International Journal of Analysis and Applications Volume 17, Number 6 (2019), 994-1018 URL: https://doi.org/10.28924/2291-8639 DOI: 10.28924/2291-8639-17-2019-994 Received 2019-05-06; accepted 2019-07-03; published 2019-11-01. 2010 Mathematics Subject Classification. 91B02. Key words and phrases. Hospitality; evaluation; forecasting; DEA; GM(1,1). ©2019 Authors retain the copyrights of their papers, and all open access articles are distributed under the terms of the Creative Commons Attribution License. 994 APPLYING DEA MODEL TO MEASURE THE EFFICIENCY OF HOSPITALITY SECTOR: THE CASE OF VIETNAM NHU-TY NGUYEN*, LINH-XUAN THI NGUYEN School of Business, International University – Vietnam National University HCMC; Quarter 6, Linh Trung ward, Thu Duc district, HCMC, Vietnam; E-Mail: nhutynguyen@hcmiu.edu.vn (N.-T., Nguyen) *Corresponding author: nhutynguyen@hcmiu.edu.vn ABSTRACT. Tourism industry is one of the world’s largest industries with a global economic contribution of over 7.6 trillion dollars in 2016 which provides an equal or even surpasses the business volume of oil exports, and food and beverage.As the current climate of the globe, Vietnam’s tourism in general, hospitality in particular has attracted investment from not only domestic enterprises but many international hospitality corporations which create a fierce competitive than ever.Identifying inefficient activities and providing improvement in whole process is crucial. The present research aims to study and evaluate the performance of Vietnam hospitality industry through 20 chosen companies that qualify criteria of Data Envelopment Analysis (DEA) model and Malmquist productivity index. It would be a useful tool in benchmarking the efficient firms and inefficient ones operating in the industry and help the former to improve their efficiency. The researcher uses 5 input variables (Cost of good sales; sales expense; operation expense; fixed assets and owner equity) and 2 output variables (Revenues and Profit after tax).DMU1 and DMU8 face with huge fluctuation in efficiency which acquires the management board to review and improve their operation process to ensure the sustainable development of the firm in current competitive market. https://doi.org/10.28924/2291-8639 https://doi.org/10.28924/2291-8639-17-2019-994 mailto:nhutynguyen@hcmiu.edu.vn Int. J. Anal. Appl. 17 (6) (2019) 995 1. Introduction Nowadays, tourism industry is one of the world’s largest industries with a global economic contribution of over 7.6 trillion dollars in 2016 which provides an equal or even surpass the business volume of oil exports, and food and beverage. Tourism has significant grown and various diversification to be one of the fastest growing industry. The development of national economy and encircles a growing number of new destinations encouraged the development of modern tourism. As the current climate of the globe, Vietnam’s tourism in general, hospitality in particular has attracted investment from not only domestic enterprises but many international hospitality corporations which create a fierce competitive than ever. Hospitality’s benefit will go toward the development of tourist and the competition can be expected. Global hospitalities like Six Sense, Sheraton, Nikko, ect. has joined to the competition. Hotel managers agree that enhancing their performance can be their advantages and those advantages can be identified with the competitive benchmarking ([1]). As the nature of hotel service is simultaneous and perishable, how to manage the customer demands and service capacity will affect in the profitability of company ([2]). How to efficiently operate in comparation with industrial benchmark and rivals is crucial in this industry. Identifying inefficient activities and providing improvement in whole process is crucial. The present research aims to study and evaluate the performance of Vietnam hospitality industry through 20 chosen companies that qualify criteria of Data Envelopment Analysis (DEA) model and Malquist productivity index. It would be a useful tool in benchmarking the efficient firms and inefficient ones operating in the industry and help the former to improve their efficiency The objectives of the research are summarized as following: b. To evaluate the efficiency of 20 listed firms in Vietnam hospitality industry using the data for the past 5 years from 2013 to 2017 c. To forecast the performance of those 20 DMUs in the next 5 years from 2018 to 2022 and then use the forecasted data an input to evaluate their performances d. To compare the efficiency of the past and the future between the set of data 2013- 2017 and the set of data 2018-2022 in order to evaluate the past-to-future performance. Int. J. Anal. Appl. 17 (6) (2019) 996 2. Literature Review The Grey System Theory, introduced by professor Deng Julong in 1988 whichis well known as beneficial tool to take uncertain system with “small sample and poor information” as the research object. Moreover, it is a useful method to forecast and has been applied successfully in many fields and present satisfaction results. Recently, the application of the systems has be successfully employed in calculating of input and output values of organizations, agriculture, meteorological, forestry and disaster predictions. The method is not complicated and be used easily. The small set of data can be used rather than a large number of data. Therefore, it is practically to apply the method in the paper. Data envelopment analysis (DEA) is popular in new researchers which is suggested firstly in 1978 byCharnes, Cooper and Rodes. DEA is a nonparametric method in which the researcher is allowed to use multiple inputs and outputs. The inputs and outputs are combined, the relative efficiency for a whole organization or parts of an organization (or called decision- making units [DMUs]) is calculated. In a sample of DMUs, the best performing ones is identified. An efficiency frontier is, the DMUs on the frontier are efficient (best practice) and the ones that are below efficiency frontier are inefficient. The efficiency index will have values from 0 to 1 (or 0 to 100%). The result 1 implied the efficient unit and vice versa the result less than 1 represents the inefficient. The DMUs in DEA in the sample have similar activities so that a similar group of inputs and outputs can be identified. Moreover, the DMUs have to operate in a similar environment ([3]). Different from the traditional accounting method, DEA model has the benefits that it is proper to compare the relative performance between multiple performance measures ([4]). There are two approaches in DEA: (1) The input-oriented approach and (2) The out-put oriented approach. The input-oriented approach argues minimization of input for the given outputs. Meanwhile, the output-oriented approach is the maximization of outputs for the given inputs. Table 2.1 indicates several previous studies that applied DEA model in evaluating the performance and efficiency of companies in hospitality industry in different countries and some similar industries as well. Int. J. Anal. Appl. 17 (6) (2019) 997 Table 2. 1 - Table of input and output Author INPUT OUTPUT Johns, Howcroft, and Drake (1997) [5] Available room nights, labourtimes , food and beverage costs and utilities cost Number of room night sold, reserved cost, food and beverage revenue Hwang and Chang (2003) [6] Number of full-time employees, number of rooms, food and beverage department areas, operating expenses Revenue of each divisions: room, food and beverage; and other revenue Chiang, Tsai, and Wang (2004) [7] Number of hotel rooms, food and beverage department areas, employees, total cost Yielding index, food and beverage revenue, other revenue Barros (2005) [8] Number of full-time employees, cost of labour, rooms, hotel areas, property’s book value, operation expenses, external expense Sales revenue, Number of guests, and number of night stay Barros and Santos (2006) [9] Number of full-time employees, cost of labour, capital Sales revenue, added value, earnings Önüt and Soner (2006) [10] Number of full-time employees, consumption of electricity, water and liquefied Occupancy rate, sales revenue, number of guests Chen (2007) [11] cost of labour , cost of F&B, cost of materials Total revenue Davutyan (2007) [12] Number of available beds, Number of full- time employees, operating cost Beds sold to return customers divided by number of available beds, beds sold Min et al. (2008) [13] Sales expense, total labour cost, operation expenses and non-operating expenses Revenue of each divisions: room, food and beverage; and other revenue Barros et al. (2009) [14] Number of full-time employees, physical capital Sales revenue, added value Neves and Lourenco (2009) [15] Cost of goods and services, Current assets, net fixed assets, andowner equity, Revenues and earnings (EBITDA) Perrigot et al. (2009) [16] Hotel establish ages, number of labour, Number of rooms. Number of hotel openings during the year, franchising contract: royalties in percentage, chain ranking Sales revenue, room revenues, other revenues and occupancy rate Int. J. Anal. Appl. 17 (6) (2019) 998 Yu and Lee (2009) [17] Full-time employees in each department: the room service department, the F&B department, number of hotel rooms, floor area in the F&B service department; cost for each service sector, shared input Revenue of each divisions: room, food and beverage; and other revenue Chen, Hu, and Liao (2010) [18] Number of hotel rooms; Number of employees; floor area in the F&B service department Revenue of each divisions: room, food and beverage; and other revenue Hsieh and Lin (2010) [19] Room expenses, number of employees of the room department, food and beverage cost, employees of food and beverage department; area of rooms, catering floors Revenue of room service, food and beverage service Hsieh et al. (2010) [20] Number of hotel rooms; Number of employees, facilities expenses, operation expenses Occupancy rate, sales revenue Assaf and Magnini (2011) [21] Number of hotel rooms; Number of employees, operational costs Occupancy rate, sales revenue Avkiran (2011) [22] Number of Full-time staff, permanent part- time staff, Number of room Sales Revenue and a double room cost Chen (2011) [23] Number of employees, area of floors, guest rooms, operation expenses, and depreciation expenses Occupancy rate, number of guests and guest satisfaction index, Room revenue, other revenue Yen and Othman (2011) [24] Number of full-time employees, cost of labour, rooms, hotel areas, property’s book value, operation expenses, external expense No nights occupied, number of guests; occupancy rate, revenue of room service, food and beverage service Honma and Hu (2012) [25] Number employees, number of temporary staff, number of seats in restaurants and bars, number of rooms Sales revenue Manasakis et al. (2013) [26] No. of employees, number employees, operation expenses Sales revenues and total number of spent nights Katarina Poldrugovac, MetkaTekavcic& Sandra Jankovic (2016) [27] Expenses of each division: Room, Energy, F&B, labor and other Sales revenue and occupancy rate Int. J. Anal. Appl. 17 (6) (2019) 999 3. Methodologies 3.1. Collecting DMUs The research was only conducted 20 companies whose financial reports are audited by reliable institutions and collected from Vietnam Stock Exchanges Market or company’s official website from 2013 to 2017 and denoted from DMU1 to DMU20 as the order in the table 3.1. in addition, the financial result of Vietnam hospitality companies from 2013 to 2017 are also generated in the bellow table: 4. Table 3. 2 - Decision making unit Number Code Company name Denote 1 DMU1 BEN THANH TOURIST AND SERVICE JSC BTV 2 DMU2 DONG A HOTEL CORP JSC DAH 3 DMU3 DIC TOURIST AND TRADING JSC DCD 4 DMU4 LANG SON EXIM TOURIST JSC DXL 5 DMU5 COMMERCIAL AND SERVICE JOIN STOCK JSC INVESTMENT POWER EIN 6 DMU6 HOI AN TOURIST SERVICE CO HOT 7 DMU7 POST HOTEL JSC NPH 8 DMU8 NINH VAN BAY TRAVEL REAL ESTALE JSC NVT 9 DMU9 OCEAN HOTEL & SERVICE JSC OCH 10 DMU10 PETROLEUM PHUONGDONG TOURISM JSC PDC 11 DMU11 SAI GON HOTEL JSC SGH 12 DMU12 THUY TA JSC TTJ 13 DMU13 VUNG TAU INTOURCO RESORT VIR 14 DMU14 THANH CONG TOURIST & SERVICE JSC VNG 15 DMU15 CORPORATION TOURIST OF BARIA VUNG TAU VTG 16 DMU16 Dak Lak Tourist Jsc. DLD 17 DMU17 MY TRA TOURIST & SERVICE CO MTC 18 DMU18 THE NATIONAL OIL SERVICE JSC OF VIETNAM OSCVN 19 DMU19 DONG NAI TOURIST CO DNT 20 DMU20 KIM LIEN TOURIST CO KimLien Int. J. Anal. Appl. 17 (6) (2019) 1000 3.2 DEA - Malmquist Productivity Index One of the standard approaches for measuring productivity that applied in many researches overtime is the Malmquist productivity index, especially when nonparametric specifications are applied to micro data. Malmquist productivity index was first proposed by Caves, Christensen and Diewert (1982) and then further modified later by Färe, Grosskopf, Lidgren and Roos in 1995. Malmquist productivity index (MPI) is a tool for measurement of productivity changes of a DMU over periods of time. It is defined as the product of “catch-up” and “frontier-shift” terms. The catch-up term is the degree of efforts that the DMU attained for improving its efficiency, while the frontier-shift term reflects the change in the efficient frontiers surrounding the DMU between the two time periods 1 and 2. DMU0 at periods 1 and 2 is denoted by (𝑥0 1 , 𝑦0 1) and (𝑥0 2 , 𝑦0 2). The efficiency score of DMU (𝑥0, 𝑦𝑜 ) 𝑡1 is measured by the technological frontier t2: 𝑑𝑡2 ((𝑥0, 𝑦0) 𝑡1 )(𝑡1 = 1,2 𝑎𝑛𝑑 𝑡2 = 1,2) C stands for the efficiency change (Catch- up effect) and is determined by the following formula: 𝐶 = 𝑑2((𝑥0, 𝑦𝑜 ) 2) 𝑑1((𝑥0, 𝑦𝑜 ) 1) The technological change (frontier-shift effect) denoted by F has the formula: 𝐹 = [ 𝑑1((𝑥0, 𝑦𝑜 ) 1) 𝑑2((𝑥0, 𝑦𝑜) 1) . 𝑑1((𝑥0, 𝑦𝑜 ) 2) 𝑑2((𝑥0, 𝑦𝑜 ) 2) ] 1/2 Malmquist Productivity Index (MPI) is the product of C and F, that is, MPI = (catch-up) x (frontier-shift) or 𝑀𝑃𝐼 = [ 𝑑1((𝑥0, 𝑦𝑜 ) 2) 𝑑1((𝑥0, 𝑦𝑜 ) 1) . 𝑑2((𝑥0, 𝑦𝑜) 2) 𝑑2((𝑥0, 𝑦𝑜) 1) ] 1/2 If the Malmquist productivity index (MPI) is greater than 1 (MPI>1), it indicates progress in relative efficiency from period 1 to period 2. Productivity remains unchanged if MPI equal to 1 (MPI=1) and it demonstrate a regress when MPI is less than 1 (MPI<1). 3.3 Establishing Inputs and Outputs Together with the financial report of hotel in Vietnam, there are five inputs chosen are cost of good sales, sales expenses, operation expenses (including electricity and labor cost), fixed assets and equity. The reasons for this range choosing as bellow: - Cost of good sales is direct cost that contribute to goods and service of hospitality firm. Int. J. Anal. Appl. 17 (6) (2019) 1001 - Fixed asset is a long-term tangible piece of property that a firm owns and uses in its operations to generate income. A fixed asset is bought for production or supply of goods or services, for rental to third party or for use in the organization. It has a physical form and is reported on the balance sheet as property, plant and equipment (PP&E). In this research which related to hospitality, fixed asset is considered to be important due to the information it provides. - Sales expenses are used for selling activities like incentive, marketing expense. in a hotel, marketing department is indirect factor to raise the sales revenue. - Operation expenses includes energy expenses, labor cost which is direct factor which create service to provide for customer. In hotel industry, labor or quality of human resource plays a significant role to generate profit. - Equity or owner’s equity presents the owner’s fund for business in various operation. Two chosen outputs factors are considered as sales and profit after tax (PAT). - Sales are the transactions between parties where the buyers receive goods (tangible or intangible), services and/or assets in exchange for money. It can also refer to an agreement between the buyer and seller of the selected good or service. - Profit after tax is defined as the net amount earned by a business after all taxation related expenses have been deducted. The profit after tax is often assessment of what a business is really earning and hence can use in its operations than its total revenues. In the previous research, occupancy rate is a non-financial output of equation but it is not objectively provided by Vietnamese hotel company, so we cannot put it in the output variables. 4. Empirical Result and Analysia 4.1. Empirical Result A forecast inputs/ outputs of next 5 years from 2019 to 2022 will be generated by GM (1,1) Model. A sample is presented to illustrate the procedure of GM (1,1) forecasting applied in the research. The researcher takes factor of sales revenue of Ben Thanh Tourist and Service JSC. (BTV) in period of time from 2018 to 2022 to demonstrate the calculation process and other variables are calculated in the same way. The researcher use the GM(1,1) model for trying to forecast the variance of primitive series as follows. Int. J. Anal. Appl. 17 (6) (2019) 1002 First, the primitive series is created: 𝑋(0) = (455,117 ; 601,329 ; 597,653 ; 672,090 ; 814,010) Secondly, perform the accumulated generating operation (AGO): 𝑋(1) = (455,117; 1,056,446; 1,654,099; 2,326,189; 3,140,199) 𝑥(1)(1) = 𝑥(0)(1) = 455,117 𝑥(1)(2) = 𝑥(0)(1) + 𝑥(0)(2) = 1,056,446 𝑥(1)(3) = 𝑥(0)(1) + 𝑥(0)(2) + 𝑥(0)(3) = 1,654,099 𝑥(1)(4) = 𝑥(0)(1) + 𝑥(0)(2) + 𝑥(0)(3) + 𝑥(0)(4) = 2,326,189 𝑥(1)(5) = 𝑥(0)(1) + 𝑥(0)(2) + 𝑥(0)(3) + 𝑥(0)(4) + 𝑥(0)(5) = 3,140,199 Third, create the different equations of GM(1,1) To find 𝑋(1) series, and the following mean obtained by the mean equation is 𝑧(1)(2) = 1 2 (455,117 + 1,056,446) = 755,781.5 𝑧(1)(3) = 1 2 (1,056,446 + 1,654,099) = 1,355,272.5 𝑧(1)(4) = 1 2 (1,654,099 + 2,326,189) = 1,990,144 𝑧(1)(5) = 1 2 (2,326,189 + 3,140,199) = 2,733,194 Fourth, solve the equations. To find a and b, the primitive series values are substituted into Grey differential equation to obtain 601,329 + 𝑎 𝑥 755,781.5 = 𝑏 597,653 + 𝑎 𝑥 1,355,272.5 = 𝑏 672,090 + 𝑎 𝑥 1,990,144 = 𝑏 814,010 + 𝑎 𝑥 2,733,194 = 𝑏 Then, convert the linear equations into the form of a matrix Let 𝐵 = [ −755,781.5 1 −1,355,272.5 1 −1,990,144 1 −2,733,194 1 ] , 𝜃 = [ 𝑎 𝑏 ], 𝑦𝑁 = [ 601,329 597,653 672,090 814,010 ]. And then use the least square method to find a and b: [ 𝑎 𝑏 ] = 𝜃 = (𝐵𝑇 𝑦𝑁 ) = [ −0.110619605 482,266.0641 ]. Int. J. Anal. Appl. 17 (6) (2019) 1003 Use the two coefficients a and b to generate the whitening equation of the differential equation: 𝑑𝑥(1) 𝑑𝑡 − 0.110619605 × 𝑥(1) = 482,266.0641. Find the prediction model from 𝑋(1)(𝑘 + 1) = (𝑋(0)(1) − 𝑏 𝑎 ) 𝑒 −𝑎𝑘 + 𝑏 𝑎 𝑋(1)(𝑘 + 1) = (455,117 − 482,266.0641 −0.110619605 ) 𝑒 0.110619605 + 482,266.0641 −0.110619605 = (4,814,796.860)𝑒 0.110619605 − 4,359,679.806 Substitute different values of k into the equation: 𝑘 = 0 𝑋(1)(1) = 455,117 𝑘 = 1 𝑋(1)(2) = 1,018,303 𝑘 = 2 𝑋(1)(3) = 1,647,365 𝑘 = 3 𝑋(1)(4) = 2,350,009 𝑘 = 4 𝑋(1)(5) = 3,134,841 𝑘 = 5 𝑋(1)(6) = 4,011,475 𝑘 = 6 𝑋(1)(7) = 4,990,648 𝑘 = 7 𝑋(1)(8) = 6,084,356 𝑘 = 8 𝑋(1)(9) = 7,305,994 𝑘 = 9 𝑋(1)(10) = 8,670,527 Derive the predicted value of the original series according to the accumulated generating operation and obtain 𝑥(0)(1) = 𝑥(1)(1) = 455,117 − 𝑓𝑜𝑟 𝑦𝑒𝑎𝑟 2013 𝑥(0)(2) = 𝑥(1)(2) − 𝑥(1)(1) = 563,186 − 𝑓𝑜𝑟 𝑦𝑒𝑎𝑟 2014 𝑥(0)(3) = 𝑥(1)(3) − 𝑥(1)(2) = 629,062 − 𝑓𝑜𝑟 𝑦𝑒𝑎𝑟 2015 𝑥(0)(4) = 𝑥(1)(4) − 𝑥(1)(3) = 702,643 − 𝑓𝑜𝑟 𝑦𝑒𝑎𝑟 2016 𝑥(0)(5) = 𝑥(1)(5) − 𝑥(1)(4) = 784,831 − 𝑓𝑜𝑟 𝑦𝑒𝑎𝑟 2017 𝑥(0)(6) = 𝑥(1)(6) − 𝑥(1)(5) = 876,633 − 𝑓𝑜𝑟 𝑦𝑒𝑎𝑟 2018 𝑥(0)(7) = 𝑥(1)(7) − 𝑥(1)(6) = 979,173 − 𝑓𝑜𝑟 𝑦𝑒𝑎𝑟 2019 𝑥(0)(8) = 𝑥(1)(8) − 𝑥(1)(7) = 1,093,707 − 𝑓𝑜𝑟 𝑦𝑒𝑎𝑟 2020 𝑥(0)(9) = 𝑥(1)(9) − 𝑥(1)(8) = 1,221,638 − 𝑓𝑜𝑟 𝑦𝑒𝑎𝑟 2021 𝑥(0)(10) = 𝑥 (1)(10) − 𝑥(1)(9) = 1,364,533 − 𝑓𝑜𝑟 𝑦𝑒𝑎𝑟 2022 Int. J. Anal. Appl. 17 (6) (2019) 1004 The other input and output factors’ forecasting results will be carried out same as the above process. The results of all DMUs from 2018 to 2022 could be acquired and the detailed numbers are generated in the table 4.1 to 4.5 respectively: Table 4.1. Financial data of decision-making units 2018 DENOTE I (COGS) I (SALES EXPENSES) I (OPERATIO N EXPENSE) I (EQUITY CAPITAL) I (FIXED ASSET) O (REVENUE) O (PROFIT AFTER TAX DMU1 32,908,616 32,908,616 32,908,616 32,908,616 32,908,616 32,908,616 32,908,616 DMU2 13,478,159 47,923 209,550 26,711,591 41,077,064 17,825,572 2,639,877 DMU3 76,576,376 802,055 132,735 1,357,945 5,424,210 548,536 (2,160) DMU4 483,545 55,537 132,735 14,095,615 582,156 17,757,563 418,969 DMU5 9,162,200 61,518 600,414 13,824,261 705,966 8,950,675 155,308 DMU6 6,060,193 1,181,491 1,029,223 4,872,856 3,765,479 8,046,307 335,272 DMU7 42,334 4,466 12,848 1,236,428 1,948 34,135 15,871 DMU8 4,788,941 992,087 3,287,557 38,014,824 8,803,001 9,747,591 4,601,632 DMU9 28,121,728 6,317,735 12,613,506 52,338,060 58,484,432 53,715,440 (7,762,659) DMU10 2,887,402 17,091 222,029 6,591,606 5,915,483 3,456,240 394,069 DMU11 1,193,309 - 314,599 10,513,538 1,820,995 2,393,822 992,115 DMU12 2,441,583 1,720,458 115,510 5,709,437 501,977 4,605,586 274,796 DMU13 1,763,348 - 692,579 3,960,691 2,839,775 2,782,695 348,412 DMU14 37,453,279 382,746 1,269,673 8,568,309 9,799,291 12,423,095 345,689 DMU15 3,334,348 1,390,279 2,425,122 7,386,611 5,098,755 6,958,109 9,656 DMU16 2,654,787 38,652 360,443 3,260,395 3,260,395 7,124,050 (28,570) DMU17 1,329,496 130,241 263,803 2,339,723 2,035,763 1,850,575 37,359 DMU18 9,886,471 1,978,471 626,407 3,988,739 1,634,898 12,610,975 983,349 DMU19 9,886,471 1,978,471 2,007,085 4,381,002 1,634,898 12,610,975 531,090 DMU20 6,381,260 23,930 329,038 1,655,251 555,370 6,381,260 291,194 http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm Int. J. Anal. Appl. 17 (6) (2019) 1005 Table 4.2. Financial data of decision-making units 2019 DENOTE I (COGS) I (SALES EXPENSES) I (OPERATIO N EXPENSE) I (EQUITY CAPITAL) I (FIXED ASSET) O (REVENUE) O (PROFIT AFTER TAX DMU1 37,098,046 1,890,424 2,615,960 11,466,654 2,850,215 42,572,760 981,061 DMU2 21,412,310 53,533 229,447 39,762,448 57,698,449 27,611,697 4,412,162 DMU3 68,177,527 725,295 124,253 1,333,963 5,484,919 486,002 (2,272) DMU4 432,150 53,533 124,253 14,329,849 649,092 19,072,378 492,506 DMU5 12,158,274 62,712 605,491 14,007,484 678,952 9,033,212 169,854 DMU6 6,229,626 1,851,392 1,252,261 4,905,668 3,618,884 8,493,387 278,994 DMU7 46,613 4,359 12,831 1,306,662 656 32,394 16,405 DMU8 4,991,351 976,854 3,225,616 38,288,765 6,531,108 10,084,064 7,736,101 DMU9 31,027,610 6,744,248 13,857,607 52,755,069 62,891,546 61,426,778 (8,089,791) DMU10 2,964,553 11,267 190,777 6,789,405 5,928,333 3,541,795 609,336 DMU11 1,321,688 - 317,410 15,698,135 1,689,007 2,854,516 1,945,718 DMU12 2,363,252 1,829,433 118,395 8,232,072 441,592 4,616,801 272,509 DMU13 1,797,353 - 847,900 3,994,845 2,770,284 2,859,928 327,333 DMU14 98,443,453 506,905 1,456,691 9,160,646 10,956,611 16,671,791 498,003 DMU15 3,300,936 1,449,366 2,653,246 7,303,900 5,231,296 7,135,472 9,271 DMU16 2,627,426 38,730 372,926 3,180,629 3,180,629 6,972,281 (16,456) DMU17 1,402,484 138,824 309,527 2,343,045 2,024,305 2,015,901 42,898 DMU18 12,235,304 2,016,607 847,652 4,199,560 1,530,489 14,777,243 1,220,173 DMU19 12,235,304 2,016,607 2,054,724 4,691,514 1,530,489 14,777,243 590,032 DMU20 6,711,212 24,138 218,970 1,385,341 459,497 6,711,212 241,473 http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm Int. J. Anal. Appl. 17 (6) (2019) 1006 Table 4.3. Financial data of decision-making units 2020 DENOTE I (COGS) I (SALES EXPENSES) I (OPERATIO N EXPENSE) I (EQUITY CAPITAL) I (FIXED ASSET) O (REVENUE) O (PROFIT AFTER TAX DMU1 41,820,811 1,927,365 2,826,492 11,535,781 2,705,067 47,552,493 972,973 DMU2 34,017,037 59,799 251,234 59,189,743 81,045,496 42,770,341 7,374,271 DMU3 60,699,858 655,882 116,312 1,310,405 5,546,309 430,596 (2,390) DMU4 386,217 51,601 116,312 14,567,975 723,724 20,484,545 578,951 DMU5 16,134,074 63,931 610,612 14,193,136 652,972 9,116,510 185,761 DMU6 6,403,796 2,901,122 1,523,632 4,938,702 3,477,997 8,965,309 232,162 DMU7 51,325 4,254 12,813 1,380,886 221 30,741 16,957 DMU8 5,202,316 961,855 3,164,843 38,564,681 4,845,548 10,432,152 13,005,660 DMU9 34,233,763 7,199,555 15,224,416 53,175,401 67,630,760 70,245,150 (8,430,709) DMU10 3,043,766 7,427 163,924 6,993,140 5,941,210 3,629,468 942,196 DMU11 1,463,879 - 320,246 23,439,442 1,566,584 3,403,871 3,815,907 DMU12 2,287,434 1,945,311 121,351 11,869,299 388,472 4,628,044 270,242 DMU13 1,832,013 - 1,038,055 4,029,293 2,702,493 2,939,305 307,529 DMU14 258,752,069 671,340 1,671,255 9,793,932 12,250,612 22,373,540 717,427 DMU15 3,267,857 1,510,964 2,902,829 7,222,116 5,367,283 7,317,356 8,901 DMU16 2,600,346 38,808 385,840 3,102,814 3,102,814 6,823,745 (9,479) DMU17 1,479,478 147,973 363,177 2,346,372 2,012,912 2,195,997 49,259 DMU18 15,142,176 2,055,478 1,147,038 4,421,524 1,432,748 17,315,624 1,514,033 DMU19 15,142,176 2,055,478 2,103,495 5,024,033 1,432,748 17,315,624 655,516 DMU20 7,058,225 24,348 145,722 1,159,444 380,174 7,058,225 200,241 http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm Int. J. Anal. Appl. 17 (6) (2019) 1007 Table 4.4. Financial data of decision making units 2021 DENOTE I (COGS) I (SALES EXPENSES) I (OPERATIO N EXPENSE) I (EQUITY CAPITAL) I (FIXED ASSET) O (REVENUE) O (PROFIT AFTER TAX DMU1 47,144,808 1,965,028 3,053,969 11,605,324 2,567,311 53,114,705 964,952 DMU2 54,041,754 66,798 275,089 88,108,905 113,839,670 66,250,982 12,324,995 DMU3 54,042,336 593,112 108,879 1,287,264 5,608,385 381,507 (2,514) DMU4 345,167 49,738 108,879 14,810,058 806,937 22,001,272 680,569 DMU5 21,409,977 65,172 615,775 14,381,248 627,985 9,200,576 203,158 DMU6 6,582,836 4,546,045 1,853,810 4,971,958 3,342,594 9,463,452 193,192 DMU7 56,514 4,151 12,796 1,459,326 74 29,173 17,528 DMU8 5,422,198 947,087 3,105,214 38,842,585 3,595,001 10,792,256 21,864,655 DMU9 37,771,215 7,685,600 16,726,037 53,599,081 72,727,100 80,329,479 (8,785,994) DMU10 3,125,096 4,896 140,851 7,202,988 5,954,115 3,719,311 1,456,888 DMU11 1,621,367 - 323,107 34,998,261 1,453,036 4,058,950 7,483,687 DMU12 2,214,048 2,068,528 124,381 17,113,585 341,741 4,639,314 267,993 DMU13 1,867,341 - 1,270,854 4,064,038 2,636,362 3,020,885 288,923 DMU14 680,112,603 889,116 1,917,424 10,470,997 13,697,439 30,025,286 1,033,533 DMU15 3,235,111 1,575,181 3,175,889 7,141,247 5,506,805 7,503,876 8,545 DMU16 2,573,546 38,887 399,202 3,026,902 3,026,902 6,678,373 (5,460) DMU17 1,560,700 157,725 426,126 2,349,704 2,001,582 2,392,183 56,563 DMU18 18,739,663 2,095,099 1,552,167 4,655,220 1,341,249 20,290,038 1,878,664 DMU19 18,739,663 2,095,099 2,153,422 5,380,121 1,341,249 20,290,038 728,268 DMU20 7,423,180 24,560 96,976 970,383 314,545 7,423,180 166,050 http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm Int. J. Anal. Appl. 17 (6) (2019) 1008 Table 4.5. Financial data of decision making units 2022 DENOTE I (COGS) I (SALES EXPENSES) I (OPERATIO N EXPENSE) I (EQUITY CAPITAL) I (FIXED ASSET) O (REVENUE) O (PROFIT AFTER TAX DMU1 53,146,575 2,003,427 3,299,752 11,675,287 2,436,571 59,327,529 956,998 DMU2 85,854,367 74,616 301,210 131,157,506 159,903,649 102,622,345 20,599,393 DMU3 48,115,006 536,350 101,921 1,264,531 5,671,156 338,014 (2,644) DMU4 308,480 47,944 101,921 15,056,164 899,718 23,630,302 800,023 DMU5 28,411,119 66,438 620,983 14,571,854 603,955 9,285,418 222,185 DMU6 6,766,881 7,123,632 2,255,540 5,005,438 3,212,462 9,989,274 160,763 DMU7 62,226 4,051 12,779 1,542,221 25 27,684 18,118 DMU8 5,651,373 932,545 3,046,709 39,122,491 2,667,197 11,164,790 36,758,085 DMU9 41,674,200 8,204,458 18,375,766 54,026,138 78,207,476 91,861,505 (9,156,251) DMU10 3,208,598 3,228 121,025 7,419,133 5,967,049 3,811,379 2,252,739 DMU11 1,795,798 - 325,994 52,257,143 1,347,717 4,840,100 14,676,872 DMU12 2,143,017 2,199,551 127,487 24,674,985 300,631 4,650,612 265,763 DMU13 1,903,351 - 1,555,863 4,099,082 2,571,848 3,104,729 271,442 DMU14 1,787,630,73 8 1,177,536 2,199,853 11,194,869 15,315,139 40,293,928 1,488,917 DMU15 3,202,692 1,642,127 3,474,635 7,061,284 5,649,953 7,695,151 8,204 DMU16 2,547,022 38,966 413,027 2,952,848 2,952,848 6,536,099 (3,145) DMU17 1,646,380 168,119 499,985 2,353,041 1,990,317 2,605,895 64,950 DMU18 23,191,844 2,135,483 2,100,385 4,901,267 1,255,593 23,775,387 2,331,112 DMU19 23,191,844 2,135,483 2,204,535 5,761,447 1,255,593 23,775,387 809,093 DMU20 7,807,007 24,774 64,536 812,150 260,245 7,807,007 137,697 Because the smallest value is – 9,156,251 USD which is forecast value of factor profit after tax of DMU9, all values will be scale up USD10,000,000 for carrying the DEA model. http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/NVT-ctcp-bat-dong-san-du-lich-ninh-van-bay.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm http://finance.vietstock.vn/OCH-ctcp-khach-san-va-dich-vu-dai-duong.htm Int. J. Anal. Appl. 17 (6) (2019) 1009 4.2 Forecasting Accuracy The predicting data of DMUs for next 5 years from 2018 to 2022 generated by GM (1,1) model is used as inputs for DEA – Malmquist to study the performance of them in future. Since the study mentioned in previous chapter, forecasting data should be accuracy to ensure that result of future data and upcoming analysis. Therefore, MAPE (Mean Absolut Percentage Error) is employed to calculate forecasting error between the two sets of data. The results are summarized in the following table: Table 4. 1 MAPE 2013-2017 Denote DMU Average MAPE (%) BTV DMU1 5.16% DAH DMU2 11.30% DCD DMU3 15.06% DXL DMU4 9.50% EIN DMU5 10.40% HOT DMU6 5.65% NPH DMU7 10.76% NVT DMU8 13.43% OCH DMU9 10.49% PDC DMU10 12.46% SGH DMU11 9.06% TTJ DMU12 5.71% VIR DMU13 2.65% VNG DMU14 12.68% VTG DMU15 1.72% DLD DMU16 2.05% MTC DMU17 2.46% OSCVN DMU18 2.96% DNT DMU19 1.78% KimLien DMU20 9.75% Average MAPE 7.75% The result of average MAPE is only 7.75 which proves that G(1,1) model is qualified to apply to forecast the future value of DMUs in this research. Int. J. Anal. Appl. 17 (6) (2019) 1010 4.3 Pearson Correlation The Pearson correlation is conducted to confirm the relationship between input and output. Pearson test confirms that the lower correlation implies the less correlated and higher correlation implies the closer correlated between two variables. The correlation value is always from -1 to 1. The closer to -1 and 1 is correlation, the more perfect is linear relationship formed. Tables from 4.6 to 4.15 confirm that the correlations comply well with the earlier condition of the DEA model as their correlation coefficients show strong positive associations. Hence, it proves that the input and output are chosen appropriately. And there is no elimination of any variable. Table 4. 6. Correlation coefficient 2013 COGS SALES EXPENSES OPERATION EXPENSE EQUITY CAPITAL FIXED ASSET REVENUE PROFIT AFTER TAX COGS 1.0000 0.2600 0.0118 0.0402 0.0911 0.0198 0.1703 SALES EXPENSES 0.2600 1.0000 0.7352 0.8324 0.7104 0.8123 0.8735 OPERATION EXPENSE 0.0118 0.7352 1.0000 0.8156 0.7452 0.8539 0.8434 EQUITY CAPITAL 0.0402 0.8324 0.8156 1.0000 0.8850 0.8381 0.8921 FIXED ASSET 0.0911 0.7104 0.7452 0.8850 1.0000 0.6227 0.7515 REVENUE 0.0198 0.8123 0.8539 0.8381 0.6227 1.0000 0.9156 PROFIT AFTER TAX 0.1703 0.8735 0.8434 0.8921 0.7515 0.9156 1.0000 Int. J. Anal. Appl. 17 (6) (2019) 1011 Table 4. 7. Correlation coefficient 2014 COGS SALES EXPENSES OPERATION EXPENSE EQUITY CAPITAL FIXED ASSET REVENUE PROFIT AFTER TAX COGS 1.0000 0.2343 0.0904 -0.0025 0.1016 0.0505 -0.1021 SALES EXPENSES 0.2343 1.0000 0.8323 0.6870 0.6726 0.8217 -0.7957 OPERATION EXPENSE 0.0904 0.8323 1.0000 0.8062 0.8456 0.7739 -0.9894 EQUITY CAPITAL -0.0025 0.6870 0.8062 1.0000 0.8333 0.7424 -0.7460 FIXED ASSET 0.1016 0.6726 0.8456 0.8333 1.0000 0.6132 -0.8230 REVENUE 0.0505 0.8217 0.7739 0.7424 0.6132 1.0000 -0.7203 PROFIT AFTER TAX -0.1021 -0.7957 -0.9894 -0.7460 -0.8230 -0.7203 1.0000 Table 4. 8. Correlation coefficient 2015 COGS SALES EXPENSES OPERATION EXPENSE EQUITY CAPITAL FIXED ASSET REVENUE PROFIT AFTER TAX COGS 1.0000 0.2107 -0.0014 -0.0277 0.0483 0.0105 0.0769 SALES EXPENSES 0.2107 1.0000 0.8204 0.6763 0.6628 0.6466 0.1385 PERATION EXPENSE -0.0014 0.8204 1.0000 0.9400 0.8725 0.6454 -0.2134 EQUITY CAPITAL -0.0277 0.6763 0.9400 1.0000 0.8499 0.6655 -0.3462 FIXED ASSET 0.0483 0.6628 0.8725 0.8499 1.0000 0.4731 -0.1748 REVENUE 0.0105 0.6466 0.6454 0.6655 0.4731 1.0000 0.2546 PROFIT AFTER TAX 0.0769 0.1385 -0.2134 -0.3462 -0.1748 0.2546 1.0000 Int. J. Anal. Appl. 17 (6) (2019) 1012 Table 4. 9. Correlation coefficient 2016 COGS SALES EXPENSES PERATION EXPENSE EQUITY CAPITAL IXED ASSET REVENUE PROFIT AFTER TAX COGS 1.0000 0.2970 0.1834 0.0597 0.1903 0.1770 -0.2140 SALES EXPENSES 0.2970 1.0000 0.8827 0.6186 0.6957 0.7956 -0.7954 PERATION EXPENSE 0.1834 0.8827 1.0000 0.8139 0.8506 0.8320 -0.8946 EQUITY CAPITAL 0.0597 0.6186 0.8139 1.0000 0.8293 0.7135 -0.6195 FIXED ASSET 0.1903 0.6957 0.8506 0.8293 1.0000 0.6900 -0.7823 REVENUE 0.1770 0.7956 0.8320 0.7135 0.6900 1.0000 -0.6654 PROFIT AFTER TAX -0.2140 -0.7954 -0.8946 -0.6195 -0.7823 -0.6654 1.0000 Table 4. 10. Correlation coefficient 2017 COGS SALES EXPENSES PERATION EXPENSE EQUITY CAPITAL IXED ASSET REVENUE PROFIT AFTER TAX COGS 1.0000 0.2738 0.0782 0.1658 0.2289 0.2283 -0.0705 SALES EXPENSES 0.2738 1.0000 0.5854 0.6972 0.7026 0.7681 0.0229 PERATION EXPENSE 0.0782 0.5854 1.0000 0.6625 0.6444 0.5513 0.7644 EQUITY CAPITAL 0.1658 0.6972 0.6625 1.0000 0.9313 0.8728 0.1421 FIXED ASSET 0.2289 0.7026 0.6444 0.9313 1.0000 0.7571 0.1400 REVENUE 0.2283 0.7681 0.5513 0.8728 0.7571 1.0000 0.0368 PROFIT AFTER TAX -0.0705 0.0229 0.7644 0.1421 0.1400 0.0368 1.0000 Int. J. Anal. Appl. 17 (6) (2019) 1013 Table 4.11. Correlation coefficient 2018 COGS SALES EXPENSES PERATION EXPENSE EQUITY CAPITAL IXED ASSET REVENUE PROFIT AFTER TAX COGS 1.0000 0.2980 0.3054 0.1616 0.3360 0.2457 0.1812 SALES EXPENSES 0.2980 1.0000 0.9777 0.4537 0.4661 0.5485 0.8969 PERATION EXPENSE 0.3054 0.9777 1.0000 0.5956 0.5894 0.6744 0.8136 EQUITY CAPITAL 0.1616 0.4537 0.5956 1.0000 0.8376 0.8329 0.2441 FIXED ASSET 0.3360 0.4661 0.5894 0.8376 1.0000 0.8638 0.1923 REVENUE 0.2457 0.5485 0.6744 0.8329 0.8638 1.0000 0.2097 PROFIT AFTER TAX 0.1812 0.8969 0.8136 0.2441 0.1923 0.2097 1.0000 Table 4. 12. Correlation coefficient 2019 COGS SALES EXPENSES PERATION EXPENSE EQUITY CAPITAL IXED ASSET REVENUE PROFIT AFTER TAX COGS 1.0000 0.1606 0.1695 0.0740 0.2340 0.2927 -0.1422 SALES EXPENSES 0.1606 1.0000 0.9058 0.5273 0.5455 0.7524 -0.6269 PERATION EXPENSE 0.1695 0.9058 1.0000 0.6972 0.6597 0.7924 -0.6064 EQUITY CAPITAL 0.0740 0.5273 0.6972 1.0000 0.8241 0.6996 -0.0339 FIXED ASSET 0.2340 0.5455 0.6597 0.8241 1.0000 0.7172 -0.3054 REVENUE 0.2927 0.7524 0.7924 0.6996 0.7172 1.0000 -0.4332 PROFIT AFTER TAX -0.1422 -0.6269 -0.6064 -0.0339 -0.3054 -0.4332 1.0000 Int. J. Anal. Appl. 17 (6) (2019) 1014 Table 4. 13. Correlation coefficient 2020 COGS SALES EXPENSES PERATION EXPENSE EQUITY CAPITAL IXED ASSET REVENUE PROFIT AFTER TAX COGS 1.0000 0.0376 0.0861 0.0304 0.1510 0.2398 -0.0629 SALES EXPENSES 0.0376 1.0000 0.8935 0.3862 0.4328 0.6811 -0.5096 PERATION EXPENSE 0.0861 0.8935 1.0000 0.5470 0.5512 0.7493 -0.4604 EQUITY CAPITAL 0.0304 0.3862 0.5470 1.0000 0.8510 0.6913 0.2533 FIXED ASSET 0.1510 0.4328 0.5512 0.8510 1.0000 0.7468 -0.0658 REVENUE 0.2398 0.6811 0.7493 0.6913 0.7468 1.0000 -0.2555 PROFIT AFTER TAX -0.0629 -0.5096 -0.4604 0.2533 -0.0658 -0.2555 1.0000 Table 4. 14. Correlation coefficient 2021 COGS SALES EXPENSES PERATION EXPENSE EQUITY CAPITAL IXED ASSET REVENUE PROFIT AFTER TAX COGS 1.0000 -0.0101 0.0399 -0.0058 0.0874 0.2049 -0.0409 SALES EXPENSES -0.0101 1.0000 0.8530 0.2162 0.3065 0.5597 -0.3953 PERATION EXPENSE 0.0399 0.8530 1.0000 0.3708 0.4389 0.6768 -0.3315 EQUITY CAPITAL -0.0058 0.2162 0.3708 1.0000 0.8758 0.6976 0.4465 FIXED ASSET 0.0874 0.3065 0.4389 0.8758 1.0000 0.7912 0.1196 REVENUE 0.2049 0.5597 0.6768 0.6976 0.7912 1.0000 -0.0761 PROFIT AFTER TAX -0.0409 -0.3953 -0.3315 0.4465 0.1196 -0.0761 1.0000 Int. J. Anal. Appl. 17 (6) (2019) 1015 Table 4. 15. Correlation coefficient 2022 COGS SALES EXPENSES PERATION EXPENSE EQUITY CAPITAL IXED ASSET REVENUE PROFIT AFTER TAX COGS 1.0000 -0.0164 0.0227 -0.0297 0.0480 0.1919 -0.0448 SALES EXPENSES -0.0164 1.0000 0.7701 0.0648 0.1816 0.3946 -0.3049 PERATION EXPENSE 0.0227 0.7701 1.0000 0.2107 0.3329 0.5756 -0.2408 EQUITY CAPITAL -0.0297 0.0648 0.2107 1.0000 0.8907 0.7249 0.5323 FIXED ASSET 0.0480 0.1816 0.3329 0.8907 1.0000 0.8434 0.2355 REVENUE 0.1919 0.3946 0.5756 0.7249 0.8434 1.0000 0.0708 PROFIT AFTER TAX -0.0448 -0.3049 -0.2408 0.5323 0.2355 0.0708 1.0000 5. Conclusion In conclusion, the research employs DEA Model and Malmquist Production Index model to study and evaluate performance of past-to-future performance of 20-listed company in Vietnam hospitality industry. At beginning, researcher collects data of 20 qualified listed companies (Decision Making Unit) with 2 sets of data: the original set of data and the future set of data. The original set of data is collected on vietstock.vn, cophieu68.com and website of companies where their financial reports are audited by reliable institution. The researcher uses 5 input variables (Cost of good sales; sales expense; operation expense; fixed assets and owner equity) and 2 output variables (Revenues and Profit after tax). The association level of those variables is examined by Pearson Correlation and the result confirms the close correlated relationship among variables which is qualified to the requirement of DEA model. Then DEA-Malmquist is applied first time to analysis the original data set (past data from 2013 to 2017) in order to evaluate performance of DMUs in the past. Next, the future data set is generated by employed GM (1,1). The error of forecasting model is calculated by Mean Absolute Percentage Error (MAPE). The average MAPE of all DMUs is 7.75% which is in acceptant range (less than 10%). The forecast data will be input for DEA-Malmquist model to evaluate performance of DMUs in next 5 year from 2018 to 2022. Int. J. Anal. Appl. 17 (6) (2019) 1016 In the first-time employing DEA model with the past data, the average score of all DMUs is 1.053 score. The most efficient company is DMU1 (average score is 1.053), however, it inconsistently performs during studying period. In the first two period of time from 2013 to 2015, DMU1 (BTV) has the highest score 1.051 and 1.140 respectively. In the other hand, DMU8 (NVT) is the least efficient firm from 2014 to 2015. Ninh Van Tourist experienced loss in period time from 2014 to 2015 due to their huge investment to real estate investment and development, but the real estate market was in deep crisis at that time. Together with the down trend of the world economy, Ninh Van Tourist made consecutive losses. Interestingly, period of year 2015 to 2016 experiences a converse trend where DMU1 is the lease efficient firm with 0.878 score and DUM8 is the most one with 1.209 score. The down trend of DMU1 is since the event of oil rig Haiyang Shi You 981 in Southeast Sea between Vietnam and China in July 2015 cause the dramatically reduce in Chinese visitors. Ben Thanh tourist should diversify their target customers avoid the similar event rather than mostly relying visitors from any one country. Sai Gon Hotel JSC (DMU11) as its MPI score has tendency to reduce and be the least efficiency in period 2016 to 2017 which requests its management board to improve as soon as possible in order not to be kick out of the market.Although the overall trend of hospitality industry is table, DMU1 and DMU8 face with huge fluctuation in efficiency which acquire the management board to review and improve their operation process to ensure the sustainable development of the firm in current competitive market. 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