Academic journal article Journal of Business Economics and Management

An Integrated Model for Extending Brand Based on Fuzzy ARAS and ANP Methods

Academic journal article Journal of Business Economics and Management

An Integrated Model for Extending Brand Based on Fuzzy ARAS and ANP Methods

Article excerpt

Introduction

Brand extension, defined as the use of an established brand name for new-product categories, is one of the most common strategies used in developing an existing brand, which can reduce risk and increase investment by enhancing consumer perception. Brand extension strategies are beneficial because they reduce new product introduction costs, and perceived risk of the new product, hence increasing the chances of success (Aaker 1990; Keller 1998). Approximately 80% of new products introduced each year are brand extensions (Keller 1998). This is due to the fact that launching a new product is not only time consuming; but also, needs a big budget to create awareness and to promote a product's benefits (Tauber 1981). On the other hand, a victorious brand can help a company to more easily launch new products in novel categories.

Generally, two main advantages of brand extensions could be underlined: the ability to facilitate new-product acceptance; and provide positive feedback to the parent brand and company (1). Therefore, it is important for marketing researchers and brand managers to understand how consumers evaluate them (Estes et al. 2012). A successful brand message strategy relies on a congruent communication and a clear brand image (Sjodin, Torn 2006).

Although there are significant benefits in brand extension strategies, there are also significant risks, resulting in a diluted or severely damaged brand image. Poor choices for brand extension may dilute and deteriorate the core brand and damage the brand equity (Aaker 1990). In spite of the positive impact of brand extension, negative association and wrong communication strategy do harm to the parent brand and even the brand family (Tauber 1981; Aaker 1990). Therefore, for propose of decreasing the level of risk in the process of brand extension, it is necessary to take into account both qualitative and quantitative parameters influencing the problem in order to get the deeper insight into the problem area. This helps an organisation to properly model the problem of brand extension.

On the other hand, the merit of using multi-criteria decision making (MCDM) techniques is to model a complex and sophisticated problem by applying a well-organised and systematic approach. The MCDM methods provide tools for considering both tangible and intangible parameters involved in the process of modelling in order to make a proper and accurate decision. These methods are strongly recommended as helpful in reaching important decisions that cannot be determined in a straightforward manner (Wu et al. 2010; Fouladgar et al. 2012). Different MCDM techniques have been developed to solve multi criteria problems. These methods can be classified into three main categories (Belton, Stewart 2002): (i) value measurement model such as analytical hierarchy process (AHP), (ii) outranking models such as Preference Ranking Organisation METHod for Enrichment Evaluation (PROMETHEE), and (iii) goal aspiration and reference level models such as Technique to Order Preference by Similarity to Ideal Solution (TOPSIS) and Additive Ratio Assessment Method (ARAS).

ARAS, first introduced by Zavadskas and Turskis (2010), is a branch of the MCDM techniques that solve a complex problem by using simple relative comparisons. This method uses the basic concept of degree of optimality for selecting the best alternative among a pool of alternatives by calculating the ratio of the sum of normalized and weighted criteria scores to the sum of the values of normalized and weighted criteria. The ARAS method is employed by different researchers to rank the possible alternatives in order to select the best ones (Zavadskas et al. 2010; Zavadskas, Turskis 2010; Bakshi, Sarkar 2011; Bakshi, Sinharay 2011; Dadelo et al. 2012; Zavadskas et al. 2012; Kutut et al. 2013). This is due to the fact that the ARAS method has several advantages: (i) the computations defined in the process of modelling a decision making problem are straightforward, (ii) the concepts have a profound logic (iii) this method contains a simple mathematical form in the pursuit of the best alternative, and (iv) the relative weights are incorporated into the comparison procedures. …

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