Search results
1 – 10 of over 42000The purpose of this paper is to explore and describe research presented in the International Journal of Quality & Reliability Management (IJQRM), thereby creating an increased…
Abstract
Purpose
The purpose of this paper is to explore and describe research presented in the International Journal of Quality & Reliability Management (IJQRM), thereby creating an increased understanding of how the areas of research have evolved through the years. An additional purpose is to show how text mining methodology can be used as a tool for exploration and description of research publications.
Design/methodology/approach
The study applies text mining methodologies to explore and describe the digital library of IJQRM from 1984 up to 2014. To structure and condense the data, k-means clustering and probabilistic topic modeling with latent Dirichlet allocation is applied. The data set consists of research paper abstracts.
Findings
The results support the suggestion of the occurrence of trends, fads and fashion in research publications. Research on quality function deployment (QFD) and reliability management are noted to be on the downturn whereas research on Six Sigma with a focus on lean, innovation, performance and improvement on the rise. Furthermore, the study confirms IJQRM as a scientific journal with quality and reliability management as primary areas of coverage, accompanied by specific topics such as total quality management, service quality, process management, ISO, QFD and Six Sigma. The study also gives an insight into how text mining can be used as a way to efficiently explore and describe large quantities of research paper abstracts.
Research limitations/implications
The study focuses on abstracts of research papers, thus topics and categories that could be identified via other journal publications, such as book reviews; general reviews; secondary articles; editorials; guest editorials; awards for excellence (notifications); introductions or summaries from conferences; notes from the publisher; and articles without an abstract, are excluded.
Originality/value
There do not seem to be any prior text mining studies that apply cluster modeling and probabilistic topic modeling to research article abstracts in the IJQRM. This study therefore offers a unique perspective on the journal’s content.
Details
Keywords
Benjamin Leiby and Darryl Ahner
This paper aims to examine how the regional variable in country conflict modeling affects forecast accuracy and identifies a methodology to further improve the predictions.
Abstract
Purpose
This paper aims to examine how the regional variable in country conflict modeling affects forecast accuracy and identifies a methodology to further improve the predictions.
Design/methodology/approach
This paper uses statistical learning methods to both evaluate the quantity of data for clustering countries along with quantifying accuracy according to the number of clusters used.
Findings
This study demonstrates that increasing the number of clusters for modeling improves the ability to predict conflict as long as the models are robust.
Originality/value
This study investigates the quantity of clusters used in conflict modeling, while previous research assumes a specific quantity before modeling.
Details
Keywords
Yogendra Kumar, Runa Sarkar and Sanjeev Swami
The purpose of this paper is to present a modeling approach for aggregate and disaggregate level models for cluster‐based diffusion of a new technology. The aggregate approach…
Abstract
Purpose
The purpose of this paper is to present a modeling approach for aggregate and disaggregate level models for cluster‐based diffusion of a new technology. The aggregate approach refers to the diffusion modeling of a product at the overall population level, while the disaggregate approach refers to the diffusion process at the individual entity level.
Design/methodology/approach
The pattern of diffusion of a new technology in a representative two‐cluster situation is studied. In the aggregate level modeling, a diffusion model is developed in which potential adopters of both clusters learn about the new technology from each other. This is done by a Lotka‐Volterra type of dynamical system of equations. Then, to focus on relatively micro‐level phenomena, such as different propensities of imitation and innovation of firms within a cluster, an agent‐based disaggregate model for cluster‐based diffusion of technology is proposed. In these disaggregate models, the effects of heterogeneity and the inter‐cluster and intra‐cluster distances between the agents are captured.
Findings
The results highlight two major points: first, both aggregate and disaggregate models are in agreement with each other, and second, both of the models exhibit a form similar to the Bass model. Thus, consistent with the general theme of why the Bass model fits without decision variables, it is found that the Bass model, when extended appropriately, can be expected to work well also in the cluster‐based technology diffusion situation.
Practical implications
This modeling approach can be applied to the modeling of those situations in which heterogeneous industrial units are present in geographical clusters. It can also be applied in the related contexts such as diffusion of practices (e.g. quality certifications) within a multi‐divisional organization or across various networked clusters.
Originality/value
For a homogenous population, the Bass model has been used extensively to predict the sales of newly introduced consumer durables. In comparison, little attention has been given to the modeling of the technology adoption by the industrial units present in disparate groups, called clusters. The major contribution of this paper is to propose a framework for cluster‐based diffusion of technological products, and then to present an analysis of that framework using two different methodologies.
Details
Keywords
Business process management (BPM) has attracted much focus throughout the years, yet there have been calls questioning the future of BPM. The purpose of this paper is to explore…
Abstract
Purpose
Business process management (BPM) has attracted much focus throughout the years, yet there have been calls questioning the future of BPM. The purpose of this paper is to explore the current state of the field through a dynamic literature review and identify the main challenges for its future development.
Design/methodology/approach
A dynamic co-citation network analysis identifies the “evolution” of knowledge of BPM and the most influential works. The results present the developed BPM subthemes in the form of clusters.
Findings
The focus within the field has shifted from facilitating wide-ranging business performance improvements to creating introverted optimizations within a particular BPM subgroup. The BPM field has thus experienced strong fragmentation throughout the years and has accrued into self-fueling subareas of BPM research such as business process modeling and workflow management. Those subareas often neglect related disciplines in other management, process modeling and organizational improvement fields.
Research limitations/implications
The study is limited by the initial keyword choice of the authors. The subsequent co-citation analysis ameliorates the subjectivity since it produces a data set and contributions based on references.
Originality/value
A new combination of historical development and the state-of-the-art of the BPM field, by employing a co-citation and cluster analysis. This dynamic literature review presents the current state of the theoretical core and attempts to identify the crossroads that BPM has reached. The study can be replicated in the future to track the changes in the field.
Details
Keywords
The purpose of this paper is to explore and describe how research on quality management (QM) has evolved historically. The study includes the complete digital archive of three…
Abstract
Purpose
The purpose of this paper is to explore and describe how research on quality management (QM) has evolved historically. The study includes the complete digital archive of three academic journals in the field of QM. Thereby, a unique depiction of how the general outlines of the field as well as trends in research topics have evolved through the years is presented.
Design/methodology/approach
The study applies cluster and probabilistic topic modeling to unstructured data from The International Journal of Quality & Reliability Management, The TQM Journal and Total Quality Management & Business Excellence. In addition, trend analysis using support vector machine is performed.
Findings
The study identifies six central, perpetual themes of QM research: control, costs, reliability and failure; service quality; TQM – implementation and performance; ISO – certification, standards and systems; Innovation, practices and learning and customers – research and product design. Additionally, historical surges and shifts in research focus are recognized in the study. From these trends, a decrease in interest in TQM and control of quality, costs and processes in favor of service quality, customer satisfaction, Six Sigma, Lean and innovation can be noted during the past decade. The results validate previous findings.
Originality/value
Of the identified central themes, innovation, practices and learning appears not to have been documented as a fundamental part of QM research in previous studies. Thus, this theme can be regarded as a new perspective on QM research and thereby on QM.
Details
Keywords
Mikhail A. Bek, Nadezda N. Bek, Marina Y. Sheresheva and Wesley J. Johnston
The purpose of this paper is to develop and test models explaining the unsatisfactory innovation activity of Russian firms and the main obstacles to innovation cluster development.
Abstract
Purpose
The purpose of this paper is to develop and test models explaining the unsatisfactory innovation activity of Russian firms and the main obstacles to innovation cluster development.
Design/methodology/approach
Based on statistical analysis and the results of a pilot survey of 192 local businessmen, followed by imitation modeling analysis, the study tests hypotheses regarding the impact of unsatisfactory institutional environments, including weak property rights protection, on innovation cluster development in Russia.
Findings
The analysis shows that the impact of adverse factors on innovation activities of cluster members is crucial, and estimates to what extent the negative factors' influence should be reduced to prevent cluster degradation processes.
Research limitations/implications
The models provide a number of sensitivity tests of the parameters; however, data from clusters with different levels of support and protection need to be obtained. Government experiments could be conducted to test the models and find ranges of optimal parameters for cluster development. Short of this, examination of actual data from different cluster in similar environments would allow estimated of optimal strategies for support. Longitudinal data can also help determine the actual cause and effect of successful innovation cluster development.
Practical implications
The paper provides managerial implications for organizations involved in innovation clusters, which can be used to improve cluster members' performance and collaborative innovation activities by means of creating acceptable institutional environments.
Originality/value
The paper provides evidence of the connection between collaborative activities of clustering organizations in Russia and their performance caused by expectations concerning institutional conditions on the macro level in Russia.
Details
Keywords
Ibrahim M. Awad and Alaa A. Amro
The purpose of this paper is to map the cluster in the leather and shoes sector for improving the competitiveness of the firms. Toward this end, the study is organized to examine…
Abstract
Purpose
The purpose of this paper is to map the cluster in the leather and shoes sector for improving the competitiveness of the firms. Toward this end, the study is organized to examine the impact of clustering on competitiveness improvement. The influence of competitive elements and performance (Porter’s diamond) and balanced score card was utilized.
Design/methodology/approach
A random sample of 131 respondents was chosen during the period from May 2016 to July 2016. A structural equation modeling (SEM) analysis was applied to investigate the research model. This approach was chosen because of its ability to test casual relationships between constructs with multiple measurement items. Researchers proposed a two-stage model-building process for applying SEM. The measurement model was first examined for instrument validation, followed by an analysis of the structural model for testing associations hypothesized by the research model.
Findings
The main findings show that there is a unidirectional causal relationship between improvements of performance and achieve competitiveness and also reveal that the Palestinian shoes and leather cluster sector is vital and strong, and conclude that clustering can achieve competitiveness for small- and medium-sized enterprises.
Research limitations/implications
Future research can examine the relationship between clustering and innovation. The effect of clustering using other clustering models other than Porter’s model is advised to be used for future research.
Practical implications
The relationships among clustering and competitiveness may provide a practical clue to both, policymakers and researchers on how cluster enhances economic firms such as a skilled workforce, research, development capacity, and infrastructure. This is likely to create assets such as trust, synergy, collaboration and cooperation for improved competitiveness.
Originality/value
The findings of this study provide background information that can simultaneously be used to analyze relationships among factors of innovation, customer’s satisfaction, internal business and financial performance. This study also identified several essential factors in successful firms, and discussed the implications of these factors for developing organizational strategies to encourage and foster competitiveness.
Details
Keywords
Noorullah Renigunta Mohammed and Moulana Mohammed
The purpose of this study for eHealth text mining domains, cosine-based visual methods (VM) assess the clusters more accurately than Euclidean; which are recommended for tweet…
Abstract
Purpose
The purpose of this study for eHealth text mining domains, cosine-based visual methods (VM) assess the clusters more accurately than Euclidean; which are recommended for tweet data models for clusters assessment. Such VM determines the clusters concerning a single viewpoint or none, which are less informative. Multi-viewpoints (MVP) were used for addressing the more informative clusters assessment of health-care tweet documents and to demonstrate visual analysis of cluster tendency.
Design/methodology/approach
In this paper, the authors proposed MVP-based VM by using traditional topic models with visual techniques to find cluster tendency, partitioning for cluster validity to propose health-care recommendations based on tweets. The authors demonstrated the effectiveness of proposed methods on different real-time Twitter health-care data sets in the experimental study. The authors also did a comparative analysis of proposed models with existing visual assessment tendency (VAT) and cVAT models by using cluster validity indices and computational complexities; the examples suggest that MVP VM were more informative.
Findings
In this paper, the authors proposed MVP-based VM by using traditional topic models with visual techniques to find cluster tendency, partitioning for cluster validity to propose health-care recommendations based on tweets.
Originality/value
In this paper, the authors proposed multi-viewpoints distance metric in topic model cluster tendency for the first time and visual representation using VAT images using hybrid topic models to find cluster tendency, partitioning for cluster validity to propose health-care recommendations based on tweets.
Details
Keywords
Alekh Gour, Shikha Aggarwal and Mehmet Erdem
The dynamic yet volatile nature of tourism and travel industry in a competitive environment calls for enhanced marketing intelligence and analytics, especially for those entities…
Abstract
Purpose
The dynamic yet volatile nature of tourism and travel industry in a competitive environment calls for enhanced marketing intelligence and analytics, especially for those entities with limited marketing budgets. The past decade has witnessed an increased use of user-generated content (UGC) analysis as a marketing tool to make better informed decisions. Likewise, textual data analysis of UGC has gained much attention among tourism and hospitality scholars. Nonetheless, most of the scholarly works have focused on the singular application of an existing method or technique rather than using a multi-method approach. The purpose of this study is to propose a novel Web analytics methodology to examine online reviews posted by tourists in real time and assist decision-makers tasked with marketing strategy and intelligence.
Design/methodology/approach
For illustration, the case of tourism campaign in India was undertaken. A total of 305,298 reviews were collected, and after filtering, 276,154 reviews were qualified for analysis using a string of models. Descriptive charts, sentiment analysis, clustering, topic modeling and machine learning algorithms for real-time classification were applied.
Findings
Using big data from TripAdvisor, a total of 145 tourist destinations were clustered based on tourists’ perceptions. Further exploration of each cluster through topic modeling was conducted, which revealed interesting insights into satisfiers and dissatisfiers of different clusters of destinations. The results supported the use of the proposed multi-method Web-analytics approach.
Practical implications
The proposed machine learning model demonstrated that it could provide real-time information on the sentiments in each incoming review about a destination. This information might be useful for taking timely action for improvisation or controlling a service situation.
Originality/value
In terms of Web-analytics and UGC, a comprehensive analytical model to perform an end-to-end understanding of tourist behavior patterns and offer the potential for real-time interpretation is rarely proposed. The current study not only proposes such a model but also offers empirical evidence for a successful application. It contributes to the literature by providing scholars interested in textual analytics a step-by-step guide to implement a multi-method approach.
Details
Keywords
Nastaran Hajiheydari, Mojtaba Talafidaryani, SeyedHossein Khabiri and Masoud Salehi
Although the business model field of study has been a focus of attention for both researchers and practitioners within the past two decades, it still suffers from concern about…
Abstract
Purpose
Although the business model field of study has been a focus of attention for both researchers and practitioners within the past two decades, it still suffers from concern about its identity. Accordingly, this paper aims to clarify the intellectual structure of business model through identifying the research clusters and their sub-clusters, the prominent relations and the dominant research trends.
Design/methodology/approach
This paper uses some common text mining methods including co-word analysis, burst analysis, timeline analysis and topic modeling to analyze and mine the title, abstract and keywords of 14,081 research documents related to the domain of business model.
Findings
The results revealed that the business model field of study consists of three main research areas including electronic business model, business model innovation and sustainable business model, each of which has some sub-areas and has been more evident in some particular industries. Additionally, from the time perspective, research issues in the domain of sustainable development are considered as the hot and emerging topics in this field. In addition, the results confirmed that information technology has been one of the most important drivers, influencing the appearance of different study topics in the various periods.
Originality/value
The contribution of this study is to quantitatively uncover the dominant knowledge structure and prominent research trends in the business model field of study, considering a broad range of scholarly publications and using some promising and reliable text mining techniques.
Details