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Article
Publication date: 18 May 2012

Sunghae Jun, Sang Sung Park and Dong Sik Jang

The purpose of this paper is to propose an objective method for technology forecasting (TF). For the construction of the proposed model, the paper aims to consider new…

Abstract

Purpose

The purpose of this paper is to propose an objective method for technology forecasting (TF). For the construction of the proposed model, the paper aims to consider new approaches to patent mapping and clustering. In addition, the paper aims to introduce a matrix map and K‐medoids clustering based on support vector clustering (KM‐SVC) for vacant TF.

Design/methodology/approach

TF is an important research and development (R&D) policy issue for both companies and government. Vacant TF is one of the key technological planning methods for improving the competitive power of firms and governments. In general, a forecasting process is facilitated subjectively based on the researcher's knowledge, resulting in unstable TF performance. In this paper, the authors forecast the vacant technology areas in a given technology field by analyzing patent documents and employing the proposed matrix map and KM‐SVC to forecast vacant technology areas in the management of technology (MOT).

Findings

The paper examines the vacant technology areas for MOT patent documents from the USA, Europe, and China by comparing these countries in terms of technology trends in MOT and identifying the vacant technology areas by country. The matrix map provides broad vacant technology areas, whereas KM‐SVC provides more specific vacant technology areas. Thus, the paper identifies the vacant technology areas of a given technology field by using the results for both the matrix map and KM‐SVC.

Practical implications

The authors use patent documents as objective data to develop a model for vacant TF. The paper attempts to objectively forecast the vacant technology areas in a given technology field. To verify the performance of the matrix map and KM‐SVC, the authors conduct an experiment using patent documents related to MOT (the given technology field in this paper). The results suggest that the proposed forecasting model can be applied to diverse technology fields, including R&D management, technology marketing, and intellectual property management.

Originality/value

Most TF models are based on qualitative and subjective methods such as Delphi. That is, there are few objective models. In this regard, this paper proposes a quantitative and objective TF model that employs patent documents as objective data and a matrix map and KM‐SVC as quantitative methods.

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Article
Publication date: 28 January 2020

Mohamed Zaki and Janet R. McColl-Kennedy

The purpose of this paper is to offer a step-by-step text mining analysis roadmap (TMAR) for service researchers. The paper provides guidance on how to choose between…

Abstract

Purpose

The purpose of this paper is to offer a step-by-step text mining analysis roadmap (TMAR) for service researchers. The paper provides guidance on how to choose between alternative tools, using illustrative examples from a range of business contexts.

Design/methodology/approach

The authors provide a six-stage TMAR on how to use text mining methods in practice. At each stage, the authors provide a guiding question, articulate the aim, identify a range of methods and demonstrate how machine learning and linguistic techniques can be used in practice with illustrative examples drawn from business, from an array of data types, services and contexts.

Findings

At each of the six stages, this paper demonstrates useful insights that result from the text mining techniques to provide an in-depth understanding of the phenomenon and actionable insights for research and practice.

Originality/value

There is little research to guide scholars and practitioners on how to gain insights from the extensive “big data” that arises from the different data sources. In a first, this paper addresses this important gap highlighting the advantages of using text mining to gain useful insights for theory testing and practice in different service contexts.

Details

Journal of Services Marketing, vol. 34 no. 1
Type: Research Article
ISSN: 0887-6045

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Article
Publication date: 18 January 2021

Shahla U. Umar and Tarik A. Rashid

The purpose of this study is to provide the reader with a full study of the bat algorithm, including its limitations, the fields that the algorithm has been applied…

Abstract

Purpose

The purpose of this study is to provide the reader with a full study of the bat algorithm, including its limitations, the fields that the algorithm has been applied, versatile optimization problems in different domains and all the studies that assess its performance against other meta-heuristic algorithms.

Design/methodology/approach

Bat algorithm is given in-depth in terms of backgrounds, characteristics, limitations, it has also displayed the algorithms that hybridized with BA (K-Medoids, back-propagation neural network, harmony search algorithm, differential evaluation strategies, enhanced particle swarm optimization and Cuckoo search algorithm) and their theoretical results, as well as to the modifications that have been performed of the algorithm (modified bat algorithm, enhanced bat algorithm, bat algorithm with mutation (BAM), uninhabited combat aerial vehicle-BAM and non-linear optimization). It also provides a summary review that focuses on improved and new bat algorithm (directed artificial bat algorithm, complex-valued bat algorithm, principal component analyzes-BA, multiple strategies coupling bat algorithm and directional bat algorithm).

Findings

Shed light on the advantages and disadvantages of this algorithm through all the research studies that dealt with the algorithm in addition to the fields and applications it has addressed in the hope that it will help scientists understand and develop it.

Originality/value

As far as the research community knowledge, there is no comprehensive survey study conducted on this algorithm covering all its aspects.

Details

World Journal of Engineering, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1708-5284

Keywords

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Article
Publication date: 8 February 2013

Stefan Dietze, Salvador Sanchez‐Alonso, Hannes Ebner, Hong Qing Yu, Daniela Giordano, Ivana Marenzi and Bernardo Pereira Nunes

Research in the area of technology‐enhanced learning (TEL) throughout the last decade has largely focused on sharing and reusing educational resources and data. This…

Abstract

Purpose

Research in the area of technology‐enhanced learning (TEL) throughout the last decade has largely focused on sharing and reusing educational resources and data. This effort has led to a fragmented landscape of competing metadata schemas, or interface mechanisms. More recently, semantic technologies were taken into account to improve interoperability. The linked data approach has emerged as the de facto standard for sharing data on the web. To this end, it is obvious that the application of linked data principles offers a large potential to solve interoperability issues in the field of TEL. This paper aims to address this issue.

Design/methodology/approach

In this paper, approaches are surveyed that are aimed towards a vision of linked education, i.e. education which exploits educational web data. It particularly considers the exploitation of the wealth of already existing TEL data on the web by allowing its exposure as linked data and by taking into account automated enrichment and interlinking techniques to provide rich and well‐interlinked data for the educational domain.

Findings

So far web‐scale integration of educational resources is not facilitated, mainly due to the lack of take‐up of shared principles, datasets and schemas. However, linked data principles increasingly are recognized by the TEL community. The paper provides a structured assessment and classification of existing challenges and approaches, serving as potential guideline for researchers and practitioners in the field.

Originality/value

Being one of the first comprehensive surveys on the topic of linked data for education, the paper has the potential to become a widely recognized reference publication in the area.

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Article
Publication date: 10 February 2020

Veronika Fenyves, Kinga Emese Zsido, Ioan Bircea and Tibor Tarnoczi

Changes in food retailing (globalization, concentration) have negative impacts on smaller, “traditional” food retail businesses. Their market share decreasing year by…

Abstract

Purpose

Changes in food retailing (globalization, concentration) have negative impacts on smaller, “traditional” food retail businesses. Their market share decreasing year by year. The purpose of this study is to examine and compare the financial performances of these businesses under the given circumstances and current economic environment in a Hungarian and a Romanian county.

Design/methodology/approach

The study is based on two complete databases, including all companies that behoove retail food activity (considering the NACE cod) in the counties of Hajdu-Bihar (Hungary) and Cluj (Romania). The database analyzed contains the financial statements for five consecutive years for 212 and 690 businesses. Databases were examined by the most typical financial indicators using the multivariate and univariate analysis of variance and the k-medoid cluster analysis methods.

Findings

The results of the analysis have shown that there are differences in the number of retail food companies in the case of two counties, both in number and in financial performance. Companies in Hajdú-Bihar county perform better in terms of financial ratios than those in Cluj county. The groups created by k-medoids cluster analysis are relatively well distinguished in the case of Hajdú-Bihar county, while the picture is much more mixed in the case of Kolozs county. However, it is also important to note that the companies analyzed should generally perform better to survive.

Research limitations/implications

Among the limitations of the study, it is important to note that the findings are relevant only to the two counties examined. Another limiting factor is that quite several companies had to be excluded from the analysis due to missing data or outliers.

Practical implications

The study presents for the corporate decision-makers the current performance of the companies of the sector examined in the two counties. The results of the study highlight the business areas of concern in management. The findings show that they need to change this performance to strengthen their market position. We believe that it is not enough to complain about the expansion of the supermarket chains, but they should take appropriate actions to improve their situation. Based on the results of the study, it can be concluded that there is a need to improve the financial efficiency of retail food companies in both counties to survive in the long run. This improvement is essential because retailers can play an important role in smaller settlements and narrower residential environments.

Originality/value

Comparative analysis of retail food companies in similar counties in these two neighboring countries has not been conducted using complex financial analysis. The study revealed the common and/or individual characteristics of these companies.

Details

British Food Journal, vol. 122 no. 11
Type: Research Article
ISSN: 0007-070X

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Article
Publication date: 18 January 2013

Bianka Kühne, Xavier Gellynck and Robert D. Weaver

Relational aspects between actors in a chain have been found to influence innovation capacity. Yet, many studies focus rather on groups of chain members, without…

Abstract

Purpose

Relational aspects between actors in a chain have been found to influence innovation capacity. Yet, many studies focus rather on groups of chain members, without investigating personalized links between the chain members. Other research involved case‐studies on a limited number of individual chains. The purpose of this paper is to examine quantitatively how the perceived relationship quality among three relational linked chain members affects the innovation capacity in traditional food chains beyond the dyad.

Design/methodology/approach

Evidence is drawn from a survey of 90 triplets of firms (three interlinked chain members), with each triplet belonging to a single individual traditional food chain. Research was conducted in three European countries and six traditional food product categories. Heterogeneity across these chains is examined based on cluster analysis. Binary logistic regression is used to examine the influence of relationship quality on the innovation capacity in the chains.

Findings

Three distinct clusters are identified and interpreted as reflecting three levels of innovation capacity: high, medium, and low. Relationship quality is defined through characteristics such as trust, social satisfaction, non‐coercive power, and reputation. Results suggest that the characteristics of the chain relationship quality may be important factors for the improvement of the innovation capacity in chains.

Research limitations/implications

As chain relationship quality fosters sharing of resources necessary for innovation as well as the distribution of incentives, these results further strengthen the emerging conclusion from the literature that innovation can be catalyzed by collaboration strategies and building strong relationships.

Originality/value

This paper contributes to the ongoing discussion on widening the supply chain approach from dyads to triads, as it is argued that a dyad of firms is intensively influenced by the network they are imbedded in. In this case, the paper explores the influence of the relationship quality among the food manufacturer and its main supplier and customer (and vice versa) on the innovation capacity of the whole chain.

Details

Supply Chain Management: An International Journal, vol. 18 no. 1
Type: Research Article
ISSN: 1359-8546

Keywords

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Article
Publication date: 18 June 2021

Shuai Luo, Hongwei Liu and Ershi Qi

The purpose of this paper is to recognize and label the faults in wind turbines with a new density-based clustering algorithm, named contour density scanning clustering

Abstract

Purpose

The purpose of this paper is to recognize and label the faults in wind turbines with a new density-based clustering algorithm, named contour density scanning clustering (CDSC) algorithm.

Design/methodology/approach

The algorithm includes four components: (1) computation of neighborhood density, (2) selection of core and noise data, (3) scanning core data and (4) updating clusters. The proposed algorithm considers the relationship between neighborhood data points according to a contour density scanning strategy.

Findings

The first experiment is conducted with artificial data to validate that the proposed CDSC algorithm is suitable for handling data points with arbitrary shapes. The second experiment with industrial gearbox vibration data is carried out to demonstrate that the time complexity and accuracy of the proposed CDSC algorithm in comparison with other conventional clustering algorithms, including k-means, density-based spatial clustering of applications with noise, density peaking clustering, neighborhood grid clustering, support vector clustering, random forest, core fusion-based density peak clustering, AdaBoost and extreme gradient boosting. The third experiment is conducted with an industrial bearing vibration data set to highlight that the CDSC algorithm can automatically track the emerging fault patterns of bearing in wind turbines over time.

Originality/value

Data points with different densities are clustered using three strategies: direct density reachability, density reachability and density connectivity. A contours density scanning strategy is proposed to determine whether the data points with the same density belong to one cluster. The proposed CDSC algorithm achieves automatically clustering, which means that the trends of the fault pattern could be tracked.

Details

Data Technologies and Applications, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2514-9288

Keywords

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Article
Publication date: 3 October 2016

Philipp Max Hartmann, Mohamed Zaki, Niels Feldmann and Andy Neely

The purpose of this paper is to derive a taxonomy of business models used by start-up firms that rely on data as a key resource for business, namely data-driven business…

Abstract

Purpose

The purpose of this paper is to derive a taxonomy of business models used by start-up firms that rely on data as a key resource for business, namely data-driven business models (DDBMs). By providing a framework to systematically analyse DDBMs, the study provides an introduction to DDBM as a field of study.

Design/methodology/approach

To develop the taxonomy of DDBMs, business model descriptions of 100 randomly chosen start-up firms were coded using a DDBM framework derived from literature, comprising six dimensions with 35 features. Subsequent application of clustering algorithms produced six different types of DDBM, validated by case studies from the study’s sample.

Findings

The taxonomy derived from the research consists of six different types of DDBM among start-ups. These types are characterised by a subset of six of nine clustering variables from the DDBM framework.

Practical implications

A major contribution of the paper is the designed framework, which stimulates thinking about the nature and future of DDBMs. The proposed taxonomy will help organisations to position their activities in the current DDBM landscape. Moreover, framework and taxonomy may lead to a DDBM design toolbox.

Originality/value

This paper develops a basis for understanding how start-ups build business models capture value from data as a key resource, adding a business perspective to the discussion of big data. By offering the scientific community a specific framework of business model features and a subsequent taxonomy, the paper provides reference points and serves as a foundation for future studies of DDBMs.

Details

International Journal of Operations & Production Management, vol. 36 no. 10
Type: Research Article
ISSN: 0144-3577

Keywords

Content available
Article
Publication date: 3 June 2021

Lulu Ge, Zheming Yang and Wen Ji

The evolution of crowd intelligence is a mainly concerns issue in the field of crowd science. It is a kind of group behavior that is superior to the individual’s ability…

Abstract

Purpose

The evolution of crowd intelligence is a mainly concerns issue in the field of crowd science. It is a kind of group behavior that is superior to the individual’s ability to complete tasks through the cooperation of many agents. In this study, the evolution of crowd intelligence is studied through the clustering method and the particle swarm optimization (PSO) algorithm.

Design/methodology/approach

This study proposes a crowd evolution method based on intelligence level clustering. Based on clustering, this method uses the agents’ intelligence level as the metric to cluster agents. Then, the agents evolve within the cluster on the basis of the PSO algorithm.

Findings

Two main simulation experiments are designed for the proposed method. First, agents are classified based on their intelligence level. Then, when evolving the agents, two different evolution centers are set. Besides, this paper uses different numbers of clusters to conduct experiments.

Practical implications

The experimental results show that the proposed method can effectively improve the crowd intelligence level and the cooperation ability between agents.

Originality/value

This paper proposes a crowd evolution method based on intelligence level clustering, which is based on the clustering method and the PSO algorithm to analyze the evolution.

Details

International Journal of Crowd Science, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2398-7294

Keywords

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Article
Publication date: 19 June 2017

Khai Tan Huynh, Tho Thanh Quan and Thang Hoai Bui

Service-oriented architecture is an emerging software architecture, in which web service (WS) plays a crucial role. In this architecture, the task of WS composition and…

Abstract

Purpose

Service-oriented architecture is an emerging software architecture, in which web service (WS) plays a crucial role. In this architecture, the task of WS composition and verification is required when handling complex requirement of services from users. When the number of WS becomes very huge in practice, the complexity of the composition and verification is also correspondingly high. In this paper, the authors aim to propose a logic-based clustering approach to solve this problem by separating the original repository of WS into clusters. Moreover, they also propose a so-called quality-controlled clustering approach to ensure the quality of generated clusters in a reasonable execution time.

Design/methodology/approach

The approach represents WSs as logical formulas on which the authors conduct the clustering task. They also combine two most popular clustering approaches of hierarchical agglomerative clustering (HAC) and k-means to ensure the quality of generated clusters.

Findings

This logic-based clustering approach really helps to increase the performance of the WS composition and verification significantly. Furthermore, the logic-based approach helps us to maintain the soundness and completeness of the composition solution. Eventually, the quality-controlled strategy can ensure the quality of generated clusters in low complexity time.

Research limitations/implications

The work discussed in this paper is just implemented as a research tool known as WSCOVER. More work is needed to make it a practical and usable system for real life applications.

Originality/value

In this paper, the authors propose a logic-based paradigm to represent and cluster WSs. Moreover, they also propose an approach of quality-controlled clustering which combines and takes advantages of two most popular clustering approaches of HAC and k-means.

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