APPLYING ARTIFICIAL NEURAL NETWORK TO ANALYZE TWO-DIMENSIONAL QUALITY MODEL

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1 APPLYING ARTIFICIAL NEURAL NETWRK T ANALYZE TW-DIMENSINAL QUALITY MDEL Li-Hsing Ho 1, Kun-Yu Wu 2, Chi-Feng Peng 3* 1 Li-Hsing Ho, Department of Technology Management, Chung Hua University, No.707, Sec.2, WuFu Rd., Hsinchu 300,Taiwan, address: ho@chu.edu.tw 2 Kun-Yu Wu, Ph.D. Program of Technology Management, Chung Hua University, No.707, Sec.2, WuFu Rd., Hsinchu 300,Taiwan, address: wing.ky.wu@gmail.com 3 Chi-Feng Peng, Department of Technology Management, Chung Hua University, No.707, Sec.2, WuFu Rd., Hsinchu 300,Taiwan, address: paine.peng@gmail.com ABSTRACT *Corresponding author Kano s model of two-dimension quality can display the correlation between the actual quality performance and customer satisfaction, and has been widely applied in various fields, and also its efficient integration with other quality management methods. Since its introduction, many different methods have been proposed to improve the original model. However, too little attention is paid to the development of the Kano s model or the design of new methods to replace the original evaluation table. Most of the existing methods are based on the operational validity and cannot fully reflect the non-linear relationship between the quality attributes and customer satisfaction. In addition, one-dimension quality model might underestimate the attractive quality attributes but overestimate the must-be quality and indifferent quality attributes. In this study, we propose a modified evaluation sheet of Kano s model. And applying Artificial Neural Network (ANN) to be analysis and verified. The result is found that, we propose an enhanced quality model to clarify the correlation between quality attributes and customer satisfaction. Moreover, the priorities of quality improvement should be decided for optimizing customer satisfaction. Through our case study, the proposed enhanced quality model is proved to be feasible and valid. KEYWRDS: Kano model, Artificial Neural Network, Two-Dimensional Quality Model, Independent Variable Importance INTRDUCTIN Research has discovered that the relationship between quality attributes and overall customer satisfaction is nonlinear and asymmetrical. However, few studies have further explored the relationships between each attribute and customer satisfaction. Kenny (1988) is the first to propose a cognitive model accessing customers quality perception, which considers three types of quality. Noriaki Kano assumes that the relationships between quality attributes and overall customer satisfaction are nonlinear and asymmetrical and proposes a two-dimensional quality model through empirical research (Kano, Seraku, Takahashi, and Tsuji, 1984), dividing quality attributes into five categories: attractive, one-dimensional, must-be, indifferent and reverse types of quality. In the model, the relationships between attributes and satisfaction vary among different features, which indicates the unique needs and performance 38

2 requirements of each attribute. This model is usually called Kano s two-dimensional model (Lee and Chen, 2006; Ho, Wang, Teng and Lin, 2007). Some scholars hold that this model can be used to understand demand and even increase customers desire to purchase. In addition, the model facilitates planning of competitive strategies for product and service development (Lee and Newcomb, 1997; Matzler and Hinterhuber, 1998). The Kano model is widely used in various fields. Integrating the Kano model with other methods of quality management is also effective. However, few papers have focused on further developing the Kano model or proposing new approaches to replace its current evaluation table. Additionally, numerous scholars have questioned this model. For example, they have reported that the model briefly conceptualizes the quality attribute categorization of a certain product function. Moreover, evaluation tables used in this method are arbitrary because they are constructed by experts and scholars on the basis of their own experiences and suggestions without any objective evidence. Accordingly, the self stated importance claimed by customers does not necessarily reflect the relative importance of a certain quality attribute when using the Kano model (Lin, Yang, Chan and Sheu, 2010; Yang and Lee, 2016). To solve this problem, some scholars have evaluated relative importance by using inferential statistics. Matzler and Sauerwein (2002) conduct multiple regression analysis to derive the relative importance of quality attributes, named the implicitly derived importance. Regarding the correlations between various quality attributes, Sampson and Showalter (1999) employ structure analysis to derive correlations between quality attributes; impact factors have also been previously used to determine relative importance (Lee et al., 2008). The aforementioned studies propose new evaluation tables for categorizing quality attributes based on the Kano model to improve upon the original evaluation table. Nonetheless, most of these methods focus on the operation process, which does not fully reflect the nonlinear and asymmetrical relationships between quality attributes and customer satisfaction. Numerous studies have determined that most attributes are identified by using the conventional Kano evaluation table. This table cannot reflect the actual nonlinear relationships between quality attributes and customer satisfaction. Additionally, the old evaluation table is likely to underestimate attractive quality and overestimate must-be and indifferent quality (Chen and Lee, 2009; Lee, Lin and Hsiao, 2016; Rao and Lee, 2016). In this study, we employ an artificial neural network (ANN) to perform analysis of Kano s two-dimensional model. This enables improving the Kano model and ensures that the observed nonlinear relationship between each attribute and satisfaction further matches the actual concept of the Kano model. Finally, a case study is conducted to verify the feasibility and validity of the modified two-dimensional model. 39

3 LITERATURE REVIEW Kano Model The Kano model is derived from research on product quality development in factories for manufacturing televisions and decorative clocks (Kano et al., 1984). The research has discovered that consumers hold a two-dimensional rather than a one-dimensional opinion of product features. Accordingly, the two-factor theory proposed by Herzberg, Mausner, and Snyderman (1959) is modified to propose Kano s two-dimensional model. In the representation of the Kano model in Fig. 1, the x-axis represents the degree to which a quality attribute is fulfilled, from completely unfulfilled (the left extreme) to completely fulfilled (the right extreme). The y-axis depicts the level of customer satisfaction, from completely dissatisfied (the lower extreme) to completely satisfied (the upper extreme). The five quality attributes are thus categorized by the location in the x and y directions. Figure 1. Kano s two-dimension quality model. 40

4 Lee and Ho (2008) explain the five attributes as follows: (1) Attractive quality: Among the five attributes, attractive quality has the strongest influence on customer satisfaction. Generally, features with attractive quality are often unknown or unspoken by customers. When attractive quality is delivered, customers are satisfied and their satisfaction exhibits exponential growth as the attractiveness increases. If attractive quality is not delivered, however, dissatisfaction is not elicited. (2) ne-dimensional quality: Customer satisfaction is directly proportional to the level of one-dimensional quality. Thus, customer satisfaction is higher when quality is fulfilled but lower when quality is only partially or completely unfulfilled. (3) Must-be quality: This attribute refers to product features that are taken for granted by customers. If must-be quality is not delivered, customers are extremely dissatisfied. In other words, customers care about fundamental product features. Nonetheless, delivering must-be quality does not increase customer satisfaction because the fundamental features of any product are considered necessary by customers. (4) Indifferent quality: This attribute refers to features (of any product and service) that do not affect customer satisfaction. (5) Reverse quality: When reverse quality is delivered, customers are dissatisfied. By contrast, if it is not delivered, customers are satisfied. Artificial Neural Network An ANN is a computing algorithm that simulates the structure of the human brain to process information. First proposed in 1890, this concept is adopted to build an ANN mathematical model (McCulloch and Pitts, 1943). Subsequently, the perceptron machine is introduced (Rosenbaltt, 1958), which involves repetitive learning processes by constantly adjusting its weights to achieve high performance. Use of ANNs first thrived between 1957 and However, the slow computational speed of ANN algorithms at that time limited such models learning capability. ANNs were then rarely mentioned until 1985 when new algorithms were invented, constituting a major breakthrough. ANNs have since been widely used once more. The design of an ANN is inspired by biological neural networks. By imitating the cognitive processes of sentient beings, ANN algorithms can create problem-solving plans based on their learning and cognitive history. Numerous ANN models have been proposed, including the most influential the backpropagation neural (BPN) model (Zhang, Patuwo and Hu, 1998). The framework of a BPN model represents multilayer perception, and such models adopt a learning algorithm called error backpropagation. The backpropagation algorithm is a type of multilayer feedforward neural network and is used to process nonlinear mapping relationships between input and output data through supervised learning. A perceptron often 41

5 contains multiple layers, but they can be divided into three major layers: the input, hidden, and output layers. In the input layer, each neuron corresponds to a predictor variable. The number of neurons thus represents the number of predictor variables. In the output layer, the number of neurons represents the number of response variables. The hidden layer may contain one or more layers. METHDLGY The literature indicates that the quality attributes of the Kano model are categorized according to five curves, and the most crucial attributes are attractive, one-dimensional, and must-be quality. By observing the slopes of those quality attributes (with the x-axis being the performance in a specified quality attribute and the y-axis being satisfaction), we summarize the three quality patterns as follows: 42

6 (1) The slope of a feature with attractive quality increases with the performance level (β 3 > β 2 > β 1 ). (2) The slope of a feature with one-dimensional quality is constant (β 3 = β 2 = β 1 ). (3) The slope of a feature with must-be quality decreases with the performance level (β 3 < β 2 < β 1 ). Kano et al. (1984) emphasize that the relationships between performance of fulfilling attributes and overall customer satisfaction should be one- and two-dimensional. Because an ANN does not need to know the relationships between different functions in advance, the network can simultaneously process linear and nonlinear functions. According to Kano s model, one-dimensional linearity does not always reflect relationships between attribute performance and customer satisfaction. Therefore, we conduct independent variable importance analysis to modify the Kano evaluation table. The importance of an independent variable is a measure of how much the network's model-predicted value changes for different values of the independent variable. Sensitivity analysis is performed, which computes the importance of each predictor in determining the neural network. CASE STUDY The case study investigates the satisfaction of customers of Starbucks in Taiwan (Liu, Huang and Chen, 2012). We explore Kano s two-dimensional quality model by using an ANN. Consequently, the study provides a reference for Starbucks regarding strategic planning and service development to enhance customer satisfaction. The questionnaire explores the relationships between customer satisfaction and the performance in quality attributes among coffee retailors in Taiwan and is designed after consulting experts and scholars of relevant fields. The following five dimensions are included in the questionnaire: tangibles, reliability, responsiveness, assurance, and empathy. A 5-point Likert scale is adopted to evaluate performance in fulfilling quality attributes and overall satisfaction. The first 22 items assess performance in fulfilling each quality factor; a high score indicates high participant satisfaction. Item 23 identifies the participants overall satisfaction with Starbucks; with high scores suggesting high participant satisfaction. The participants of this study are customers who have purchased products in branches of Starbucks in Taiwan. The questionnaire is distributed to 700 customers, and 669 valid responses are received; a response rate of 95.3%. The first 22 items are marked Q 1 Q 22, and Item 23 is marked Y for convenience. Subsequently, the ANN multilayer perceptron (MLP) in SPSS is employed to analyze the collected data. The items Q 1 Q 22, which represent the five dimensions 43

7 for evaluating performance, are treated as the input variables; one hidden layer is adopted with sigmoid as the activation function; and item Y (overall satisfaction) is treated as the output variable, with softmax used as the activation function. The ANN analysis of independent variable importance identifies six attractive quality attributes (of the total 22 items): Q 1 (high quality coffee), Q 7 (business hours), Q 8 (frequent promotions), Q 10 (customer interaction), Q 15 (public image), and Q 21 (frequent offering of fashionable merchandise). When performance is high regarding these six attributes, customers are satisfied; conversely, customers do not feel dissatisfied if the attributes are not fulfilled. Attractive quality is often considered the factor that differentiates a product. Moreover, attractive quality greatly impresses customers by surprising or delighting them. Therefore, product differentiation and competitive advantage are achieved when attractive quality is high (Deng and Lee, 2006). These two attributes are taken for granted by customers but result in dissatisfaction if not fulfilled (Deng and Lee, 2006). The remaining 14 items are categorized as must-be attributes after consultation with several experts and scholars. When a product or service provides these attributes, customers are satisfied; when they do not offer the attributes, customers are dissatisfied (Deng and Lee, 2006). The results of item categorization using ANN analysis are displayed in Table 1. The ANN detects two must-be attributes, Q 4 (clean and comfortable environment) and Q 9 (beyond connection, smiling at customers). 44

8 Table 1 Quality Categories btained Using an ANN Dimension Questions Quality Category Q 1. High quality coffee A Tangibility Reliability Responsiveness Assurance Empathy verall Satisfaction Q 2. Unique graceful adornment with artistry Q 3. Sufficient seats Q 4. Clean and comfortable environment M Q 5. Reasonable prices Q 6. Clear categories Q 7. Business hours A Q 8. Promotion often A Q 9. Beyond connected, smile to customers M Q 10. Customers interactions A Q 11. Payment rapid Q 12. A lot of products choices Q 13. Non-smoking segment Q 14. Barista expert attitude Q 15. Public image A Q 16. Cards with easy and budget-friendly way Q 17. Shared Values Blog Q 18. Customized coffee Q 19. Full of humanity service provide Q 20. Combination meals Q 21. Fashionable merchandised offering often A Q 22. Coffee and merchandised diversified Y. verall satisfaction with the customer service The results of the ANN analysis indicate that Q 1, Q 7, Q 8, Q 10, Q 15, and Q 21 belong to the attractive quality category. The ANN multilayer perceptron calculates the importance of the independent variables to determine the quality attribute of each item. The slope for each item reveals how variable importance changes from low to high levels of performance. The slopes of Q 1, Q 7, Q 8, Q 10, Q 15, and Q 21 increase with performance level, matching the line graph of attractive quality in the Kano model. Accordingly, they are categorized as attractive quality attributes (Figs. 2 7). 45

9 Figure 2. ANN analysis Q 1 drawing (Attractive). Figure 3. ANN analysis Q 7 drawing (Attractive). Figure 4. ANN analysis Q 8 drawing (Attractive). 46

10 Figure 5. ANN analysis Q 10 drawing (Attractive). Figure 6. ANN analysis Q 15 drawing (Attractive). Figure 7. ANN analysis Q 21 drawing (Attractive). Subsequently, the ANN multilayer perceptron is used to calculate the importance of the independent variables to determine the quality attributes of Q 4 and Q 9. The slopes of both items decrease as performance level rises. This pattern matches the line graph of must-be quality in the Kano model, and we thus consider Q 4 and Q 9 as must-be quality attributes (Fig. 8 and 9). 47

11 Figure 8. ANN analysis Q 4 drawing (Must-Be). Figure 9. ANN analysis Q 9 drawing (Must-Be). CNCLUSINS This study categorizes Kano s two-dimensional attributes by using an ANN. Unlike other categorization methods, ANNs have been proven to be feasible and valid by previous empirical research. In this study, a 5-point Likert scale is employed to divide performance into five levels from low to high, and an ANN is used to calculate the importance of the independent variables. We then observe the slope patterns to determine the quality attributes of all the items according to the Kano model. The application of the Kano model in assessing Likert scale responses mainly involves using simple rules to explain the relationships between quality attributes and customer satisfaction and to summarize the multilayer structure in the data. The categorization principles of the Kano model are adjusted and applied to a new categorization approach and evaluation table concerning the two-dimensional model. The objective of this study is to logically analyze the quality factors and overall satisfaction. Dummy variable regression is the first proposed approach for the categorization of quality attributes and exploration of attribute performance and customer satisfaction. This type of regression is popular because of its 48

12 simplicity. Subsequently, several researchers have proposed a successful new approach for categorizing Kano s two-dimensional quality attributes that involves using a quasilinear regression model. This present study contributes to the literature by using the ANN multilayer perceptron to calculate independent variable importance and determine the quality attribute of each item. The results determine that items with increasing slopes are identified as having attractive quality. Conversely, items with decreasing slopes are categorized as having must-be quality. We also modify the Kano model by using the ANN analysis results, clarifying the relationships between each attribute and satisfaction and thereby determining the priorities of providing product and service features according to their efficiency in satisfying customers. In the case study herein, the modified Kano model demonstrates both feasibility and validity. 49

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