CONJOINT ANALYSIS OF CONSUMERS PURCHASE BEHAVIOUR OF COMPUTER PRODUCT PORTFOLIOS: A PRACTICAL SIMULATION CASE

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1 CONJOINT ANALYSIS OF CONSUMERS PURCHASE BEHAVIOUR OF COMPUTER PRODUCT PORTFOLIOS: A PRACTICAL SIMULATION CASE Yu-Je Lee Takming University of Science and Technology, Taiwan pyj@takming.edu.tw Abstract This paper intends to explore consumers purchase behaviour of computer product portfolios by using conjoint analysis as the experiment tool for portfolio analysis. The data concerning the product attributes of three leading computer brands was collated. This is followed with a conjoint analysis on utilities, importance and correlations to derive the best mix of computer products in the purchase decision of consumers in Kaohsiung, Taiwan. Keywords: product portfolio, consumers preferences, conjoint analysis INTRODUCTION The data of this study concerning the product attributes of three leading computer brands was collated and the products prices surveyed shall be final during the surveyed period at that time. The questionnaire is shown on the appendix 1. This is followed with a conjoint analysis on utilities, importance and correlations to derive the best mix of computer products in the purchase decision of consumers in Kaohsiung, Taiwan. Basis of Conjoint Analysis (1) Regressions are performed to understand overall preferences and calculate the utilities of different attribute levels; (2) Attributes are a nominal variable and need to be translated into a dummy variable for analysis; and (3) Once the utility for a specific attribute level is estimated, it is possible to design a new product/service to meet the maximum expectations from consumers. Assumptions of Conjoint Analysis (1) The combination of product attribute levels must be meaningful; (2) Product utilities must be a simple function of the utilities of product attribute levels; (3) Consumers purchase the products offering 40

2 the maximum utilities; (4) The description of attributes and products in the survey are relevant and appropriate; (5) There are no redundant combinations; and (6) Consumers share the same view as the respondents in the survey for the strategy of product purchases. Statistics and Conditions of Conjoint Analysis (1) Part-worth (Utility) functions: Consumers express their subjective judgment in the form of product preferences by quantifying the utility for each attribute level; (2) Relative importance weights: These are the levels of importance that affect the choice by consumers; (3) Attributes: Attributes are an important factor of consumers decisions, and hence, they are the independent variable; (4) Levels: They represent the different selections made by consumers for specific attributes. These selections must be sufficiently differentiated; (5) Full profiles: All the attributes and level combinations are incorporated in the assessment for experiment designs. If each of the three attributes has three levels, the number of possible combinations will be 27 (3*3*3); (6) Fractional factorial designs: A too large number of product portfolios will make consumers assessment difficult. Therefore, only part of the portfolios is used to evaluate the overall situation; (7) Orthogonal arrays: These are a special category of partial factor designs. Orthogonal arrays aim to improve the efficiency in the assessment of main effects. The number of minimum combinations =total number of levels the number of attributes=9-3+1=7; (8) Profiles (stimuli): Profiles are the combination of product attribute levels; (9) Validation profiles (a.k.a. holdout profiles): They are not used for utility assessment. Rather, validation profiles are meant for the evaluation and forecast of the effectiveness of assessment results; and (10) Internal validity: This includes holdout profiles and the correlations of assessment results ( 1 ; 2 ). EXPERIMENT DESIGN AND ANALYSIS OF PRODUCT PORTFOLIOS Product Attributes, Attribute Levels, Utility Estimates and Importance Ranges Figure 1 illustrates the product attributes, attribute levels, utility estimates and importance ranges in this study. 41

3 Product Attribute level Utility estimate Importance range attribute Brand U(Mac) = 2.03 U(Sony) = MAC SONY ASUS U(Asus) = 0.0 RAM 8 G 4 G 2 G Price 31,000 42,000 U(8G) = 4.4 U(4G) = 2.43 U(2G) = 0.0 U(3.1) = 2.12 U(4.2) = 0.0 Figure 1: product attributes, attribute levels, utility estimates and importance ranges Addition notes to the above equation of importance: IMP k = Range k n ΣRange k=1 *100 Remarks: (1) Range: The gap between the minimum and the maximum utility values (2) If the variable subject is incorporated in the SPSS conjoint analysis, the results will include the mean of individual groups. Research design for the purchase behavior of computers list of cards Table 1:List of Cards Card ID Brand RAM Price 1 1 ASUS 2G MAC 4G ASUS 8G MAC 2G

4 5 5 SONY 2G ASUS 4G SONY 4G SONY 8G MAC 8G 3.1 Fractional factorial designs: 9 combinations sufficient because this number is greater than the minimum number of combinations & Chang 2 Calculating minimum number of profile (stimuli) (1) Total number of levels across all the attributes Number of attributes + 1 (2) Brands (3) + RAMs (3) + Prices (2) = 8 (3) 8 Attributes (3) + 1 = 6 (4) Number of the minimum product portfolios = 6 Research design for the purchase behavior of computers experiment design Table 2: Research design for the purchase behavior of computers experiment design ID Brand RAM Price Score 1 Mac 8 GB 31, Sony 4 GB Asus 2 GB 31, Mac 2 GB 42, Sony 8 GB 31, Asus 4 GB 31, Mac 4 GB 31, Sony 2 GB 31, Asus 8 GB 42,000 5 derived from Chang 2 and Wu 3 43

5 Attribute Levels Translated into Dummy Table 3:Conversion into Dummy Variables Variables Use of Software for Conversion ID Dmac Dsony DR8G DR4G DP3.1 Score A DUMMY VARIABLE REGRESSION U= b0+b1dmac+b2dsony+b3dr8g+b4dr4g+b5 DP3.1 +e U= Dmac+.3Dsony+ 4.4DR8G+ 2.43DR4G+ 2.12DP3.1 +e Table 4 DUMMY VARIABLE REGRESSION DV IV Unstd. Std. B SE β t P Score Const Dmac Dsony Dr8G DR4G DP

6 CONJOINT ANALYSIS RESULTS U= Dmac +.3Dsony + 4.4DR8G Data Analysis +2.43DR4G +2.12DP3.1 + e Utilities Table 5: Utility comparison of brands, memories and prices U(Mac) = 2.03 The utility of the Mac brand is 2 units higher than that of U(Sony) =.30 Asus. U(Asus) = 0.0 U(8G) = 4.4 The utility of 8GW RAM is 4.4 units higher than that of U(4G) = G RAM. U(2G) = 0.0 U(3.1) = 2.12 The utility of the price tag is 2.12 units U(4.2) = 0.0 higher than that of NT$ 42,000 price tag. Utility Values Explained Table 6 presents the explanations of the Table 6: utility values explained Price attribute (NT$ 1,000) 43-37= 8 Utility value per NT$ 1, /8 =.265 Utility difference between Mac and Asus = 2.03 The brand value of Mac higher than that of Asus 2.03/.265 = 7.66 utility values in this study. U (3.1) = 2.12 U (4.2) = 0.0 U (Mac) = 2.03 U (Sony) =.30 U (Asus) = 0.0 Prediction of the Utilities for Product Portfolios The regression equation for the prediction of the utility of a given product portfolio is as follows: U = Dmac +.3Dsony DR8G +2.43DR4G+ 2.12DP3.1 The calculation of the utility of a given product portfolio: Mac +4GRAM + NT$ 42,000 (1, 0, 0, 1, 0) U= (1) +.3(0) + 4.4(0) +2.43(1) + 45

7 2.12(0) = 5.37 VECTOR MODEL REGRESSION Table 7 VECTOR MODEL REGRESSION ID MAC SONY RAM Price Score Note: RAM stats based on actual numbers. Pricing data based on effect coding. VECTOR MODEL REGRESSION U = MAC +.3Sony +.699RAM 1.06EP Table 8 VECTOR MODEL REGRESSION DV IV Unstd. Std. t p B SE β Score Const DB DB RAM PIRCE

8 Attribute Importance Table 9 Product Attribute Importance Maximum utility minus minimum utility of an attribute Importance of brand = U(Mac) U(Asus) = 2.03 Importance of RAM = U(8G) U(2G) = 4.40 Importance of price = U() U(NT$ 42,000) = 2.12 U (Mac) = 2.03 U (Sony) =.30 U (Asus) = 0.0 U(8G) = 4.4 U(4G) = 2.43 U(2G) = 0.0 U(3.1) = 2.12 U(4.2) = 0.0 Correlations Correlations are between attributes and utility Table 10: correlation table values. Similar with correlation analysis, this approach sheds light to the quality of the model. Value Significance Pearson s R Kendall s tau Reserved Kendall s tau Note: Observed correlation with estimated preferences The correlation table indicates that both Pearson s R and Kendall s tau are statistically significant. This suggests a correlation between the attributes and utility values in the consumers purchase of computer product packages. In other words, purchase decisions are based on product attributes, utility values and the correlation between these two factors. CONCLUSIONS AND SUGGESTIONS Conclusions This paper examines the purchase behaviour of consumers by conducting a conjoint analysis on the combination of computer product portfolios ( 4 ; 5 ). The first step is to delve into the product attributes of the three sampled computer brands and process the data with conjoint analysis to determine 47

9 correlations, importance and utilities of the attributes. The purpose is to understand the best combination consumers have in mind for the purchase of computer products. (1) As far as product attributes and correlations are concerned, brands, memories and prices are the key considerations for consumers. In fact, the most important factor for buyers is the size of memories, followed by prices and then brands; (2) In terms of attribute levels and importance, MAC is the most favored brand among the three sampled brands (Sony and Asus being the other two). Asus is the least favored brand of the three; (3) Mac has with the maximum utility value for its brand. Among the different size of memories (8GB, 4GB and 2GB), the largest size of 8GB is the most popular with consumers. Meanwhile, a lower price tag of is more acceptable to buyers. In sum, the conjoint analysis on the sampled purchase decisions over computer product portfolios reveals that the most favoured package is the Mac, 8G memories and a price tag of. This is followed by the combination of the Sony brand, 8GB memories and a price tag of NT$ $31,000 and then the combination of the Mac brand, 4GB memories and a price tag of. The least popular package is the Mac brand, 2GB memories and a price tag of NT$ 42,000. Research Contributions The application of conjoint analysis to identify the consumers preferences for computer product portfolios is an innovative approach in theoretical application. As far as the innovation in practice, the research findings can serve as a reference to marketing executives in the computer industry by providing insight to the behaviour and preferences of consumers. Suggestions This paper only surveyed the consumers in Kaohsiung, Taiwan and only sampled the data of three brands, i.e. Mac, Sony and Asus. Follow-up studies can expand their coverage of geographic areas and product mixes or even combinations with other products (e.g. fast foods). It is also possible to use Big Data and cluster analysis to observe the structural differences in consumer preferences of different market segments. References 1 Wu, W. Y. (2008). Business Research Method, Third Edition, Taipei: Hwa-Tai Publishing, Chang, W. H. (2016). Practical Examples of SPSS in Conjoint Analysis (Handout); Taiwan, Tristar Statistics. 3 Wu, M. L. (2009). SPSS Operation and Application: The Practice of Quantitative 48

10 Analysis of Questionnaire Data, Second Edition, Taipei: Wu-Nan Book Inc. 4 Lin, C. H. (2001). Marketing Management Case Publishing Co., Ltd. 5 Tai, K. L. (2008). Marketing Management - Marketing Practices and Local Case Studies, Studies, First Edition, Taipei: Best-Wise Appendix 1 Questionnaire (A total of 9 Sets) First Edition, Taipei: Gau Lih Book. MAC 8G RAM MAC 2G RAM NT$ 42,000 MAC 4G RAM SONY 4G RAM NT$ 42,000 SONY 8G RAM SONY 2G RAM ASUS 2G RAM ASUS 4G RAM ASUS 8G RAM NT$42,000 49

11 Appendix 2 Data entries for conjoint analysis Three data types for conjoint analysis (1) Rank Card 1~ Card 6: Enter the sequence of preferences at the bottom of the card. For instance, the number set 2, 3, 1, 6, 5, 4 indicates the strongest preference for Card 3 and the least preference for Card 4. (2) Score Card 1~ Card 6: Enter the preference ratings (0~10) at the bottom of the card. A high rating indicates a strong preference. For example, the number set 7, 6, 9, 3, 4, 5 means the strongest preference is for Card 3 and the lowest preference is for Card 4. (3) Sequence PREF 1~PREF 6: Enter the card numbers at the bottom. The number set 2, 3, 1, 6, 5, 4 suggests the strongest preference is for Card 2 and the weakest preference is for Card 4. 50