A DEA-Based Portfolio Model for Performance Management of Online Games

This paper proposes a strategic portfolio model for managing performance of online games. The portfolio matrix is composed of two dimensions: financial performance and non-financial performance. Financial performance is measured by the conventional measure, average revenue per user (ARPU). In terms of non-financial performance, five non-financial key performance indicators (KPIs) that have been widely used in the online game industry are utilized: RU (Register User), VU (Visiting User), TS (Time Spent), ACU (Average Current User), MCU (Maxi- mum Current User). Data envelopment analysis (DEA) is then employed to produce a single performance mea- sure aggregating the five KPIs. DEA is a linear programming model for measuring the relative efficiency of decision making unit (DMUs) with multiple inputs and outputs. This study employs DEA as a tool for multiple criteria decision making (MCDM), in particular, the pure output model without inputs. Combining the two types of performance produces the online game portfolio matrix with four quadrants: Dark Horse, Stop Loss, Jack Pot, Luxury Goods. A case study of 39 online games provided by company 'N' is provided. The proposed portfolio model is expected to be fruitfully used for strategic decision making of online game companies.

[1]  Theodor J. Stewart,et al.  Relationships between Data Envelopment Analysis and Multicriteria Decision Analysis , 1996 .

[2]  Laurens Cherchye,et al.  AN INTRODUCTION TO 'BENEFIT OF THE DOUBT' , 2006 .

[3]  Laurens Cherchye,et al.  An Introduction to ‘Benefit of the Doubt’ Composite Indicators , 2007 .

[4]  Ramakrishnan Ramanathan,et al.  Data envelopment analysis for weight derivation and aggregation in the analytic hierarchy process , 2006, Comput. Oper. Res..

[5]  Naveen Donthu,et al.  Retail productivity assessment using data envelopment analysis , 1998 .

[6]  Jesús T. Pastor,et al.  Radial DEA models without inputs or without outputs , 1999, Eur. J. Oper. Res..

[7]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[8]  Choongsoo Lee.  Evaluation Model of On-line Game using Analytic Hierarchy Process , 2008 .

[9]  Rodney H. Green,et al.  Data envelopment analysis and multiple criteria decision making , 1993 .

[10]  Sungmook Lim,et al.  Method of Benchmarking Route Choice Based on the Input Similarity Using DEA , 2011 .

[11]  Valerie Belton,et al.  Demystifying DEA — A Visual Interactive Approach Based on Multiple Criteria Analysis , 1993 .

[12]  Choong-Soo Lee,et al.  Effects of Online Game Service Quality on Customer Satisfaction and Loyalty , 2009 .

[13]  Jung Min Hong,et al.  A Multiattribute Attitude Analysis of On-line Game Attributes and User's Perceptions , 2002 .

[14]  Chin-Lung Hsu,et al.  Why do people play on-line games? An extended TAM with social influences and flow experience , 2004, Inf. Manag..

[15]  篠原 正明,et al.  William W.Cooper,Lawrence M.Seiford,Kaoru Tone 著, DATA ENVELOPMENT ANALYSIS : A Comprehensive Text with Models, Applications, References and DEA-Solver Software, Kluwer Academic Publishers, 2000年, 318頁 , 2002 .

[16]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .