Academic journal article Journal of Business Economics and Management

Application of Stepwise Data Envelopment Analysis and Grey Incidence Analysis to Evaluate the Effectiveness of Export Promotion Programs

Academic journal article Journal of Business Economics and Management

Application of Stepwise Data Envelopment Analysis and Grey Incidence Analysis to Evaluate the Effectiveness of Export Promotion Programs

Article excerpt

1. Introduction

Economic scholars believed that export had a major and direct impact on economic conditions and growth of the country. At a micro level, the export of goods and services has become increasingly important for the survival of growth oriented domestic firms. At a macro level, exporting is important for dealing with trade deficit problems experienced by many countries (Julian, Ali 2009). These impacts persuade governments to design and provide some programs in order to promote the magnitude and diversity of export in their countries.

Export promotion programs (EPPs) are a class of policies that governments make to encourage and reinforce domestic exporters to expand their activities. It can be defined as an incentive program designed for attracting firms into export by offering help with product and market identification and development (Korsakiene, Tvaronaviciene 2012; Travkina, Tvaronaviciene 2011; Valuckaite, Snieska 2007; Zhou et al. 2010), prescription and post-shipment, financing, training, payment guaranty schemes, trade fairs, trade visits, foreign representation, etc. (Shamsuddoha et al. 2009; Lages et al. 2008) used electronic information retrieval methods (Burinskas et al. 2010; Azimi et al. 2011; Buyukozkan 2004) and systems (Kaklauskas et al. 2002a,b, 2003, 2010; Zavadskas et al. 2005).

Some studies have shown a positive direct impact of EPPs on export performance (Balassa 1978; Kumar Roy 1993; Ramaseshan, Soutar 1996; Billings et al. 2003; Francis, Collins-Dodd 2004; Shamsuddoha, Ali 2006; Zia 2008; Julian, Ali 2009; Larbi, Chymes 2009; Lederman et al. 2010; Freixanet 2011; Argent 2011). Also, Armah and Epperson (1997), Knowles and Mathur (1997), and Onunkwo and Epperson (2000) have tried to measure the global impact of specific promotion interventions. Some studies have indirectly evaluated program effects, considering them among other factors to explain export performance (Crick, Chaudhry 1997; Katsikeas et al. 1996; Walters 1983).

This study is done to determine the effects of EPPs on Iran food industry. A set of different EPPs are proposed to food product exporters in Iran. This diversity in programs and their requested funds forces decision makers to appraise the effects of different EPPs and assign financial resources based on a logical and structured manner. The aim of this study to determine and clarify the effectiveness of EPPs in Iran is satisfied through a hybrid application of stepwise data envelopment analysis (stepwise DEA) and grey incidence analysis (GIA) methods.

The paper is organized as follows: section 2 discusses the concept of stepwise DEA, section 3 briefly introduces the GIA method and section 4 explores the framework of data gathering. The analysis and their results are presented in section 5. Finally, section 6 consists of conclusions and future work.

2. Stepwise data envelopment analysis

Data envelopment analysis was originally proposed by Charnes et al. (1978) as a method to evaluate the relative efficiency of a set of units that consume a set of m inputs and transform them into a set of s outputs. For more details on DEA refer to Cooper et al. (2002) and Ray (2004). For reviewing applications for DEA see Emrouznejad et al. (2008).

The classic CCR model can be introduced as follows. Suppose there are a set of m homogenous units. Each unit, [DMU.sub.j], j = 1,2, ... n, use a set of m inputs [X.sub.j] = ([x.sub.lj], [x.sub.2j], ..., [x.sub.mj]) to produce a set of s outputs [Y.sub.j] = ([y.sub.1j], [y.sub.2j], [y.sub.sj]). The input oriented CCR model to evaluate the relative efficiency of these DMUs for each [DMU.sub.0], 0 [member of] {l,2, ..., n} is developed as follows:

min [[theta].sub.0]

[n.summation over (j=1)][[lambda].sub.j][x.sub.ij] [less than or equal to] [[theta].sub.0] [x.sub.i0], i = 1, 2 ..., m,

[n.summation over (j=1)][[lambda].sub.j][y.sub.rj] [greater than or equal to] [y. …

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