adopted. Pearson correlation analysis method is used to find the relationships between course grade and the student eigenvalues.

Findings

Days of access has the highest correlation with course grade, followed by hits count, and connection time is less relevant to students’ course grade. Student age and gender have the lowest correlation with course grade. Binary classification models have much higher prediction accuracy than multi-class classification models. Data normalization and data discretization can effectively improve the prediction accuracy of machine learning models, such as ANN model in this paper.

Originality/value

This paper may help teachers to find some clue to identify students with learning difficulties in advance and give timely help through the online learning behavior data. It shows that acceptable prediction models based on machine learning can be built using a small and limited data set. However, introducing external data into machine learning models to improve its prediction accuracy is still a valuable and hard issue.

Details

Asian Association of Open Universities Journal, vol. 14 no. 2
Type: Research Article
ISSN: 2414-6994

Keywords

To view the access options for this content please click here
Article
Publication date: 10 December 2020

Dareen Ryied Al-Tawal, Mazen Arafah and Ghaleb Jalil Sweis

Cost estimation is one of the most significant steps in construction planning, which must be undertaken in the preliminary stages of any project; it is required for all…

Abstract

Purpose

Cost estimation is one of the most significant steps in construction planning, which must be undertaken in the preliminary stages of any project; it is required for all projects to establish the project's budget. Confidence in these initial estimates is low, primarily due to the limited availability of suitable data, which leads the construction projects to frequently end up over budget. This paper investigated the efficacy of artificial neural networks (ANNs) methodologies in overcoming cost estimation problems in the early phases of the building design process.

Design/methodology/approach

Cost and design data from 104 projects constructed over the past five years in Jordan were used to develop, train and test ANN models. At the detailed design stage, 53 design factors were utilized to develop the first ANN model; then the factors were reduced to 41 and were utilized to develop the second predictive model at the schematic design stage. Finally, 27 design factors available at the concept design stage were utilized for the third ANN model.

Findings

The models achieved average cost estimation accuracy of 98, 98 and 97% in the detailed, schematic and concept design stages, respectively.

Research limitations/implications

This paper formulated the aims and objectives to be applicable only in Jordan using historical data of building projects.

Originality/value

The ANN approach introduced as a management tool is expected to provide the stakeholders in the engineering business with an indispensable tool for predicting the cost with limited data at the early stages of construction projects.

Details

Engineering, Construction and Architectural Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0969-9988

Keywords

To view the access options for this content please click here

Abstract

Details

An Input-output Analysis of European Integration
Type: Book
ISBN: 978-0-44451-088-4

To view the access options for this content please click here
Article
Publication date: 12 June 2014

Liwen Vaughan

The purpose of this paper is to examine the feasibility of discovering business information from search engine query data. Specifically the study tried to determine…

Abstract

Purpose

The purpose of this paper is to examine the feasibility of discovering business information from search engine query data. Specifically the study tried to determine whether search volumes of company names are correlated with the companies’ business performance and position data.

Design/methodology/approach

The top 50 US companies in the 2012 Fortune 500 list were included in the study. The following business performance and position data were collected: revenues, profits, assets, stockholders’ equity, profits as a percentage of revenues, and profits as a percentage of assets. Data on the search volumes of the company names were collected from Google Trends, which is based on search queries users enter into Google. Google Trends data were collected in the two scenarios of worldwide searches and US searches.

Findings

The study found significant correlations between search volume data and business performance and position data, suggesting that search engine query data can be used to discover business information. Google Trends’ worldwide search data were better than the US domestic search data for this purpose.

Research limitations/implications

The study is limited to only one country and to one year of data.

Practical implications

Publicly available search engine query data such as those from Google Trends can be used to estimate business performance and position data which are not always publicly available. Search engine query data are timelier than business data.

Originality/value

This is the first study to establish a relationship between search engine query data and business performance and position data.

Details

Online Information Review, vol. 38 no. 4
Type: Research Article
ISSN: 1468-4527

Keywords

To view the access options for this content please click here
Article
Publication date: 16 November 2015

Anika Ludwig and Mary Marshall

Research into crime is reliant on data that is recorded and published by criminal justice agencies; data which is collected for other purposes. Considering the suitability…

Abstract

Purpose

Research into crime is reliant on data that is recorded and published by criminal justice agencies; data which is collected for other purposes. Considering the suitability of geocoded crime data for academic research purposes, this paper will demonstrate the difficulties faced regarding the availability, integrity and reliability of readily accessible criminal justice data.

Design/methodology/approach

Data from two countries – England and Germany – were considered and set in a wider European Union (EU) context. Using the data received from requests made to the Freedom of Information Act (FOIA) in England and openly published reports and data available from Germany, the authors provide a contextual picture of the availability and operability of data recorded by these agencies. Geocoded data that enable cross-national comparisons with respect to immigration, ethnicity and crime are particularly hard to locate, and conducting research using data (such as crime data) whose “integrity” is questionable in an academic environment becomes increasingly problematic.

Findings

Analysing secondary data produced by a number of agencies are amplified due to the different methods of collection, management, retention and dissemination. It was found that even within England, the information provided by police forces varied greatly. Data in Germany were found to be more openly available and published electronically by a number of different criminal justice agencies; however, many of the issues apparent in English data regarding data integrity were also identified here.

Originality/value

The need for good record-keeping and information sharing practices has taken on added significance in today’s global environment. The better availability of comparable criminal justice data has the potential to provide academics with increased opportunities to develop an evidence base for policymaking.

Details

Records Management Journal, vol. 25 no. 3
Type: Research Article
ISSN: 0956-5698

Keywords

To view the access options for this content please click here
Book part
Publication date: 1 November 2007

Irina Farquhar and Alan Sorkin

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized…

Abstract

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

Details

The Value of Innovation: Impact on Health, Life Quality, Safety, and Regulatory Research
Type: Book
ISBN: 978-1-84950-551-2

1 – 10 of over 193000