Performance measurement of Vietnamese publishing firms by the integration of the GM (1,1) model and the Malmquist model

Nguyen, Xuan-Huynh; Quoc Chien Luu Article Performance measurement of Vietnamese publishing firms by the integration of the GM (1,1) model and the Malmquist model Business Systems Research (BSR) Provided in Cooperation with: IRENET - Society for Advancing Innovation and Research in Economy, Zagreb Suggested Citation: Nguyen, Xuan-Huynh; Quoc Chien Luu (2021) : Performance measurement of Vietnamese publishing firms by the integration of the GM (1,1) model and the Malmquist model, Business Systems Research (BSR), ISSN 1847-9375, Sciendo, Warsaw, Vol. 12, Iss. 1, pp. 17-33, https://doi.org/10.2478/bsrj-2021-0002 This Version is available at: https://hdl.handle.net/10419/318747 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ 17 Business Systems Research | Vol. 12 No. 1 |2021 Performance Measurement of Vietnamese Publishing Firms by the Integration of the GM (1,1) Model and the Malmquist Model Xuan-Huynh Nguyen Vietnam National University, Vietnam Quoc Chien Luu Thanh Dong University, Vietnam Abstract Background: In the new technology context, the publishing industry cannot continue to maintain its business operations and to develop relying solely on traditional product offerings, such as books, magazines, and newspapers. There needs to be an expansion into innovative products, such as e-books, micro-publishing, and websites. Objectives: The paper addresses the factors influencing financial reports of Vietnamese publishing firms using two methodological approaches, namely the Grey first-order one variables (GM,1,1) model in the Grey theory and the Malmquist model in the data envelopment analysis (DEA). Methods/Approach: The GM(1,1) model predicts the future period of 2020–2023 based on the historical time series analysis. The Malmquist model presents catch-up, frontier-shift, and Malmquist Productivity Index (MPI) in whole terms. Results: The analysis provides an overview of the publishing industry in Vietnam. The final empirical results show that twelve companies reached a production efficiency higher than 1 and fourteen companies are expected to attain a productivity score higher than 1. Conclusions: Only a few firms do not need to change significantly; however, the remaining firms must re-evaluate their current operations. Keywords: Vietnamese publishing firms; GM(1,1) model; Malmquist model; production efficiency JEL classification: G17, N25, P34 Paper type: Research article Received: 15 Nov 2020 Accepted: 21 Mar 2021 Citation: Nguyen, X-H., Luu, Q.C. (2021). “Performance Measurement of Vietnamese Publishing Firms by the Integration of the GM (1,1) Model and the Malmquist Model”, Business Systems Research, Vol. 12, No. 1, pp. 17-33. DOI: https://doi.org/10.2478/bsrj-2021-0002 The acknowledgments: The authors would like to thank the editors and reviewers for their constructive comments related to this article. 18 Business Systems Research | Vol. 12 No. 1 |2021 Introduction Industry 4.0 affects directly and deeply the publishing industry because it is an effective support tool for the rapid transfer of information. Hence, the number of traditional publications has been reduced and replaced by electronic publications. As the publishing industry applies Industry 4.0, it can bring a high degree of effectiveness around the world. Nowadays, Vietnam has adopted new globally achieved techniques to catch up with the growth of electronic publishing and ebooks on the internet. The development process of the publishing industry has always met with difficulties in the process of change, regarding innovative technology implementation and economic growth (Lacy, 1979). In recent years, digital publishing has had a great effect on publications’ market shares (Lin, Chiou & Huang, 2013; Sara & Markus, 2014), and publishing firms need to have a production plan, schedule and control, inventory management, and reverse logistics (Meysam, Mohammad, Ebrahim & Ali, 2013). Besides, they need to investigate the consumer’s requirements and optimize excess product offerings (Anat, John & Pat, 2003; Edoardo, Antonello & Laura, 2008). Any nation applying digital techniques to the publishing industry also builds up its growth strategies (McCready & Molls, 2018; Edelmann & Schoßböck, 2020). Operational strategies are a key factor for the maintainability and sustainability development process to occur so that the publishing company can receive good revenues. A publishing firm of educational books (Lee & Liang, 2018), media content (Alexander & Thomas, 2007), software (Matthew, 2008) makes an effort to overcome specific challenges and reach full effectiveness. These previous studies have explored the development of the global publishing industry via many different methods. In this study, we utilize GM(1,1) model in the Grey theory system and the Malmquist model in DEA. GM(1,1) model is a forecasting tool that can deal with the minimum historical time series as four terms. The Grey theory supports solving characteristics of poor and insufficient information (Wu, Liu, Fang & Xu, 2015), thus it is useful to deal with the lack of available data. Previous research has utilized the GM(1,1) model for predictive purposes. For instance, Liu, Peng, Bai, Zhu & Liao (2014) forecasted future values of the factors affecting the tourist flow by GM(1,1) model. Maciej & Czeslaw (2015) used a set of GM(1,1) models to predict values of vibration symptoms of fan mills in a combined heat and power generation plant. Qian & Wang (2020) approached the GM(1,1) model to predict wind power generation in China based on the historical data from 2013 to 2019. Nguyen (2020) utilized the GM(1,1) model to forecast the operational efficiency of Vietnamese construction companies. Nguyen, Le, Ngo & Hoang (2020) investigate the business efficiency of global electric cars in 14 countries around the world. Data envelopment analysis (DEA) is a statistical analysis method that can be applied to various industries, such as economics (Gunes & Guldal, 2019), education (Montoneri, Lin, Lee & Huang, 2012), manufacturing (Ehsan & Hadi, 2014), banking (Bošković & Krstić, 2020), and others. Therefore, DEA is a useful method allowing the efficiency of a specific decision-making unit (DMU) to be measured by the ratio between virtual output and virtual input. The relative efficiency of a homogenous set of a specific DMU is computed by the presence of multiple inputs and outputs. DEA has many different models so that each model may have a separate functionality. For example, Liu & Wang (2008) evaluated the efficiency of Taiwanese semiconductor companies when using the Malmquist model with three components, including technical change, frontier forward shift, and frontier backward shift. Chen, Tzeremes & Tzeremes (2018) discovered the productivity levels of the Chinese airline market 19 Business Systems Research | Vol. 12 No. 1 |2021 through the Malmquist model. Mavi, et al., (2019) found out the efficiency of freight transportation in Iran and determined a declining trend in terms of eco-innovation and environmental efficiency when observed by the Malmquist model approach. Wang, Tibo, Nguyen & Duong (2020) measured the relative performance of New Zealand universities by use of a Malmquist productivity approach. This study uses the GM(1,1) model to forecast the main factors of financial reporting, including current assets, non-current assets, fixed assets, liabilities, owner’s equity, net revenue, gross profit, and net profitafter-tax in Vietnamese publishing firms. With the actual and forecasted data, the DEA has been applied. Whereas to have a good economic comparison of productivity efficiency among Vietnamese publishing companies, the Malmquist model in DEA is used to compute the score in every term, and the average score in whole terms. The estimated values throughout 2020–2023 are predicted utilizing the GM(1,1) model based on historical data obtained from 2015 to 2019, and then the productivity efficiencies in both previous and future terms are conducted by the Malmquist model. Combining the two above models, namely GM(1,1) model, and the Malmquist model, we can figure out the overall picture of Vietnamese publishing firms from the past to the near future. The empirical results reveal effective variations and identify the operational trends. Research results may serve as references that can help publishing firms in emerging economies foresee their operational processes and make a suitable plan for future development. Besides, the investors may choose the best partners in future terms for better profitability based on the presented analysis. The paper is arranged as follows. The introduction gives an overview of the publishing industry, the background of GM(1,1), and the Malmquist model. The second chapter sets up the conceptual research, materials, and methods. The third chapter presents the results of the empirical results. The fourth chapter discusses the main analysis results, while the last chapter summarizes the key empirical results, and it indicates the limitations and future research directions. Materials and methods Research process The research process is carried on a step-by-step basis, as shown in Figure 1. Step 1. The study determined the objective research, and then it collected all related data including inputs and outputs. All selected data must be positive values, and they must be removed and reselected if they are positive values. Step 2. The estimated data from 2020 to 2023 are calculated by a GM(1,1) model based on the primary data. These predicted values must check the accuracy level through the Mean Absolute Percentage Error (MAPE). All forecasted values owning unsuitable MAPE are removed and used with another forecast model. Step 3. The Malmquist model in DEA is used for conducting the efficiency score and determining the position of the publishing firms under consideration. Before the actual and forecasted data are utilized to conduct the scores, they must test the Pearson correlation between variables. Any unappreciated values must be returned and reselected as other values. The appreciated data is applied to computing the productivity efficiency from past to future time. Step 4. Major analysis results are given and discussed, which can be used to define the extent of efficient and inefficient cases. 20 Business Systems Research | Vol. 12 No. 1 |2021 Figure1 Research framework Source: Author’s illustration Materials The global publishing industry includes newspapers, periodicals, books, directories, and software. The growth of companies in the Vietnamese publishing industry is analyzed based on the actual data published on Vietstock (2020). The purpose of this study is to measure the financial performance of publishing firms in Vietnam throughout 2016–2023. The input and output variables of nineteen Vietnamese publishing firms are taken and then collected from 2016 to 2019. The names of nineteen companies are given, as shown in Table 1. 21 Business Systems Research | Vol. 12 No. 1 |2021 Table 1 List of publishing firms No. Firm code Name of publishing firms 1 ADC Art Design And Communication JSC 2 BDB Binh Dinh Book & Equipment Joint Stock Company 3 BED Danang Books & School Equipment JSC 4 BST Binh Thuan Book And Equiptment JSC 5 DAD Da Nang Education Development & Investment JSC 6 DAE Educational Book JSC in Da Nang City 7 EBS Educational Book JSC in Hanoi City 8 EID Education Cartography And Illustration JSC 9 HAP Hanoi Education Development & Investment JSC 10 HBE Ha Tinh Book And Equipment Education JSC 11 KBE Higher Education And Vocational Book JSC 12 LBE Long An School Book & Equipment JSC 13 NBE North Books and Educational Equipment Joint Stock Company 14 QST Quang Ninh Book & Educational Equipment JSC 15 SED Phuong Nam Education Investment & Development JSC 16 SGD Educational Book JSC in Ho Chi Minh City 17 SMN South books and Educational Equipment JSC 18 STC Book & Education Equipment JSC Of HCMC 19 TPH Ha Noi Textbooks Printing Joint Stock Company Source: Vietstock (2020) The following input and output factors have been taken into the consideration, which are the key parts of a financial statement that can help to give a deeper identification of an enterprise’s operational enterprise. These indexes equip to calculate the performance in operating progress that displays upward or downward trends in each term. Each of the highest and lowest efficiency points is identified to observe the worst or best business status in every term. All historical data are gathered from Vietstock and summarized as shown in Table 2. Table 2 indicates that the minimum value of CA, NA, FA, LS, OE, NR, GP, and PT is 8623; 1463; 621; 1342; 12698; 16619; 3911; 693, respectively. Therefore, all historical data realize positive values, they are appreciable to use for forecasting future data via the LTS (A, A, A) model and to measure the efficiency via the Malmquist model as well as the GM(1,1) model. Input factors: o Current assets (CA): Cash, cash equivalents, accounts, stock inventory, marketable securities, and other liquid assets. o Non-current assets (NA): The long-term investments made by a specific publishing firm. o Fixed assets (FA): The long-term tangible assets that are comprised of property, facilities, and equipment. o Liabilities (LS): Loans, mortgages, deferred revenues, bonds, warranties, and accrued expenses. o Owner’s equity (OE): The owner’s investment in the operational business of the enterprise. Output factors: o Net revenue (NR): Total sales of a company are received from selling the goods. o Gross profit (GP): The profit of a publishing firm after deduction of the charges for making and selling products. 22 Business Systems Research | Vol. 12 No. 1 |2021 o Net profit-after-tax (NT): The net income of a publishing firm after deducting taxes. Table 2 Historical data of publishing firms Code CA NA FA LS OE NR GP PT 2016 Max 283150 140198 41922 190209 233138 516773 142396 36504 Min 8623 1872 940 1764 12959 16619 3911 1072 Ave 64480 29805 10271 32599 61687 180175 38930 8512 SD 72056 30062 10126 43442 57503 154352 38439 10030 2017 Max 306786 121173 40172 181606 246353 577062 159499 36223 Min 10561 1590 621 2164 12779 19203 4107 760 Ave 70959 28218 10285 33976 65201 194884 43002 8637 SD 85262 26327 11104 45775 63944 165895 43333 10016 2018 Max 340056 106057 38331 183902 262211 599103 163408 40947 Min 11084 1463 864 1487 12698 23200 4430 707 Ave 76252 26824 11585 34043 69034 206116 45731 10609 SD 92511 23979 10775 48671 67717 176569 46316 11864 2019 Max 372315 95769 38590 185068 283016 652590 174643 45363 Min 12405 1484 747 1342 12993 26322 5464 693 Ave 77590 28906 12777 36628 69868 225307 49314 9702 SD 99761 24275 11549 53582 71268 197027 51390 11845 Note: Ave: average; SD: Standard deviation Source: Vietstock (2020) GM(1,1) model The GM(1,1) model is a forecasting tool based on the Grey theory system utilizing the previous time series (Deng, 1989). This model only calculates future data when the historical time series maintains positive data and is computed in the following order: From the primary data (0) (0) (0) (0) ( (1), (2),..., ( ))A A A A n= , the consequence (1) A is calculated by: 1 (0) ( ) (1) / ( 0,1,..., ) k i A h A h n==  (1) where, 10 1 0 0 1 0 0 0 0, (1) (1) 1, (2) (1) (2) , ( ) (1) (2) ... ( ) h A A h A A A h n A n A A A n == = = + = = + + + when having (1) A series, the mean equation (1) Z is built up: (1) (1) (1) 1 ( ) ( ( ) ( 1)) / ( 1,2,..., ) 2 A h A h A h h n= + − = (2) where, 23 Business Systems Research | Vol. 12 No. 1 |2021 (1) (1) (1) (1) (1) (1) 1 1, (1) ( (1) (0)) 2 1 2, (2) ( (1) (2)) 2 = = + = = + h A A A h A A A (1) (1) (1) (1) 1 , (2) ( (1) (2) ( )) 2 = = + +h n A A A A n The mathematical equation for a and b is determined by: (1) (1) ( ) ( ) / ( 2,3,..., )A h a Z h b h n+  = = (3) where a and b are coefficients. The linear equation of a matrix is presented by: (0) (0) (0) (0) (0) (0) 1 (2) (2) 1 (3) (3) 1 , / ( 1,2,... ) ( ) 1 () () TT AZ AZ E D h n Zh Ah a and D D D E b  −   −   −   = = =     −     =  (4) The whitening equation is formed: (1) (1) dA a A b dt +  = (5) Set up the estimated values   (0) (0) (0) (0) (1), (2),..., ( ) ( 0,1,2,..., )A a a a n n n== , the predicted equation is built up: (1) (0) ( 1) (1) / ( 1,2,.., ) ah bb A h A e h n aa −   + = − + =     (6) The forecasted values must examine the accuracy level via the mean absolute error percentage (MAPE): (0) (0) (0) 1 100 ( ) ( ) / ( 1,2,.., ) () n t A h A h MAPE h n nAh = − ==  (7) According to Lewis (1982), the MAPE indicator may be used to distinguish four groups, such as the excellent group (smaller than 10%); good group (10-20%); reasonable group (20-50%); and, poor group (higher than 50%). The unsuitable tested values require the usage of another forecast model or reselection of the primary data. Malmquist model The Malmquist model defines the efficiency change of a DMU between two consecutive periods (Tone, 2004). This study uses the Malmquist-radial model for measuring the efficiency of publishing firms in Vietnam over the period from 2016 to 24 Business Systems Research | Vol. 12 No. 1 |2021 2023. The distance functions of inputs and outputs at t and 1+t is 0 0 0 ( , ) t DF x y , and 1 0 0 0 ( , ) +t DF x y , respectively. The catch-up effect is estimated as follows: 1 1 1 0 0 0 0 0 0 ( , ) ( , ) t t t t t t DF x y CUI DF x y + + + = (8) The frontier-shift effect is computed: 1/2 11 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 ( , ) ( , ) ( , ) ( , ) t t t t t t t t t t t t DF x y DF x y FSI DF x y DF x y ++ + + + +  =   (9) The Malmquist Productivity Index (MPI) has four terms including 0 0 0 ( , ) t t t DF y , 1 0 0 0 ( , ) t t t DF y + , 11 0 0 0 ( , ) t t t DF y ++ and 1 1 1 0 0 0 ( , ) t t t DF y + + + , the mathematical equation of MPI is calculated: 1/2 1 1 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 ( , ) ( , ) ( , ) ( , ) t t t t t t t t t t t t DF x y DF x y MPI DF x y DF x y + + + + + +  =   (10) Let input and output matrices at the period ()p as 1 ( , ) p p p n X x x= and 1 ( , ) p p p n Y y y= respectively. The input-oriented radial MPI is presented by the scores ()  that are given by the linear programs as follows: , 0 0 min ( , ) p p p DF x y   = (11) where, 00 , , 0 p p p p x X y Y        Results Based on the collected data of nineteen Vietnamese publishing companies, we predict the future values. When having appreciated actual and forecasted data, the technical efficiency scores are computed by the Malmquist model. Estimated values The primary data of nineteen publishing companies in Vietnam is used for predicting the forecasted values utilizing the GM(1,1) model. We utilize the input variable (CA) of the ADC firm to illustrate the predictive process. Let the primary time series (0) A . (0) 80,055;88,350;96,533)(66,306;A= (12) Calculate time series (1) A . (1) 66,306;1 46,361; 234,711; 331,244)(A= (13) Count the mean sequence (1) Z . (1) 1 06,334;1 90,536; 282,978)(Z= (14) Formulate a and b. 31 Business Systems Research | Vol. 12 No. 1 |2021 overall picture of the publishing industry. Thirdly, our paper not only uses the actual data and forecasts the future data but also measures the performance of publishing firms by using the Malmquist model approach. We built a set of variables that are related to the operational process, and the findings evaluate the efficiency level of these firms and recommend a strategy to develop and improve their efficiency score in the future. Conclusion This research integrates GM(1,1) model and the Malmquist model to observe the publishing firms in Vietnam. The GM(1,1) model estimates the high degree of accuracy, as indicated by the average MAPE of 3.00176%. The operational progress of nineteen publishing companies in Vietnam is measured by the technical efficiency (catch-up), technological change (frontier-shift), and MPI when applying the Malmquist model in DEA. An analysis of actual and predicted data by the use of the Malmquist model describes a picture of the Vietnamese publishing industry. The catch-up efficiencies of publishing firms indicate that six firms are in no need of substantial change; however, remaining firms must re-evaluate current operations since these firms can improve their performance by employing increased values of revenue and profitability while reducing the values of assets, equity, and liability. On the other hand, frontier-shift results describe only a slight change for all publishing firms. Similarly, MPI also denotes that in recent years, publishing companies have experienced considerable fluctuation. Nine publishing companies, including BDB, BED, DAD, EID, HBE, NBE, QST, SED, and STC did not reach the desired efficiency level in previous years, but they are expected to have a good result in the future when their forecasted efficiency scores will be higher than one number. 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(2020), “Effects of the Performance-Based Research Fund and Other Factors on the Efficiency of New Zealand Universities: A Malmquist Productivity Approach”, Sustainability, Vol. 12 No. 15, pp. 1–18. 31. Wang, Y. N., Wei, F. F., Sun, C. Q., Li, Q. Z. (2016), "The Research of Improved Grey GM (1, 1) Model to Predict the Postprandial Glucose in Type 2 Diabetes”, BioMed Research International, Vol. 2016, Article ID 6837052. 32. Wu, L. F., Liu, S. F., Fang, Z. G., Xu, H. Y. (2015), “Properties of the GM(1,1) with fractional order accumulation”, Applied Mathematics and Computation, Vol. 252, pp. 287–293. About the authors Xuan-Huynh Nguyen is a lecturer at Hanoi School of Business and Management, Vietnam National University. He obtained his Ph.D. in International Business at the National Kaohsiung University of Science and Technology in Taiwan. His research areas include business administration, business development, and business strategy. The author can be contacted at [email protected]. Quoc-Chien Luu is a lecturer of the Department of State Management, Thanh Dong University in Vietnam. He obtained his Ph.D. in Industrial and engineering management at the National Kaohsiung University of Science and Technology, Taiwan. His research areas include business administration, business development. The author can be contacted at [email protected].