The current issue and full text archive of this journal is available at www.emeraldinsight.com/1463-5771.htmBIJ18,5 Benchmarking for investment decisions: a case of food production694 Anatoliy G. Goncharuk Department of Management and Finance, Odessa National Academy of Food Technologies, Odessa, Ukraine Abstract Purpose – The paper aims to focus on improving the methodology and developing the model of choice of optimal investment object using benchmarking tools that eliminate the drawbacks of existing approaches. Design/methodology/approach – The methodological basis of the proposed model is frontier analysis, namely the nonparametric data envelopment analysis. Using this and other benchmarking tools, the author introduces the concept and mathematical model for evaluation of super-attractiveness for investors that allows a full ranking of potential objects for investment. Findings – The concept of variable investment decision that combines various periods, varying degrees of risk and other decision characteristics with a common purpose of maximizing the beneﬁts from investments is deﬁned. The model for the making of variable investment decisions is developed. Practical implications – The proposed model enables strategic and portfolio investors to implement the optimal choice of investment object. It is demonstrated on a case of the food production of Ukraine. Originality/value – This paper adopts benchmarking tools to the decision-making process to optimal choice of investment object. Keywords Benchmarking, Investment attractiveness, Super-efﬁciency, Investment decision, Data Envelopment Analysis (DEA), Food production, Investments, Food industry Paper type Research paper Introduction As the previous study (Goncharuk, 2009a) shows, benchmarking detects the best practices, factors and reserves for performance improvement. These features of benchmarking can be useful not only for enterprise managers and owners, but for potential investors which look for an optimal way for investing. Making the right decision about where to invest is an important objective of any investor. Depending on the purpose of investing, investment decisions may be different: from the portfolio that is aimed at earning the expected returns at the lowest acceptable risk, to strategic with long-term goals of ownership and governance of the investment object. Such solutions can differ not only in goals, but also in the degree of risk, amounts and forms of investment, etc. Considering the totality of such decisions, we deﬁne the concept of variable investment decisions as decisions of the investor on the investing of ﬁnancialBenchmarking: An International resources for various periods in objects with varying degrees of risk and otherJournal characteristics, but with the common purpose – to maximize the beneﬁts from theseVol. 18 No. 5, 2011pp. 694-704 investments. In our opinion, the determining factor of this decision should be the resultq Emerald Group Publishing Limited of the selection process for the object of investment by the established criteria.1463-5771DOI 10.1108/14635771111166820 The ﬁnancial and economic crisis has shown that existing methodological approaches
to selecting investment targets are ineffective. Its primary disadvantages are the Benchmarkinglimited purposes, static character, focus only on ﬁnancial indicators, large number for investmentof used factors and the complexity of interpreting of the results. In our opinion, thebenchmarking tools can eliminate many of these shortcomings. decisions Thus, this study focuses on improving the methodology and developing the modelof choice of optimal investment object using benchmarking tools that eliminate thedrawbacks of existing approaches. To examine this model, we consider a case of 695Ukrainian food industry in order to ﬁnd the optimal enterprises for investing. Investment attractiveness of enterprises’ potential is usually used in the science andpractice as a criterion for choosing the optimal investment object (enterprise). Thescientiﬁc literature does not develop a common approach to the deﬁnition of thisconcept. Zahorodniy and Voznyuk (2008) and Nosova (2007) deﬁne it as a generalizeddescription of the advantages and disadvantages of investing in certain areas andobjects from the perspective of a particular investor. Others consider the investmentattractiveness more simply – as the expediency of free capital investments in anenterprise (Rusak and Rusak, 1997), or more comprehensively as an integral featureof individual enterprises as objects of future investments from the prospects ofdevelopment of production and sales, efﬁcient use of assets, their liquidity, the state ofsolvency and ﬁnancial stability (Bryukhovetska and Khasanova, 2009). Summarizing the existing treatments, we deﬁned the concept of “investmentattractiveness of enterprises” as characteristics describing the system of integratedindicators of expediency of investments in a company, which reﬂects the totality ofexisting conditions and factors that promote or hinder the process of investing. There are different opinions on a choice of the methodology and model to measurean investment attractiveness. Traditionally, investors use two criteria for choosingbetween capital investment projects – the net present value (NPV) and the internal rateof return (IRR). They often provide inconsistent rankings. This inconsistency is hotlydebated about which criterion is better. The debate has lasted more than a century.Some explorers (Osborne, 2010) suggest new methods of calculating for NPV, theother (Kierulff, 2008) modify IRR. However, the suggestions to determine the level ofinvestment attractiveness of enterprises and create an adequate rating with a singleindicator are debatable. Practice (Bennouna et al., 2010) conﬁrms both the widespreaduse of NPV and IRR and making of poor decisions based on them only. There are multi-criteria approaches to solving this problem. Dudka (2006) considersa system of statistically signiﬁcant indicators as the most appropriate method for this.This system should include a general indicator and several levels of interrelatedindividual indicators, which fully characterize the object under investigation and havea common dimension and structure. Blank (2001) suggests that you ﬁrst determine thestage of the life cycle of the company, which will evaluate its investment attractiveness.Balatsky (2004) and the other scholars tend to use the expert-rating systems for theevaluation of investment attractiveness, which are widespread in developed countries.All these approaches have their disadvantages. Practice of using them in the ﬁnancialcrisis leads to a distortion of reality and making suboptimal decisions related toinvestments in this or that object. Most of the existing methodological approaches to the measurement of investmentattractiveness of company are poor or include many heterogeneous indicators and ratios,which can hardly be perceived as a whole and on the basis of which it is practically
BIJ impossible to provide real prospects of the development of a company and its environment. Therefore, in our view, it is necessary to develop a special model and pay18,5 more attention to the most important aspects of any enterprise – efﬁciency and proﬁtability. It is a reliable estimate of the efﬁciency of the company and its growth potential that can protect investors from the risk of loss of funds. The joint evaluation of investment attractiveness and the relative efﬁciency of its activity makes it possible696 to take into account the situation of enterprises in the environment and the prospects for its development. Methodology Under existing conditions of limited investment resources, we offer a model for selecting investment targets based on the three-level approach, including the consistent application of inter-industry, intra-industry and corporate analysis of investment attractiveness, efﬁciency, and proﬁtability. The methodological basis of proposed model is frontier analysis, namely the nonparametric method called Data Envelopment Analysis (hereinafter referred to as DEA) that was for the ﬁrst time proposed by Charnes et al. (1978) and then has received extensive theoretical development and practical application in various spheres of human activity over the past three decades. DEA is the usage of linear programming methods for constructing nonparametric piecewise surfaces (frontier) according to the data of enterprises of sample, and calculation of efﬁciency index concerning this surface (Coelli et al., 2005). DEA is now one of the most popular tools for performance measurement and benchmarking in the various ﬁelds, for example, in manufacturing (Goncharuk, 2009a), power generation and distribution (Farzipoor Saen, 2010; Goncharuk, 2008), transportation (Abraham George and Rangaraj, 2008), communication (Mitra Debnath and Shankar, 2008), trade ( Joo et al., 2009), medicine (Lambert et al., 2009), etc. This study uses DEA super-efﬁciency model by Anderson and Petersen (1993) for complete ranking (hierarchy) of the enterprises of sample as to the efﬁciency. We propose to modify this model to assess the relative investment attractiveness and ranking of companies on its level. In this case, the obtained model of the super-attractiveness for investor (SIA) can be mathematically expressed as follows: X n min a sup ; subject to : vj xij þ s2 ¼ a sup xiq ; i ¼ 1; 2; . . . ; m i j¼1;–q ð1Þ X n vj yrj 2 sþ ¼ yrq ; r ¼ 1; 2; . . . ; s vj ; s2 ; sþ $ 0; r i r j¼1;–q where, a sup – the value of super-attractiveness of object (companies, industry); x and y – the values of inputs and outputs of the model, respectively; s2 – the deviation of i input of the ith type of the frontier; sþ – the deviation of output parameter of r the rth type of frontier; vj – weights; m – number of inputs, r – number of outputs, n – number of objects. The practical application of this model (1) consists in the possibility of obtaining the ratings of the level of relative investment attractiveness of each object (enterprise, industry) of the sample. By analogy with the measurement of effectiveness, we offer
to take the denominators of the basic indicators of investment attractiveness as inputs, Benchmarkingi.e. material resources, as well as their numerators as outputs, i.e. ﬁnancial results. for investment A set of basic indicators should reﬂect the current rate of return on investment inthe operating activities of the object, its capital goods and ﬁnancial risk of investment decisionsin the object. As inputs, we propose to use the following indicators: total operatingcosts, depreciation of ﬁxed assets, and total liabilities. As outputs, we use the net salesand net working capital. 697 For the ranking of objects in terms of efﬁciency, in our view, it is advisable to usethe DEA super-efﬁciency model with major operating cost items as inputs (materialcosts, wages, depreciation, and miscellaneous costs) and net sales as an output. In orderto rank the level of proﬁtability, we offer to use the return on total assets. The two-dimensional graphic comparison of investment attractiveness andefﬁciency will enable a strategic investor (SI) to select the best objects for investment.It is necessary to make such ranking and comparison of at least two periods in order tomake a deliberate decision on assessing trends in the analyzed characteristics of objects. We offer to use the Malmquist total factor productivity index (MPI) (Goncharuk, 2007)for identifying common trends in the efﬁciency of sampling and site speciﬁc. This indexintroduced by Caves et al. (1982) is derived for general production structures. MPI deﬁnesthe fundamental characteristic of a productivity index as a ratio between an outputquantity change index and an input quantity change index (Bjurek, 1996). It characterizesthe general changes between the two periods of technical efﬁciency and technologicaldevelopments that involve the development of new products and technologies that enablethe rapid growth of output compared with an increase in consumption of resources.In our view, this is an important determinant of business prospects that inﬂuence thedecision making on strategic investment in the object. Moreover, the return to scale maybe an important factor of the success of strategic business development. Its estimationfor selected enterprises of analyzed sample would be indicating the level of desirableexpansion of production (business) in terms of efﬁciency. To make a decision about portfolio investment, we offer graphic comparison of theevaluation of investment attractiveness and proﬁtability as portfolio investors (PI) aremore interested in these characteristics. To increase the validity of decisions, it isappropriate to evaluate and compare these characteristics during at least two intervalsin order to assess trends of their changes.ModelWe offer the model for making the variable investment decisions (MVID model)particularly for SIs and PI that are based on benchmarking tools. The essence of itsphases is outlined below. At the ﬁrst stage, we offer to rank the industries (activities) in which an investorwishes to invest. For the SI, it is advisable to use the rating that is built on the basis ofsuper-efﬁciency estimates (SE) obtained by means of using an appropriate DEAsuper-efﬁciency model by Anderson and Petersen (1993). To improve the reliability ofthe choice, it is better to make this rating for the two periods and select one of theleading industries. For PI, we suggest using the rating of industries that is built on thebasis of estimates of returns on assets, on equity capital or product proﬁtability.Making such rating of two periods will make it possible to identify the industry withthe most stable and high proﬁtability.
BIJ The ﬁrst phase of the MVID model detects one or two sectors for potential18,5 investment. The second stage consists in intra-industry analysis of investment attractiveness. Its main goal is to choose one or two companies of each of the industries, which were selected at the previous stage, in which it is expedient to invest. The initial stage of the MVID model consists in estimating SIA scores using the model (1). For the SI, we offer698 to compare SIA scores with SE scores for each selected company (industry) in the two-dimensional coordinate system. By means of separating the resulting distribution of enterprises in four quadrants by analogy with the efﬁciency-proﬁtability matrix by Dyson et al. (1990), we will select just that group of companies, which covers the upper right quadrant called area of attractiveness for a SI. Such procedure is desirable to be carried out during the interval of two periods, in order to avoid appearance of accidental “stars” and reduce the risk of investing. We also offered to evaluate MPIs for each of the selected enterprises and for the whole sample of enterprises for each of the selected industries. The best index will indicate the highest rate of efﬁciency growth and will be one of the determinants of the decision making of SI. At this stage, MVID model offers the PI to compare the SIA scores with the proﬁtability of assets for enterprises of the sample in two-dimensional coordinate system. Companies which are in the upper right quadrant called area of attractiveness for PI for two periods are potential targets for portfolio investments. The third stage consists in an in-depth analysis of ﬁnancial condition of selected companies and evaluating their opportunities of implementation of institutional arrangements for the acquisition of their shares. SI should: . study the composition and structure of investments of existing shareholders (owners); . evaluate their own ﬁnancial capabilities; . formulate investment proposals; and . negotiate with major owners the possibility of purchasing their stake in the business or buying the additional issue of shares. PI should be in the stock market for shares of selected enterprises. In case of a closed form of business organization, an investor should negotiate the possible occurrence of the shareholders of the company with the owners. Thus, the MVID model enables SI and PI to make an informed choice of investment objects (companies) and provide afﬁliation to its owners. Case study In order to demonstrate the practical aspects of using the proposed model, we will make a careful study of its work by the example of the food industry in Ukraine. Taking into consideration the importance to society and the growing demand for food products, food industry has always been important for investors from different countries (Skripnitchenko and Koo, 2005; Makki et al., 2004). Our calculations are dated 2006 and 2008. First stage. Rating was formed for the four-digit NACE items of economic activities (industries) that enter into the composition of the food industry. The results of the top of the SE score rating for the food industries of Ukraine are given in Table I.
This rating indicates that the manufacture of beer is the most attractive for Benchmarkinginvestors among the Ukrainian food industries. It is this industry that becomes the for investmentobject for further analysis. Second stage. While analyzing the investment attractiveness of the beer industry, decisionsit should be noted that over 90 percent of the industry belongs to four major producers:SUN InBev Ukraine, Baltic Beverages Holding Ukraine, Company “Obolon” and SarmatBrewery Company. The rest of the market is divided among dozens of small companies. 699Thus, you need either major investments (hundreds of millions US$) or relatively smallinvestments (few millions US$) in order to enter this market as SI. The sample of 25 beercompanies in Ukraine for a period of two years has been analyzed. The SIA estimatesreceived with the help of the model (1) for this sample are presented in Table II. Despite the general deterioration in the performance of the beer industry in 2008 dueto the inﬂuence of economic and ﬁnancial crisis and other negative factors, some of theleading enterprises of the industry in terms of investment attractiveness, includingKhmelpivo, Sarmat Brewery Company and BNC Radomyshl hold their positionsconﬁdently. It should be also highlighted that the loss of the relative investmentattractiveness of one of the market leaders Company “Obolon” and the signiﬁcantimprovement of the position of SUN InBev Ukraine (it has risen to third place in theSIA rating in 2008) took place. We compared the SIA estimates with the SE in two-dimensional coordinate systemfor two years in order to select the best companies for SI (Figure 1). Comparison shown that if in 2006 the area of attractiveness for SI were only threeenterprises beer industry (Khmelpivo, Sarmat Brewery Company and Uman brewery),then in 2008 due to changes in the relative efﬁciency and investment attractiveness thisarea was included already six companies, namely: Khmelpivo, SUN InBev Ukraine,Ohtyrka brewery, Lviv brewery, Uman brewery and Bershad brewery. Estimates ofthe MPI and return to scale for these companies are presented in Table III. Given that all the selected companies from the area of attractiveness are based onindustry efﬁciency frontier, they have constant returns to scale, i.e. increase in inputsleads to a proportional increase in output. Therefore, this condition can be consideredequal to them. Low values of MPI for the “Lviv brewery”, SUN InBev Ukraine and Ohtyrka brewery,which is much lower than one, indicate the negative dynamics of total factorproductivity in these companies. Thus, they should be excluded from furtherconsideration in terms of optimality for SI. The other selected companies (Table III),despite the impact of ﬁnancial crisis, have a very positive dynamics of total productivity,which is a deﬁnite advantage in favour of their selection as a target for the SI.Four-digit economic activities Super-efﬁciency score (%) No. in rating15.96 Manufacture of beer 119.4 115.11 Manufacture of meat 117.5 215.91 Manufacture of distilled alcohol beverages 115.8 315.93 Manufacture of wine 93.5 415.13 Manufacture of meat products 86.7 5 Table I.... ... ... Ranking of the economic activities bySource: Goncharuk (2009b) super-efﬁciency
BIJ 2006 200818,5 Number in SIA score Number in SIA score Change of Company name rating (%) rating (%) rating position Khmelpivo 1 628.7 1 819.4 – Sarmat Brewery Company 2 252.3 2 345.1 –700 Company “Obolon” 3 229.5 15 94.0 212 BNC Radomyshl 4 97.6 4 145.9 – Uman brewery 5 91.6 7 109.6 22 Chernyatinske pyvo 6 83.2 24 42.8 218 Imperia-S 7 80.2 14 95.2 27 Lviv brewery 8 76.8 6 127.8 þ2 BNC Slavutich 9 65.2 9 101.6 – Dnepropetrovsk brewery “Dnipro” 10 64.2 10 100.8 – SUN InBev Ukraine 11 63.1 3 175.3 þ8 Brovar 12 59.4 12 97.9 – Bershad brewery 13 59.0 8 102.0 þ5 Ohtyrka brewery 14 56.8 11 100.2 þ3 “Poltavpivo” ﬁrm 15 53.8 20 52.2 25 Rovenki brewery 16 52.4 13 97.5 þ3 Cherkaske Pyvo 17 52.3 25 21.0 28 Riven’ 18 48.3 16 80.4 þ2 Brewery on Podol 19 48.0 5 142.2 þ 14 Novograd-Volynskiy brewery 20 47.3 23 48.1 23 Opillya 21 46.0 22 48.8 21Table II. Sevastopol brewery 22 44.1 17 74.7 þ5Scores and ranking of Zahidpyvo 23 43.8 18 60.7 þ5investment attractiveness Izyum brewery 24 43.2 21 50.7 þ3of Ukrainian breweries Pavlivskiy brewery 25 30.2 19 53.2 þ6 2006 2008 3.2 3.2 2.8 Area of 2.8 Area of attractiveness for attractiveness for 2.4 strategic investor 2.4 strategic investor Super-efficiency Super-efficiency 2 2 1.6 1.6Figure 1. 1.2 1.2Comparison ofsuper-investment 0.8 0 1 2 3 4 5 6 7 8 9 0.8 0 1 2 3 4 5 6 7 8 9attractiveness andsuper-efﬁciency for 0.4 0.4Ukrainian breweries for2006 and 2008 0 0 SIA SIA
To select the best companies for PI, we compared the SIA estimates with the Benchmarkingproﬁtability of assets for two years (Figure 2). for investment The carried-out comparison showed that whereas in 2006 only two companies ofbeer industry (Khmelpivo and Company “Obolon”) got the area of attractiveness for PI, decisionsin 2008, due to changes in the level of proﬁtability and investment potential,already ﬁve companies were in this area, namely: Khmelpivo, SUN InBev Ukraine,Ohtyrka brewery, Uman brewery and Bershad brewery. But only Khmelpivo were 701stably attractive and proﬁtable during the analyzed period, hence portfolioinvestments in this company are the least risky among the companies of the industry. Third stage. In-depth ﬁnancial analysis of selected companies indicates thefollowing: . Uman brewery and Ohtyrka brewery do not have their own circulating capital. These companies fund both the turnover and a substantial part of ﬁxed assets by loans; hence, they cannot be considered as reliable objects for investment. . SUN InBev Ukraine, in spite of the proﬁtable operation and high investment attractiveness, had negative dynamics in both productivity and proﬁtability, the latter fell for two years from 10.6 to 2.2 percent. This does not allow PI to guarantee the necessary efﬁciency of investments. Besides, this company is practically in private ownership of the largest foreign investor and the purchase of its share may be difﬁcult.Company name Malmquist TFP index Return to scaleKhmelpivo 1.722 ConstantUman brewery 1.478 Constant Table III.Lviv brewery 0.871 Constant Malmquist TFP indexSUN InBev Ukraine 0.563 Constant and return to scale forBershad brewery 1.439 Constant selected UkrainianOhtyrka brewery 0.786 Constant breweries 2006 2008 30 30 Profitability of assets (%) Profitability of assets (%) 0 0 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 Figure 2. –30 –30 Comparison of super-investment Area of attractiveness Area of attractiveness attractiveness and for portfolio investor for portfolio investor proﬁtability of assets for Ukrainian breweries for –60 –60 2006 and 2008 SIA SIA
BIJ . Khmelpivo and Bershad brewery are the optimum for both the SI and PI.18,5 Both companies are highly proﬁtable, efﬁcient, with the positive dynamics of total factor productivity. However, due to the organizational and legal form of Bershad brewery (it is a closed joint stock company), portfolio investments into this company are difﬁcult, and opportunities for SI depend on the results of negotiations with the major owner of Company “Obolon”. Taking into702 consideration the size of selected companies, amounts of funds for strategic and portfolio investments are relatively small (within one to two million US$); hence, they are accessible to many potential investors. The demonstrated case shows how an investor can ﬁnd the desired object for investment and make a balanced variable investment decision based on the results of benchmarking and comprehensive analysis in the result of the phased implementation of the proposed MVID model. Conclusions Benchmarking makes investment decisions more grounded and optimal. Studying of the methodological aspects and practical problems that arise in the result of grounding and making different investment decisions, allowed the author to elaborate a number of innovations: (1) deﬁnition of the concept of variable investment decisions that are the decisions of an investor on the investing of ﬁnancial resources for various periods in objects with varying degrees of risk and other characteristics, but with a common purpose – to maximize the beneﬁts from these investments; (2) introduction of the concept and mathematical model for evaluation of super-attractiveness for investor that allows make a full ranking of potential objects for investment; and (3) development of the model for MVID model that is based on benchmarking tools. The MVID model has important practical signiﬁcance and allows strategic and PI to implement the optimal choice of investment object. The work and effectiveness of the proposed model are demonstrated on the case of the food industry of Ukraine. The MVID model is quite versatile and can be applied for making investment decisions in other industries, not only for food production. Future directions for research on this issue will be associated with empowerment of the model and its application to other ﬁelds. References Abraham George, S. and Rangaraj, N. (2008), “A performance benchmarking study of Indian Railway zones”, Benchmarking: An International Journal, Vol. 15 No. 5, pp. 599-617. Anderson, P. and Petersen, N.C. (1993), “A procedure for ranking efﬁcient units in data envelopment analysis”, Management Science, No. 10, pp. 1261-4. Balatsky, E.F. (2004), Investment Management, University Book, Sumy.
Bennouna, K., Meredith, G.G. and Marchant, T. (2010), “Improved capital budgeting decision Benchmarking making: evidence from Canada”, Management Decision, Vol. 48 No. 2, pp. 225-47. for investmentBjurek, H. (1996), “The Malmquist total factor productivity index”, Scandinavian Journal of Economics, Vol. 98 No. 2, pp. 303-13. decisionsBlank, I.A. (2001), Investment Management, Nika-Center, Moscow.Bryukhovetska, N.Y. and Khasanova, O.V. (2009), “Evaluation of investment attractiveness”, Economics of Industry, No. 1, pp. 110-17. 703Caves, D.W., Christensen, L.R. and Diewert, W.E. (1982), “The economic theory of index numbers and the measurement of input, output and productivity”, Econometrica, Vol. 50, pp. 1393-414.Charnes, A., Cooper, W. and Rhodes, E. (1978), “Measuring the efﬁciency of decision making units”, European Journal of Operational Research, Vol. 2, pp. 429-44.Coelli, T., Prasada Rao, D.S., O’Donnel, C.J. and Battese, G.E. (2005), An Introduction to Efﬁciency and Productivity Analysis, Springer, New York, NY.Dudka, T.V. (2006), “Evaluation of investment attractiveness of the food industry based on measuring the use of reserve production capacity”, Economic Innovations, Vol. 24, pp. 168-75.Dyson, R.G., Thanassoulis, E. and Boussoﬁane, A. (1990), “Data envelopment analysis”, in Henry, L.C. and Eglese, R. (Eds), Operational Research Tutorial Papers, Operational Research Society, Birmingham, pp. 13-28.Farzipoor Saen, R. (2010), “Performance measurement of power plants in the existence of weight restrictions via slacks-based model”, Benchmarking: An International Journal, Vol. 17 No. 5, pp. 677-91.Goncharuk, A.G. (2007), “Impact of political changes on industrial efﬁciency: a case of Ukraine”, Journal of Economic Studies, No. 4, pp. 324-40.Goncharuk, A.G. (2008), “Performance benchmarking in gas distribution industry”, Benchmarking: An International Journal, Vol. 15 No. 5, pp. 548-59.Goncharuk, A.G. (2009a), “Improving of the efﬁciency through benchmarking: a case of Ukrainian breweries”, Benchmarking: An International Journal, Vol. 16 No. 1, pp. 70-87.Goncharuk, A.G. (2009b), Methods of Estimation and Analysis of Industrial Efﬁciency, Astroprint, Odessa.Joo, S.-J., Stoeberl, P.A. and Fitzer, K. (2009), “Measuring and benchmarking the performance of coffee stores for retail operations”, Benchmarking: An International Journal, Vol. 16 No. 6, pp. 741-53.Kierulff, H. (2008), “MIRR: a better measure”, Business Horizons, Vol. 51 No. 4, pp. 321-9.Lambert, T.E., Min, H. and Srinivasan, A.K. (2009), “Benchmarking and measuring the comparative efﬁciency of emergency medical services in major US cities”, Benchmarking: An International Journal, Vol. 16 No. 4, pp. 543-61.Makki, S.S., Somwaru, A. and Bolling, C. (2004), “Determinants of foreign direct investment in the food-processing industry: a comparative analysis of developed and developing economies”, Journal of Food Distribution Research, Vol. 35 No. 3, pp. 60-7.Mitra Debnath, R. and Shankar, R. (2008), “Benchmarking telecommunication service in India: an application of data envelopment analysis”, Benchmarking: An International Journal, Vol. 15 No. 5, pp. 584-98.Nosova, A.V. (2007), “The investment attractiveness”, Strategic Priorities, No. 1, pp. 120-4.
BIJ Osborne, M.J. (2010), “A resolution to the NPV-IRR debate?”, The Quarterly Review of Economics and Finance, Vol. 50 No. 2, pp. 234-9.18,5 Rusak, N.A. and Rusak, V.A. (1997), Fundamentals of Financial Analysis, High School, Minsk. Skripnitchenko, A. and Koo, W.W. (2005), “US foreign direct investment in food processing industries of Latin American countries: a dynamic approach”, Applied Economic704 Perspectives and Policy, Vol. 27 No. 3, pp. 394-401. Zahorodniy, A.G. and Voznyuk, G.L. (2008), Investment Dictionary, Beskid Beat, Lviv. Corresponding author Anatoliy G. Goncharuk can be contacted at: firstname.lastname@example.org To purchase reprints of this article please e-mail: email@example.com Or visit our web site for further details: www.emeraldinsight.com/reprints