Competitive Environment Analysis in Global Retail Companies Operation Strategy: A Data Envelopment Analysis (DEA) Based Approach 16 Competitive Environment Analysis in Global Retail Companies Operation Strategy: A Data Envelopment Analysis (DEA) Based Approach Zainal Putra 21 ⃰ and Muzakir 2 1,2 Universitas Teuku Umar, Aceh Barat, Indonesia Abstract This research aims to find out the most competitive retail company operating in current global market based on the perspective of efficiency. A well-performed company is the company that is efficient in its operations. By using Data Envelopment Analysis (DEA) approach, this research differs from prior research because we used multivariable inputs, namely: asset, operational expense and the number of employees. The output variables used in this research are: total revenue, net profit, return on equity (ROE), return on assets (ROA), return on investment (ROI), dividend yield ratio and asset turnover ratio. The analysis results shows that six retail companies are “efficient” in its operation (efficiency score of 1.00), namely: Carrefour, Costco, Kroger Company, Home Depot Inc, JD.com Inc Adr and Alibaba Group Holdings Ltd ADR. Therefore, these companies are considered the most competitive in its operation strategy in the current global market, whereas there are four retail companies falls into category of “inefficient” (efficiency score < 1.00), namely: Walmart, Amazon.com Inc, Tesco PLC and Walgreens Boots Alliance Inc. Keywords: Competitive, DEA, Efficient, Retail 1. Introduction Retailing serves the selling of goods and services toward the consumers, both for household and personal consumption. There are various types of goods available in retail shops, including: food, clothes, electronics, drugs, books, and many others (Marketos & Theodoridis, 2006). Retail industry has a long process of supply chain, starting from supplier, importer, producer, distributor, wholesaler, and retailer. Thus the retail store is at the very end of the supply chain. Based on its supply chain, retailer directly interact with the end-consumers. Advances in information technology have been applied in this sector, both for data management, supply chain, and also in terms of marketing strategies. As the result, the consumers can enjoy a quick and quality services. The retail industry is one of the sectors that is most sought after by capital owners. With a high level of sales, they managed to reap relatively large profits at the end of each year. One of the reasons is because this industry usually provides essential goods for the needs of the community. In 2019, Walmart as a giant retailer, ranked first in the largest revenue, amounting to US$ 127,991 million, followed by Amazon and Carrefour, each of which managed to record revenues of US$ 87,436 million and US$ 41,611 million. Regarding the top three retail companies which managed to record the largest revenue in 2019, we will briefly review the company's profile. The first is Walmart; the largest retail company in the world headquartered in Bentonnile, Akansas, United * Corresponding author. Email address: zainalputra@utu.ac.id AFEBI Management and Business Review (AMBR) Vol.05 No.01 June 2020 17 States. The company was established on July 2, 1962 by Sam Walton. Currently, Walmart has 11,690 outlets worldwide employing 2,200,000 employees. The second is Amazon; This e-commerce company based in Seattle Washington was established by Jeff Bezos on July 5, 1994. The company, operating under amazone.com, has separate retail websites in various countries. For example, for India, the website name is Amazon.in, and so on. Amazon has 534 outlets around the globe, employing 798,000 employees. The third is Carrefour, a retail company headquartered in Boulognr- Billancourt, France. Established on January 1, 1958 by Marcel Fournier, Denis Defforey, and Jacques Defforey, Carrefour now are operating in America, Asia, and Africa. Carrefour has around 11,935 outlets worldwide and now employs 321,383 employees. One interesting thing to note is that in terms of its revenue, Alibaba was ranked tenth in 2019 as the company managed to make a profit of US$ 7,397 million. This means that of the top ten retail companies operating in the global market, Alibaba has been ranked first in terms of profitability. Alibaba is an e-commerce giant headquartered in Binjiang District, Hangzhou, China. The company, founded by Jack Ma in 1999, now employs 116,519 employees. The top ten global retail companies based on revenue in 2019 are presented below. Table 1 Top Ten Global Retail Companies Based on the Biggest Revenue in 2019 No Companies Revenue (Million USD) Net Profit (Million USD) 1. Walmart 127,991 3,288 2. Amazon.com Inc 87,436 3,268 3. Carrefour SA 41,611 1,719 4. Tesco PLC 39,864 405 5. Costco 37,040 844 6. Walgreens Boots Alliance Inc 34,339 844 7. Kroger Company 27,974 263 8. Home Depot Inc 27,223 2,769 9. JD.com Inc Adr 24,135 514 10. Alibaba Group Holdings Ltd ADR 22,830 7,397 Source: summarized from https://id.investing.com In this regard, the researchers conclude that it is very important for company managers to conduct an environmental analysis to maintain a sustainable competitive advantage, including retail industry sector. In line with what Birkinshaw, Morrison, & Hulland (1995) suggest, that the analysis of the competitive environment is very important for corporate strategy, business strategy and competitive strategy in the global market. This statement also supported by Ward & Duray (2000) and Gong (2013), who emphasized that analysis of the competitive environment is very important for decision making in global retail organizations. Previous research on the importance of analyzing the competitive environment for companies in an industry has been carried out using a variety of methods, both using qualitative and quantitative methods, some of which we present in the table below. Competitive Environment Analysis in Global Retail Companies Operation Strategy: A Data Envelopment Analysis (DEA) Based Approach 18 Table 2 Previous Research Related to Competitive Environmental Analysis Researchers Theme Method Industry/Sector Birkinshaw et al. (1995) Structural and competitive determinants of a global integration strategy Partial Least Square (PLS) Models Trade Industry Geys (2006) Looking Across Borders: A Test of Spatial Policy Interdependence Using Local Government Efficiency Ratings Spatial Lag Model Goverment Burt et al. (2011) Standardized Marketing Strategies in Retailing? IKEA’s Marketing Strategies in Sweden, the UK and China IKEA’s marketing strategy Retail outlets operating in Sweden, UK, and China Pang et al. (2013) Information Technology and Administrative Efficiency in U.S. State Governments – A Stochastic Frontier Approach Min-Seok Stochastic Frontier Approach Goverment Ahmad et al. (2014) A Comparative Study on Service Quality in the Grocery Retailing: Evidence from Malaysia and Turkey Comparative Study Grocery retailing in Malaysia and Turkey Ko et al. (2017) Efficiency Analysis of Retail Chain Stores in Korea Data Envelopment Analysis (DEA) and Tobit Regression Model Household retailer in Korea Liu et al. (2018) A DEA-Based Approach for Competitive Environment Analysis in Global Operations Strategies Data Envelopment Analysis (DEA) Global retail outlets Gong et al. (2019) When to Increase Firms’ Sustainable Operations for Efficiency? A Data Envelopment Analysis in the Retailing Industry Data Envelopment Analysis (DEA) Global retailing industry In this study, we conduct an analysis of the competitive environment through an efficiency perspective. Retail companies that are considered the most competitive are those who are most efficient in their operating strategies in a global market environment. We measure it with the Data Envelopment Analysis (DEA) approach. To the best of our knowledge, this research is different from the previous mentioned researches , especially in terms of the measurement approach used. Although some previous studies as shown in the above table also have used the DEA approach, the input and output variables are different. As research by Ko et al. (2017), the input used in its DEA model is: store size, number of items, number of employees and rental costs. While the output used is sales revenue and number of customers. As research by Liu et al. (2018), the input used in tis DEA model is: outlets, warehouses, supplier and inhabitants. While the output used is market concentration, consumer spending and market share. As research by Gong et al. (2019) the input used in its DEA model is: supply chain coordination and sustainability level. While the output used is: cost competency, flexibility competency, social competency, environmental competency and business performance. This research is different from the previous researches since in this research we used multiple input variables, namely: assets, operating costs and number of employees. We also use multi output variables, namely: total revenue, net profit, ROE, ROA, ROI, dividend payout ratio and asset turnover ratio. AFEBI Management and Business Review (AMBR) Vol.05 No.01 June 2020 19 Based on the gap research we feel it is very important to conduct this research, with the aim of: first, to find out the most competitive retail companies operating in today's global market environment, viewed from an efficiency perspective. A well- performed company is the company that is efficient in its operations. Second, trying to give a picture of solutions to companies that are not yet competitive/efficient, on how to improve their performance. 2. Literature Review 2.1. Competitive Environment Analysis Basically, strategic management is about how to obtain and maintain competitive advantage. This term can be interpreted as anything that can be done much better by a company when compared with rival companies (David & David, 2017). In strategic management theory, it is emphasized that to be able to maintain its competitive advantage, organizations must adapt to their environment. This has long been echoed by Ginter & Duncan (1990), that environmental analysis is an integral and very important part of the strategic planning process itself. Furthermore, David & David (2017) stated that one important part of the analysis of the external environment is identifying competing companies, determining their strengths, weaknesses, capabilities, opportunities, threats, goals and strategies. Wheelen, Hunger, Hoffman, & Bamford (2018) assert that it is essential for the managers to understand the environmental context in which their organizations compete before formulating business strategy. It is impossible for companies to design strategies without a deep understanding of the external environment. Wheelen et al. (2018) further explains that environmental scanning is a comprehensive term that includes monitoring, evaluating, and disseminating information relevant to the development of organizational strategy. After management has captured all aspects of the external environment that have an impact on the business, then the company's competitive advantage can be determined. Industry is defined as a group of companies that produce similar products or services, such as soft drinks or financial services. Conducting an analysis of stakeholder groups in an industry is very important, for example an analysis of suppliers and customers in the environment of a particular company is part of the industry analysis. Meanwhile Hitt, Ireland, & Hoskisson (2017) explained that to achieve strategic competitiveness, companies must formulate and implement a value creation strategy. Strategy is a set of commitments and integrated and coordinated actions designed to exploit core competencies and gain competitive advantage. However, to achieve competitive advantage, the company must be responsive to the changing environment. In fact, even adapting to the local environment, sometimes companies have to make big changes. One of the companies which adopted and demonstrated this strategic is Abibaba, which has now become a leader in its industry as one of the most successful online sales facilitators in China and is striving to become a successful global business. According to Hitt et al. (2017) a company achieves competitive advantage when they are able to implement strategies that create superior value for customers and cannot be duplicated by the competitors or are too expensive to emulate. However, it is important to note that there is no permanent competitive advantage in the company. In 1997, Russo & Fouts conducted a research on the relationship between the company's environmental performance and the level of profit generated (profitability). The results indicate that: a) a high level of environmental performance is associated with increased profitability; b) the greater the company's growth, the greater the positive impact of environmental performance on company profitability. Russo & Fouts (1997) links corporate environmental performance with three aspects, namely: a) physical assets and technology; b) human resources and organizational capabilities; and c) intangible resources. Where intangible resources are the reputation of leadership Competitive Environment Analysis in Global Retail Companies Operation Strategy: A Data Envelopment Analysis (DEA) Based Approach 20 in caring for the environment, so that ultimately it can increase sales among customers who are sensitive to the problem (environment) (Russo & Fouts, 1997). A research by McKinsey & Company in 2011 found that an analysis of the external environment was the most important information for executives to consider when developing company strategies. The results prove that there is a positive relationship between environmental analysis and profit (Wheelen et al., 2018). Golovko & Valentini (2011) found out that internal environmental factors, in this case innovation has a positive effect on company growth. Other research by Pang et al. (2013) shows evidence that environmental factors such as market share, rural populations and politically divided governments have a moderate effect on the relationship between information technology and administrative efficiency, and several environmental factors that are treated as control variables in their research, such as population, household income and gross domestic product (GDP), also have an influence on operational efficiency. 2.2. Data Envelopment Analysis (DEA) DEA has been widely studied, used and analyzed by academics who understand linear programming. DEA was developed to measure organizational performance. This technique has been used successfully to assess the performance of a number of organizations that use a variety of similar inputs and also produce the same variety of outputs. The efficiency measured in DEA is known as Decision-Making Unit (DMU). Jadi DEA measures how efficient a DMU is in using available resources to produce a set of outputs. Linear programming is the underlying methodology that makes DEA very powerful when compared to other alternative productivity management tools. DMU can include manufacturing units, large organizations such as universities, schools, bank branches, hospitals, police stations, tax offices, companies and so on. The DMU performance assessed in DEA uses the concept of efficiency or productivity, which is the ratio of total output to total input. The best performing DMUs are given an efficiency score of one or 100 percent (Ramanathan, 2003). Taylor III (2016) also states that DEA is a linear programming application that compares a number of service units of the same type, such as banks, hospitals, restaurants and schools based on their inputs and outputs. The results of the model solution indicate whether certain units are less productive, or inefficient, compared to other units. For example, DEA has compared hospitals where input includes hospital beds and staff size and output including patient days for different age groups. Liu et al. (2018) explain that DEA is an important non-parametric method in management of production and operations. As an optimization method, DEA enables multi-input, multi-output measurement of the relative performance of production units and has been widely used to assess operational performance in various industries. In the classical DEA model there is an implicit assumption that all inputs and outputs are 'discretionary variables', which can vary depending on the policy of the company manager. The non-discretionary DEA model seeks to overcome the problem of input from the external environment. Input 'non-discretionary variables' (Cooper, Seifod, & Tone, 2006), such as economic and environmental factors, are where companies do not have the ability to decide or adjust according to their own discretion or judgment. Such inputs are important to be taken into account from the industrial environment when we evaluate operational efficiency, especially in retail companies. Initially Charnes, Cooper & Rhodes (1978) developed DEA to evaluate nonprofit and public sector organizations (Prakash & Annapoorni, 2015). Afterwards, research using DEA continues to grow rapidly for various sectors, for example in the banking sector (Ohsato & Takahashi, 2015; Sufian, Kamarudin, & Nassir, 2016), the college sector (Goksen, Dogan, & Ozkarabacak, 2015; Ohsato & Takahashi, 2015), public/ government sector organizations (Zhonghua & Ye, 2012; Jorge, Carvalho, Jorge, Medeiros, & Ferreira, 2013; Pang et al., 2013; Buleca & Mura, 2014; Chitnis & Mishra, 2019), the hospitality sector (Barros, 2005; Sanjeev, 2007), the aviation sector (Singh, AFEBI Management and Business Review (AMBR) Vol.05 No.01 June 2020 21 2011; Zhu, 2011), the retail sector (Barros, 2005; Yu & Ramanathan, 2008; Lau, 2013; Ahmad et al., 2014; Ko et al., 2017; Liu et al., 2018; Gong et al., 2019). 2.3. Operational Performance Many researchers in the operations management community are interested in evaluating the performance of retail companies and assessing the impact of improved operations, both on operational performance and on financial performance. Most retail company performance measurements use a number of financial measures, such as return on investment (ROI), return on asset ROA), or return on capital employed (ROCE) (Panigyrakis & Theodoridis, 2007). Meanwhile according to Liu et al. (2018), retail company performance can be seen through its level of productivity, such as: inventory turnover ratio, accounts receivable turnover ratio, total assets or current assets turnover ratio, gross margin, and long-term stock returns. Many researchers criticize such performance measurement models, because they fail to measure various dimensions of performance. Explicitly, there is still limited research that considers aspects of the competitive environment and operating strategies in global markets relating to the operating efficiency of retail companies. In fact, for it is very important for retail company to do environmental analysis both internal and external. It is important to note that retail companies in their operations rely on their supply chains, where they will face strong pressure from various stakeholder groups, such as end customers, industrial customers, suppliers and financial institutions (Hendricks & Singhal, 2005). Retail companies operating in various regions will certainly develop diverse business models that are in accordance with the characteristics of consumers in the region. They also must adjust to the diversity of policies, culture, legislation and politics in the area. As a result of the diversity of such treatments, it may produce varying levels of efficiency from each retail company. This is consistent with the statement of Slater & Narver (1994), that the competitive environment in various regions affects the operational efficiency of retail companies. Nowadays, globally integrated retail companies operate in a highly competitive market environment. The operations strategy can be carried out by importing governance management from home countries or mimicking the governance management practices of successful local companies. It can help them carry out their responsibilities, make it easier to make relevant decisions so they can be efficient in operations. Measuring the performance of retail companies is a complex phenomenon (Liu et al., 2018). Researchers often face difficulties in trying to get accurate financial performance measures. The most common problems are information sensitivity and the unavailability of information for the public. However Panigyrakis & Theodoridis (2007) has measured the business performance of retail companies (supermarkets) in Greece, using two dimensions, financial performance and non-financial performance. Financial performance is measured by four indicator; total sales, growth rate of sales and gross margin and Non-financial performance is measured by three indicators; market share, space productivity and stock age. The findings indicate a positive relationship between market orientation and the performance of retail companies. In other words market orientation is an important determinant of company performance. Market orientation is defined as the company's ability to understand customers, competitors and environmental factors in a sustainable manner that enables them to act according to current market trends. Meanwhile Johlke & Iyer's research (2013), regarding the performance of retail companies in the United States proves that employee ambiguity towards customers and external customer mindset sets are related to sales performance. Mertens, Recker, Kummer, Kohlborn, & Viaene (2016) conducted a research on constructive deviance as a driver of performance in retail companies. The study was conducted on food retail stores operating in Australia. The results prove that constructive deviance that occurs in retail companies can improve organizational performance. Therefore the application Competitive Environment Analysis in Global Retail Companies Operation Strategy: A Data Envelopment Analysis (DEA) Based Approach 22 of constructive deviance as a strategic tool in retail companies can be considered. Constructive deviance is deviant behavior carried out with positive intentions that aim to provide positive or beneficial results for the organization without harming other parties or violating norms at a higher level. 3. Research Method 3.1. Research Object This research focuses on the top ten retail companies operating in the global market, as presented in the table below. Table 3 Top Ten Global Retail Companies in 2019 DMU Company Code Country Industry 1. Walmart WMT United States Retail (Groceries) 2. Amazon.com Inc AMZN United States Retail (via Catalogue & Courier) 3. Carrefour SA CARR France Retail (Groceries) 4. Tesco PLC TSCO Great Britain Retail (Groceries) 5. Costco COST United States Retail (Spesific) 6. Walgreens Boots Alliance Inc WBA United States Retail (Drugs) 7. Kroger Company KR United States Retail (Groceries) 8. Home Depot Inc HD United States Retail (Households) 9. JD.com Inc Adr JD China Retail (via Catalogue & Courier) 10. Alibaba Group Holdings Ltd ADR BABA China Retail (via Catalogue & Courier) Sources: summarized from https://id.investing.com and other various sources 3.2. Research Variables and Data Collection There are three input variables used in this study, namely: assets, operating costs and number of employees. In addition there are seven output variables used, namely: total revenue, net profit, return on equity (ROE), return on assets (ROA), return on investment (ROI), dividend payout ratio and asset turnover ratio. Data was obtained from financial reports published on the investing.com website. We focus on the financial statements of the top ten retail companies operating in the global market. The operational variables in this research are presented in the table below. Table 4 Operational Variables A. Input Definition Ref. Scale Asset An asset definition according to IFRS is a source that is controlled by an entity as a result of a past event (for example creating itself or buying) and from future economic benefits (cash inflows and other assets) expected to flow to the entity. Assets are measured in United States dollars (USD). [1] Ratio Operating cost operating costs or operating expenses are all expenses directly used to produce goods, including general costs, sales costs, administrative costs, and loan interest; costs directly related to the production and distribution of goods. Operating costs are measured in United States dollars (USD). [2a, 2b] Ratio Employee Employees are the people working in an institution (office, company, etc.) with a salary (wages). Employees are measured in units of people. [3] Ratio B. Output Revenue According to IFRS revenue is defined as income arising from the normal activities of an entity and is often known by different names, such as: sales, [4] Ratio https://id.investing.com/ AFEBI Management and Business Review (AMBR) Vol.05 No.01 June 2020 23 turnover, costs, interest, dividends and royalties. Revenue is measured in United States dollars (USD). Net profit Net Profit is the amount of profit obtained after tax deduction. Profit is often used as a financial measure. Net income is measured in United States dollars (USD). [5a & 5b] Ratio ROE Return on Equity (ROE) is defined as net income divided by shareholder equity. ROE shows the ability to generate a return on investment based on the book value of the shareholders. ROE is measured in percentage (%). [6a, 6b, 6c, 6d, 6e] Ratio ROA Return on Assets is defined as net income divided by total assets. ROA is measured in percent (%). [7a, 7b, 7c, 7d] Ratio ROI Return on Investment (ROI) is defined as net operating profit divided by average operating assets. The higher the ROI of a business segment, the greater the profit generated from each dollar invested in its assets. ROI is measured in percentage (%). [8] Ratio Dividend yield ratio Dividend yield ratio is used to measured rate of return (only in the form of cash dividends) received by the investor who purchase ordinary stock at the current market price. Dividend yield ratio is calculated by dividing the dividend per share with the market price per share. This ratio is measured in percent (%). [9] Ratio Asset turnover ratio Asset turnover ratio is defined as sales divided by total assets. This ratio shows how many sales are generated from each dollar of company assets. This ratio is measured in percent (%). [10a, 10b, 10c] Ratio Notes: [1] ICAEW, 2008, page 25; [2a & 2b] KB, 2020; Scarborough & Cornwall, 2016, page 425; [3] KBBI, 2016; [4] ICAEW, 2008, page 62; [5a & 5b] IAI, 2013; ICAEW, 2008 page 24; [6a, 6b, 6c, 6d, 6e] Horne & Wachowicz, 2009, page 150; Ross, Westerfield, Jaffe, & Jordan, 2018, page 53; Gorrison et al., 2018, page 739; Brealey, Myers, & Marcus, 2018, page 95; Brigham & Houston, 2019, page 119; [7a, 7b, 7c & 7d] Horne & Wachowicz, 2009, page 157; Brealey, Myers, & Marcus, 2018, page 94; Ross, Westerfield, Jaffe, & Jordan, 2018, page 53; Brigham & Houston, 2019, page 119; [8] Gorrison et al., 2018, page 509; Horne & Wachowicz, 2009, page 150; [9] Gorrison, Noreen, & Brewer, 2018, page 742; [10a, 10b & 10c] Brealey et al., 2018, page 96; Gorrison et al., 2018, page 735; Brigham & Houston, 2019, page 113. 3.3. Data Analysis Data Envelopment Analysis (DEA) approach is used for data analysis in this reserach. DEA is a linear programming application that compares a number of company units in the same industry. The results of the model solution provide an indication whether a particular company is less productive or inefficient, compared to other units (Taylor III, 2016, p.164). According to Storto (2013), DEA has the advantage as it is able measure multi-variable input and multi-variable output. In this research, Ten retail companies operating in the global market are treated as a decision making unit (DMU). The formula used to calculate unit efficiency (Ragsdale, 2008, p.103-104; Ragsdale, 2018, p. 107-108) is as follows: 𝑢𝑛𝑖𝑡 𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 𝑖 = 𝑊𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝑜𝑢𝑡𝑝𝑢𝑡 𝑢𝑛𝑖𝑡 𝑖 𝑊𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝑖𝑛𝑝𝑢𝑡 𝑢𝑛𝑖𝑡 𝑖 = ∑ 𝑂𝑖𝑗 𝑋𝑗 𝑛0 𝑗=1 ∑ 𝐼𝑖𝑗 𝑌𝑗 𝑛𝐼 𝑗=1 (1) Where: Oij = the value of unit i on output j Iij = the value of unit i on input j Xj = non-negatif weight assigned to output j Yj = non-negatif weight assigned to input j nO = the number of output variables Competitive Environment Analysis in Global Retail Companies Operation Strategy: A Data Envelopment Analysis (DEA) Based Approach 24 nI = the number of input variables Objective function: 𝑀𝐴𝑋: ∑ 𝑂𝑖𝑗 𝑋𝑗 𝑛𝑂 𝑗=1 (2) Constraint function: ∑ 𝑂𝑘𝑗 𝑋𝑗 ≤ 𝑛0 𝑗=1 ∑ 𝐼𝑘𝑗 𝑌𝑗 𝑛𝐼 𝑗=1 Where k = 1 (3) or in other words: ∑ 𝑂𝑘𝑗 𝑋𝑗 − 𝑛0 𝑗=1 ∑ 𝐼𝑘𝑗 𝑌𝑗 𝑛𝐼 𝑗=1 ≤ 0 Where k = 1 (4) The weighted input weight for the unit under investigation (unit i) must be equal to 1 (one), then: ∑ 𝐼𝑖𝑗 𝑌𝑗 𝑛𝐼 𝑗=1 = 1 (5) To provide accurate results, in solving this problem we use the parameter solver tool available in Microsoft Excel. Macro facilities found in Excel were also used so that the calculation can be conducted simultaneously. The efficiency score of a DMU must be less than or equal to one (≤ 1.00). If the score isequal to one (= 1.00), then the DMU is said to be 'efficient', or has 'good performance'. Whereas if the score of DMU is less than one (<1.00), then the DMU is said to be 'inefficient', or has 'poor performance'. 4. Results This research uses three input variables, namely: total assets, total operating costs and number of employees. In addition, there are seven output variables used, namely: total revenue, net profit, ROE, ROA, ROI, dividend payout ratio and asset turnover ratio. Data of input variables and output variables used in the analysis are presented in the table below. Table 5 Input Variable Data Used in the Analysis, 2019 DMU Company Input Ref. Asset (Million USD) Operational Cost (Million USD) Employees (Person) 1. Walmart 239,830 136,349 2,200,000 [1] 2. Amazon.com Inc 225,248 83,557 798,000 [2] 3. Carrefour SA 55,064 40,475 321,383 [3] 4. Tesco PLC 70,758 38,447 464,505 [4] 5. Costco 51,431 35,979 149,000 [5] 6. Walgreens Boots Alliance Inc 90,807 34,339 232,000 [6] 7. Kroger Company 45,393 27,720 435,000 [7] 8. Home Depot Inc 52,309 23,276 415,700 [8] 9. JD.com Inc Adr 36,725 24,060 178,927 [9] 10. Alibaba Group Holdings Ltd ADR 186,577 17,236 116,519 [10] Table 6 Output Variable Data used in the Analysis, 2019 DMU Company Output Ref. Revenue (Million USD) Net Profit (Million USD) ROE (%) ROA (%) ROI (%) Dividend yield (%) Asset Turnover (%) 1. Walmart 127,991 3,288 20.22 6.67 10.61 1.68 2.30 [1] 2. Amazon.com Inc 87,436 3,268 21.95 5.98 10.01 17.20 1.45 [2] 3. Carrefour SA 41,611 1,719 0.33 0.44 0.91 3.26 1.51 [3] 4. Tesco PLC 39,864 405 9.71 2.28 3.65 3.93 1.12 [4] AFEBI Management and Business Review (AMBR) Vol.05 No.01 June 2020 25 5. Costco 37,040 844 24.59 8.29 16.61 0.84 3.46 [5] 6. Walgreens Boots Alliance Inc 34,339 844 14.41 4.38 6.67 4.21 1.73 [6] 7. Kroger Company 27,974 263 19.91 3.63 5.50 2.00 2.93 [7] 8. Home Depot Inc 27,223 2,769 79.26 23.61 37.38 3.02 2.31 [8] 9. JD.com Inc Adr 24,135 514 17.21 5.07 13.81 17.20 2.46 [9] 10. Alibaba Group Holdings Ltd ADR 22,830 7,397 28.54 15.36 22.16 17.20 0.44 [10] Notes: the data sources listed in table 5 and table 6 above are [1] Investing.com, 2020j; [2] Investing.com, 2020b; [3] Investing.com, 2020c; [4] Investing.com, 2020h; [5] Investing.com, 2020d; [6] Investing.com, 2020g; [7] Investing.com, 2020g; [8] Investing.com, 2020e; [9] Investing.com, 2020f; [10] Investing.com, 2020a. For information, some of the companies in this research presented their financial report in different currencies other than United States Dollar (USD) . Tesco PLC presented their financial report in Great Britain Pound (GBP), Carrefour SA uses Euro (EUR), JD.com Inc. Adr and Alibaba Group Holdings Ltd. ADR uses Chinese Yuan (CNY). Therefore, for uniformity we converted the currencies into United States dollars (USD) using a currency converter (investing.com, 2020e). By using this tool, currency conversion can be done retroactively in accordance with the date of the company's financial statements, which are at the end of 2019. To evaluate the efficiency of retail companies from unit 1 to unit 10, we formulated a linear DEA program with the implementation of the model as follows: DMU 1: Walmart Max: 127,991x1 + 3,288x2 + 20.22x3 + 6.67x4 + 10.61x5 + 1.68x6 + 2.3x7 Subject to: 127,991x1 + 3,288x2 + 20.22x3 + 6.67x4 + 10.61x5 + 1.68x6 + 2.30x7 – 239,830y1 – 136,349y2 – 2,200,000y3 ≤ 0 constrain for unit 1 87,436x1 + 3,268x2 + 21.95x3 + 5.98x4 + 10.01x5 + 17.20x6 + 1.45x7 – 225,248y1 – 83,557y2 – 798,000y3 ≤ 0 constrain for unit 2 and so on to ... 22,830x1 + 7,397x2 + 28.54x3 + 15.36x4 + 22.16x5 + 17.20x6 + 0.44x7 – 186,577y1 – 17,236y2 – 116,519y3 ≤ 0 constrain for unit 10 239,830y1 + 136,349y2 + 2,200,000y3 = 1 input constrain for unit 1 x1, x2, x3, x4, x5, x6, x7, y1, y2, y3 ≥ 0 nonnegativity conditions DMU 2: Amazon.com Max: 87,436x1 + 3,268x2 + 21.95x3 + 5.98x4 + 10.01x5 + 17.20x6 + 1.45x7 Subject to: 127,991x1 + 3,288x2 + 20.22x3 + 6.67x4 + 10.61x5 + 1.68x6 + 2.30x7 – 239,830y1 – 136,349y2 – 2,200,000y3 ≤ 0 constrain for unit 1 87,436x1 + 3,268x2 + 21.95x3 + 5.98x4 + 10.01x5 + 17.20x6 + 1.45x7 – 225,248y1 – 83,557y2 – 798,000y3 ≤ 0 constrain for unit 2 and so on to ... 22,830x1 + 7,397x2 + 28.54x3 + 15.36x4 + 22.16x5 + 17.20x6 + 0.44x7 – 186,577y1 – 17,236y2 – 116,519y3 ≤ 0 constrain for unit 10 225,248y1 + 83,557y2 + 798,000y3 = 1 input constrain Competitive Environment Analysis in Global Retail Companies Operation Strategy: A Data Envelopment Analysis (DEA) Based Approach 26 for unit 2 x1, x2, x3, x4, x5, x6, x7, y1, y2, y3 ≥ 0 nonnegativity conditions DMU 3: Carrefour SA Max: 41,611x1 + 1,719x2 + 0.33x3 + 0.44x4 + 0.91x5 + 3.26x6 + 1.51x7 Subject to: 127,991x1 + 3,288x2 + 20.22x3 + 6.67x4 + 10.61x5 + 1.68x6 + 2.30x7 – 239,830y1 – 136,349y2 – 2,200,000y3 ≤ 0 constrain for unit 1 87,436x1 + 3,268x2 + 21.95x3 + 5.98x4 + 10.01x5 + 17.20x6 + 1.45x7 – 225,248y1 – 83,557y2 – 798,000y3 ≤ 0 constrain for unit 2 and so on to ... 22,830x1 + 7,397x2 + 28.54x3 + 15.36x4 + 22.16x5 + 17.20x6 + 0.44x7 – 186,577y1 – 17,236y2 – 116,519y3 ≤ 0 constrain for unit 10 55,064y1 + 40,475y2 + 321,383y3 = 1 input constrain for unit 3 x1, x2, x3, x4, x5, x6, x7, y1, y2, y3 ≥ 0 nonnegativity conditions DMU 4: Tesco PLC Max: 39,864x1 + 405x2 + 9.71x3 + 2.28x4 + 3.65x5 + 3.93x6 + 1.12x6 Subject to: 127,991x1 + 3,288x2 + 20.22x3 + 6.67x4 + 10.61x5 + 1.68x6 + 2.30x7 – 239,830y1 – 136,349y2 – 2,200,000y3 ≤ 0 constrain for unit 1 87,436 x1 + 3,268x2 + 21.95x3 + 5.98x4 + 10.01x5 + 17.20x6 + 1.45x7 – 225,248y1 – 83,557y2 – 798,000y3 ≤ 0 constrain for unit 2 and so on to ... 22,830x1 + 7,397x2 + 28.54x3 + 15.36x4 + 22.16x5 + 17.20x6 + 0.44x7 – 186,577y1 – 17,236y2 – 116,519y3 ≤ 0 constrain for unit 10 70,758y1 + 38,447y2 + 464,505y3 = 1 input constrain for unit 4 x1, x2, x3, x4, x5, x6, x7, y1, y2, y3 ≥ 0 nonnegativity conditions DMU 5: Costco Max: 37,040x1 + 844x2 + 24.59x3 + 8.29x4 + 16.61x5 + 0.84x6 + 3.46x7 Subject to: 127,991x1 + 3,288x2 + 20.22x3 + 6.67x4 + 10.61x5 + 1.68x6 + 2.30x7 – 239,830y1 – 136,349y2 – 2,200,000y3 ≤ 0 constrain for unit 1 87,436 x1 + 3,268x2 + 21.95x3 + 5.98x4 + 10.01x5 + 17.20x6 + 1.45x7 – 225,248y1 – 83,557y2 – 798,000y3 ≤ 0 constrain for unit 2 and so on to ... 22,830x1 + 7,397x2 + 28.54x3 + 15.36x4 + 22.16x5 + 17.20x6 + 0.44x7 – 186,577y1 – 17,236y2 – 116,519y3 ≤ 0 constrain for unit 10 AFEBI Management and Business Review (AMBR) Vol.05 No.01 June 2020 27 51,431y1 + 35,979y2 + 149,000y3 = 1 input constrain for unit 5 x1, x2, x3, x4, x5, x6, x7, y1, y2, y3 ≥ 0 nonnegativity conditions DMU 6: Walgreens Boots Alliance Inc Max: 34,339x1 + 844x2 + 14.41x3 + 4.38x4 + 6.67x5 + 4.21x6 + 1.73x7 Subject to: 127,991x1 + 3,288x2 + 20.22x3 + 6.67x4 + 10.61x5 + 1.68x6 + 2.30x7 – 239,830y1 – 136,349y2 – 2,200,000y3 ≤ 0 constrain for unit 1 87,436x1 + 3,268x2 + 21.95x3 + 5.98x4 + 10.01x5 + 17.20x6 + 1.45x7 – 225,248y1 – 83,557y2 – 798,000y3 ≤ 0 constrain for unit 2 and so on to ... 22,830x1 + 7,397x2 + 28.54x3 + 15.36x4 + 22.16x5 + 17.20x6 + 0.44x7 – 186,577y1 – 17,236y2 – 116,519y3 ≤ 0 constrain for unit 10 90,807y1 + 34,339y2 + 232,000y3 = 1 input constrain for unit 6 x1, x2, x3, x4, x5, x6, x7, y1, y2, y3 ≥ 0 nonnegativity conditions DMU 7: Kroger Company Max: 27,974x1 + 263x2 + 19.91x3 + 3.63x4 + 5.50x5 + 2.00x6 + 2.93x7 Subject to: 127,991x1 + 3,288x2 + 20.22x3 + 6.67x4 + 10.61x5 + 1.68x6 + 2.30x7 – 239,830y1 – 136,349y2 – 2,200,000y3 ≤ 0 constrain for unit 1 87,436x1 + 3,268x2 + 21.95x3 + 5.98x4 + 10.01x5 + 17.20x6 + 1.45x7 – 225,248y1 – 83,557y2 – 798,000y3 ≤ 0 constrain for unit 2 and so on to ... 22,830x1 + 7,397x2 + 28.54x3 + 15.36x4 + 22.16x5 + 17.20x6 + 0.44x7 – 186,577y1 – 17,236y2 – 116,519y3 ≤ 0 constrain for unit 10 45,393y1 + 27,720y2 + 435,000y3 = 1 input constrain for unit 7 x1, x2, x3, x4, x5, x6, x7, y1, y2, y3 ≥ 0 nonnegativity conditions DMU 8: Home Depot Inc Max: 27,223x1 + 2,769x2 + 79.26x3 + 23.61x4 + 37.38x5 + 3.02x6 + 2.31x7 Subject to: 127,991x1 + 3,288x2 + 20.22x3 + 6.67x4 + 10.61x5 + 1.68x6 + 2.30x7 – 239,830y1 – 136,349y2 – 2,200,000y3 ≤ 0 constrain for unit 1 87,436x1 + 3,268x2 + 21.95x3 + 5.98x4 + 10.01x5 + 17.20x6 + 1.45x7 – 225,248y1 – 83,557y2 – 798,000y3 ≤ 0 constrain for unit 2 and so on to ... Competitive Environment Analysis in Global Retail Companies Operation Strategy: A Data Envelopment Analysis (DEA) Based Approach 28 22,830x1 + 7,397x2 + 28.54x3 + 15.36x4 + 22.16x5 + 17.20x6 + 0.44x7 – 186,577y1 – 17,236y2 – 116,519y3 ≤ 0 constrain for unit 10 52,309y1 + 23,276y2 + 415,700y3 = 1 input constrain for unit 8 x1, x2, x3, x4, x5, x6, x7, y1, y2, y3 ≥ 0 nonnegativity conditions DMU 9: JD.com Inc Adr Max: 24.135x1 + 514x2 + 17,21x3 + 5,07x4 + 13,81x5 + 17,20x6 + 2,46x7 Subject to: 127.991x1 + 3.288x2 + 20,22x3 + 6,67x4 + 10,61x5 + 1,68x6 + 2,30x7 - 239.830y1 - 136.349y2 - 2.200.000y3 ≤ 0 constrain for unit 1 87.436x1 + 3.268x2 + 21,95x3 + 5,98x4 + 10,01x5 + 17,20x6 + 1,45x7 – 225.248y1 - 83.557y2 - 798.000y3 ≤ 0 constrain for unit 2 and so on to ... 22.830x1 + 7.397x2 + 28,54x3 + 15,36x4 + 22,16x5 + 17,20x6 + 0,44x7 – 186.577y1 - 17.236y2 - 116.519y3 ≤ 0 constrain for unit 10 36.725y1 + 24.060 y2 + 178.927y3 = 1 input constrain for unit 9 x1, x2, x3, x4, x5, x6, x7, y1, y2, y3 ≥ 0 nonnegativity conditions DMU 10: Alibaba Group Holdings Ltd ADR Max: 22,830x1 + 7,397x2 + 28.54x3 + 15.36x4 + 22.16x5 + 17.20x6 + 0.44x7 Subject to: 127,991x1 + 3,288x2 + 20.22x3 + 6.67x4 + 10.61x5 + 1.68x6 + 2.30x7 – 239,830y1 – 136,349y2 – 2,200,000y3 ≤ 0 constrain for unit 1 87,436x1 + 3,268x2 + 21.95x3 + 5.98x4 + 10.01x5 + 17.20x6 + 1.45x7 – 225,248y1 – 83,557y2 – 798,000y3 ≤ 0 constrain for unit 2 and so on to ... 22,830x1 + 7,397x2 + 28.54x3 + 15.36x4 + 22.16x5 + 17.20x6 + 0.44x7 – 186,577y1 – 17,236y2 – 116,519y3 ≤ 0 constrain for unit 10 186,577y1 + 17,236y2 + 116,519y3 = 1 input constrain for unit 10 x1, x2, x3, x4, x5, x6, x7, y1, y2, y3 ≥ 0 nonnegativity conditions To solve this complex problem, we used parameter solver tool and macro facilities found in Microsoft Excel for data processing. The results of data processing as shown in the table below. AFEBI Management and Business Review (AMBR) Vol.05 No.01 June 2020 29 Table 7 Global Retail Company Efficiency Score data, 2019 DMU Company Efficiency Score Conclusion 1. Walmart 0.86 Inefficient 2. Amazon.com Inc 0.94 Inefficient 3. Carrefour SA 1.00 Efficient 4. Tesco PLC 0.94 Inefficient 5. Costco 1.00 Efficient 6. Walgreens Boots Alliance Inc 0.92 Inefficient 7. Kroger Company 1.00 Efficient 8. Home Depot Inc 1.00 Efficient 9. JD.com Inc Adr 1.00 Efficient 10. Alibaba Group Holdings Ltd ADR 1.00 Efficient Source: DEA result 5. Discussion Based on table 7 above, we obtained the information that from the top ten (top ten) retail companies operating in the global market, there are six companies that are efficient in carrying out their operations, thus the companies are considered to be the most competitive company in the global market, They are: Carrefour, Costco, Kroger Company, Home Depot Inc., JD.com and Alibaba Group Holdings Ltd. While the remaining four companies, namely: Walmart, Amazon.com, Tesco and Walgreens Boots Alliance are considered to be inefficient in conducting operations in the global market. Thus it can be concluded that the companies are less competitive. Although retail companies such as Walmart has the largest total revenue and Amazon.com has the second largest total revenue, the results of the model solutions show that they are inefficient. That is because they are unable to utilize available abundant input resources to produce maximum output. Below we outline the DEA's suggestions for these inefficient retail companies to be more competitive in the global market environment, which can be seen from the composite value and sensitivity report. 0.75 0.80 0.85 0.90 0.95 1.00 Walmart Amazon.com Carrefour Tesco Costco Walgreens Kroger Company Home Depot JD.com Alibaba 0.86 0.94 1.00 0.94 1.00 0.92 1.00 1.00 1.00 1.00 Figure 1 Radar Chart of Global Retail Companies efficiency Figure 2 Bar Chart of Global Retail Companies efficiency Competitive Environment Analysis in Global Retail Companies Operation Strategy: A Data Envelopment Analysis (DEA) Based Approach 30 Table 8 Inefficient Global Retail Company, 2019 DMU Company Efficiency Score Conclusion 1. Walmart 0.86 Inefficient 2. Amazon.com Inc 0.94 Inefficient 4. Tesco PLC 0.94 Inefficient 6. Walgreens Boots Alliance Inc 0.92 Inefficient Based on the composite value, to achieve high level of performance (efficiency score 1.00), for Walmart, Amazon.com Inc., Tesco PLC and Walgreens Boots Alliance Inc., it is recommended to decrease the number of inputs and increase a certain number of outputs, sequentially shown below this. DMU 1: Walmart Input Initial Final Increase (Decrease) Asset (Million USD) 239,830 206, 28 (33,602) Operational Cost (Million USD) 136,349 117,246 (19,103) Employees 2,200,000 1,453,520 (746,480) Output Initial Final Increase (Decrease) Revenue (Million USD) 127,991 127,991 0 Net Profit (Milliom USD) 3,288 9,010 5,722 ROE (%) 20.22 179.92 159.70 ROA (%) 6.67 54.14 47.47 ROI (%) 10.61 86.05 75.44 Dividend Yield (%) 1.68 12.04 10.36 Asset Turnover (%) 2.30 7.64 5.34 DMU 2: Amazon.com Inc Input Initial Final Increase (Decrease) Asset (Million USD) 225,248 212,830 (12,418) Operational cost (Million USD) 83,557 78,951 (4,606) Employees 798,000 754,007 (43,993) Output Initial Final Increase (Decrease) Revenue (Million USD) 87,436 87,436 0 Net Profit (Million USD) 3,268 7,816 4,548 ROE (%) 21.95 138.37 116.42 ROA (%) 5.98 45.41 39.43 ROI (%) 10.01 75.95 65.94 Dividend Yield (%) 17.20 17.20 0.00 Asset Turnover (%) 1.45 7.14 5.69 AFEBI Management and Business Review (AMBR) Vol.05 No.01 June 2020 31 DMU 4: Tesco PLC Input Initial Final Increase (Decrease) Asset (Million USD) 70,758 66,647 (4,111) Operational cost (Million USD) 38,447 36,213 (2,234) Employees 464,505 437,516 (26,989) Output Initial Final Increase (Decrease) Revenue (Million USD) 39,864 39,864 0 Net Profit (Million USD) 405 2,772 2,368 ROE (%) 9.71 70.72 61.01 ROA (%) 2.28 21.37 19.09 ROI (%) 3.65 35.01 31.36 Dividend Yield (%) 3.93 3.93 0.00 Asset Turnover (%) 1.12 3.11 1.99 DMU 6: Walgreens Boots Alliance Inc Input Initial Final Increase (Decrease) Asset (Million USD) 90,807 83,543 (7,264) Operational cost (Million USD) 34,339 31,592 (2,747) Employees 232,000 213,442 (18,558) Output Initial Final Increase (Decrease) Revenue (Million USD) 34,339 34,339 0 Net profit (Million USD) 844 2,719 1,875 ROE (%) 14.41 39.62 25.21 ROA (%) 4.38 13.85 9.47 ROI (%) 6.67 23.67 17.00 Dividend yield (%) 4.21 4.83 0.62 Asset turnover (%) 1.73 2.80 1.07 Based on the sensitivity report, for Walmart, Amazon.com, Tesco and Walgreens Boots Alliance to be "efficient", It is suggested for these companies to increase their output. There are two options available for Walmart. They are either increasing output by 159.52% when referring to DMU 3 (Carrefour), or by 226.33% when referring to DMU 8 (Home Depot). For Amazon.com there are four options available. They are either to increase output by 101.04% when referring to DMU 5 (Costco), or by 120.43% when referring to DMU 8 (Home Depot), or by 26.73% when referring to DMU 9 (JD.com), or by 47.19% when referring to DMU 10 (Alibaba.com). In addition, Tesco has four options available. They are either increasing output by 18.10% when referring to DMU 3 (Carrefour), or by 25.61% when referring to DMU 5 (Costco), or by 80.33% when referring to DMU 8 (Home Depot), or by 4.06% when referring to DMU 9 (JD.com). For Walgreens Boots Alliance there are three options available. They are either to increase output by 63.93% when referring to DMU 5 Competitive Environment Analysis in Global Retail Companies Operation Strategy: A Data Envelopment Analysis (DEA) Based Approach 32 (Costco), or by 22.90% when referring to DMU 8 (Home Depot), or by 21.01% when referring to on DMU 10 (Alibaba.com). 6. Conclusion The analysis results shows that from top ten global retail companies operating in current global market, six retail companies are “efficient” in its operation (efficiency score of 1.00), Namely: Carrefour, Costco, Kroger Company, Home Depot Inc, JD.com Inc Adr and Alibaba Group Holdings Ltd ADR. Therefore, these companies are considered the most competitive in its operation strategy in the current global market, whereas there are four retail companies falls into category of “inefficient” (efficiency score < 1.00), namely: Walmart, Amazon.com Inc, Tesco PLC and Walgreens Boots Alliance Inc. It is implied that these companies were unable to utilize their resources (input) to produce maximum output. 7. Research Limitation This research has limitations since a number of inputs such as: number of outlets, number of product items sold and number of suppliers have not been included in the analysis. In addition, a number of non-financial performance outputs such as: market share, customer satisfaction and number of customers have also not been included in the analysis. That is because the authors are having difficulty to obtain these data. It is expected that the limitation of this research can be refined further by future researchers, and it is possible for the next researcher to increase the number of years of observation to a minimum of five years. References Ahmad, F. S., Ihtiyar, A., & Omar, R. (2014). A Comparative Study on Service Quality in the Grocery Retailing: Evidence from Malaysia and Turkey. Procedia-Social and Behavioral Sciences, (109), 763–767. https://doi.org/10.1016/j.sbspro.2013.12.541 Barros, C. P. (2005). Measuring Efficiency in the Hotel Sector. Annals OfTourism Research, 32(2), 456–477. https://doi.org/10.1016/j.annals.2004.07.011 Birkinshaw, J., Morrison, A., & Hulland, J. (1995). Structural and Competitive Determinants of a Global Integration Strategy. Strategic Management Journal, 16, 637–655. Brealey, R. A., Myers, S. C., & Marcus, A. J. (2018). Fundamentals of Corporate Finance (9th ed.). United States of America: McGraw-Hill Education. Brigham, E. F., & Houston, J. F. (2019). Fundamentals of Financial Management (15th ed.). Boston, USA: Cengage Learning. Buleca, J., & Mura, L. (2014). Quantification of the Efficiency of Public Administration by Data Envelopment Analysis. Procedia Economics and Finance, (15), 162–168. https://doi.org/10.1016/S2212-5671(14)00469-9 Burt, S., & Johansson, U. (2011). Standardized Marketing Strategies in Retailing? IKEA’s Marketing Strategies in Sweden, the UK and China. Journal of Retailing and Consumer Services, (18), 183–193. https://doi.org/10.1016/j.jretconser.2010.09.007 Chitnis, A., & Mishra, D. K. (2019). Performance Efficiency of Indian Private AFEBI Management and Business Review (AMBR) Vol.05 No.01 June 2020 33 Hospitals Using Data Envelopment Analysis and Super-Efficiency DEA. Journal of Health Management, 1–15. https://doi.org/10.1177/0972063419835120 Cooper, W. W., Seifod, L. M., & Tone, K. (2006). Introduction to Data Envelopment Analysis and Its Uses With DEA-Solver Software and References. United States of America: Springer. David, F. R., & David, F. R. (2017). Strategic Management: A Competitive Adventage Approach (16th ed.). United States of America: Pearson Education, Inc. Geys, B. (2006). Looking Across Borders: A Test of Spatial Policy Interdependence Using Local Government Efficiency Ratings. Journal of Urban Economics, (60), 443–462. https://doi.org/10.1016/j.jue.2006.04.002 Ginter, P. M., & Duncan, W. J. (1990). Macroenvironmental Analysis for Strategic Management. Long Range Planning, 23(6), 91–100. Goksen, Y., Dogan, O., & Ozkarabacak, B. (2015). A Data Envelopment Analysis Application for Measuring Efficiency of University Departements. Procedia Economics and Finance, 19(15), 226–237. https://doi.org/10.1016/S2212- 5671(15)00024-6 Golovko, E., & Valentini, G. (2011). Exploring the Complementarity Between Innovation and Export for SMEs’ Growth. Journal of International Business Studies, 42, 362–380. https://doi.org/10.1057/jibs.2011.2 Gong, Y. (2013). Global Operations Strategy: Fundamental and Practice. New York: Springer. Gong, Y., Liu, J., & Zhu, J. (2019). When to Increase Firms’ Sustainable Operations for Efficiency? A Data Envelopment Analysis in the Retailing Industry. European Journal of Operational Research, (277), 1010–1026. https://doi.org/10.1016/j.ejor.2019.03.019 Gorrison, R. H., Noreen, E. W., & Brewer, P. C. (2018). Managerial Accounting (16th ed.). New York: McGraw-Hill Education. Hendricks, K. B., & Singhal, V. R. (2005). An Empirical Analysis of the Effect of Supply Chain Disruptions on Long-Run Stock Price Performance and Equity Risk of the Firm. Production and Operations Management Society, 14(1), 35– 52. Hitt, M. A., Ireland, R. D., & Hoskisson, R. E. (2017). Strategic Management: Competitiveness & Globalization (12th ed.). Boston, USA: Cengage Learning. Horne, J. C. Van, & Wachowicz, J. M. (2009). Fundamentals of Financial Management (13th ed.). London: Pearson Education Limited. IAI. (2013). PSAK 46: Pajak Penghasilan. In Pernyataan Standar Akuntansi Keuangan (November). Jakarta: Ikatan Akuntan Indonesia. ICAEW. (2008). International Financial Reporting Standards. England: The Institute of Chartered Accountants in England and Wales (ICAEW). Investing.com. (2020a). Alibaba Group Holdings Ltd ADR (BABA). Retrieved April 16, 2020, from https://id.investing.com/equities/alibaba-financial-summary Investing.com. (2020b). Amazon.com Inc (AMZN). Retrieved April 16, 2020, from https://id.investing.com/equities/amazon-com-inc-financial-summary Competitive Environment Analysis in Global Retail Companies Operation Strategy: A Data Envelopment Analysis (DEA) Based Approach 34 Investing.com. (2020c). Carrefour SA (CARR). Retrieved April 16, 2020, from https://id.investing.com/equities/carrefour-financial-summary Investing.com. (2020d). Costco Wholesale Corp (COST). Retrieved April 16, 2020, from https://id.investing.com/equities/costco-whsl-corp-new-financial-summary Investing.com. (2020e). Currency Converter. Retrieved April 16, 2020, from https://www.investing.com/currency-converter/ Investing.com. (2020f). Home Depot Inc (HD). Retrieved April 16, 2020, from https://id.investing.com/equities/home-depot-financial-summary Investing.com. (2020g). JD.com Inc Adr (JD). Retrieved April 16, 2020, from https://id.investing.com/equities/jd.com-inc-adr-financial-summary Investing.com. (2020h). Kroger Company (KR). Retrieved April 16, 2020, from https://id.investing.com/equities/kroger-co-financial-summary Investing.com. (2020i). Tesco PLC (TSCO). Retrieved April 16, 2020, from https://id.investing.com/equities/tesco-financial-summary Investing.com. (2020j). Walgreens Boots Alliance Inc (WBA). Retrieved April 16, 2020, from https://id.investing.com/equities/walgreen-co-financial-summary Investing.com. (2020k). Walmart Inc (WMT). Retrieved April 16, 2020, from https://id.investing.com/equities/wal-mart-stores-financial-summary Johlke, M. C., & Iyer, R. (2013). A Model of Retail Job Characteristics, Employee Role Ambiguity, External Customer Mind-set , and Sales Performance. Journal of Retailing and Consumer Services, (20), 58–67. https://doi.org/10.1016/j.jretconser.2012.10.006 Jorge, M. J., Carvalho, F. A. de, Jorge, M. F., Medeiros, R. de O., & Ferreira, D. de S. (2013). Efficiency Analysis in Public Health Organizations in Brazil. Journal of Modelling in Management, 8(2), 241–254. https://doi.org/10.1108/JM2-03- 2010-0015 KB. (2020). Operating Cost. Retrieved April 20, 2020, from https://www.kamusbesar.com/operating-cost KBBI. (2016). Karyawan. Retrieved April 21, 2020, from https://kbbi.kemdikbud.go.id/entri/karyawan Ko, K., Chang, M., Bae, E., & Kim, D. (2017). Efficiency Analysis of Retail Chain Stores in Korea. Sustainability, (9), 1–14. https://doi.org/10.3390/su9091629 Lau, K. H. (2013). Measuring Distribution Efficiency of a Retail Network Through Data Envelopment Analysis. Int. J. of Production Economics, 2(146), 598–611. https://doi.org/10.1016/j.ijpe.2013.08.008 Liu, J., Gong, Y. (Yale), Zhu, J., & Zhang, J. (2018). A DEA-Based Approach for Competitive Environment Analysis in Global Operations Strategies. International Journal of Production Economics, (203), 110–123. https://doi.org/10.1016/j.ijpe.2018.05.029 Marketos, G., & Theodoridis, Y. (2006). Measuring Performance in the Retail Industry. Springer-Verlag Berlin Heidelberg, (September), 129–140. https://doi.org/10.1007/11837862 Mertens, W., Recker, J., Kummer, T., Kohlborn, T., & Viaene, S. (2016). Constructive Deviance as a Driver for Performance in Retail. Journal of Retailing and AFEBI Management and Business Review (AMBR) Vol.05 No.01 June 2020 35 Consumer Services, 30, 193–203. https://doi.org/10.1016/j.jretconser.2016.01.021 Ohsato, S., & Takahashi, M. (2015). Management Efficiency in Japanese Regional Banks : A Network DEA. Procedia - Social and Behavioral Sciences, (172), 511–518. https://doi.org/10.1016/j.sbspro.2015.01.394 Pang, M., Tafti, A., & Krishnan, M. (2013). Information Technology and Administrative Efficiency in U.S. State Governments: A Stochastic Frontier Approach. MIS Quarterly, 38(4), 1–59. https://doi.org/10.2139/ssrn.1612820 Panigyrakis, G. G., & Theodoridis, P. K. (2007). Market Orientation and Performance: An Empirical Investigation in the Retail Industry in Greece. Journal of Retailing and Consumer Services, (14), 137–149. https://doi.org/10.1016/j.jretconser.2006.05.003 Prakash, V., & Annapoorni, D. (2015). Performance Evaluation of Public Hospitals in Tamil Nadu. Journal of Health Management, 17(4), 417–424. Ragsdale, C. T. (2008). Spreadsheet Modeling and Decision Analysis (5th ed.). Mason, USA: Thomson Corporation. Ragsdale, C. T. (2018). Spreadsheet Modeling and Decision Analysis (8th ed.). Boston, USA: Cengage Learning. Ramanathan, R. (2003). An Introduction to Data Envelopment Analysis, A Tool for Performance Measurement. New Delhi: Sage Publication. Ross, S. A., Westerfield, R. W., Jaffe, J. F., & Jordan, B. D. (2018). Corporate Finance: Core Principles & Applications (5th ed.). New York: McGraw-Hill Education. Russo, M. V, & Fouts, P. A. (1997). A Resource-Based Perspective on Corporate Environmental Performance and Profitability. Academy of Management Jauma, 40(3), 534–559. https://doi.org/10.2307/257052 Sanjeev, G. M. (2007). Measuring Efficiency of the Hotel and Restaurant Sector: the Case of India. International Journal of Contemporary Hospitality Management, 19(5), 378–387. https://doi.org/10.1108/09596110710757543 Scarborough, N. M., & Cornwall, J. R. (2016). Essentials of Entrepreneurship and Small Business Management (8th ed.). Harlow, England: Pearson Education Limited. Singh, A. K. (2011). Performance Evaluation of Indian Airline Industry: An Application of DEA. Asia-Pacific Business Review, VII(2), 92–103. Slater, S. F., & Narver, J. C. (1994). Does Environment Competitive Moderate the Market Orientation-Performance Relationship? Journal of Marketing, 58(January), 46–55. Storto, C. I. (2013). Evaluating Technical Efficiency of Italian Major Municipalities: A Data Envelopment Analysis Model. Procedia and Behavioral Science (Elsevier-Science Direct), (81), 346–350. Sufian, F., Kamarudin, F., & Nassir, A. M. (2016). Determinants of Efficiency in the Malaysian Banking Sector : Does Bank Origins Matter? Intellectual Economics, (10), 38–54. https://doi.org/10.1016/j.intele.2016.04.002 Taylor III, B. W. (2016). Introduction to Management Science (12th ed.). Harlow, England: Pearson Education Limited. Competitive Environment Analysis in Global Retail Companies Operation Strategy: A Data Envelopment Analysis (DEA) Based Approach 36 Ward, P. T., & Duray, R. (2000). Manufacturing Strategy in Context : Environment, Competitive Strategy and Manufacturing Strategy. Journal of Operations Management, (18), 123–138. Wheelen, T. L., Hunger, J. D., Hoffman, A. N., & Bamford, C. E. (2018). Strategic Management and Business Policy: Globalization, Innovation and Sustainability (15th ed.). Harlow, England: Pearson Education Limited. Yu, W., & Ramanathan, R. (2008). An Assessment of Operational Efficiencies in the UK Retail Sector. International Journal of Retail & Distribution Management, 36(11), 861–882. https://doi.org/10.1108/09590550810911656 Zhonghua, C., & Ye, W. (2012). Research Frontiers in Public Sector Performance Measurement. Physics Procedia, (25), 793–799. https://doi.org/10.1016/j.phpro.2012.03.159 Zhu, J. (2011). Airlines Performance via Two-Stage Network DEA Approach. Journal of CENTRUM Cathedra, 4(2), 260–269.