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Article
Publication date: 1 February 1986

This article has been withdrawn as it was published elsewhere and accidentally duplicated. The original article can be seen here: 10.1108/eb045709. When citing the…

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Abstract

This article has been withdrawn as it was published elsewhere and accidentally duplicated. The original article can be seen here: 10.1108/eb045709. When citing the article, please cite: James D. Hlavacek, N. Mohan Reddy, (1985), “Identifying and Qualifying Industrial Market Segments”, Marketing Intelligence & Planning, Vol. 3 Iss: 1, pp. 41 - 56.

Details

European Journal of Marketing, vol. 20 no. 2
Type: Research Article
ISSN: 0309-0566

Article
Publication date: 1 January 1991

N. Mohan Reddy

The search for a better grasp of the what, why and how of productvalue is analogous to the quest for the Holy Grail. Just when you thinkthat you finally have a handle on…

Abstract

The search for a better grasp of the what, why and how of product value is analogous to the quest for the Holy Grail. Just when you think that you finally have a handle on it, the mirage fades away and another appears on the horizon, full of hope. So go our efforts to understand customer valuation of products. There exists a value definition at every turn, but one is never too sure how they all fit together (if they do at all!). This article attempts to reconcile the various definitions in the search for an operational framework of product value.

Details

Management Decision, vol. 29 no. 1
Type: Research Article
ISSN: 0025-1747

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Article
Publication date: 1 August 1990

N. Mohan Reddy

Speeding technology to market, and to the rightmarket, is increasingly held out as the hallmarkof well run technology‐based organisations. Thispaper details an inexpensive…

Abstract

Speeding technology to market, and to the right market, is increasingly held out as the hallmark of well run technology‐based organisations. This paper details an inexpensive methodology to evaluate the economic viability of technologies that have potential applicability in a broad range of market segments.

Details

Management Decision, vol. 28 no. 8
Type: Research Article
ISSN: 0025-1747

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Article
Publication date: 1 January 1985

James D. Hlavacek and N. Mohan Reddy

This article exposes common pitfalls in the practice of segmenting industrial markets and shows how previous industrial segmentation research has been of limited…

Abstract

This article exposes common pitfalls in the practice of segmenting industrial markets and shows how previous industrial segmentation research has been of limited managerial value. An operational approach to conducting industrial market segmentation is presented and explained.

Details

Marketing Intelligence & Planning, vol. 3 no. 1
Type: Research Article
ISSN: 0263-4503

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Article
Publication date: 1 March 1988

Ellen Day, Richard J. Fox and Sandra M. Huszagh

Although the viability of global marketing is disputed, the best opportunities for pursuing basically the same strategy across national borders are in industrial…

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Abstract

Although the viability of global marketing is disputed, the best opportunities for pursuing basically the same strategy across national borders are in industrial marketing. However, because of the disparities across world markets, segmentation is essential to assessing opportunities for a standardised marketing approach. Segmentation based on economic indicators represents the first step in identifying potential markets. In this study, 96 countries were grouped into six segments. Implications for industrial marketers are presented, along with issues relating to using stages of economic development as a basis for segmentation and using a factor analytic and clustering approach to the segmentation of the global market.

Details

International Marketing Review, vol. 5 no. 3
Type: Research Article
ISSN: 0265-1335

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Article
Publication date: 4 February 2014

Anita Louise Hamilton, Jo Coldwell-Neilson and Annemieke Craig

Digital technology has changed how people interact with information and each other. Being able to access and share information ensures healthcare practitioners can keep

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Abstract

Purpose

Digital technology has changed how people interact with information and each other. Being able to access and share information ensures healthcare practitioners can keep abreast of new and ever changing information and improve services. The purpose of this paper is to present an information management-knowledge transfer (IM-KT) framework which emerged from a study looking at digital literacy in the occupational therapy profession.

Design/methodology/approach

The research was undertaken in three stages. First an in-depth literature review was undertaken, which enabled the creation of an initial conceptual framework which in turn, informed the second stage of the research: the development of a survey about the use of digital technologies. Occupational therapy students, academics and practitioners across five different countries completed the survey, after which refinements to the framework were made. The IM-KT framework presented in this paper emerged as a result of the third stage of the study, which was completed using the Delphi technique where 18 experts were consulted over four rounds of qualitative questionnaires.

Findings

The IM-KT framework assists individuals and groups to better understand how information management and knowledge transfer occurs. The framework highlights the central role of information literacy and digital literacy and the influence of context on knowledge transfer activities.

Originality/value

The IM-KT framework delineates clearly between information and knowledge and demonstrates the essential role of information literacy and digital literacy in the knowledge era. This framework was developed for the occupational therapy profession and may be applicable to other professions striving to keep up to date with best evidence.

Details

VINE: The journal of information and knowledge management systems, vol. 44 no. 1
Type: Research Article
ISSN: 0305-5728

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Article
Publication date: 8 April 2021

Jagan Mohan Reddy K., Neelakanteswara Rao A., Krishnanand Lanka and PRC Gopal

Pull production systems have received much attention in the supply chain management environment. The number of Kanbans is a key decision variable in the pull production…

Abstract

Purpose

Pull production systems have received much attention in the supply chain management environment. The number of Kanbans is a key decision variable in the pull production system as it affects the finished goods inventory (FGI) and backorders of the system. The purpose of this study is to compare the performance of the fixed and dynamic Kanban systems in terms of operational metrics (FGI and backorders) under the demand uncertainty.

Design/methodology/approach

In this paper, the system dynamics (SD) approach was used to model the performance of fixed and dynamic Kanban based production systems. SD approach has enabled the feedback mechanism and is an appropriate tool to incorporate the dynamic control during the simulation. Initially, a simple Kanban based production system was developed and then compared the performance of production systems with fixed and dynamic controlled Kanbans at the various demand scenarios.

Findings

From the present study, it is observed that the dynamic Kanban system has advantages over the fixed Kanban system and also observed that the variation in the backorders with respect to the demand uncertainty under the dynamic Kanban system is negligible.

Research limitations/implications

In a just-in-time production system, the number of Kanbans is a key decision variable. The number of Kanbans is mainly depended on the demand, cycle time, safety stock factor (SSF) and container size. However, this study considered only demand uncertainty to compare the fixed and dynamic Kanban systems. This paper further recommends researchers to consider other control variables which may influence the number of Kanbans such as cycle time, SSF and container size.

Originality/value

This study will be useful to decision-makers and production managers in the selection of the Kanban systems in uncertain demand applications.

Details

Journal of Modelling in Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1746-5664

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Article
Publication date: 27 July 2020

Vamsee Krishna S., Sudhakara Reddy P. and Chandra Mohan Reddy S.

A third-order discrete time sigma delta modulator (SDM) is proposed with optimum performance by addressing instability and power dissipations issues, and a novel SDM…

Abstract

Purpose

A third-order discrete time sigma delta modulator (SDM) is proposed with optimum performance by addressing instability and power dissipations issues, and a novel SDM architecture is designed and verified in behavioural modelling in MATLAB/SIMULINK environment. Simulation results show that performance parameters of proposed modulator achieved SNR of 105.41 dB, SNDR of 101.96 dB and DR of 17 bits for the signal bandwidth of 20 kHz.

Design/methodology/approach

This paper describes single-loop SDM design with optimum selection of integrator weights for physiological signal processing in IoT applications.

Findings

The proposed discrete time modulator designed with 1-bit quantizer and optimum oversampling ratio proved as power efficient. Integrator scaling coefficients are generated in LabVIEW environment for pure third-order noise shaping.

Originality/value

This paper contains the novelty in the work, and it is suitable for cognitive Internet of Things applications.

Details

International Journal of Pervasive Computing and Communications, vol. 17 no. 3
Type: Research Article
ISSN: 1742-7371

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Article
Publication date: 29 September 2021

Swetha Parvatha Reddy Chandrasekhara, Mohan G. Kabadi and Srivinay

This study has mainly aimed to compare and contrast two completely different image processing algorithms that are very adaptive for detecting prostate cancer using…

Abstract

Purpose

This study has mainly aimed to compare and contrast two completely different image processing algorithms that are very adaptive for detecting prostate cancer using wearable Internet of Things (IoT) devices. Cancer in these modern times is still considered as one of the most dreaded disease, which is continuously pestering the mankind over a past few decades. According to Indian Council of Medical Research, India alone registers about 11.5 lakh cancer related cases every year and closely up to 8 lakh people die with cancer related issues each year. Earlier the incidence of prostate cancer was commonly seen in men aged above 60 years, but a recent study has revealed that this type of cancer has been on rise even in men between the age groups of 35 and 60 years as well. These findings make it even more necessary to prioritize the research on diagnosing the prostate cancer at an early stage, so that the patients can be cured and can lead a normal life.

Design/methodology/approach

The research focuses on two types of feature extraction algorithms, namely, scale invariant feature transform (SIFT) and gray level co-occurrence matrix (GLCM) that are commonly used in medical image processing, in an attempt to discover and improve the gap present in the potential detection of prostate cancer in medical IoT. Later the results obtained by these two strategies are classified separately using a machine learning based classification model called multi-class support vector machine (SVM). Owing to the advantage of better tissue discrimination and contrast resolution, magnetic resonance imaging images have been considered for this study. The classification results obtained for both the SIFT as well as GLCM methods are then compared to check, which feature extraction strategy provides the most accurate results for diagnosing the prostate cancer.

Findings

The potential of both the models has been evaluated in terms of three aspects, namely, accuracy, sensitivity and specificity. Each model’s result was checked against diversified ranges of training and test data set. It was found that the SIFT-multiclass SVM model achieved a highest performance rate of 99.9451% accuracy, 100% sensitivity and 99% specificity at 40:60 ratio of the training and testing data set.

Originality/value

The SIFT-multi SVM versus GLCM-multi SVM based comparison has been introduced for the first time to perceive the best model to be used for the accurate diagnosis of prostate cancer. The performance of the classification for each of the feature extraction strategies is enumerated in terms of accuracy, sensitivity and specificity.

Details

International Journal of Pervasive Computing and Communications, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1742-7371

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Article
Publication date: 25 October 2021

Venkata Dasu Marri, Veera Narayana Reddy P. and Chandra Mohan Reddy S.

Image classification is a fundamental form of digital image processing in which pixels are labeled into one of the object classes present in the image. Multispectral image…

Abstract

Purpose

Image classification is a fundamental form of digital image processing in which pixels are labeled into one of the object classes present in the image. Multispectral image classification is a challenging task due to complexities associated with the images captured by satellites. Accurate image classification is highly essential in remote sensing applications. However, existing machine learning and deep learning–based classification methods could not provide desired accuracy. The purpose of this paper is to classify the objects in the satellite image with greater accuracy.

Design/methodology/approach

This paper proposes a deep learning-based automated method for classifying multispectral images. The central issue of this work is that data sets collected from public databases are first divided into a number of patches and their features are extracted. The features extracted from patches are then concatenated before a classification method is used to classify the objects in the image.

Findings

The performance of proposed modified velocity-based colliding bodies optimization method is compared with existing methods in terms of type-1 measures such as sensitivity, specificity, accuracy, net present value, F1 Score and Matthews correlation coefficient and type 2 measures such as false discovery rate and false positive rate. The statistical results obtained from the proposed method show better performance than existing methods.

Originality/value

In this work, multispectral image classification accuracy is improved with an optimization algorithm called modified velocity-based colliding bodies optimization.

Details

International Journal of Pervasive Computing and Communications, vol. 17 no. 5
Type: Research Article
ISSN: 1742-7371

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