J. Inf. Technol. Appl. Manag. 23(4): 83~100, September 2016 ISSN 1598-6284 (Print) http://dx.doi.org/10.21219/jitam.2016.23.4.083 ISSN 2508-1209 (Online)
Application of DEA to Investigate Distinctive Regional Characteristics for Asia-Pacific Telco Management
Ancilla K. Kustedjo*․Hyun-Soo Han**
Abstract
In this paper, we present the DEA (Data Envelopment Analysis) application case study to investigate the regionally distinctive telco management characteristics of the Asia-Pacific countries. This study attempts to exploit the implications of DEA for the assessments of core process capabilities of telcos. Accordingly, we extract input variables of CAPEX (capital expenditure), operating expense, marketing expense, and number of employees, each to reflect the competitiveness of the core processes such as fixed asset utilization, operation & sales efficiency, and white collar productivity. In conjunction with the input variables, the output variables are chosen as EBITDA (Earnings Before Interest, Taxes, Depreciation and Amortization), ARPU (Average Revenue per User), and number of subscribers. The computational testing results, conducted with total 37 telcos of the 12 Asia-Pacific countries, are analyzed in various ways to understand the distinctive performance characteristics across the region. The managerial implication captured from this study provides useful insight for using DEA as the international telco management purpose.
Keywords：DEA, Telco Management, Telco Process Capability, Asia-Pacific Region, ARPU, International Management
1)
Received：2016. 11. 30. Revised : 2016. 12. 29. Final Acceptance：2016. 12. 29. ※ This work was supported by the research fund of Hanyang University (HY-2015). * Ph.D. Candidate, Hanyang University, School of Business, e-mail：[email protected] ** Corresponding Author, Hanyang University, School of Business, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763 Korea, Tel：+82-2-2220-1822, e-mail：[email protected] 84 JOURNAL OF INFORMATION TECHNOLOGY APPLICATIONS & MANAGEMENT
1. Introduction faceted performance measurement scheme along with financial measures would be more appro- Telecommunication service is regarded as priate. DEA (Data Envelopment Analysis) me- critical and valuable instrument necessary for thod is one useful technique for this purpose. socio-economic development of the nation. In- DEA is a non-parameter technique for eva- deed, information and communication services luating the efficiency of decision making units provided by telcos contribute to the growth of (DMUs) [Charnes et al., 1978]. DEA distingui- the national economy in the sense of reducing shes efficient versus inefficient units by em- transaction cost, facilitating information flows ploying multiple inputs and outputs variables. [Mitra and Shankar, 2008]. So, in many coun- The critical advantage of DEA in performance tries, the policy level control is made to maintain measurement is it does not need some assump- proper numbers and total capacity of domestic tions for the specific relationship between inputs telcos, and that the accurate measurement of and outputs. For instance, DEA can be used to telco’s performances is regarded as important. identify specific inefficiencies that may not be Moreover, the international comparison of telcos measurable by common analysis methods such performance provides more objective assess- as ratio analysis or linear regression [Hong et ment in judging domestic telco management al., 1999]. The practices of DEA for benchmark- efficiency [Hung and Lu, 2007]. For instance, ing are diverse for coffee shop [Joo et al., 2009], sometimes a domestic telcos yielding positive third party logistics [Min and Joo, 2009] and so profit may not be relatively efficient when com- on. In telecom industry, various DEA analysis pared to other foreign telcos under similar envi- were adopted with different purposes, which in- ronment. clude finding best service provider [Mitra and Our research motivation stems from this wor- Shankar, 2008], impact assessment of wireless thiness of international comparison of telcos. Par- communications merging [Kwon et al., 2008], ticularly, our interest is to compare the perfor- performance modelling of mobile phone pro- mances of Asia-Pacific telcos having similar cul- viders [Kwon, 2014], and others. tural and socio economic environment. We think Most DEA telco studies for efficiency and the comparison result could provide some useful productivity were conducted with domestic play- insights in understanding regional characteri- ers [Yang and Chang, 2009; Lam and Shiu, 2010; stics which should be helpful for collaboration Hisali and Yawe, 2011; Liao and Lien, 2012]. or establishment of telecom related new busi- Some exceptions are Hung and Lu [2007] and nesses in Asia-Pacific region. However, under Hu and Chu [2008]. They extended the scope of international context, the performance compar- telco performance comparison internationally. ison is not so straightforward to use financial However, as the authors mentioned, both studies statement only. Because there are so many fac- are unclear about what are the distinguishing tors which are different across the region, multi- international factors of those operators that have Vol.23 No.4 Application of DEA to Investigate Distinctive Regional Characteristics for Asia-Pacific Telco Management 85 been more successful in achieving efficiency than lyzed the impact of new technology adoption in others. They suggested this as the further work US telco industry using DEA. The analysis was agenda. We focused on this. As a first step of conducted in cross-sectional manner to reveal this further research agenda, as for the Asia- the positive relationship between the levels of Pacific telecom operators, we attempt to find out new technology output and utilization and the regionally distinctive telco management charac- firm performance. Koski and Majumdar [2000] teristics using DEA. reported efficiency enhancement effect in case The remaining of this paper is organized as of fixed and cellular communication convert- follow. The next section provides brief lite- gence through expansion and acquisition of com- ratures on telco efficiency studies using DEA. plementary telco infrastructures. Section 3 outlines research method of DEA ap- Lien and Peng [2001] investigated the effi- plication including variables selection. In Section ciency of telcos of 24 OECD countries, and veri- 4, we report the computational testing and ana- fied that telcos under tough competition were lysis result. And finally, conclusions are re- more efficient. Calabrese et al. [2002] compared marked in Section 5. efficiency of 13 OECD telcos having distinctive patterns of capital accumulation. Tsai et al. [2006] 2. Literatures for Telco DEA verified the validity of new DEA methodology through comparison of 39 Forbes 2000 ranked Several studies adopted DEA technique to mea- global telecom operators. Hung and Lu [2007] sure the efficiency and productivity of the telco investigate the relationship between the size of industries. The benefit of DEA method include the telcos and performance, and found that con- the description of relative productivity among solidation of small units to grow bigger will po- decision making units (DMU) by measuring the sitively impact on the telco efficiency. The em- efficiency between the inputs and outputs having pirical testing results indicated better perfor- heterogeneous dimensions [Charnes et al., 1978]. mance for European telcos more than those of DEA method has been revolutionized through Asia Pacific and America. Hu and Chu [2008] development of new analysis techniques so that studied the impact of industrial policy of com- it has become one of the standard analytical tool petition and privatization of telco industry for for comparing performances of the various busi- the Asia Pacific telco firms. Yang and Chang ness units [Cooper et al., 2000]. [2009] suggested that telcos can improve scale There have been several studies that measure efficiency through acquisitions but could get en- efficiency and productivity of telco industries countered with poor technical efficiency in the using DEA methods. Fuss [1994] studied pro- short run. ductivity growth of Canadian telcos by TFT Hisali and Yawe [2011] assembled the panel (Total Factor Productivity) whcih is estimated data set, and adopted common set of input and through DEA analysis. Majumdar [1995] ana- output indicators to compare the estimation of 86 JOURNAL OF INFORMATION TECHNOLOGY APPLICATIONS & MANAGEMENT
DEA for the Telco Industry
Year Author Objective Input variables Output variables Total Factor Local service revenue, Toll service 1994 Fush Capital, Labor, Mainline Productivity Growth revenue, Miscellaneous revenue Impact of new The number of switches, The number of local calls, The number 1995 Majumdar technology Access lines, Employees of toll calls Convergence in The number of the main telephones lines, Koski and Investment of operators, 2000 telecommunication The number of cellular subscribers, Majumdar Total staffs infrastructure Percent of digital main lines Competitions and Employee, Mainline, 2001 Lien and Peng Revenue Production Efficiency Investment 2002 Calabrese et al. Capital accumulation Labor, Mainline Revenue Total assets, CAPEX, 2006 Tsai et al. Productivity efficiency Revenue, EBITDA, Operating profit Number of employees Examine managerial Total Asset, CAPEX, 2007 Hung and Lu Revenue, EBITDA, EBIT, Net Income performance Employee Efficiency and The number of employees, Revenue of fixed-line services and 2008 Hu and Chu productivity The amount of fixed assets non-fixed line services Asset, Operating cost, Operating revenue, Mobile phone, 2009 Yang and Chang Efficiency Operating expense Subscribers, Mobile phone calls Cost of investments, Total Factor Non-wage operational cost, Telephone calls, International calls, 2011 Hisali and Yawe Productivity Growth Number of employees, Revenue Staff costs Expenditure in crores, Call Service access delay, Complaints per 2012 Nigam et al. Performance efficiency success rate, Call drop rate, 1,000 bill, No of subscriber, Gross Voice quality revenue the Malmquist Total Factor Productivity index urement, the CCR and the BCC model [Charnes via input-oriented Data Envelopment Analysis. et al., 1978; Banker et al., 1984]. The CCR model Nigam et al. [2012] adopted DEA for the purpose is calculated with the preconception of the con- of benchmarking Indian mobile telecom opera- stant return to scale (CRS) and BCC model is tors. Sensitivity analysis was conducted with calculated with the presumption of variable re- the DEA result to identify the differences among turn to scale (VRS). DEA efficiency measure- the efficiency and inefficient units.
ment could consider two kinds of orientation. briefly lists the key variables of previous studies First, the input-oriented model minimizes input on DEA analysis for telco industry. while satisfying, at least, the given outputs level. Second, the output-oriented model maximizes 3. Research Methods outputs without demanding more of any of the realized input levels. Particularly, the input- 3.1 DEA Method oriented model calculates to minimize the cost and output-oriented model calculates to maxi- There are two type of DEA efficiency meas- mize revenue. Vol.23 No.4 Application of DEA to Investigate Distinctive Regional Characteristics for Asia-Pacific Telco Management 87
Regional Grouping of 37 Telcos
Group Country Company Malaysia Maxis Communication, Celcom Axiata, DiGi Telecommunications Philippines Globe Telecom, Smart Communication, Sun Celular Group 1 Indonesia PT Telkom Tbk, PT Indosat Tbk, PT XL Axiata Tbk Thailand Advanced Info Service (AIS), DTAC, True Corporation Australia Telstra, Optus, Vodafone Japan KDDI, NTT, Softbank Group 2 South Korea SKT, KT, LGT Taiwan Chungwa Telecom, FarEast Tone, Taiwan Telecom Singapore SingTel, M1, Starhub Group 3 Hong Kong SmarTone Telecommunication, PCCW, Hutchison Telecommunication China China Mobile, China Unicom, China Telecom Group 4 India Idea Cellular, Airtel India, Reliance Communication, Vodafone India
The analysis focus is, considering BCC model 3.2 Telco Selections and Regional Grouping is based on VRS assumption, the (the effici- The telcos are selected from the BMI [Busi- ency of the DMUi) can be pure technical effici- ness Monitor International, 2015] report which ency (PTE) while in CCR model represents list competitive telcos of Asia pacific countries. technical efficiency (TE). The BCC models gene- On the basis of telco scores, we 37 telcos from rate a VRS efficiency frontier and estimates both 12 countries. With reference to BMI information, technical efficiency and scale efficiency three or four major telcos are selected from each [Nigam et al., 2012]. Nigam et al. [2012] country. Next, we classified these telcos into consider that “technical efficiency is the efficiency four groups as illustrated in
. The of converting inputs to outputs, while scale effi- classification is made as per the cultural sim- ciency recognizes that economy of scales will ilarity, income level and regional characteristics. not obtain at all scales of production and there Group 1 is organized with Malaysia, Philip- is only one most productive scale size where pines, Indonesia, and Malaysia, which are part the scale efficiency is 100%. Therefore, the DMU of ASEAN countries in Southeast. These coun- is said to be efficient if and only if it is both tech- tries have common characteristics in their eco- nical and scale efficient.” When the target DMU nomic growth pattern and social cultural evolu- has a SE value equal to one, it operates at constant tion. Group 2 consists of South Korea, Japan, return scale. In case SE score is less than one Australia, and Taiwan. Each country in group for the target DMU, then it is scale inefficient 2 is ranked relatively high in GDP and also over- [Hung and Lu, 2007] and distinguished as either all ICT technology level is advanced. Furthe- IRS (Increasing Returns to Scale) or DRS (De- rmore, we grouped Hong Kong and Singapore creasing Returns to Scale). together into group 3 since they have similarities 88 JOURNAL OF INFORMATION TECHNOLOGY APPLICATIONS & MANAGEMENT in economic structure, size of region, and po- The number of employees are selected to ana- pulation. The last group 4 consists of India and lyze the labor productivity. Labor productivity, China. They are the two biggest population coun- in some sense white collar productivity, is one tries in the world. of the critical benchmarking item. In this paper, we compare the original DEA result with alter- 3.3 Variables Selection native DEA result which are calculated without the number of employee variable. By comparing The selection of input and output variables the changes of telco status of efficient versus in DEA is very fundamental to derive mean- inefficient, we can find out the critical role of ingful and reasonable results. In previous liter- employee productivity to make a particular telco atures on telco DEA analysis, the input and out- from inefficient to efficient or vice versa. For put variable selections are diverse as illustrated example, if a particular telco’s efficiency status in
. With reference to variables used is changed from efficient to inefficient after for telco DEA analysis and to extract perform- omitting number of employee as input variable, ance implications of telco management, we se- then we could judge that employee productivity lect the four input variables and three output of the telco plays a critical role to make it variables. The criteria for input variable selec- efficient. Indeed, we intend to adopt this kind of tions is to assess the core process capabilities analysis to measure critical managerial varia- of telcos, which include fixed asset utilization, ble’s performance across the Asia-pacific re- operation and sales efficiency, and white collar gions, and individual telcos within a specific productivity. In conjunction with the input vari- region. ables, the criteria for selecting output variables Similarly, the input variable of CAPEX (capit- is to incorporate critical telco performance in- al expenditure) is selected to assess the relative dicators such as operating profit, average reve- efficiency of infrastructure and facilities utili- nue per user, and the number of subscribers. zation. The other input variables of marketing As such, the selected input variables are the expense and operating expense are selected to number of employees, capital expenditure (CA- analyze the relative efficiency of business proc- PEX), marketing expense and operating expense. esses of marketing and operations. In sum, the They are already employed in other telco liter- input variables selected in this paper are most ature, for example, employee and capital expen- frequently used input variable in the literature diture in Koski and Majumdar [2000], Lien and and will be used for efficiencies of labor, capital, Peng [2001], Tsai et al. [2006], Hung and Lu [2007], and business processes of marketing and ope- Hu and Chu [2008], Liau and Lien [2012]; opera- ration. ting expenses in Yang and Chang [2009], Kweh The output variables selected in this paper are et al. [2014], Wang et al. [2014], and marketing ARPU (Average Revenue Per Subscriber), EBI- expenses in Mason et al. [2016]. TDA (Earnings Before Interest, Taxes, Deprecia- Vol.23 No.4 Application of DEA to Investigate Distinctive Regional Characteristics for Asia-Pacific Telco Management 89 tion and Amortization), and the number of sub- vement efforts of upgrading or enlarging total scribers. They are widely used in telco DEA anal- service portfolio items. If a telco turns out to ysis, for example, ARPU in Mason et al. [2016], be inefficient when DEA is run excluding ARPU subscribers in Koski and Majumdar [2000]; Kwon from the set of output variables, then it gives et al. [2008], Mitra and Shakar [2008], Yang and us the information that the ARPU of the telco Chang [2009], Nigam et al. [2012], Liau and Lien is critical to make it as efficient. In other words, [2012], Mason et al. [2016]; EBITDA in Tsai et al. the telco was inefficient without ARPU as an [2006], Hung and Lu [2007]. output, but since the ARPU of the telco is so As the input variables are selected for the superior, the inclusion of ARPU into the output purpose of analyzing functional performance com- makes it to become efficient. The same kind parisons of telco management such as employee of analysis will be conducted for the number productivity, the output variable are also selec- of subscribers and EBITDA across the region ted for telco management performance analysis. and among the telcos within the region. The ARPU is selected to obtain some managerial details of variable selection is organized in insights to identify critical management impro-
.
Selection of Input and Output Variables
Category Variable Description Researcher
Koski and Majumdar [2000], Lien and Peng [2001], Tsai et al. [2006], Employee All staffs engaged in telco operations. Hung and Lu [2007], Hu and Chu [2008], Liau and Lien [2012]
Koski and Majumdar [2000], The cost related to principal sources to CAPEX Lien and Peng [2001], Tsai et al. [2006], Input develop telco services to customers. Hung and Lu [2007], Liau and Lien [2012]
Total administrative expenses to run the Yang and Chang [2009], Operating Exp telco. Kweh et al. [2014]
All expenses used for marketing, selling, Marketing Exp Mason et al. [2016] and advertising
Revenue divided by the number of ARPU subscribers; the price paid by the Mason et al. [2016] customer each month.
Tsai et al. [2006], EBITDA Firm’s net operating performance result Output Hung and Lu [2007]
Koski and Majumdar [2000], Subscribers having accounts of network Kwon et al. [2008], Mitra and Shakar [2008], Subscribers service provider. Yang and Chang [2009], Nigam et al. [2012], Liau and Lien [2012], Mason et al. [2016] 90 JOURNAL OF INFORMATION TECHNOLOGY APPLICATIONS & MANAGEMENT
3.4 Data Collection and Descriptive Statistics Finally, we verified the number of DMUs with the number of variables. In DEA application, The data for 37 telcos are collected from con- Cooper et al. [1999] have a rule of thumb for solidated financial report posted annually on DMU analysis which max [×], company web site. The year 2015 annual report where n is a number of DMU, m is a number data are used for computation testing.
shows the descriptive statistics for input and analysis, we used 37 telcos, and the number is output variables adopted for DEA analysis. Fur- larger than triple times of the number of inputs thermore, we conducted the correlation analysis (4) and outputs (3). That is, 37 > 3(4+3) = 21. among the input and output variables, which is organized in
. The result shows that 4. Results of DEA Analysis all the input variables are positively correlated with output variables. And, among the output We used DEAP 2.1 program [Coelli, 1996] to variables, the correlation between ARPU and run the DEA model. Input oriented CCR and BCC subscribers is shown to be negative. It is possi- is adopted which investigates the efficiency ble for ASEAN countries because the increase change resulting from changing the input value of user, for example due to the pre-paid card against a fixed output value [Kim et al., 2014]. users, sometimes inversely related with the The computational testing results organized on ARPU in telco industries [Firli et al., 2015]. group basis are presented in
India 1.000 1.000 1.000 CRS 3 India 1.000 1.000 1.000 CRS 1 India 1.000 1.000 1.000 CRS 4 India 0.781 0.839 0.930 DRS 0 Japan 0.987 1.000 0.987 DRS 1 Japan 0.624 1.000 Japan 0.412 1.000 0.41 China 1.000 1.000 1.000 CRS 6 China 0.915 0.964 0.949 DRS 0 China 0.972 1.000 0.972 DRS 1 Taiwan 0.850 0.9 Country TE Australia 0.780 1. Australia 0.509 0.637 Australia 0.732 South South South
Telecom Taiwan 0.464 0.512 0.907 DRS 0
India Deviation Deviation Comm
Mobile Taiwan 0.797 0.859 0.928 DRS 0
Mobile Unicom Telecom India
Cellular
Standard Mean Standard Mean 2 4
D013 Vodafone Group Group SE RTS Freq DMU Company Country TE SE RTS Freq DMU Company
DEA results DEA
organizedin different 4 groups VRS VRS PTE PTE CRS CRS 0.984 1.000 0.984 0.694 0.907 0.765 0.032 0.000 0.032 0.218 0.129 0.202 Country TE Country TE Philipina 0.352 1.000 0.352 IRS 1 D003 Vodafone Philipina 0.463 0.960 0.483 IRS 0 D016 SoftBank Philipina 0.391 0.624 0.628 IRS 0 D020 Chunghwa Thailand 0.606 0.746 0.812 IRS 0 D001 Optus Thailand 1.000 1.000 1.000 CRS 2 D018 LGT Thailand 0.623 0.949 0.656 IRS 0 D002 Telstra Malaysia 0.693 0.797 0.869 IRS 0 D022 Taiwan Indonesia 1.000 1.000 1.000 CRS 4 D019 SKT Indonesia 0.891 1.000 0.891 IRS 1 D015 NTT Singapore 1.000 1.000 1.000 CRS 2 D012 Idea Singapore 0.921 1.000 0.921 IRS 1 D005 China Singapore 0.983 1.000 0.983 DRS 0 D006 China Hongkong 1.000 1.000 1.000 CRS 1 D010 Reliance Comm Hongkong 1.000 1.000 1.000 CRS 2 D011 Airtel Comm Hongkong 1.000 1.000 1.000 CRS 9 D004 China
Tbk Indonesia 0.738 1.000 0.738 IRS 0 D017 KT
Tbk Tbk
Mobil
Global
Deviation Deviation
Axiata (AXIATA) Malaysia 0.745 0.811 0.919 IRS 0 D021 FarEasTone
Comm
Comm Telecom
Corporation
Communication Malaysia 0.831 0.998 0.832 IRS 0 D014 KDDI Celular
Telkom Indosat XL
Standard Mean Standard Mean 1 3
D024 PT. D035 AIS D023 PT. D008 Hutchison D034 Starhub D025 PT. D027 Digi D009 SmartTone D007 PCCW D033 SingTel D030 Sun D029 Smart D032 M1 D037 True D028 Maxis D036 DTAC D031 Globe D026 Celcom DMU Company DMU Company Group Group 92 JOURNAL OF INFORMATION TECHNOLOGY APPLICATIONS & MANAGEMENT
Among the 37 telcos, 12 telcos were shown to 2 is only 2 out of 12 (16.7%). The reason why be efficient (32%) when CCR model was applied group 2 telcos of Korea, Japan, Taiwan and (TE = 1), and the total number of efficient telcos Australia runs relatively inefficient status could when BCC model was applied, was 23 (62%). be explainable by the DRS (Decreasing Returns Some interesting result is that both Hutchison to Scale) operating level. Among the 10 in- Global Communication in Hong Kong and SKT efficient telcos of group 2, total 9 telcos were in Korea were shown to be highly competent, operating at DRS. These telcos could be reduced in terms of frequency shown in
, to in size to achieve better scale efficiency. The become a benchmark telco. The frequency of telecom capacity of group 2 on a national basis particular efficient DMU means the number of could be over capacitated to run at optimal re- count referred by other inefficient DMUs. The turns of scale. count measures the DMU’s importance as a Similar performance enhancement opportuni- benchmark [Smith and Mayston, 1987], and the ties in opposite way were available to group 1 high frequency referenced DMUs are considered telcos of ASEAN countries. All the total 10 in- to be reference DMUs by other inefficient DMUs. efficient telcos of group 1 run at IRS (Increasing Returns to Scale). It means that these telcos 4.1 Regional Telco Efficiency Analysis could be consolidated to achieve the scale effici- ency. The CCR inefficient two telcos in group The mean value of TE in group 1, which con- 3 showed mixed result of IRS and DRS each. sists of ASEAN countries, was shown to be With respect to PTE criterion, the mean effi- 0.694. As indicated in
, TE value of ciency score of group 4 is 0.972, which indicated group 1 was far below than the average TE that the telcos in group 4 were operated more score of 0.809. The group 3 which consists of efficiently under the less scale efficient environ- telcos in Hong Kong and Singapore showed ment. Furthermore, group 1’s lowest mean value most efficient scores of TE = 0.984 and PTE = of scale efficiency and TE revealed strong in- 1.000. While the number of efficient telcos in dustrial need of consolidating small scale telcos group 3 was 4 out of 6 (67%) when BCC model to achieve better economy of scale. Furthermore, is applied, the number of efficient telcos in group the overall mean TE score of 0.809 and mean
Group based Telco Efficiency Analysis
Number of Telcos Mean Score Group CCR BCC Total CRS IRS DRS TE PTE SE efficient efficient CRS VRS Group 1 12 2 5 2 10 0 0.694 0.907 0.765 Group 2 12 2 7 2 1 9 0.752 0.906 0.834 Group 3 6 4 6 4 1 1 0.984 1.000 0.984 Group 4 7 4 5 4 0 3 0.953 0.972 0.979 Total/Avg. 37 12 23 12 12 13 0.809 0.934 0.863 Vol.23 No.4 Application of DEA to Investigate Distinctive Regional Characteristics for Asia-Pacific Telco Management 93
SE score of 0.863 indicated that the telco in- higher than the former, so that the DEA model efficiencies in Asia-Pacific countries were at- of the latter will be less constrained to result tributable to both the scale and pure technical in higher efficiency score [Jenkis and Anderson, inefficiencies. 2003]. Following the stepwise DEA output variable 4.2 Regional Telco Output Factor Analysis inclusion procedure, we first analyzed the im- pact of ARPU and number of subscribers on tel- The output variables of ARPU, number of co efficiency. In , the computational subscribers, and EBITDA are selected to repre- testing result is illustrated. When ARPU was sent the key performances of telco. In order to not included in the output variable set, the num- extract the contribution effect of the particular ber of efficient telcos in group 2 and group 3 output variable on telco’s efficiency, we run DEA was three and two each. However, after ARPU analysis two times in stepwise manner [Wagner was included as the output variable, the number and Shimshak, 2007] and compare the results of of efficient telcos turned out to increase up-to the two. Considering that the scale efficiency seven and six each. Indeed, in both group 2 and was assessed in the former section, we used 3, additional four telcos had become efficient PTE figures of BCC to judge the telco efficiency. when ARPU was included as one of the output At first, we run the DEA without including focal variable. It clearly verifies that the critical com- output variable, and next run the DEA with com- petitive factor of telcos in region 2 and 3 is plete variable set. The two DEA results could ARPU. be different, and in any case the efficiency score On the other hand, the similar analysis con- of the latter DEA will always be at least higher ducted with the number of subscribers revealed than the score of the former DEA. It is because the contrary result. This time, without the num- the number of variables of the latter DEA is ber of subscribers, the number of efficient telcos
The Impact of ARPU and Subscribers 94 JOURNAL OF INFORMATION TECHNOLOGY APPLICATIONS & MANAGEMENT
The Impact of EBITDA
The Impact of Marketing Expense and Number of Employee in group 1 was two. However, when the number ference for the group 1, 2, 3. Only the group 3 of subscribers is included in the output variable, telcos showed somewhat significant efficiency the number of efficient telco has been increased gains from EBITDA inclusion to output. The up-to five. The result definitely indicates that number of efficient telcos in group 3 was four the major performance factor to make group 1 without EBITDA, and the number increased telco efficient is the number of subscribers. It up- to six after EBITDA was included in the is interesting to note that, the number of sub- output. scribers does not affect the relative efficiency of the telcos in group 2 and group 3. 4.3 Regional Telco Input Factor Analysis The EBITDA variable analysis, as illustrated in , did not indicate significant dif- We analyzed the relative contribution effect Vol.23 No.4 Application of DEA to Investigate Distinctive Regional Characteristics for Asia-Pacific Telco Management 95
The Impact of Operating Expense and CAPEX of managerial factors reflected as the DEA input variable. We think that this is partially due to variables. As was done with the output varia- the portion of pre-paid cards since the market- bles, stepwise DEA method was adopted also. ing effort is required relatively less for pre-paid We first run the DEA by omitting one focal in- card subscription compared to post-paid sub- put variable. Then, we compared the result with scription. The other notable difference is found the other DEA result with complete input varia- for the number of employee variable in group ble set. The input variables used for input factor 3. The number of efficient telcos in group 3 was analysis was marketing expense, operating ex- four when the number of employees was not in- pense, CAPEX (Capital Expenditure), and the cluded, and the number was increased to up-to number of employees. As with the output varia- six. ble analysis, we used PTE scores, and the com- In general, the regionally distinguishing ef- parison results were presented in
fect of the input variables was not shown to and
. be strong. The less constrained effect of effi- Indeed, the computational testing result in- ciency increase attributable to the number of dicated a few notable input factor contribution input variables increase was assumed to be effects which could be distinguishable across shown for the other input variables. However, the region. The one was marketing expense of the impact of input variables on performance group 1. Without marketing expense as an input on telco efficiency was significant among the variable, the number of efficient telco in group telcos within the group. As shown in
, the rank of telco efficiency changed signi- as illustrated in
, when the market- ficantly across the telcos particularly in group ing expense was included as one of the input 1 and group 2. 96 JOURNAL OF INFORMATION TECHNOLOGY APPLICATIONS & MANAGEMENT
inChange Ranking with and Without Variable PTE Rank Korea 1.000 1 0.781 5
Japan 1.000 1 1.000 1 Japan 1.000 1 1.000 1 Japan 1.000 1 1.000 1 Taiwan 0.906 3 0.906 3 Taiwan 0.859 4 0.859 4 Country Philipina 0.624 9 0.635 11 Philipina 1.000 1 0.782 8 Philipina 0.960 4 0.960 3 Thailand 0.746 9 0.746 9 Thailand 1.000 1 1.000 1 Malaysia 0.797 9 0.797 7 Australia 0.959 2 0.959 2 Australia 0.637 5 0.637 6 Australia 1.000 1 1.000 1 South South Korea 1.000 1 1.000 1 South Korea 1.000 1 1.000 1 Tbk Indonesia 1.000 1 0.712 10
Tbk Indonesia 1.000 1 0.812 5
Telecom Taiwan 0.512 6 0.512 7
Axiata Mobile (AXIATA) Malaysia 0.811 6 0.811 6
Comm
Comm Telecom
Corporation Thailand 0.949 5 0.949 4
Communication Malaysia 0.998 2 0.998 2 Celular
Indosat XL
1 2
D036 DTAC D031 Globe D028 Maxis D037 True D026 Celcom D029 Smart D018 LGT D035 AIS D023 PT. D027 Digi D021 FarEasTone D022 Taiwan D001 Optus D020 Chunghwa D014 KDDI D016 SoftBank D003 Vodafone D002 Telstra D025 PT. D030 Sun D019 SKT D017 KT D015 NTT D024 PT. Telkom Tbk Indonesia 1.000 1 1.000 1 DMU Company Group Group Vol.23 No.4 Application of DEA to Investigate Distinctive Regional Characteristics for Asia-Pacific Telco Management 97
5. Conclusion and scale inefficiencies in data envelopment analysis”, Management Science, Vol. 30, No. The contribution of this paper is rather prac- 9, 1984, pp. 1078-1092. tical. By applying stepwise DEA in terms of an- [2] BMI, Business Monitor International Re- alyzing particular input/out variable addition ef- search-Indonesia Telecommunications Report fect, the regionally distinguishing telco manage- Q4, 2015, Accessed on October 2016, Avai- ment variables are revealed. We applied DEA lable at http://search.proquest.com/docview/ to extract distinctive regional characteristics for 1702173245/. telco management. The computational testing [3] Calabrese, A., Campisi, D., and Mancuso, P., was conducted with 37 telcos across the 12 coun- “Productivity change in the telecommuni- tries. cations industries of 13 OECD countries”, The analysis results revealed that one of the International Journal of Business and Eco- major cause of ASEAN telco inefficiency is due nomics, Vol. 1, No. 3, 2002, p. 209. to operating at IRS (Increasing Returns to Scale) [4] Charnes, A., Cooper, W. W., and Rhodes, E., status, which indicates consolidation opportunity “Measuring the efficiency of decision ma- to achieve efficiency. On the other hand, some king units”, European Journal of Opera- telcos of Korea, Japan and Taiwan are found to tional Research, Vol. 2, No. 6, 1978, pp. be less efficient due to operating status at DRS 429-444. (Decreasing Returns to Scale) stage. Also, ARPU [5] Coelli, T., A guide to DEAP version 2.1 : and number of subscribers are shown to play a data envelopment analysis (computer) pro- a different role across the region in achieving gram, Centre for Efficiency and Producti- the telco efficiency. Although the regional dis- vity Analysis, University of New England, tinction is not strong, the impact of input variables Australia, 1996. of marketing and operating expenses, capital ex- [6] Cooper, W. W., Seiford, L. M., and Tone, K., penditure, and number of employees, on the telco Data Envelopment Analysis : A Compre- efficiency were shown to be significant. hensive Text with Models, Applications, Re- Theoretically, this paper could be added upon ferences, and DEA-Solver Software, Kluwer the literatures of stepwise DEA applications, and Academic Publishers, Boston, 1999. international management studies. On the basis [7] Cooper, W. W., Seiford, L. M., and Tone, of this paper, more elaborate comparative telco K., Data Envelopment Analysis : A Com- management research could be extendible. prehensive Text with Models, Applications, Kluwer Academic Publisher, Boston, MA, References 2000. [8] Firli, A., Primiana, I., and Kaltum, U., “The [1] Banker, R. D., Charnes, A., and Cooper, W. Impact of Increasing CAPEX on Customer W., “Some models for estimating technical Number, Profit, and ROI in Indonesia Tele- 98 JOURNAL OF INFORMATION TECHNOLOGY APPLICATIONS & MANAGEMENT
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Author Profile Ancilla Katherina Kustedjo Hyun-Soo Han Ancilla Katherina Kustedjo is Hyun-Soo Han is a Profe- a Ph.D Candidate in Hanyang ssor in the Business School University in Seoul, Korea (Ma- of Hanyang University in Seoul, jor : Service Operation Manage- Korea. He received a B.S. in ment). She received a B.S. in Industrial Engineering from Chemical Engineering from Parahyangan Uni- Seoul National University, and M.S. in Manage- versity and MBA in Institute Technology Ban- ment Science at KAIST. He earned Ph.D. in dung, Indonesia. Her research areas are operation Management from University of Massachusetts management, supply chain management, and Amherst, USA. He formerly worked at the Korean management science. IT service company as a director of the IS con- sulting service department. His technical pub- lications appear in various international and do- mestic journals including Information & Manage- ment, Decision Support Systems, International Journal of Technology Management, Internatio- nal Journal of Satellite Communications and Networking, International Journal of Operations and Quantitative Management, European Journal of Operational Research, Annals of Operations Research, and so on. His research interests in- cludes operational innovation, smart SCM & business model, and others.