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Article
Publication date: 9 June 2023

Honey Yadav and Mahim Sagar

India has the biggest number of active users on social media platforms, particularly Twitter. The purpose of this paper is to examine public sentiment on COVID-19 vaccines and…

Abstract

Purpose

India has the biggest number of active users on social media platforms, particularly Twitter. The purpose of this paper is to examine public sentiment on COVID-19 vaccines and COVID Appropriate Behaviour (CAB) by text mining (topic modeling) and network analysis supported by thematic modeling.

Design/methodology/approach

A sample dataset of 115,000 tweets from the Twitter platform was used to examine the perception of the COVID-19 vaccination and CAB from January 2021 to August 2021. The research applied a machine-learning algorithm and network analysis to extract hidden and latent patterns in unstructured data to identify the most prevalent themes. The COVID-19 Vaccine Hesitancy Amplification Model was formulated, which included five key topics based on sample big data from social media.

Findings

The identified themes are Social Media Adaptivity, Lack of Knowledge Providing Mechanism, Perception of Vaccine Safety Measures, Health Care Infrastructure Capabilities and Fear of Coronavirus (Coronaphobia). The study implication assists communication strategists and stakeholders design effective communication strategies using digital platforms. The study reveals CAB themes as with Mask Wearing Issues and Employment Issues as relevant themes discussed on digital channels.

Research limitations/implications

The themes extracted in the present study provide a roadmap for policy-makers and communication experts to utilize social media platforms for communicating and understanding the perception of preventive measures of vaccination and CAB. As evidenced by the increased engagement on social media platforms during the COVID-19-induced lockdown, digital platforms are indeed valuable from the communication perspective to be proactive in the event of a similar situation. Moreover, significant themes, including social media adaptivity, absence of knowledge-providing mechanism and perception of safety measures of the vaccine, are the critical parameters leading to an amplified effect on vaccine hesitancy.

Practical implications

The COVID-19 Vaccine Hesitancy Amplification Themes (CVHAT) equips stakeholders and government strategists with a preconfigured paradigm to tackle dedicated communication campaigns and assess digital community behavior during health emergencies COVID-19.

Social implications

The increased acceptance of vaccines and the following of CAB decrease the advocacy of mutation of the virus and promote the healthy being of the people. As CAB has been mentioned as a preventive strategy against the COVID-19 pandemic, the research preposition promotes communication intervention which helps to mitigate future such pandemics. As developing, economies require effective communication strategies for vaccine acceptance and CAB, this study contributes to filling the gap using a digital environment.

Originality/value

Chan et al. (2020) recommended using social media platforms for public knowledge dissemination. The study observed that the value of a communication strategy is increased when communication happens using highly trusted and accessible channels such as Twitter and Facebook. With the preceding context, the present study is a novel approach to contribute toward digital communication strategies related to vaccination and CAB.

Details

Kybernetes, vol. 52 no. 7
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 14 July 2023

Hamid Hassani, Azadeh Mohebi, M.J. Ershadi and Ammar Jalalimanesh

The purpose of this research is to provide a framework in which new data quality dimensions are defined. The new dimensions provide new metrics for the assessment of lecture video…

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Abstract

Purpose

The purpose of this research is to provide a framework in which new data quality dimensions are defined. The new dimensions provide new metrics for the assessment of lecture video indexing. As lecture video indexing involves various steps, the proposed framework containing new dimensions, introduces new integrated approach for evaluating an indexing method or algorithm from the beginning to the end.

Design/methodology/approach

The emphasis in this study is on the fifth step of design science research methodology (DSRM), known as evaluation. That is, the methods that are developed in the field of lecture video indexing as an artifact, should be evaluated from different aspects. In this research, nine dimensions of data quality including accuracy, value-added, relevancy, completeness, appropriate amount of data, concise, consistency, interpretability and accessibility have been redefined based on previous studies and nominal group technique (NGT).

Findings

The proposed dimensions are implemented as new metrics to evaluate a newly developed lecture video indexing algorithm, LVTIA and numerical values have been obtained based on the proposed definitions for each dimension. In addition, the new dimensions are compared with each other in terms of various aspects. The comparison shows that each dimension that is used for assessing lecture video indexing, is able to reflect a different weakness or strength of an indexing method or algorithm.

Originality/value

Despite development of different methods for indexing lecture videos, the issue of data quality and its various dimensions have not been studied. Since data with low quality can affect the process of scientific lecture video indexing, the issue of data quality in this process requires special attention.

Details

Library Hi Tech, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0737-8831

Keywords

Article
Publication date: 10 March 2023

Rainald Löhner, Lingquan Li, Orlando Antonio Soto and Joseph David Baum

This study aims to evaluate blast loads on and the response of submerged structures.

Abstract

Purpose

This study aims to evaluate blast loads on and the response of submerged structures.

Design/methodology/approach

An arbitrary Lagrangian–Eulerian method is developed to model fluid–structure interaction (FSI) problems of close-in underwater explosions (UNDEX). The “fluid” part provides the loads for the structure considers air, water and high explosive materials. The spatial discretization for the fluid domain is performed with a second-order vertex-based finite volume scheme with a tangent of hyperbola interface capturing technique. The temporal discretization is based on explicit Runge–Kutta methods. The structure is described by a large-deformation Lagrangian formulation and discretized via finite elements. First, one-dimensional test cases are given to show that the numerical method is free of mesh movement effects. Thereafter, three-dimensional FSI problems of close-in UNDEX are studied. Finally, the computation of UNDEX near a ship compartment is performed.

Findings

The difference in the flow mechanisms between rigid targets and deforming targets is quantified and evaluated.

Research limitations/implications

Cavitation is modeled only approximately and may require further refinement/modeling.

Practical implications

The results demonstrate that the proposed numerical method is accurate, robust and versatile for practical use.

Social implications

Better design of naval infrastructure [such as bridges, ports, etc.].

Originality/value

To the best of the authors’ knowledge, this study has been conducted for the first time.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 33 no. 6
Type: Research Article
ISSN: 0961-5539

Keywords

Article
Publication date: 28 April 2023

Linlin Zhang, Haoran Jiang, Tongwen Hu and Zhenduo Zhang

Drawing upon person–supervisor fit theory, a model is developed to illustrate how leader–member trait mindfulness (in)congruence may impact leader–member exchange (LMX) and how…

Abstract

Purpose

Drawing upon person–supervisor fit theory, a model is developed to illustrate how leader–member trait mindfulness (in)congruence may impact leader–member exchange (LMX) and how such trait mindfulness (in)congruence can indirectly influence taking charge.

Design/methodology/approach

Polynomial regression and response surface methodology are used to analyze 237 valid matched leader–member dyads.

Findings

LMX increases as leaders' and members' trait mindfulness become more aligned; LMX is higher when leader–member dyads are congruent at high levels (vs low levels). In the case of incongruence, LMX is higher when the member's trait mindfulness exceeds that of the leader. Furthermore, the relationship between leader–member trait mindfulness (in)congruence and taking charge is mediated by LMX.

Practical implications

The joint and interactive role of high trait mindfulness in leader–member dyads can help them to generate high-quality interpersonal exchange, as well as to cope with challenges posed by present and future changes.

Originality/value

The linear, nonlinear, simultaneous and interactive effects of dyadic trait mindfulness expand previous research, clarifying that the evaluation of leader–member congruence and incongruence at various degrees, and for various patterns of trait mindfulness, is more informative than examining the direct effect alone.

Details

Journal of Managerial Psychology, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0268-3946

Keywords

Open Access
Article
Publication date: 23 January 2024

Wang Zengqing, Zheng Yu Xie and Jiang Yiling

With the rapid development of railway-intelligent video technology, scene understanding is becoming more and more important. Semantic segmentation is a major part of scene…

Abstract

Purpose

With the rapid development of railway-intelligent video technology, scene understanding is becoming more and more important. Semantic segmentation is a major part of scene understanding. There is an urgent need for an algorithm with high accuracy and real-time to meet the current railway requirements for railway identification. In response to this demand, this paper aims to explore a variety of models, accurately locate and segment important railway signs based on the improved SegNeXt algorithm, supplement the railway safety protection system and improve the intelligent level of railway safety protection.

Design/methodology/approach

This paper studies the performance of existing models on RailSem19 and explores the defects of each model through performance so as to further explore an algorithm model dedicated to railway semantic segmentation. In this paper, the authors explore the optimal solution of SegNeXt model for railway scenes and achieve the purpose of this paper by improving the encoder and decoder structure.

Findings

This paper proposes an improved SegNeXt algorithm: first, it explores the performance of various models on railways, studies the problems of semantic segmentation on railways and then analyzes the specific problems. On the basis of retaining the original excellent MSCAN encoder of SegNeXt, multiscale information fusion is used to further extract detailed features such as multihead attention and mask, solving the problem of inaccurate segmentation of current objects by the original SegNeXt algorithm. The improved algorithm is of great significance for the segmentation and recognition of railway signs.

Research limitations/implications

The model constructed in this paper has advantages in the feature segmentation of distant small objects, but it still has the problem of segmentation fracture for the railway, which is not completely segmented. In addition, in the throat area, due to the complexity of the railway, the segmentation results are not accurate.

Social implications

The identification and segmentation of railway signs based on the improved SegNeXt algorithm in this paper is of great significance for the understanding of existing railway scenes, which can greatly improve the classification and recognition ability of railway small object features and can greatly improve the degree of railway security.

Originality/value

This article introduces an enhanced version of the SegNeXt algorithm, which aims to improve the accuracy of semantic segmentation on railways. The study begins by investigating the performance of different models in railway scenarios and identifying the challenges associated with semantic segmentation on this particular domain. To address these challenges, the proposed approach builds upon the strong foundation of the original SegNeXt algorithm, leveraging techniques such as multi-scale information fusion, multi-head attention, and masking to extract finer details and enhance feature representation. By doing so, the improved algorithm effectively resolves the issue of inaccurate object segmentation encountered in the original SegNeXt algorithm. This advancement holds significant importance for the accurate recognition and segmentation of railway signage.

Details

Smart and Resilient Transportation, vol. 6 no. 1
Type: Research Article
ISSN: 2632-0487

Keywords

Article
Publication date: 21 December 2023

Lan H. Phan and Peter T. Coleman

For decades, conflict resolution (CR) educators working cross-culturally have struggled with a fundamental dilemma – whether to offer western, evidence-based approaches through a…

Abstract

Purpose

For decades, conflict resolution (CR) educators working cross-culturally have struggled with a fundamental dilemma – whether to offer western, evidence-based approaches through a top-down (prescriptive) training process or to use a bottom-up (elicitive) strategy that builds on local cultural knowledge of effective in situ conflict management. This study aims to explore which conditions that prompted experienced CR instructors to use more prescriptive or elicitive approaches to such training in a foreign culture and the implications for training outcomes.

Design/methodology/approach

There are two parts to this study. First, the authors conducted a literature review to identify basic conditions that might be conducive to conducting prescriptive or elicitive cross-cultural CR training. The authors then tested the identified conditions in a survey with experienced CR instructors to identify different conditions that afforded prescriptive or elicitive approaches. Exploratory factor analysis and regression were used to assess which conditions determined whether a prescriptive or elicitive approach produced better outcomes.

Findings

In general, although prescriptive methods were found to be more efficient, elicitive methods produced more effective, culturally appropriate, sustainable and culturally sensitive training. Results revealed a variety of instructor, participant and contextual factors that influenced whether a prescriptive or elicitive approach was applied and found to be more suitable.

Originality/value

This study used empirical survey data with practicing experts to provide insight and guidance into when to use different approaches to CC-CR training effectively.

Details

International Journal of Conflict Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1044-4068

Keywords

Article
Publication date: 22 December 2022

Yankai Shen and Chen Wei

The research of unmanned air/ground vehicle (UAV/UGV) cooperation has attracted much attention due to its potential applications in disaster rescue and target surveillance. This…

Abstract

Purpose

The research of unmanned air/ground vehicle (UAV/UGV) cooperation has attracted much attention due to its potential applications in disaster rescue and target surveillance. This paper aims to focus on the UAV/UGV cooperative target tracking and enclosing, considering the limits of detection and sensor failures.

Design/methodology/approach

The UAV/UGV cooperation structure is designed, contributing to homogeneous consistency and heterogeneous communication. The target tracking of UAVs is converted into a constraint optimization problem involving tracking cost, and the target enclosing of UGVs is modeled as formation control.

Findings

The energy estimation pigeon-inspired optimization is developed to generate control inputs for UAVs. And the controller combined with switchable topology is proposed, where the switching rule is flexible in dealing with some emergencies.

Practical implications

The proposed structure and algorithms can be easily applied to practice and help design the UAV/UGV control system.

Originality/value

The energy estimation mechanism is proposed for the target tracking of UAVs, and the rules of switching topologies ensure the target enclosing process of UGVs.

Article
Publication date: 19 March 2024

Aubid Hussain Parrey and Gurleen Kour

Career adaptability is emerging as an important research area in today's uncertain, volatile world of work created by the COVID-19 pandemic. The present study focuses on career…

Abstract

Purpose

Career adaptability is emerging as an important research area in today's uncertain, volatile world of work created by the COVID-19 pandemic. The present study focuses on career adaptability research post-COVID-19 by scientifically capturing the literature evolution, hotspots and future trends using bibliometric analysis.

Design/methodology/approach

The Scopus database, due to its vast and quality literature, was used to search the papers from the period 2020 to 2023. Bibliometric data were extracted and analyzed from the relevant literature. For further scientific mapping, VOSviewer and Biblioshiny software tools were used.

Findings

Findings of the analysis suggest a positive research trend related to career adaptability research post-Covid. Keyword analysis revealed noteworthy clusters and important themes. Bibliometric visual networks regarding authors, sources, citations, future themes, etc. are also presented from the 441 analyzed publications with comprehensive interpretation.

Research limitations/implications

The literature for carrying out the bibliometric analysis was confined to the Scopus database. Other databases in combination with different software can be used for future niche research. From the analysis, future research avenues and practical interventions are presented which have significant implications for future researchers, career counselors and managers.

Originality/value

The study summarizes the recent literature on career adaptability in the aftermath of the pandemic and makes a novel contribution to the existing literature. A reliable study has been provided by the authors using the scientific bibliometric technique. The study highlights emerging research trends post the pandemic. The results are concluded with further suggestions which can guide future research related to the topic.

Details

International Journal of Organization Theory & Behavior, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1093-4537

Keywords

Article
Publication date: 4 May 2022

Artur Swierczek

This study considers transitive service triads, which consist of three dyads formed by three actors: supplier, logistics service provider and customer, who remain directly linked…

Abstract

Purpose

This study considers transitive service triads, which consist of three dyads formed by three actors: supplier, logistics service provider and customer, who remain directly linked by one or more of the upstream and downstream flows of products, information and finances. This paper aims to explore the link between information governance, decentralized information technologies and supply chain self-organization, and their resulting impact on network performance in the transitive service triads.

Design/methodology/approach

Drawing upon the tenets of the theory of complex adaptive systems and supply chain practice view, this paper involves an empirical investigation that uses survey data gathered from transitive service triads in the European countries. The study uses partial least squares structural equation modeling to estimate the formative-reflective hierarchical component model and test the research hypotheses.

Findings

Information governance defines how supply chain information flows are controlled, accessed and used by a focal organization and its business partners. As empirically evidenced in this study, it can be depicted as a latent construct consisting of three distinct dimensions of information custody, information ownership and right to data access. Likewise, the study also indicates that supply chain self-organization, as a second-order construct, consists of three interactive self-organization actions undertaken by specific firms participating in the triadic arrangement. Supply chain self-organization is thus produced by firms that are reciprocally interrelated and interacting, having effects on one another. Furthermore, the study also highlights that information governance creates an environment for applying decentralized information technologies, which then positively affects supply chain self-organization. Finally, the research also empirically operationalizes the construct of network performance within the transitive service triads.

Research limitations/implications

Although the results provide several major contributions to theory and implications for practitioners, the study still demonstrates some methodological constraints. Specifically, although the study uses a relatively large research sample of 350 transitive service triads, it still focuses only on a selected group of industries and is limited to investigating solely a particular type of service triads.

Originality/value

Given the increasing interest in investigating triads, this study examines how information governance and decentralized information technologies support supply chain self-organization to yield network performance in transitive service triads.

Details

Supply Chain Management: An International Journal, vol. 28 no. 3
Type: Research Article
ISSN: 1359-8546

Keywords

Article
Publication date: 8 March 2023

Jordi Vila-Pérez, Matteo Giacomini and Antonio Huerta

This study aims to assess the robustness and accuracy of the face-centred finite volume (FCFV) method for the simulation of compressible laminar flows in different regimes, using…

Abstract

Purpose

This study aims to assess the robustness and accuracy of the face-centred finite volume (FCFV) method for the simulation of compressible laminar flows in different regimes, using numerical benchmarks.

Design/methodology/approach

The work presents a detailed comparison with reference solutions published in the literature –when available– and numerical results computed using a commercial cell-centred finite volume software.

Findings

The FCFV scheme provides first-order accurate approximations of the viscous stress tensor and the heat flux, insensitively to cell distortion or stretching. The strategy demonstrates its efficiency in inviscid and viscous flows, for a wide range of Mach numbers, also in the incompressible limit. In purely inviscid flows, non-oscillatory approximations are obtained in the presence of shock waves. In the incompressible limit, accurate solutions are computed without pressure correction algorithms. The method shows its superior performance for viscous high Mach number flows, achieving physically admissible solutions without carbuncle effect and predictions of quantities of interest with errors below 5%.

Originality/value

The FCFV method accurately evaluates, for a wide range of compressible laminar flows, quantities of engineering interest, such as drag, lift and heat transfer coefficients, on unstructured meshes featuring distorted and highly stretched cells, with an aspect ratio up to ten thousand. The method is suitable to simulate industrial flows on complex geometries, relaxing the requirements on mesh quality introduced by existing finite volume solvers and alleviating the need for time-consuming manual procedures for mesh generation to be performed by specialised technicians.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 33 no. 6
Type: Research Article
ISSN: 0961-5539

Keywords

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