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Book part
Publication date: 26 October 2017

Ronald K. Klimberg, Samuel Ratick and Harvey Smith

Multiple linear regression (MLR) is a commonly used statistical technique to predict future values. In this paper, we examine the situation in which a given time series…

Abstract

Multiple linear regression (MLR) is a commonly used statistical technique to predict future values. In this paper, we examine the situation in which a given time series dataset contains numerous observations of important predictor variables that can effectively be classified into groups based on their values. In such situations, cluster analysis is often employed to improve the MLR models predictive accuracy, usually by creating separate regressions for each cluster. We introduce a novel approach in which we use the clusters and cluster centroids as input data for the predictor variables to improve the predictive accuracy of the MLR model. We illustrate and test this approach with a real dataset on fleet maintenance.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-1-78743-069-3

Keywords

Book part
Publication date: 21 November 2018

Nur Syazwin Mansor, Norhaiza Ahmad and Arien Heryansyah

This study compares the performance of two types of clustering methods, time-based and non-time-based clustering, in the identification of river discharge patterns at the…

Abstract

This study compares the performance of two types of clustering methods, time-based and non-time-based clustering, in the identification of river discharge patterns at the Johor River basin during the northeast monsoon season. Time-based clustering is represented by employing dynamic time warping (DTW) dissimilarity measure, whereas non-time-based clustering is represented by employing Euclidean dissimilarity measure in analysing the Johor River discharge data. In addition, we combine each of these clustering methods with a frequency domain representation of the discharge data using Discrete Fourier Transform (DFT) to see if such transformation affects the clustering results. The clustering quality from the hierarchical data structures of the identified river discharge patterns for each of the methods is measured by the Cophenetic Correlation Coefficient (CPCC). The results from the time-based clustering using DTW based on DFT transformation show a higher CPCC value as compared to that of non-time-based clustering methods.

Details

Improving Flood Management, Prediction and Monitoring
Type: Book
ISBN: 978-1-78756-552-4

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Book part
Publication date: 9 June 2020

Anna Purwaningsih and Indra Wijaya Kusuma

This study examines associations between accrual earnings management (AEM) and real earnings management (REM), and earnings quality between countries considered under…

Abstract

This study examines associations between accrual earnings management (AEM) and real earnings management (REM), and earnings quality between countries considered under insider economics and outsider economics clusters. Countries included in the outsider economics cluster are Singapore, Malaysia, and Hong Kong. Meanwhile, countries included in the insider economics cluster are Indonesia, the Philippines, and South Korea. Earnings management practices have changed from AEM to REM since the publication of the Sarbanes Oxley Act and DFA 954 implementation of the Claws back provision policy in the United States.

Research data were obtained from the Bloomberg database, 2010–2016. Regression analysis and t-test were utilized. This study compared AEM and REM to determine which is stronger based on country clusters, as well as the association between AEM or REM and earnings quality.

The results of this study indicate that AEM and REM are associated with the quality of earnings in the insider economics cluster. However, AEM and REM are not associated with earnings quality in the outsider economics cluster. Furthermore, associations between AEM and earnings quality are stronger than associations between REM and earnings quality in insider economics cluster.

Abstract

Details

Rutgers Studies in Accounting Analytics: Audit Analytics in the Financial Industry
Type: Book
ISBN: 978-1-78743-086-0

Article
Publication date: 29 November 2022

Armaghan Eslami, Atsuko Kanai and Miyuki Matsumoto

First, this study aimed to investigate the association of time perspective (TP) profiles with work engagement and workaholism. Second, it tested TP profiles as the…

Abstract

Purpose

First, this study aimed to investigate the association of time perspective (TP) profiles with work engagement and workaholism. Second, it tested TP profiles as the moderator of perfectionism with work engagement and workaholism relationship.

Design/methodology/approach

The sample of this study comprised 148 Japanese employees, and snowball sampling was used for data collection. The authors found the TP profiles in the first step using cluster analysis with five TP dimensions. Next, the authors tested workaholism and work engagement in three clusters. The two dimensions of perfectionistic strivings and perfectionistic concerns were extracted through the exploratory factor analysis of Sakurai and Ohtani's (1997) perfectionism measure. Further, their relationship with workaholism and work engagement was tested in the TP profiles using multiple group analysis in structural equation modeling (SEM).

Findings

Three TP profiles were found, which the authors named: Future (F), Hedonistic and Balanced. There was a significant difference between the three groups. Notably, working compulsively was significantly higher in the Future cluster in the three clusters. The moderator analysis results indicated that perfectionistic concerns positively affected workaholism in the Future cluster but not for the Balanced cluster.

Originality/value

To the best of authors’ knowledge, this is the first study to investigate the relationship between perspective profiles, workaholism and work engagement. The relationship between these factors can be a stepping stone for further research.

Details

International Journal of Workplace Health Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1753-8351

Keywords

Open Access
Article
Publication date: 22 November 2022

Kedong Yin, Yun Cao, Shiwei Zhou and Xinman Lv

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index…

Abstract

Purpose

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Design/methodology/approach

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Findings

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

Originality/value

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

Details

Marine Economics and Management, vol. 5 no. 2
Type: Research Article
ISSN: 2516-158X

Keywords

Article
Publication date: 30 November 2022

Mahendrawathi ER, Ika Nurkasanah and Annisa Rizki Pratama

This study aims to develop a taxonomy of organizations according to business process orientation (BPO) maturity and investigate the difference between clusters in terms of…

Abstract

Purpose

This study aims to develop a taxonomy of organizations according to business process orientation (BPO) maturity and investigate the difference between clusters in terms of performance outcome.

Design/methodology/approach

A survey of various organizations in Indonesia is conducted. The main variables are critical practices (CPs) as the measurement variables of BPO maturity and performance outcome. Cluster analysis is performed to obtain an empirical taxonomy of the organizations. ANOVA test is used to test if there are statistically different performance outcomes across different clusters.

Findings

Cluster analysis resulted in six archetypes labeled according to their characteristics: Beginners, Non-technical, Domestics, IT laggards, Excellers, and Champions. The ANOVA test results show that the archetypes with high CPs tend to have high perceived performance results.

Research limitations/implications

This study is limited because the authors use a single dataset from organizations in Indonesia. Further study involving more organizations will be beneficial to validate and enrich the taxonomy of organizational archetypes.

Practical implications

Results of the study can be used as a benchmarking tool by organizations to identify their positions against other organizations and set their areas for improvement. It can also help them identify a roadmap for improvement that will benefit their organization.

Originality/value

Using the CPs as a measure of BPO enables the authors to identify supplier orientation and information and technology (IT) implementation as the primary differentiators within the taxonomy. The use of IT differentiates the bottom, middle and top clusters.

Details

Benchmarking: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-5771

Keywords

Open Access
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. 5 no. 2
Type: Research Article
ISSN: 2398-7294

Keywords

Article
Publication date: 9 November 2022

Zahra Ahmadi Alvar, Davood Feiz and Meysam Modarresi

This study aims to reach a perception of the advance of research on deviant organisational behaviours.

Abstract

Purpose

This study aims to reach a perception of the advance of research on deviant organisational behaviours.

Design/methodology/approach

This research has been done through the text mining method. By reviewing, the papers were selected 360 papers between 1984 and 2020. Based on the Davis–Boldin index, 11 optimal clusters were gained. Then the roots were ranked in any group, using the Simple Additive Weighting technique. Data were analysed by RapidMiner and MATLAB software.

Findings

According to the results obtained, clusters are included leadership styles, job attitudes, spirituality in the workplace, work psychology, personality characteristics, classification and management of deviant workplace behaviours, service and customer orientation, deviation in sales, psychological contracts, group dynamics and inappropriate supervision.

Originality/value

This study provides a landscape and roadmap for future investigation on deviant organisational behaviours.

Details

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

Keywords

Article
Publication date: 8 November 2022

María Del Pilar Pascual-Fraile, Pilar Talón-Ballestero, Teresa Villacé-Molinero and Antonio-Rafael Ramos-Rodríguez

This study aims to provide an overview, the state-of-the-art “research fronts”, the emerging themes of investigation and a research agenda of crisis communication for…

Abstract

Purpose

This study aims to provide an overview, the state-of-the-art “research fronts”, the emerging themes of investigation and a research agenda of crisis communication for destinations’ image.

Design/methodology/approach

This research is conducted with a bibliographic coupling study, complemented with an H-Classic classification and a thematic analysis of the articles included in the four clusters provided by the bibliometric methodology (papers dating from 2017 to 2021, both years included).

Findings

Based on the bibliometric analysis, four thematic clusters were identified. Two of these clusters supply the “research fronts”, the most current themes in a scientific field: Cluster 1 addresses communication related to tourists’ safety, and cluster 2 enhances the role of stakeholders’ collaboration to create destinations resilience in crisis communication. The other two clusters highlight emerging themes for future investigation: Cluster 3 focuses on recovery marketing communication strategies for a post-crisis era, and cluster 4 analyses how crisis communication strategies contribute to reduce tourists’ risk perception and boosting travel intention. Finally, a future research agenda is proposed, based on the emerging themes from this study.

Originality/value

To the best of the authors’ knowledge, this is the first bibliometric study to analyse crisis communication for destinations’ image (pre-crisis, crisis and post-crisis). This study, which covers the most recent academic literature in this field, provides insights of communication strategies from recent crises and disasters within the “research fronts”. Besides, a research agenda useful for future scholar investigation is proposed with its emerging themes. These rising topics and learnings from past events could be used by destination marketing organisations in crisis communication for destination image recovery in the current post-pandemic scenario or in upcoming crises or disasters.

危机和灾难中目的地形象沟通:回顾和未来研究议程

摘要

目的

本研究提供了目的地形象危机沟通的概述、最先进的“研究前沿”、新兴的研究主题以及研究议程。

方法

本文进行了书目耦合研究, 辅以 H-经典分类和对文献计量方法提供的四个集群中包含的文章的主题分析 (论文日期为 2017–2021 年间)

结果

根据文献计量分析, 确定了四个主题集群。 其中两个集群提供“研究前沿”, 这是科学领域的最新主题:集群 一 解决与游客安全相关的沟通, 集群 二 加强利益相关者合作的作用, 以在危机沟通中创造目的地恢复力。 其他两个集群突出了未来调查的新兴主题:集群 三 侧重于后危机时代的复苏营销传播策略, 集群 四分析危机传播策略如何有助于降低游客的风险感知和提高旅行意愿。最后, 本文提出了基于新兴主题的未来研究议程。

原创性/价值

据我们所知, 这是第一个分析了目的地形象的危机传播(危机前、危机和危机后)的, 独特的文献计量研究。该研究涵盖了该领域最新的学术文献, 通过其“研究前沿”提供了有关近期危机和灾难的沟通策略的见解。此外, 本文还提出了具有新兴主题的研究议程。这些新兴话题以及从过去事件中吸取的教训, 可以被目的地营销组织 (DMO) 用来进行灾难沟通, 以便在当前的大流行后情景或未来的危机或灾难中恢复目的地的形象。

Comunicación Para la imagen de destinos en crisis y desastres: revisión y futura agenda de investigación

Resumen

Propósito

Este estudio proporciona una perspectiva general, los “research fronts”- los temas más actuales de una disciplina científica-, los temas emergentes y una agenda de investigación sobre comunicación de crisis de la imagen de los destinos turísticos.

Metodología

La investigación está basada en un análisis bibliográfico coupling, complementado con una clasificación h-Classics y un análisis temático de todos los artículos examinados con esta metodología bibliométrica (artículos fechados entre 2017 y 2021, ambos años incluidos).

Resultados

Con este análisis bibliométrico, se identifican cuatro clusters temáticos. Dos de ellos, presentan los “research fronts”, los temas más vigentes de un campo científico: el cluster 1 se refiere a la comunicación realizada para transmitir el concepto de seguridad a los turistas, y el cluster 2 destaca la relevancia de la colaboración de todos los agentes turísticos para crear resiliencia en los destinos en la comunicación de crisis. Los otros dos clusters recogen los temas emergentes de investigación futura: el cluster 3 se centra en las estrategias de marketing para la época de postcrisis y el cluster 4 analiza cómo la comunicación contribuye a reducir la percepción de riesgo de los turistas y, por tanto, a potenciar su intención de viaje. Por último, el artículo propone una agenda de investigación basada en estos temas emergentes.

Originalidad/valor

Hasta donde tenemos conocimiento, éste es el primer estudio bibliométrico especialmente enfocado a la comunicación de crisis para la imagen de los destinos turísticos (con sus tres etapas, precrisis, crisis y poscrisis). Esta investigación, que analiza la literatura más reciente en este campo, proporciona conocimiento sobre la comunicación de las crisis y desastres más recientes, a través de sus “research fronts”. Asimismo, propone una agenda con nuevos temas que están surgiendo en esta disciplina, útil para futuras investigaciones académicas. Dichos temas, junto con los aprendizajes de incidentes pasados, pueden ser usados por las Organizaciones de Marketing de Destinos (DMO, en sus siglas en inglés) para incorporarlos en su comunicación de crisis destinada a la recuperación de la imagen de los destinos turísticos en el actual escenario post pandemia o en futuras crisis o desastres.

Details

Tourism Review, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1660-5373

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