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1 – 10 of over 1000Mamta Kayest and Sanjay Kumar Jain
Document retrieval has become a hot research topic over the past few years, and has been paid more attention in browsing and synthesizing information from different documents. The…
Abstract
Purpose
Document retrieval has become a hot research topic over the past few years, and has been paid more attention in browsing and synthesizing information from different documents. The purpose of this paper is to develop an effective document retrieval method, which focuses on reducing the time needed for the navigator to evoke the whole document based on contents, themes and concepts of documents.
Design/methodology/approach
This paper introduces an incremental learning approach for text categorization using Monarch Butterfly optimization–FireFly optimization based Neural Network (MB–FF based NN). Initially, the feature extraction is carried out on the pre-processed data using Term Frequency–Inverse Document Frequency (TF–IDF) and holoentropy to find the keywords of the document. Then, cluster-based indexing is performed using MB–FF algorithm, and finally, by matching process with the modified Bhattacharya distance measure, the document retrieval is done. In MB–FF based NN, the weights in the NN are chosen using MB–FF algorithm.
Findings
The effectiveness of the proposed MB–FF based NN is proven with an improved precision value of 0.8769, recall value of 0.7957, F-measure of 0.8143 and accuracy of 0.7815, respectively.
Originality/value
The experimental results show that the proposed MB–FF based NN is useful to companies, which have a large workforce across the country.
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Maryana Scoralick De Almeida Tavares, Cláudia Fabiana Gohr, Sandra Morioka and Thereza Rakel da Cunha
This paper aims to map literature about innovation capabilities (IC) taking into consideration industrial clusters to propose a conceptual framework that synthetizes the main…
Abstract
Purpose
This paper aims to map literature about innovation capabilities (IC) taking into consideration industrial clusters to propose a conceptual framework that synthetizes the main factors and subfactors responsible for ICs; in addition, the paper also proposes a research agenda.
Design/methodology/approach
A systematic literature review (SLR) was performed; academic papers were analyzed qualitatively and quantitatively.
Findings
The authors provide a descriptive analysis followed by a thematic synthesis, in which we present 05 enablers and 20 critical factors (CF) of IC in clusters. The proposed framework emphasizes what needs to be done or improved to increase IC in cluster-based companies. Based on this systematic review and the framework proposed, the authors identified opportunities for future research.
Research limitations/implications
The enablers and CF identified through SLR were not validated empirically. Therefore, future studies on the current topic are required to validate the framework by investigating which factors are more relevant to cluster-based companies that intend to improve their innovative performance.
Practical implications
The present findings have important implications for the identification of the factors and subfactors that may contribute to the development of IC, which may help managers and decision-makers in recognizing which factors are the most responsible for business innovation.
Originality/value
The paper identifies enablers related to the development of IC in industrial cluster and presents a research agenda. The framework represents a guideline for companies to achieve better innovation performance.
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The most commonly used approaches for cluster validation are based on indices but the majority of the existing cluster validity indices do not work well on data sets of different…
Abstract
Purpose
The most commonly used approaches for cluster validation are based on indices but the majority of the existing cluster validity indices do not work well on data sets of different complexities. The purpose of this paper is to propose a new cluster validity index (ARSD index) that works well on all types of data sets.
Design/methodology/approach
The authors introduce a new compactness measure that depicts the typical behaviour of a cluster where more points are located around the centre and lesser points towards the outer edge of the cluster. A novel penalty function is proposed for determining the distinctness measure of clusters. Random linear search-algorithm is employed to evaluate and compare the performance of the five commonly known validity indices and the proposed validity index. The values of the six indices are computed for all nc ranging from (nc min, nc max) to obtain the optimal number of clusters present in a data set. The data sets used in the experiments include shaped, Gaussian-like and real data sets.
Findings
Through extensive experimental study, it is observed that the proposed validity index is found to be more consistent and reliable in indicating the correct number of clusters compared to other validity indices. This is experimentally demonstrated on 11 data sets where the proposed index has achieved better results.
Originality/value
The originality of the research paper includes proposing a novel cluster validity index which is used to determine the optimal number of clusters present in data sets of different complexities.
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This study research contributes in fulfilling the gap by carrying out a systematic literature review (SLR) of contemporary research studies in closed-loop supply chain (CLSC). To…
Abstract
Purpose
This study research contributes in fulfilling the gap by carrying out a systematic literature review (SLR) of contemporary research studies in closed-loop supply chain (CLSC). To the best of the author’s knowledge, an SLR rooted in bibliometric analysis has not been carried focusing on advent developments in CLSC. SLR employs scientific methodologies to select papers from standard databases. The SLR using advanced bibliometric and network analysis enables unveiling the key features of the contemporary literature.
Design/methodology/approach
The author has analyzed over 333 documents published from 2008 and onward. Using the contemporary tools from bibliometric analysis tools, the author presented an exploratory analysis. A network analysis is utilized to visualize literature and create clusters for the cocited research studies, keywords and publication sources. A detailed multivariate analysis of most influential works published based top 100 articles via a cocitation matrix is done. The multivariate analysis used k-means clustering in which optimal number of clusters are estimated. The analysis is further extended by using a factor analysis, which enables determining the most influential clusters in the k-means clustering analysis.
Findings
The SLR using a bibliometric and network analysis enables unveiling the key features of the contemporary literature in CLSC. The author examined published research for influential authors, sources, region, among other key aspects. Network analysis enabled visualizing the clusters of cocited research studies, cowords and publication sources. Cluster analysis of cocited research studies is further explored using k-means clustering. Factor analysis extends findings by identifying most contributing grouping of research areas within CLSC research. Each clustering technique disclosed a unique grouping structure.
Originality/value
CLSC has received considerable attention, and its core areas start with focusing on reverse logistics concepts relating reuse, recycling, remanufacturing, among others. Contemporarily, the studies have enhanced reverse logistics core functionalities interfaced with the other interesting avenues related to CO2 emission reduction, greening and environmental protection, sustainability, product design and governmental policies. Earlier studies have presented a literature review of CLSC; however, these reviews are commonly conducted in the traditional manner where the authors select papers based on their area of expertise, interest and experience. As such these reviews fall short in utilizing the advanced tools from bibliometric analysis.
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Katharina Maria Hofer, Lisa Maria Niehoff and Gerhard A. Wuehrer
In this paper, we examine the elements of pricing approaches in export businesses and their performance in an international environment. The elements of pricing approaches consist…
Abstract
Purpose
In this paper, we examine the elements of pricing approaches in export businesses and their performance in an international environment. The elements of pricing approaches consist of cost-based, competitor-based, and value-based decisions made by different levels of management. By providing an integrated, holistic view, we investigate how different types of export-pricing strategies influence export performance, and which elements strengthen or attenuate the outcomes of strategic actions.
Methodology/approach
Using data from a survey of 172 export managers, we test our hypotheses in a two-step approach. First, we use an unsupervised approach to group the export companies and to validate the cluster solution internally and externally. Second, we test our hypotheses regarding export performance.
Findings
The results show that the types of export-pricing strategies are unequally distributed, and the elements of the strategies have different complexities. Export performance varies significantly by type of pricing orientation used.
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In the extant organizational change literature scant attention has been given to the communication and cognitive processes consequential to organizational transformation. From the…
Abstract
Purpose
In the extant organizational change literature scant attention has been given to the communication and cognitive processes consequential to organizational transformation. From the communication and sense-making perspectives, this study discusses the role of positive communication involving stories, metaphors or axioms in fostering socio-cognitive routines necessary for organizational change. The study further examines the empirical link between positive communication and organizational transformation with the survey data from professionals who have experienced the organizational change episode. The paper aims to discuss these issues.
Design/methodology/approach
The study examines the empirical link between the positive communication and organizational transformation with a survey data collected from 174 management professionals who have recently experienced the organizational change episodes such as restructuring, reengineering, TQM adoption or new strategy implementation. With the content analysis of narratives containing metaphors, axioms and stories, the study unravels the underlying clusters of organizational and socio-cognitive dimensions associated with organizational transformation.
Findings
The study results affirm the importance of positive communication and its effects on the emotional buy-in, learning and transformation occurring at the individual level, and attest to the transformational effect of positive axioms, metaphors or stories on the organization. The study also revealed that the positive communication diffusing social, cognitive or emotional attributes such as commitment, trust or optimism produces the desired transformational effect.
Practical implications
It is imperative for managers to understand the relationship between socio-linguistic processes and cognitive attributes such as trust, commitment and learning. With the help of right metaphors, stories and axioms that resonate with changing industry conditions, managers can effectively orchestrate the strategic intent and organizational transformation.
Originality/value
Most studies on the relationship between managerial communication and organizational transformation are primarily qualitative case studies focusing on the dialectics of organizational change. This study carries the strong external validity by capturing the connection between managerial communications and their transformational effect with the help of data collected from the management professionals across multiple industries.
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Mahsan Esmaeilzadeh, Bijan Abdollahi, Asadallah Ganjali and Akbar Hasanpoor
The purpose of this paper is to introduce an evaluation methodology for employee profiles that will provide feedback to the training decision makers. Employee profiles play a…
Abstract
Purpose
The purpose of this paper is to introduce an evaluation methodology for employee profiles that will provide feedback to the training decision makers. Employee profiles play a crucial role in the evaluation process to improve the training process performance. This paper focuses on the clustering of the employees based on their profiles into specific categories that represent the employees’ characteristics. The employees are classified into following categories: necessary training, required training, and no training. The work may answer the question of how to spend the budget of training for the employees. This investigation presents the use of fuzzy optimization and clustering hybrid model (data mining approaches) as a fuzzy imperialistic competitive algorithm (FICA) and k-means to find the employees’ categories and predict their training requirements.
Design/methodology/approach
Prior research that served as an impetus for this paper is discussed. The approach is to apply evolutionary algorithms and clustering hybrid model to improve the training decision system directions.
Findings
This paper focuses on how to find a good model for the evaluation of employee profiles. The paper introduces the use of artificial intelligence methods (fuzzy optimization (FICA) and clustering techniques (K-means)) in management. The suggestion and the recommendations were constructed based on the clustering results that represent the employee profiles and reflect their requirements during the training courses. Finally, the paper proved the ability of fuzzy optimization technique and clustering hybrid model in predicting the employee’s training requirements.
Originality/value
This paper evaluates employee profiles based on new directions and expands the implication of clustering view in solving organizational challenges (in TCT for the first time).
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The immense quantity of available unstructured text documents serve as one of the largest source of information. Text classification can be an essential task for many purposes in…
Abstract
Purpose
The immense quantity of available unstructured text documents serve as one of the largest source of information. Text classification can be an essential task for many purposes in information retrieval, such as document organization, text filtering and sentiment analysis. Ensemble learning has been extensively studied to construct efficient text classification schemes with higher predictive performance and generalization ability. The purpose of this paper is to provide diversity among the classification algorithms of ensemble, which is a key issue in the ensemble design.
Design/methodology/approach
An ensemble scheme based on hybrid supervised clustering is presented for text classification. In the presented scheme, supervised hybrid clustering, which is based on cuckoo search algorithm and k-means, is introduced to partition the data samples of each class into clusters so that training subsets with higher diversities can be provided. Each classifier is trained on the diversified training subsets and the predictions of individual classifiers are combined by the majority voting rule. The predictive performance of the proposed classifier ensemble is compared to conventional classification algorithms (such as Naïve Bayes, logistic regression, support vector machines and C4.5 algorithm) and ensemble learning methods (such as AdaBoost, bagging and random subspace) using 11 text benchmarks.
Findings
The experimental results indicate that the presented classifier ensemble outperforms the conventional classification algorithms and ensemble learning methods for text classification.
Originality/value
The presented ensemble scheme is the first to use supervised clustering to obtain diverse ensemble for text classification
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Cláudia Fabiana Gohr, Maryana Scoralick de Almeida Tavares and Sandra Naomi Morioka
This paper aims to propose an assessment framework to evaluate companies' innovation capability in the context of industrial clusters.
Abstract
Purpose
This paper aims to propose an assessment framework to evaluate companies' innovation capability in the context of industrial clusters.
Design/methodology/approach
The assessment framework was built based on the Graph-Theoretic Approach (GTA) to measure the influence of the factors and sub-factors of innovation capabilities. To quantify the level of interdependence between factors and sub-factors of innovation capability Delphi method was adopted. The authors developed five case studies in firms from an Information and Communications Technology and Creative Economy cluster in Northeastern Brazil to test the framework's applicability.
Findings
The results showed that identifying and evaluating the factors of innovation capability allows a larger understanding of what affects these capabilities to a greater or lesser extent and contributes to strategic decision-making.
Research limitations/implications
The framework evaluates the innovation capability of each firm, not providing an index for the whole industrial cluster. Besides, the framework does not consider the innovations developed by the companies through the innovation's capabilities. As the Delphi technique was adopted to analyze the levels of influence or interdependence between factors and sub-factors of innovation capability, different experts may lead to different results.
Practical implications
Among the managerial implications, the authors can highlight the innovation capability index as a practical performance measure to stimulate improvement initiatives regarding innovations in industrial clusters. Besides, as the proposed framework is generic, research organizations, public institutions and regional governments can adopt it to analyze innovation capabilities in cluster-based companies.
Originality/value
Previous industrial cluster studies have concentrated on knowledge transfer as the main attribute influencing innovation capabilities. The literature also presents assessment frameworks focusing on qualitative analyses or innovation capabilities outcomes (patents and products). Differently, the authors proposed a quantitative assessment framework considering specific factors (and sub-factors) of innovation capabilities in industrial clusters.
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ALAN GRIFFITHS, LESLEY A. ROBINSON and PETER WILLETT
This paper considers the classifications produced by application of the single linkage, complete linkage, group average and Ward clustering methods to the Keen and Cranfield…
Abstract
This paper considers the classifications produced by application of the single linkage, complete linkage, group average and Ward clustering methods to the Keen and Cranfield document test collections. Experiments were carried out to study the structure of the hierarchies produced by the different methods, the extent to which the methods distort the input similarity matrices during the generation of a classification, and the retrieval effectiveness obtainable in cluster based retrieval. The results would suggest that the single linkage method, which has been used extensively in previous work on document clustering, is not the most effective procedure of those tested, although it should be emphasized that the experiments have used only small document test collections.