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The purpose of this paper is to examine situations in which brand extensions are likely to dilute beliefs associated with family brands.

Hypotheses are developed and tested in a consumer survey that included experimental and control groups.

The findings show the congruity of the extension with the family brand is an important factor, the absence of which increases the chances of dilution of the family brand. Perceived success/failure of the extension is a more important factor that also enhances or dilutes the image of the family brand.

Brand names can be hurt by brand extensions, which contain attributes incompatible with or negating favorable family brand beliefs.

If managers feel the dilution is occurring because of an extension, they can increase perceptions that the extension is atypical of the family brand.

This paper identifies certain key attributes of extensions that, if ignored, may dilute the image of the family brand.

The purpose of this paper is to develop an automated phase segmentation model from complex microstructure. The mechanical and physical properties of metals and alloys are influenced by their microstructure, and therefore the investigation of microstructure is essential. Coexistence of random or sometimes patterned distribution of different microstructural features such as phase, grains and defects makes microstructure highly complex, and accordingly identification or recognition of individual phase, grains and defects within a microstructure is difficult.

In this perspective, computer vision and image processing techniques are effective to help in understanding and proper interpretation of microscopic image. Microstructure-based image processing mainly focuses on image segmentation, boundary detection and grain size approximation. In this paper, a new approach is presented for automated phase segmentation from 2D microstructure images. The benefit of the proposed work is to identify dominated phase from complex microstructure images. The proposed model is trained and tested with 373 different ultra-high carbon steel (UHCS) microscopic images.

In this paper, Sobel and Watershed transformation algorithms are used for identification of dominating phases, and deep learning model has been used for identification of phase class from microstructural images.

For the first time, the authors have implemented edge detection followed by watershed segmentation and deep learning (convolutional neural network) to identify phases of UHCS microstructure.

Supplier selection (SS) is one of the prime competencies in a sourcing decision. Taking into account the key role played by suppliers in facilitating the implementation of green supply chain management (GSCM), it is somewhat surprising that very little research attention has been imparted to the development of a strategic sourcing model for GSCM. This research aims to develop a strategic sourcing framework in which supplier organizations are prioritized and ranked based on their GSCM performance. Accordingly, the benchmark organization is identified and its strategy is explored for GSCM performance improvement.

The research develops an innovative GSCM performance evaluation framework using six parameters, namely, investment in corporate social responsibility, investment in research and development, utilization of renewable energy, total energy consumption, total carbon-di-oxide emissions and total waste generation. An integrated multicriteria decision-making (MCDM) approach is proposed in which the entropy method calculates criteria weights. The Complex Proportional Assessment (COPRAS) and the Grey relational analysis (GRA) methods are used to rank supplier organizations based on their performance scores. A real-world case of green supplier selection (GSS) is considered in which five leading India-based automobile manufacturing organizations (Supplier 1, Supplier 2, Supplier 3, Supplier 4 and Supplier 5) are selected. Surveys with industry experts at the strategic, tactical, and operational levels are carried out to collect relevant data.

The results reveal that total carbon dioxide emission is the most influential parameter, as it gains the highest weight. On the contrary, investment in research and development, and total waste generation have no significant impact on GSCM performance. Results show that Supplier 5 secures the top rank. Hence, it is the benchmark organization.

The proposed methodology offers an easy and comprehensive approach to sourcing decisions in the field of GSCM. The entropy weight-based COPRAS and GRA methods offer an error-free channel of decision-making and can be proficiently used to outrank various industrial sectors based on their GSCM performances. This research is specific to the automobile manufacturing supply chain. Therefore, research outcomes may vary across supply chains with distinct characteristics.

The basic propositions of this research are based on a real-world case. Hence, the research findings are practically feasible. The less significant parameters identified in this study would enable managers to impart more attention to vulnerable areas for improvement. This research may help policymakers identify the influential parameters for effective GSCM implementation. As this research considers all aspects of sustainability, the strategies of the benchmark supplier have a direct impact on organizations' overall sustainability. The study would enable practitioners to make various strategies for GSCM performance improvement and to develop a cleaner production system.

The originality of this research lies in the consideration of both economic, social, environmental and operational aspects of sustainability for assessing the GSCM performance of supplier organizations. Quantitative criteria are considered so that vagueness can be removed from the decision. The use of an integrated grey-based approach for developing a strategic sourcing model is another unique feature of this study.

The service sector is a major segment of the economy and contributes to the gross national product in a significant manner. It complements the manufacturing sector as organizations become global in nature. Sources of raw material may be quite dispersed from the manufacturing site. Further, not all manufacturing may take place in one particular location. Based on the availability of expertise and the required operations to produce the product, components, subassemblies, or assemblies could be produced in different geographical locations. This creates the necessity to transport raw material, components, or assemblies in a timely manner from one location to another based on the needs of the supply chain. All customers prefer not only an efficient delivery system but also one that is damage-free. In this chapter, we consider a model whereby service organizations offer a contract for damage protection based on product value. The objective is to determine the premium to be charged by the service organization so as to at least break even or accomplish a desired profit margin.

The primary aim in this paper is to develop and demonstrate a theory of constraints (TOC) model in which constraint resource prevents the throughput of the organization.

In this paper, the authors propose an integrated model by combining Laplace criterion and TOC into a single evaluation model in a multiproduct constraint resource environment. A case study is illustrated to demonstrate the effectiveness of this model. The outsourcing decision model compares three alternatives: standard cost accounting, standard theory‐of‐constraints, and our own solution.

The numerical results show that the new approach is superior to Standard cost accounting and Theory of Constraints and presents a more realistic state of optimum allocation of resources and measures the performance of the model.

This research is limited to the production processes that do not have multiple constraints.

This is the first time that the integrated model comprising of Laplace‐TOC model has been used to maximize the product throughput. Instead of calculating $return per constraint minute, this method decides the priority of product that maximizes the product throughput in the constraint resource environment. It makes a significant contribution to the manufacturing Organization where one can compare the financial performance of the Organization by selecting the right decision model.

The purpose of this paper is to develop and demonstrate an outsourcing model in which constraint resource prevents the throughput of the organization.

The paper proposes an integrated model by combining the Hurwicz criterion, the theory‐of‐constraints (TOC) and linear programming (LP) into a single evaluation model in a multi‐product constraint resource environment. A case study is presented to demonstrate the effectiveness of this model. The decision model compares four alternatives: standard cost accounting, standard TOC, LP analysis, and our own solution, which is an approach that combines TOC, LP, and the Hurwicz criterion.

The numerical results show that this model is superior and more realistically optimizes resource allocation and measures the performance of the model.

This research is limited to the production processes that do not have multiple constraints.

This is the first time that the integrated model comprising of Hurwicz‐TOC‐LP model has been used to maximize the product throughput. Instead of calculating $ return per constraint minute, this method decides the priority of product and resource center that maximizes the product throughput in the constraint resource environment. It makes a significant contribution to the manufacturing organization where one can compare the financial performance of the organization by selecting the right decision model.

The aim of this paper is to develop and demonstrate an outsourcing decision model in which constraint resource prevents the throughput of the organization.

An integrated model is proposed by incorporating the weighted product model (WPM) of AHP in estimating the priority of each product in a multi‐product constraint resource environment. A numerical example is presented to demonstrate the effectiveness of this model. The outsourcing decision model compares four alternatives: Standard cost accounting, standard theory‐of‐constraints (TOC), LP analysis and an original solution.

The numerical results show that the proposed model is superior and more realistically optimizes resource allocation and measures the performance of the model.

This research is limited to the production processes that do not have multiple constraints.

This research is applicable to the companies which produce multi‐products in a situation in which market demand exceed the company's production capacity.

This is the first time that the WPM of AHP/TOC has been used to maximize the product throughput. Instead of calculating $return per constraint minute, it decides the priority of product that maximizes the product throughput in the constraint resource environment. It makes a significant contribution to the manufacturing organization where one can compare the financial performance of the organization by selecting the right decision model.

Reducing greenhouse gas emissions from fossil fuel consumption is a big challenge on the view of global warming and climate changes caused by greenhouse gases as per recent scientific reports. This paper aims to identify the major challenges of green energy sources (GES) to the future power systems and suggests an appropriate GES based on the preference by the decision maker on the various issues to meet these challenges.

The proposed work presents a multi‐criteria decision analysis (MCDA) – the analytic hierarchy process (AHP) to evaluate the GES – photovoltaic (PV), wind generator (WG), biomass (BM) and micro‐hydel (MH) and to find the appropriate selection in general, by evaluating its main operational characteristic. In this research, the choices of the green energy alternatives on the basis of various factors have been taken into consideration. MATLAB simulation of different criteria to ascertain their clear‐cut effects on GES selection under multiple uncertainties are presented.

Related articles appearing in the recently published (1995‐2010) works, based on green energy scope and practical implementations, and earlier approaches in the field of energy are gathered and analyzed so that the following questions can be answered: Which evaluating criteria are paid more attention to? Which source is the best GES? Which is the most critical factor in the green energy selection. This research not only provides the application of MCDA to evaluate the operation of the GES – PV, WG, BM and MH but also aids the researchers and decision makers in applying the approaches effectively.

This is the first analysis in the green energy selection which considers future uncertainties of the GES. Instead of focusing only on cost factor, the proposed work considers main uncertainties of the GES. The best GES will be decided based on the preference of the criterion chosen by the end‐user.

This study adds to the ongoing analysis of the long‐term impact of Asian financial crisis on the stock markets of eight Asian‐Pacific countries. Using current data to capture postcrisis behavior of returns, multivariate cointegration analysis reveals that a cointegrating relationship exists among the markets that transcend the financial crisis. Both vector error correction (VEC) and Granger causality tests demonstrate the profound effect of financial crisis in Korea on the returns of other countries. Granger causality tests further reveal that the events surrounding the crisis in Thailand and Indonesia largely dictate their own short‐run returns behavior since the advent of the crisis. Compared to earlier period, the post‐crisis era also experiences a closer relationship among the index returns of Hong Kong, Korea, and Singapore and a heightened degree of convergence among the returns of Asian markets.

This chapter challenges the urban bias in studies on the middle class in India and underlines the need to focus on the significant role rural middle class plays in economic diversification. Given that more than 23% of the upper middle class are located in rural India, it is surprising to note that their contribution in supporting experience economy remains under-researched. Thus, this chapter fills a key gap in existing studies on rural tourism in India by underlining how rural middle-class has triggered a huge demand for travel within the country itself for rural cultural programmes and different schemes to promote rural heritage sights.