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The purpose of this case study is to describe the Costa Rican coffee roaster and retail company Grupo Britt and provides sufficient information for readers to discuss the company's growth options in the US and Latin American markets. It has been prepared to facilitate discussion and learning about appropriate growth strategies and the building, positioning and branding of companies from emerging economies within the internationalization processes.

The case study is based on primary research conducted in conjunction with the company, including interviews with senior management and an ample review of documents. Secondary research was also conducted into the relevant environmental, industry and competitor trends and characteristics.

The case presents the ways in which Grupo Britt has been successful in developing a new business model based on the concept of a “sense of place.” This translates as meaning that the company generates products and services that highlight the particular characteristics of the place(s) in which it operates.

This is a complex, in‐depth case study suitable for use with advanced MBA students and practitioners. Depending on the aims of the instructor, different aspects of the case can be highlighted and it can be used in a course or module focussing on strategies for growth or internationalization, international business or marketing, or brand management

The main objective of this study is to identify the determinants of TV ads avoiding behavior between light and heavy avoiders in greater Cairo. To achieve the study objective, five hypotheses have been developed and tested by such statistical techniques as discriminant analysis, t‐tests, MannWhitney tests, and Chi Square tests. A questionnaire has been designed to collect data from a systematic random sample of adults in social clubs and shopping centers in greater Cairo. The number of usable questionnaires in data analysis was 364. The study findings show that all respondents except 3 were doing one or more of TV ads avoiding behavior. Cognitive avoiding represents the most frequently used avoiding behavior by light and heavy TV ads avoiders. The results also demonstrate that perceptions, attitudes toward advertising, and some motives were determinants of TV ads avoiding behavior. On the other hand, it was found that all demographic and socioeconomic characteristics, except types of channels, were not determinants of TV ads avoiding behavior between light and heavy TV ads avoiders. The study concludes with a number of academic and practical recommendations.

The marketing manager has little or no guidance in formulating competitive strategies. This article presents a marketing strategy planning tool based on customers' perceptions of the positions of competing brands across various product attributes. The method, called “Simultaneous Importance‐Performance Analysis,” advocates focusing attention on relevant competitors' positions and attacking or defending market territory selectively. An example of its application is provided to illustrate its usefulness. The tool provides a framework for prioritizing alternative marketing strategies and is helpful in deciding on the allocation of limited marketing resources to design an efficient short‐range marketing plan. We will first discuss the nature of competitive advantage strategy and look at the marketing manager's dilemma on how to select tactics to develop a competitive advantage. We will then describe and illustrate “simultaneous importance — performance analysis,” based on importance‐performance analysis. Finally, we will suggest how this technique might be integrated into a company's strategic planning system.

The purpose of this paper is to compare how some firms from China and some from other emerging countries (EC) are using a variety of branding strategies to improve their global competitiveness. A total of 14 firms have been compared on criteria related to possible acquisition of foreign brands, development of local brands, personality of the leaders and in some cases use of ideological messages.

The paper is mostly based on case studies coming from literature, interviews from marketing executives of major enterprises from China or other EC. It is mainly exploratory in its approach.

The critical success factors for the competitiveness of emerging countries brands are either coming from the choice to create a local brand from scratch, to buy an existing famous brand, or to imitate successful foreign brands. Few strategic differences appear between Chinese firms and the ones from other EC. The factors explaining success or failure are linked to the type of industry and the way it relates to the country of origin effect, the level of marketing “maturity” as well as the personality and visibility of the entrepreneur.

The study does not aim at being statistically representative, the firms which are selected may not be a full representation of Chinese firms branding strategy or from emerging nations.

The definition of the brand strategy for emerging countries firms is a relatively new subject and this study is a contribution to helping enterprises in finding the best approach as well as giving examples for academic studies on Chinese firms marketing efficiency.

The purpose of this paper, is to predict the various phases and crystal structure from multi-component alloys. Nowadays, the concept and strategies of the development of multi-principal element alloys (MPEAs) significantly increase the count of the potential candidate of alloy systems, which demand proper screening of large number of alloy systems based on the nature of their phase and structure. Experimentally obtained data linking elemental properties and their resulting phases for MPEAs is profused; hence, there is a strong scope for categorization/classification of MPEAs based on structural features of the resultant phase along with distinctive connections between elemental properties and phases.

In this paper, several machine-learning algorithms have been used to recognize the underlying data pattern using data sets to design MPEAs and classify them based on structural features of their resultant phase such as single-phase solid solution, amorphous and intermetallic compounds. Further classification of MPEAs having single-phase solid solution is performed based on crystal structure using an ensemble-based machine-learning algorithm known as random-forest algorithm.

The model developed by implementing random-forest algorithm has resulted in an accuracy of 91 per cent for phase prediction and 93 per cent for crystal structure prediction for single-phase solid solution class of MPEAs. Five input parameters are used in the prediction model namely, valence electron concentration, difference in the pauling negativeness, atomic size difference, mixing enthalpy and mixing entropy. It has been found that the valence electron concentration is the most important feature with respect to prediction of phases. To avoid overfitting problem, fivefold cross-validation has been performed. To understand the comparative performance, different algorithms such as K-nearest Neighbor, support vector machine, logistic regression, naïve-based approach, decision tree and neural network have been used in the data set.

In this paper, the authors described the phase selection and crystal structure prediction mechanism in MPEA data set and have achieved better accuracy using machine learning.

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.

Some consumer durables, such as automobiles, involve warranties involving two attributes. These are time elapsed since the sale of the product and the usage of the product at a given point in time. Warranty may be invoked by the customer if both time and usage are within the specified warranty parameters and product failure occurs. In this chapter, we assume that usage and product age are related through a random variable, the usage rate, which may have a certain probabilistic distribution as influenced by consumer behavior pattern. Further, product failure rate is influenced by the usage rate and product age. Of importance to the organization is to contain expected warranty costs and select appropriate values of the warranty parameters accordingly. An avenue to impact warranty costs is through research on product development. This has the potential to reduce the failure rate of the product. The objective then becomes to determine warranty parameters, while constraining the sum of the expected unit warranty costs and research and development (R&D) costs per unit sales, under a limited R&D budget.

This chapter considers warranty policies involving two attributes, such as the time elapsed since sale of the product and product usage at a given point in time. Examples of such policies are found for automobiles, where warranty may be invoked by the consumer if both time and usage are within specified warranty parameters when a product failure occurs. Here, we assume that usage and product age are related through a random variable, the usage rate, which may have a certain probabilistic distribution as influenced by consumer behavior patterns. Furthermore, product failure rate is influenced by the usage rate and product age as well as research and development expenditures per unit. It is assumed that, in production, there is a learning effect with time. The attained market share of a product will be influenced by the warranty policy parameters of warranty time and usage limit and also by the product price and product quality. An integrated model is developed to address multiobjective goals such as attainment of a specified level of market share and net profit per unit when manufacturing and warranty costs are taken into account. The impact of the goal priorities are investigated on the attained warranty policy parameters.

Two-attribute warranty policies are considered that incorporate, for example, the time elapsed since sale of the product and product usage at a given point in time. Such policies occur in consumer products, such as automobiles, where warranty may be exercised if both time and usage are within specified warranty parameters when a product failure occurs. In this chapter, it is assumed that usage and product age are related through a random variable, the usage rate, which may have a certain probabilistic distribution as influenced by consumer behavior patterns. Product quality is modeled through the product failure rate, which is influenced by unit research and development expenditures as well as the usage rate and product age. The attained market share of the product is modeled as a function of the warranty policy parameters of price, warranty time, and usage limit, with product quality also having an influence. Attainment of single and multiple objectives are explored. Such objectives encompass expected total unit costs as a proportion of unit product price and market share.