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This article has been withdrawn as it was published elsewhere and accidentally duplicated.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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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

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.

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

Water lubrication is significant for its environmental friendliness. Composite journal bearing is liable to deform for the huge pressure of water film. This paper aims to study the influence of elastic deformation on how lubrication functions in water-lubricated journal bearings and to provide references for designing composite journal bearings.

The combination of computational fluid dynamics and fluid-structure interaction is adopted in this paper to study the lubrication performance of water-lubricated compliant journal bearings. The influences of elasticity modulus and Poisson’s ratio on load-carrying capacity and elastic deformation are studied for different rotational speeds. Predictions in this work are compared with the published experimental results, and the present work agrees well with the experimental results.

A reference whether elastic deformation should be considered for composite journal bearings is proposed under different working conditions. Besides, a reference to determine water-lubricated plain journal bearings dimensions under different loads and rotational speeds is developed with the effect of both elastic deformation and cavitation being accounted.

The present research provides references as to whether elastic deformation should be considered in operation and to determine compliant journal bearings’ dimensions in the design process.

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.

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.

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.

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.