USING SELF-ORGANIZED MAPS AND ANALYTIC HIERARCHY PROCESS FOR EVALUATING CUSTOMER PREFERENCES IN NETBOOK DESIGNS

International Journal of Electronic Business Management, Vol. 7, No. 4, pp. 297-303 (2009) 297 USING SELF-ORGANIZED MAPS AND ANALYTIC HIERARCHY PROCESS FOR EVALUATING CUSTOMER PREFERENCES IN NETBOOK DESIGNS Nai-Hua Chen *, Min-Hsin Chen and Ruey-Chi Jao Department of Information Management Chienkuo Technology University Chang-Hua (500), Taiwan ABSTRACT Netbooks are small, stripped-down laptops that are inexpensive ($400-ish) and lightweight (3 lb). A Taiwanese company called ASUS first introducing the netbook, the Eee-PC, which has captured the market since 2007. Sales of netbooks increased from 182,000 in 2007 to 11m in 2008, and will reach 21m in 2009. Many famous PC makers are dedicated in making netbooks following the trend of the ultraportable netbook market. However, netbooks are still new to the market. There are problems that do not meet customer requirements. In this study, the Self-Organized Maps (SOM) is first used for market segmentation. Then the Analytic Hierarchy Process (AHP) is used to analyze selection process of netbook attributes. Empirical studies stress the important attributes which are the keyboard size, the monitor size and low-pitch sound. Results can provide related PC industry for further improvement. Keywords: Self-organized Maps, Analytic Hierarchy Process, Netbook * 1. INTRODUCTION Netbooks are small, stripped-down laptops that are inexpensive ($400-ish) and lightweight (3 lb). The netbook revaluation started from the One Laptop Per Child, OLPC" project of the MIT media Lab. The idea is to design a computer device for children in developing nations with open source software and off-the-shelf technologies. Although, the OLPC project has some bottlenecks during executing, the idea of simple and cheap laptop surged. A Taiwanese company called ASUS first introducing the netbook, the Eee-PC, which has captured the market since 2007. The concept of the netbook is to let customers who can access internet and word-processing wherever they want. In addition, people start to consider their basic needs after the downturn of the global economic. Sales of netbooks increased from 182,000 in 2007 to 11m in 2008, and will reach 21m this year, according to IDC, a market-research firm. Many famous PC makers included HP and Acer also dedicating in making netbooks following the trend of the ultraportable netbook market. However, netbooks are still new to the market. There are problems that do not meet customer requirements. For example, the keyboard size is smaller and hard to type or screen is too small to read. A growth number of researches adopt data mining in various areas due to the progressive * Corresponding author:nhc@ctu.edu.tw development in computer techniques. Data mining emerges computational intelligent to extract valuable information from databases. Major techniques of data mining include association rule, classification and prediction, clustering and mining complex types of data [3]. The clustering is usually used in segmentation data with different characteristics into different groups and similar characteristics into same group. The self-organizing map (SOM) is an unsupervised clustering method based on neural network techniques. The SOM projects a multidimensional input space into 2D. The strength of SOM lays in its data visualization and classification capabilities. Many researches apply the SOM in many areas [8]. Problems become more complex when the number of decision quantities is large or the decision quantities are in mutual complex. Multiple criteria decision making (MCDM) is a powerful tool used widely for evaluation and ranking complex decisions. In line with the multidimensional customer preferences of the netbook, MCDM provides an efficient framework for evaluating multiple attributes in netbook components. Many researches use the analytic hierarchy process (AHP) in MCDM approaches [1,2,10]. In this study, the SOM is first used for market segmentation. Then the Analytic Hierarchy Process (AHP) is adopted to analyze selection process of netbook attributes to calculate the priority of customer preferences of the netbook in different groups.

298 International Journal of Electronic Business Management, Vol. 7, No. 4 (2009) 2. METHODOLOGY 2.1 Questionnaire Design Participants supplied a knowing of how to operate computers. Three main attributes emerged to netbook designs which are demographic characteristics, the conscious for netbooks and computer using behaviors. Each attribute influences the preference of netbooks. A questionnaire was next designed, reviewed by experts, and pre-tested with a group of 10 respondents. Questionnaire variables were anchored on a 7-point Likert scale. Customer responses and discussion of netbooks are collected from the internet forums to design the questionnaire of customer needs for netbooks. Their needs are divided into the monitor, the battery, the memory, device design, and the audio sections. The monitor section is subdivided into the size of monitor and the resolution based on the AHP definition. The battery section is subdivided into the duration and the charging efficiency. The memory section is subdivided into the size and access time. The device design section is divided into the design of the fan and the keyboard size. The audio part is subdivided into the surrounding sound and the low-pitched sound. 2.2 Market Segmentation Based on SOM Segmentation groups respondents by similar characteristics [12]. Recently, neural networks have been used for classifying and clustering complicated data across a wide range of managerial issues [9]. Self-organizing maps (SOM), developed by Kohonen in the early 1980s, is a neural network method that trains data in an unsupervised fashion [5, 6]. One of its most advantages is easy to understand via graphic representation. The SOM method converts high-dimensional data items into low-dimensional items and is useful in visualizing data. The algorithm for SOM simulation is summarized as follows [6]. 1. Initialize the neuron weight, mi (t), on the map randomly, where mi (t) is the weight of each neuron point on the map, i indicates the neuron numbers and t is the iteration time. Suppose the total number of neuron is N, let i = 1, 2, N. 2. Compute the Euclidean distance, di= x-mi(t), between x and every neuron on the map. Here, the vector x is the service quality and demographics in the current study. 3. The best-matched neuron is defined as the shortest distance among all neurons. The shortest distance, dc, is defined as 4. dc= x-mc(t) =min{ x-mi(t) } for all neurons, and mc(t) is called the winning neuron. 5. Update the weight vectors of the winning neuron c and its neighboring according to the learningrule, mi(t+1)=mi(t)+α(t)hc,i(t)[x-mi(t)], where α(t) is an adaptive function, 0 α(t) 1. In this study, the adaptive function is set as 0.5 and it decreases with time t. The hc,i(t) is the neighborhood kernel defined as 2 rc ri ci, t 2 h () exp( ) (1) () t where r c and r i are coordinates of neurons c and i, andσis neighborhood radius at time t and is suitable decreasing function of time. 6. Repeat step 2 until the weights have stabilized. Furthermore, the two-stage clustering method [7,9] was used in this study for data clustering. The SOM was first used to determine the number of clusters and then the K-means method was employed to find the final solution. 2.3 The AHP Theory The AHP was introduced by Thomas L. Satty to analyze complex decision problems [13]. The AHP enable the decision-makers to build a complex problem in the form of a simple hierarchy. Various studies adopted the AHP in decision making [13, 14]. The processes of the AHP are simply outlined in the following steps. The first step is the decomposition of the decisions problem into a hierarchy form. The second is the judgment of individual elements. In this step, elements compared pair wise according to a ratio scale 1, 3, 5, 7, 9. The third step is to estimate the relative weights of elements by the eignvalue method. Finally, these weights are aggregated for ranking given decision alternatives. 3. RESULTS 3.1 Data Collection and Description A total of 450 questionnaires were given to respondents, and usable questionnaires totaled 357, an 79% valid response rate. Demographically, 51.8% of the respondents were male and 48.2% female. About 58.5% are students. Over 85% of the respondents use computer for more than 5 years. The percentage of respondents who uses computer more than 5 hours is close 64%. The most used function of computer is browsing websites, the second is writing email and the third is playing games. Close 29% of the respondents think they will purchase the netbook in the next six years. (or months???) 3.2 Market Segmentation In this research, data was first trained on an 4 6 hexagon map (Figure 1). The batch training was adopted in the algorithm, which means data set was presented to the SOM as a whole, and the new weight vectors were weighted averages of the data vectors [6] (Figure 2). The demographic variables, their consumption behavior in purchasing computers and their behavior in operating computers were first

N. H. Chen et al.: Using Self-organized Maps and Analytic Hierarchy Process 299 clustered by SOM. Data were formed in three groups (Figure 3) and then the K-means method was employed to group data into six clusters (Figure 4). Figure 1: The self-organizing map. Figure 2: The weight distance map. Each of the six clusters had a very typical profile in respondents background, netbook viewpoints and usage behavior in computers. Six clusters of customers are illustrated in Appendix 1. Cluster 1 (Potential and Careful) indicates respondents has highest intention in purchasing the netbook. All of them own PC with highest price among clusters. They are royal to the brand and will test the computer carefully before purchasing. Cluster 2 (Low Involvement) refers to respondents who involved in the information techniques very low. Their careers are not related to IT comparing to other clusters. They think the netbook is positive to their social position. Cluster 3 (Positive Attitude User) profiles the respondents who has highest rate in owning the netbook. They think the netbook is easy to operate and make their life more convenient. They are also sensitive to price and have positive attitude for the netbook. Cluster 4 (Beginner) indicates respondents all have notebook but have the lowest rate in owning PC. Their experience in operating computer is low. Cluster 5 (Professor) referred to respondents with higher percentage in doing IT job. They have confidence in operating the computer devices. However, their intension in purchasing the netbook is low. Cluster 6 (Belonging) think the netbook can make them earning the sense of belonging. 3.3 The AHP Results The respective eignvalues show preferences of each cluster (Appendix 2). The keyboard size is the most choice attributes for the first priority. The low involvement cluster, positive attitude user and the belonging cluster choose the monitor size as the second priority. Other attributes such as the low-pitch sound, the fan design the battery charging efficiency are chosen by different cluster as the third priority. 4. CONCLUSION AND DISCUSSION Figure 3: SOM results Figure 4: Two-stage results Customer preferences in different segmentation are implicated by the AHP in this study. Respondents are groups in six clusters based on their demographic variables, the computer usage behaviors and their opinions of the netbook. The positive attitude user cluster has the highest rate of owning a netbook, the keyboard size, monitor size and the fan deign are the top three priory in the list. They care about operating the computer conveniently. The practical function is the main element for this cluster to purchase the netbook. The potential and careful cluster has the highest rate in purchasing the netbook in the next 6 months. They put emphasis on the design of the keyboard size, the fan and the battery charging efficiency. This cluster want the power of a computer can last longer and the fan can work well. The beginner cluster has lowest experience in operating the computer, this cluster can be one of the targets

300 International Journal of Electronic Business Management, Vol. 7, No. 4 (2009) marketing cluster. They choose the keyboard and the low-pitch sound as the important attributes. It is because the second most used function for the netbook is multi-media software besides the internet. Overall, empirical studies stress the important attributes which are the keyboard size, the monitor size and low-pitch sound. Results can provide related PC industry for further improvement. REFERENCES 1. Armacost, R. L., Componation, P. J. and Mullens, M. A., 1994, An AHP framework for prioritizing customer requirement in QFD: An industririalized housing application, IIE Transactions, Vol. 26, pp.72-79. 2. Babic, Z. and Plazibat, N., 1998, Ranking of enterprise based on multicriterial analysis, International Journal Product Economics, Vol. 56/57, No. 20, pp. 29-35. 3. Han, J. and Kamber, M., 2001, Data Mining: Concepts and Techniques, San Francisco, CA: Kaufmann. 4. Kohonen, T., 1982, Self-organized formation of topologically correct feature maps, Biological Cybernetics, Vol. 43, No. 1, pp.59-69. 5. Kohonen, T., 1998, The self-organizing map, Neurocomputing, Vol. 21, No. 1/2, pp. 1-6. 6. Kohonen, T., 2001, Self-organizing Maps, Springer. 7. Kuo, R., Ho, L. and Hu, C., Integration of self-organizing feature map and K-means algorithm for market segmentation, Computers and Operations Research, Vol. 29, No. 11, pp. 1475-1493. 8. Oja, M., Kaski, S. and Kohonen, T., 2003, Bibliography of set-organizing map (SOM) papers: 1998-2001 addendum, Neural Computing Surveys, Vol. 3, pp. 1-156. 9. Punj, G. and Stewart, D., 1983, Cluster analysis in marketing research: Review and suggestions for application, Journal of Marketing Research, Vol. 20, pp. 134-148. 10. Sutterfield, J. S., Swirsky, S. and Ngassam, C., 2008, Project management software selection using analytical hierarchy process, Academy of Information and Management Science Journal, Vol. 11, pp. 79-93. 11. Smith, K. A. and Gupta, J. N. D., 2000, Neural networks in business: Techniques and applications for the operations researcher, Computers and Operations Research, Vol. 27, No. 11/12, pp. 1023-1044. 12. Satty, T. L., 1980, The Analysis Hierarchy Process, New York: McGraw-Hill. 13. Uzoka, F. M. E., 2005, Analytical hierarchy process-based system for strategic evaluation of financial information, Information Knowledge System Management, Vol. 5, No. 1, pp. 49-61. 14. Sutterfield, J. S., Swirsky, S. and Ngassam, C., 2008, Project management software selection using analytical hierarchy process, Academy of Information and Management Science Journal, Vol. 11, No. 2. pp. 79-104. ABOUT THE AUTHORS Nai-Hua Chen is an Assistant Professor in the Department of Information management at Chienkuo Technology University (CTU), Taiwan R.O.C. She received her Ph.D. degree in Computational Science and Informatics at George Mason University in 2004. Her current research and teaching interests are in the general area of Computational Statistics and Soft Computing. Min-Hsin Chen is an associate professor in the department of Information Management at Chienkuo Technology University (CTU), Taiwan R.O.C. She received her Ph.D. degree in Organizational Leadership at University of the Incarnate Word in 2003. Her current research focuses on using instructional technology to increase learning results and performance appraisal in an organization.. Rury-Chi Jao is an Associate Professor in the Department of Information management at Chienkuo Technology University (CTU), Taiwan R.O.C. He received his Ph.D. degree in Bio-Industrial Mechatronics Engineering at Taiwan University in 2003. His current major researches are in the RFID technology. (Received October 2009, revised November 2009, accepted November 2009)

N. H. Chen et al.: Using Self-organized Maps and Analytic Hierarchy Process 301 APPENDIX 1 Table A: Characteristics of each cluster Cluster1 (n=66) Cluster2 (n=174) Cluster3 (n=15) Cluster4 (n=41) Cluster5 (n=12) Cluster6 (n=49) ANOVA Mean Mean Mean Mean Mean Mean F-value P-value Own a Notebook (1: No, 2: Yes) 1.08 1.02 1.93 2.00 2.00 2.00 532.44 0.00* Price of the Purchased Notebook (NTD)+ 10157 10157 19300 34334 12505 34851 794.05 0.00* Usage Year of the Notebook (Year) 1.20 0.92 1.36 1.57 1.45 2.02 78.16 0.00* Own a Netbook (1: No, 2: Yes) 0.02 0.00 0.20 0.05 0.08 0.08 5.57 0.00* Own a PC (1: No, 2: Yes) 2.00 1.94 2.00 1.07 1.42 2.00 136.94 0.00* Price of the Purchased PC (NTD)+ 33150 18383 21212 9633 13000 26373 205.08 0.00* Usage Year of the PC (yr.) 2.43 2.75 3.49 1.83 3.06 2.96 17.71 0.00* Hours of Using Computers Daily (hr.) 6.26 6.49 6.00 6.32 6.29 6.92 0.20 0.96 Possibility in Buying Netbook (Scale :1-4) 2.39 2.16 2.20 1.95 1.67 1.86 4.66 0.00* Years of Using Computers (yr.) 8.61 8.34 7.96 9.14 8.58 9.95 1.98 0.08 Gender (1:male, 2: female) 1.55 1.45 1.27 1.59 1.67 1.45 1.66 0.14 Age (yr.) 23.09 23.37 25.6 24.56 25 26.04 2.48 0.03* Marriage (1: No, 2: Yes) 1.06 1.06 1.13 1.15 1.25 1.04 2.00 0.08 Education (1:Under High School,2: High School 3:College, 4: Graduate) 2.95 2.77 3.07 3.24 3.25 3.47 10.58 0.00* Living place (1: North, 2: Central, 3: South) 2.05 1.82 1.27 1.61 1.75 1.94 3.50 0.00* Carrere is Related to IT (1: No, 2:Yes) 1.23 1.18 1.47 1.34 1.77 1.29 4.90 0.00* Income (1: below 40000 NTD,2: 40001-800000 NTD, 3:80001-120000 NTD, 1.73 1.50 3.07 1.78 2.00 1.61 9.43 0.00* 4:120001-160000 NTD, 5: above 160001 NTD) + Functions (Scale :1-7) 4.69 4.60 5.18 4.65 4.25 4.61 0.80 0.55 sense of belonging (Scale :1-7) 4.26 4.04 4.18 4.23 4.22 4.27 0.61 0.69 Confidence in Computer Knowledge (Scale :1-7) 4.13 3.73 5.16 3.90 3.89 3.99 4.48 0.00* Showing Personality (Scale :1-7) 3.47 3.48 4.22 3.42 3.70 3.30 1.54 0.18 Excitement (Scale :1-7) 4.09 4.06 4.38 4.31 4.00 3.99 0.48 0.79 Price (Scale :1-7) 5.50 5.52 5.78 5.62 5.47 5.54 0.24 0.95 Familiar Operator the Computer (Scale :1-7) 5.33 5.07 5.49 5.32 5.92 5.44 2.44 0.03* Social Network (Scale :1-7) 4.80 4.57 5.11 4.83 4.14 4.39 2.04 0.07 Netbook Liker (Scale :1-7) 4.10 4.39 4.87 4.24 4.67 3.82 3.95 0.00* Computer Skill (Scale :1-7) 5.04 5.02 5.53 4.94 5.61 5.12 1.41 0.22 Brand Royalty (Scale :1-7) 4.58 4.34 4.40 4.54 4.38 4.16 0.69 0.63 Carefully Test (Scale :1-7) 5.33 4.93 4.90 4.98 5.17 4.74 1.57 0.17 Positive Attitude Netbook (Scale :1-7) 4.44 4.30 4.67 4.50 4.08 4.59 1.13 0.35 Social Position (Scale :1-7) 3.21 3.44 3.33 3.26 3.29 2.93 1.13 0.34 * p<.05 + 1 NTD= 33 USD

302 International Journal of Electronic Business Management, Vol. 7, No. 4 (2009) Hierarchy Level Attributes APPENDIX 2 Table B: AHP eignvalues and their rank of each cluster Cluster 1 Cluster 2 Cluster3 Cluster 4 (Potential (Low (Positive (Beginner) and Careful) Involvement) Attitude User) Cluster 5 (Professor) Cluster 6 (Belonging) Monitor Size 0.0978(5) 0.1214(2) 0.1311(2) 0.1099(4) 0.1984(1) 0.1449(2) Resolution 0.0865(8) 0.0699(8) 0.1127(5) 0.0892(5) 0.0857(6) 0.0782(7) Memory Capacity 0.0434(9) 0.0561(9) 0.0869(7) 0.0640(9) 0.0464(9) 0.0419(9) Access Speed 0.0331(10) 0.0403(10) 0.0476(10) 0.0384(10) 0.0164(10) 0.0233(10) Battery Duration 0.0963(6) 0.0872(6) 0.0550(9) 0.0690(8) 0.0828(7) 0.0816(6) Charging Efficiency 0.1308(2) 0.1135(4) 0.0868(8) 0.1228(3) 0.0933(5) 0.1268(4) Design Fan 0.1213(3) 0.1093(5) 0.1228(3) 0.0848(7) 0.0523(8) 0.0920(5) Keyboard Size 0.1778(1) 0.1989(!) 0.1345(1) 0.1825(1) 0.1572(2) 0.2057(1) Audio Surrounding Sound 0.0993(7) 0.0835(7) 0.1198(4) 0.0863(6) 0.1304(4) 0.0731(8) Low-Pitch Sound 0.1196(4) 0.1200(3) 0.1029(6) 0.1530(2) 0.1369(3) 0.1324(3)

N. H. Chen et al.: Using Self-organized Maps and Analytic Hierarchy Process 303 運用自我組織圖與層級階層程序於消費者之小筆電喜好設計研究陳乃華 * 陳明星饒瑞佶建國科技大學資訊管理系彰化市介壽北一路 1 號摘要小筆電為輕巧 ( 約 1.5g) 與低價 ( 約 400 美元以下 ) 之筆記型電腦, 台灣華碩公司於 2007 年首先於 2007 年推出小筆電 (Eee-PC), 其銷售額由 2007 年之 182,000 美元至 2008 年的 1 億 1 仟萬美元, 預計於 2009 年將達到 2 億 1 仟萬美元小筆電的構想來自於讓消費者可隨時隨地上網與文書處理, 意外成為市場新寵, 許多電腦製造廠商均紛紛投入小筆電的研發製造, 小筆電為一創新產品, 仍有許多設計無法滿足消費者需求本研究首先調查各論壇對於小筆電設計的抱怨項目進行歸納整理, 運用自我組織圖 (Self-Organized Maps,SOM) 進行集群分析, 並利用層級階層程序法 (Analytic Hierarchy Process,AHP) 找出各集群之權重排序結果顯示多數集群認為鍵盤大小螢幕尺寸以及低音效為最重視的項目, 建議業者可由此方向進行改善, 以提升顧客滿意度關鍵詞 : 自我組織圖層級階層程序小筆電 (* 聯絡人 :nhc@ctu.edu.tw)