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Article
Publication date: 1 April 2002

Sally Dibb, Philip Stern and Robin Wensley

This paper reports findings from a study into how marketing academics and MBA students view segmentation. The research indicates that both respondent groups view…

Abstract

This paper reports findings from a study into how marketing academics and MBA students view segmentation. The research indicates that both respondent groups view segmentation as being more valuable in helping to understand customers than improving business performance. For MBA students there appears to be no relationship between their reported marketing knowledge and the value attributed to using market segmentation. The findings for academics suggest inconsistencies in how they interpret the value of segmentation and appraise the usefulness of analytical and evaluation approaches.

Details

Marketing Intelligence & Planning, vol. 20 no. 2
Type: Research Article
ISSN: 0263-4503

Keywords

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Article
Publication date: 16 August 2021

Khaldoon Nusair, Hamed Alazri, Usamah F. Alfarhan and Saeed Al-Muharrami

The purpose of this paper is to contribute to international tourism market segmentation research by proposing a comprehensive framework that examines behavioral, benefits…

Abstract

Purpose

The purpose of this paper is to contribute to international tourism market segmentation research by proposing a comprehensive framework that examines behavioral, benefits and lifestyle segmentations. The moderating roles of geographic segmentation (nationality) and advertising media types are also discussed.

Design/methodology/approach

Tourists volunteered to participate in a self-administered survey at random during peak seasons. Total number of collected questionnaires was 966. The authors used WarpPLS 6.0 software to analyze data.

Findings

Results from a sample of 919 tourists show that tourists in the benefit segmentation cluster had intentions to revisit the destination but they were unlikely to recommend it to others. Another finding indicates that marketing campaigns on different advertising media types might have different results when targeting different activities.

Originality/value

Leaning on the foundations of the marketing literature and the market segmentation theory, this research attempts to create a theoretical contribution that can be used to segment international tourists based on their travel motivations. Additionally, this study highlights the power of conditional probability approach, as it could be of more value than the predominant path coefficient approach.

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Article
Publication date: 1 June 2021

Na Li and Kai Ren

Automatic segmentation of brain tumor from medical images is a challenging task because of tumor's uneven and irregular shapes. In this paper, the authors propose an…

Abstract

Purpose

Automatic segmentation of brain tumor from medical images is a challenging task because of tumor's uneven and irregular shapes. In this paper, the authors propose an attention-based nested segmentation network, named DAU-Net. In total, two types of attention mechanisms are introduced to make the U-Net network focus on the key feature regions. The proposed network has a deep supervised encoder–decoder architecture and a redesigned dense skip connection. DAU-Net introduces an attention mechanism between convolutional blocks so that the features extracted at different levels can be merged with a task-related selection.

Design/methodology/approach

In the coding layer, the authors designed a channel attention module. It marks the importance of each feature graph in the segmentation task. In the decoding layer, the authors designed a spatial attention module. It marks the importance of different regional features. And by fusing features at different scales in the same coding layer, the network can fully extract the detailed information of the original image and learn more tumor boundary information.

Findings

To verify the effectiveness of the DAU-Net, experiments were carried out on the BRATS 2018 brain tumor magnetic resonance imaging (MRI) database. The segmentation results show that the proposed method has a high accuracy, with a Dice similarity coefficient (DSC) of 89% in the complete tumor, which is an improvement of 8.04 and 4.02%, compared with fully convolutional network (FCN) and U-Net, respectively.

Originality/value

The experimental results show that the proposed method has good performance in the segmentation of brain tumors. The proposed method has potential clinical applicability.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 14 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

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Abstract

Details

The Organic Growth Playbook: Activate High-Yield Behaviors to Achieve Extraordinary Results – Every Time
Type: Book
ISBN: 978-1-83982-687-0

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Article
Publication date: 15 December 2020

Soha Rawas and Ali El-Zaart

Image segmentation is one of the most essential tasks in image processing applications. It is a valuable tool in many oriented applications such as health-care systems…

Abstract

Purpose

Image segmentation is one of the most essential tasks in image processing applications. It is a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc. However, an accurate segmentation is a critical task since finding a correct model that fits a different type of image processing application is a persistent problem. This paper develops a novel segmentation model that aims to be a unified model using any kind of image processing application. The proposed precise and parallel segmentation model (PPSM) combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions. Moreover, a parallel boosting algorithm is proposed to improve the performance of the developed segmentation algorithm and minimize its computational cost. To evaluate the effectiveness of the proposed PPSM, different benchmark data sets for image segmentation are used such as Planet Hunters 2 (PH2), the International Skin Imaging Collaboration (ISIC), Microsoft Research in Cambridge (MSRC), the Berkley Segmentation Benchmark Data set (BSDS) and Common Objects in COntext (COCO). The obtained results indicate the efficacy of the proposed model in achieving high accuracy with significant processing time reduction compared to other segmentation models and using different types and fields of benchmarking data sets.

Design/methodology/approach

The proposed PPSM combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions.

Findings

On the basis of the achieved results, it can be observed that the proposed PPSM–minimum cross-entropy thresholding (PPSM–MCET)-based segmentation model is a robust, accurate and highly consistent method with high-performance ability.

Originality/value

A novel hybrid segmentation model is constructed exploiting a combination of Gaussian, gamma and lognormal distributions using MCET. Moreover, and to provide an accurate and high-performance thresholding with minimum computational cost, the proposed PPSM uses a parallel processing method to minimize the computational effort in MCET computing. The proposed model might be used as a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc.

Details

Applied Computing and Informatics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2634-1964

Keywords

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Article
Publication date: 4 December 2020

Fang-Jye Shiue, Hsin-Yun Lee, Meng-Cong Zheng, Akhmad F.K. Khitam and Sintayehu Assefa

For large projects, project segmentation and planning the size of contract packages in construction bids is a complex and critical issue. Due to the nature of construction…

Abstract

Purpose

For large projects, project segmentation and planning the size of contract packages in construction bids is a complex and critical issue. Due to the nature of construction projects, which frequently have large budgets, long durations and many activities with complex procedures, project segmentation involves complicated decision-making. To fill this gap, this study aims to develop an integrated model for planning project segmentation.

Design/methodology/approach

The proposed model integrates a simulation and multiple attribute decision-making method. The simulation is used to evaluate the bidding outcome of various project segmentations. The owner can then determine the bid-price behavior of contractors in response to varying work package sizes. The multiple attribute decision-making method is used to select the optimal segmentation solution from the simulated scenarios.

Findings

The proposed model is applied to a large road preservation project in Indonesia and incorporates bid participants and market conditions. The model provides seven scenarios for segmentation. The range of scenarios captures increasing competitiveness in the construction with the average bid price becoming gradually more beneficial for the owner. The model also utilizes a multiple attribute decision-making method to select the optimum scenario for the owner.

Originality/value

This study presents an applicable model for project segmentation that is useful for both project owners and contractors. By utilizing the proposed model, a project owner can segment a large project into smaller contract packages to create improved project pricing.

Details

Engineering, Construction and Architectural Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0969-9988

Keywords

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Article
Publication date: 20 December 2019

Guolei Wang, Xiaotong Hua, Jing Xu, Libin Song and Ken Chen

This paper aims to achieve automatically surface segmentation for painting different kinds of aircraft efficiently considering the demands of painting robot.

Abstract

Purpose

This paper aims to achieve automatically surface segmentation for painting different kinds of aircraft efficiently considering the demands of painting robot.

Design/methodology/approach

This project creatively proposed one method that accepts point cloud, outputs several blocks, each of which can be handled by ABB IRB 5500 in one station. Parallel PointNet (PPN) is proposed in this paper for better handling six dimensional aircraft data including every point normal. Through semantic segmentation of PPN, each surface has its own identity information indicating which part this surface belongs to. Then clustering considering constraints is applied to complete surface segmentation with identity information. To guarantee segmentation paintable and improve painting efficiency, different dexterous workspaces of IRB 5500 corresponding to different postures have been analyzed carefully.

Findings

The experiments confirm the effectiveness of the proposed surface segmentation method for painting different types of aircraft by IRB 5500. For semantic segmentation on aircraft data with point normal, PPN has higher precision than PointNet. In addition, the whole algorithm can efficiently segment one complex aircraft into qualified blocks, each of which has its own identity information, can be painted by IRB 5500 in one station and has fewer edges with other blocks.

Research limitations/implications

As the provided experiments indicate, the proposed method can segment one aircraft into qualified blocks automatically, which highly improves the efficiency in aircraft painting compared with traditional approaches. Moreover, the proposed method is able to provide identity information of each block, which is necessary for application of different paint parameters and different paint materials. In addition, final segmentation results by the proposed method behaves better than k-means cluster on variance of normal vector distance.

Originality/value

Inspired by semantic segmentation of 3 D point cloud, some improvements based on PointNet have been proposed for better handling segmentation of 6 D point cloud. By introducing normal vectors, semantic segmentation could be accomplished precisely for close points with opposite normal, such as wing upper and lower surfaces. Combining deep learning skills with traditional methods, the proposed method is proved to behave much better for surface segmentation task in aircraft painting.

Details

Assembly Automation, vol. 40 no. 2
Type: Research Article
ISSN: 0144-5154

Keywords

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Article
Publication date: 27 April 2020

Igor Georgievich Khanykov, Ivan Mikhajlovich Tolstoj and Dmitriy Konstantinovich Levonevskiy

The purpose of this paper is the image segmentation algorithms (ISA) classification analysis, providing for advanced research and design of new computer vision algorithms.

Abstract

Purpose

The purpose of this paper is the image segmentation algorithms (ISA) classification analysis, providing for advanced research and design of new computer vision algorithms.

Design/methodology/approach

For the development of the required algorithms a three-stage flowchart is suggested. An algorithm of quasi-optimal segmentation is discussed as a possible implementation of the suggested flowchart. A new attribute is introduced reflecting the specific hierarchical algorithm group, which the proposed algorithm belongs to. The introduced attribute refines the overall classification scheme and the requirements for the algorithms under development.

Findings

Optimal approximation generation is a computationally intensive task. The computational complexity can be reduced using a hierarchical data framework and a set of auxiliary algorithms, contributing to overall quality improvement. Because hierarchical solutions usually are distinctively suboptimal, further optimization to them was applied. A new classification attribute, proposed in this paper allows to discover previously hidden «blank spots», having decomposed the two-tier ISA classification scheme. The new classification attribute allows to aggregate algorithms, yielding multiple partitions at output and assign them to a dedicated group.

Originality/value

The originality of the paper consists in development of a high-level ISA classification, as well in introduction of a new classification attribute, pertinent to iterative algorithm groups and to hierarchically structured data presentation algorithms.

Details

International Journal of Intelligent Unmanned Systems, vol. 8 no. 2
Type: Research Article
ISSN: 2049-6427

Keywords

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Article
Publication date: 1 July 1990

Arun Sharma and Douglas M. Lambert

Customer service represents a significantopportunity for segmenting markets. This articlereviews the importance of customer service andthe conceptual issues associated…

Abstract

Customer service represents a significant opportunity for segmenting markets. This article reviews the importance of customer service and the conceptual issues associated with segmenting industrial markets on the basis of customer service. A methodology is presented which can be used by managers to classify a market into segments with different customer service needs. Empirical results from a high‐technology industry are also presented. The article emphasises the need to recognise the differing customer service requirements of segments of customers when establishing priorities for customer service expenditures.

Details

International Journal of Physical Distribution & Logistics Management, vol. 20 no. 7
Type: Research Article
ISSN: 0960-0035

Keywords

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Article
Publication date: 20 July 2012

Rusnah Muhamad, T.C. Melewar and Sharifah Faridah Syed Alwi

The purpose of this paper is to explore the different segments of consumers in the Islamic financial services industry (IFSI) and their relationship with product/brand…

Abstract

Purpose

The purpose of this paper is to explore the different segments of consumers in the Islamic financial services industry (IFSI) and their relationship with product/brand positioning for Islamic financial services (IFS).

Design/methodology/approach

In‐depth interviews were conducted with individuals in managerial positions among the key market players in the IFSI to explore the segmentation of consumers and their buying motives.

Findings

Four segments of IFS consumers emerged, namely, Religious conviction group; Religious conviction and economic rationality group; Ethical observant group; and Economic rationality group. These segmentation groups were appropriately categorized through a psychographic (value)‐based approach.

Research limitations/implications

The empirical findings of this study pave the way for embarking on promising and relevant future research, which is needed to substantiate and enrich the academic understanding and managerial practice of linking market segmentation and brand positioning for IFS in the global market. Future research should focus on analysing these issues from the perspective of consumers of IFS to identify the purchase trend.

Practical implications

The study provides empirical evidence of the bases or initial dimensions of consumer segmentation for IFS. The findings are useful in guiding the management of institutions offering IFS in making decisions relating to the marketing communication and promotion strategy as well as product and brand positioning strategy.

Originality/value

For both academia and the IFSI, this study provides useful knowledge in strategically using market segmentation to position IFS in the global market.

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