Using Artificial Neural Networks to Predict Restaurant Industry Service ...

1 downloads 0 Views 636KB Size Report
Keywords: Service Failure, Service Recovery, Artificial Neural Networks ..... [5] M. A. McCollough, S. G. Bharadwaj, "The Recovery Paradox: An Examination of ...

Using Artificial Neural Networks to Predict Restaurant Industry Service Recovery Han-Chen Huang

Using Artificial Neural Networks to Predict Restaurant Industry Service Recovery Han-Chen Huang No. 168, Hsueh-Fu Rd., Tanwen Village, Chaochiao Township, Miaoli County, 36143 Taiwan Department of Leisure Management, Yu Da University, E-mail: [email protected]

Abstract Success in the service industry requires providing high-quality service and a satisfying consumer experience. However, regardless of the level of quality, preventing service failures is always difficult. When a service failure occurs, it is critical for managers to propose a quick and accurate service recovery plan that can satisfy the consumer. After surveying consumers, the data revealed a 72.99% chance that consumers will return to the place of business if they are satisfied with the service recovery plan. Conversely, there is a 79.64% chance that consumers will not return if they are not satisfied with the service recovery plan. This indicates that managers should manage consumer complaints with extreme care. This paper uses multilayer perceptrons (MLPs) and support vector machines (SVMs) neural networks to predict service recovery. The variables which are input into the MLPs and SVMs artificial neural networks to predict consumer expectations for service recovery are service failure type, easily determined consumer characteristics, the language used in customer complaints, tone of voice, and mood. Both MLPs and SVMs are proved to be efficient and reliable. The SVMs method is more accurate (PPV=95%) than the MLPs method (PPV=87.5%).

Keywords: Service Failure, Service Recovery, Artificial Neural Networks 1. Introduction With the fast growth of economic development and individual incomes, the relative proportion of service industry businesses in Taiwan has grown increasingly higher. The service industry constituted 57% of Taiwan’s GDP in 1990, and that rate grew to 67.10% by 2010, showing the rising influence and importance of the service industry to Taiwan’s economy. As the service industry has grown, competition within the service industry has intensified. To sustain perpetual operation, a business must attract new consumers as well as keep existing consumers. Desatnick[1] indicated that the cost of attracting one new consumer is roughly equal to five times the cost of maintaining an existing consumer. Reichheld and Sasser[2] stated further that if a business can reduce the consumer loss rate by 5%, it may produce a profit of 25% to 85%, depending on the industry. Therefore, many businesses have changed from an “offensive” strategy of focusing the business on new consumers to a “defensive” strategy of satisfying and retaining existing consumers[3]. Goodwin and Ross[4] stated that a service failure at any service contact point during service delivery produces a negative reaction by the consumer, resulting in a complaint. McCollough and Bharadwaj[5] found that consumers receiving service recovery from a business have a higher level of satisfaction than do consumers who experience no service failure at all, demonstrating that effective service recovery can increase consumer satisfaction. However, Holloway and Beatty[6] showed that 57% of consumers are not satisfied with the management of service failures and that service recovery is an area where many businesses need improvement. For a business to recover from a service failure, it must first understand the situation that led to that service failure. We used a consumer survey method to examine the types of service failure encountered in the fastfood restaurants to determine the expected service recovery. In addition, we used artificial neural networks to build a service recovery prediction model that can be used onsite when providing service.

International Journal of Advancements in Computing Technology(IJACT) Volume4, Number10, June 2012 doi: 10.4156/ijact.vol4.issue 10.37

315

Using Artificial Neural Networks to Predict Restaurant Industry Service Recovery Han-Chen Huang

2. Questionnaire survey and findings We aimed to build a service recovery prediction model that can be directly used onsite when providing service to predict consumer expectations for service recovery, and we acquired the data required for the model’s training, cross-validation, and testing using a questionnaire survey. The contents of the questionnaire were divided into two parts. First part: Whether consumers had an unhappy experience when dining at the fastfood restaurant; the type of unhappy experience; whether they made a complaint; how the business handled it; how the consumer expected it to be handled; whether they were satisfied with that service recovery; and whether they will return. Second part: expression of customer complaint (language used, degree of anger, tone of voice) and basic personal information (gender, age: young, middle-aged, or elderly, and dress). We sent a total of 1,000 questionnaires in Taipei to two Chinese restaurants, two Western restaurants, and two breakfast restaurants, and received 719 validly completed questionnaires. Figure 1 is the tree diagram of questionnaire results. Unhappy experiences were reported in 61.34% of the questionnaires, with 10 types of customer complaints provided for respondents to choose from. The customer complaint that was reported most frequently was waiting too long (27.44%), followed by food quality (22.90%) and an unhygienic environment (17.69%). This indicates that businesses should increase service speed and pay attention to food quality and cleanliness. In addition, employees who directly serve customers should receive more training and improve their ability to respond to situations so that they can reduce the occurrence of errors.

Figure 1. Tree diagram of questionnaire results Figure 1 also shows that among the 250 customer complaints of service failure, business provided a poor service recovery 113 times. Under these circumstances, the percentage of consumers who did not return was 79.65%. By comparison, in situations with a good service recovery, the consumer loss rate was only 27.01%, showing that businesses must have an effective service recovery mechanism to influence whether consumers return. If the business can accurately present a remedial plan that meets consumer expectations, there is a chance that they can win back the consumer’s trust and retrieve the consumer’s business. Among the 719 valid questionnaires, 250 respondents reported having the experience of making complaints, and the three main methods of addressing complaints (constituting 66.4%) were “apology,” “exchange for equivalent product,” and “Personal explanation by manager(Table 1). However, as can be seen in Table 2, the primary method of service recovery desired by consumers is “personal explanation by the manager” (22.4%), indicating that onsite employees may lack the authority or

316

Using Artificial Neural Networks to Predict Restaurant Industry Service Recovery Han-Chen Huang

sincerity to “take corrective action” or “apologize” voluntarily, leading the consumer to expect that the manager should personally explain the problem. Table 1. Frequency distribution of business handling methods Type Frequency Percentage Apology Exchanging for equivalent product Personal explanation by manager Correction Offering free food Offering coupons Making this visit complimentary Total

70 53 43 30 23 17 14 250

28.00% 21.20% 17.20% 12.00% 9.20% 6.80% 5.60% 100.00%

Table 2. Frequency distribution of service recovery methods expected by consumers Type

Frequency

Percentage

Personal explanation by manager Apology Correction Making this visit complimentary Exchanging for equivalent product Getting free food Getting coupons or discounts

56 49 38 34 29 25 19

22.40% 19.60% 15.20% 13.60% 11.60% 10.00% 7.60%

Total

250

100.00%

3. Artificial neural networks Artificial neural networks (ANNs) are effective in addressing classification problems because they can learn from noisy data and generalize findings. The first neural network model (the perceptron) was developed by Rosenblatt in the late 1950s. Since then, several other models have been proposed; for example, generalized feed-forward networks, radial basis function networks, the Hopfield model, multilayer perceptrons, modular networks, support vector machines, and self-organizing feature maps. These models differ in architecture and in how they learn and behave; thus, they are suitable for various types of problems. Numerous applications involve ANNs to solve real-world problems. For commercial purposes, ANNs can be applied to predict profit, market movements, and price levels based on the market’s historical dataset. In medical applications, doctors can evaluate the situation of many patients depending on the historical dataset of other patients with the same illness. In industry, engineers can apply ANNs to solve various engineering problems such as classification, prediction, pattern recognition, and non-linear problems that are extremely difficult or potentially impossible to solve using normal mathematical processes[7]. In this study we used multilayer perceptrons (MLPs) and support vector machines (SVMs) neural networks to predict the service recovery expected by consumers when a service failure occurs. Table 3 list the input and output variables.

317

Using Artificial Neural Networks to Predict Restaurant Industry Service Recovery Han-Chen Huang

Table 3. Input and output variables Type

Service Failure Type Input

Expression of Complaint Consumer External Characteristics

Output

Expected Service Recovery Methods

Variables X1:Product defect, such as cold, overcooked, or bad food X2:Slow service X3:Service not delivered X4:Unclear rules, such as restaurant not accepting foreign currency or credit cards X5:Food not prepared as requested X6:Seating problem, such as refusing a customer’s request for specific seating X7:Public health and hygiene problem X8:Inappropriate employee behavior, such as being rude or impolite X9:Delivery of wrong product X10:Error with check or checkout X11:Language used for complaint (Taiwanese, Mandarin, or other) X12:Degree of anger (on scale of 4 points from calm to furious) X13:Tone of voice (on scale of 4 points from slow to urgent) X14:Gender X15:Age (young, middle-aged, or elderly) X16:Dress (formal or casual) Y=1,Free food Y=2,Coupon or discount Y=3,Personal explanation by manager Y=4,Receiving equivalent product Y=5,Corrective action Y=6,Apology Y=7,Making this visit complimentary

3.1 MLPs neural network The MLPs neural network used in this study contains three layers. The NeuroSolutions software was used to construct the required model. The constructed model consists of an input layer, a nonlinear hidden layer, and an output layer. The hidden layer and output layer apply the tanh transfer function. The MLPs neural network was trained and based on 170 questionnaires. The cross-validation process of the network uses a dataset of 40 questionnaires. The testing process is defined as data used to evaluate the performance after the training is complete. The trained network was tested based on 40 questionnaires that were not used in the training and cross-validation set. Table 4 lists the distribution of datasets. The number of nodes of the network is the number of exemplars of the training set equal to 170 and 1,000 epochs. Note that the number of nodes is configured automatically by the NeuralBuilder. 3.2 SVMs neural network As with MLPs model, a NeuroSolutions programmer was used to transform the data from an input space to a high-dimensional space using a radial basis function (RBF) network that places a Gaussian distribution at each data sample[8]. Thus, the feature space becomes as large as the number of samples. The SVMs neural network was divided into two parts to implement the RBF dimensionality expansion and a large margin classifier. As with MLPs, SVMs use the concept of back-propagation training to train the linear combination of Gaussians. SVMs are motivated by the concept of training and use only those inputs that are near the decision surface because they provide the most information about the classification[9]. The training, cross-validation, and testing processes of SVMs were conducted based on the same dataset that was used in the MLPs to ensure an exact comparison in the quality of the results between the MLPs and SVMs.

318

Using Artificial Neural Networks to Predict Restaurant Industry Service Recovery Han-Chen Huang

Dataset

Table 4. Distribution of datasets Exchanging Making Personal Getting Getting visit explanation Correctio for Total Apology free coupons or complim by n equivalent discount food entary manager product

Train validation Test

15 5 5

12 4 3

40 8 8

20 4 5

26 6 6

34 7 8

23 6 5

170 40 40

4. Empirical results The training, cross-validation, and testing dataset classification using SVMs and MLPs are listed in Tables 5, 6, and 7, respectively. To validate the proposed model, positive predicted value (PPV) was computed as PPV=(Correct results / All results) x 100%

(1)

Figure 2 shows the SVMs and MLPs learning curves. The active cost curves approaches zero which means that classification of the dataset was carried out correctly. Table 8 shows the mean square error (MSE), correlation coefficient (r), and PPV. The testing result (PPV) of the trained SVMs is higher than that of the MLPs model, with 90% accuracy. Although the prediction effect of the MLPs is less effective, it still has 83.33% accuracy; therefore, both the SVMs and MLPs models have high prediction ability. Table 5. Training dataset classification Getting Making this Personal Exchanging Clas Getting coupon explanation Tota for visit Correctio Model s or free Apology s n by equivalent complimentar l food discoun manager product y t True

14

11

40

20

26

34

23

168

False

1

1

0

0

0

0

0

2

True

12

10

38

20

23

32

21

156

False

3

2

2

0

3

2

2

14

SVMs

MLPs

Table 6. Cross-validation dataset classification Getting Making this Personal Exchanging coupon Getting Clas explanation Tota visit for Correctio Model s or free Apology s n by complimentar l equivalent food discoun manager y product t True

4

3

8

4

6

7

6

38

False

1

1

0

0

0

0

0

2

True

3

2

7

4

5

7

6

34

False

2

2

1

0

1

0

0

6

SVMs

MLPs

319

Using Artificial Neural Networks to Predict Restaurant Industry Service Recovery Han-Chen Huang

Table 7. Testing dataset classification Getting Making this Personal Exchanging coupon Getting Clas Tota visit explanatio Correcti for Model Apology s or free s complimentar l n by on equivalent food discoun y manager product t True

4

2

8

5

6

8

5

38

False

1

1

0

0

0

0

0

2

True

3

2

7

5

5

8

5

35

False

2

1

1

0

1

0

0

5

SVMs

MLPs

Figure 2. The SVMs and MLPs model learning curves Table 8. Mean square error, correlation coefficient, and PPV of our research Training data

Cross-validation data

Testing data

Model

MSE

r

PPV

MSE

r

PPV

MSE

r

PPV

SVMs

0.0338

0.9774

98.82%

0.0704

0.9381

95%

0.0833

0.9315

95%

MLPs

0.0416

0.9446

91.76%

0.0920

0.8261

85%

0.1114

0.8369

87.5%

5. Conclusions After surveying consumers, “Slow service” is the primary complaint by consumers; therefore, fast service should be one of the goals of fastfood restaurants operators. Businesses must reduce the time consumers spend waiting as much as possible or explain to consumers the standard waiting time so that consumers can feel at ease instead of anxious. When a service failure occurs, the business should provide the correct service recovery as expected by the consumer. We discovered that if the service recovery can satisfy the consumer, there is a 72.99% chance that they will return. If the consumer is not satisfied with the service recovery, there is a 79.64% chance that they will not return. Businesses should manage consumer complaints with extreme care and offer the appropriate service recovery depending on the onsite situation. This should

320

Using Artificial Neural Networks to Predict Restaurant Industry Service Recovery Han-Chen Huang

be considered as a second sales opportunity, and the maintenance of good consumer relations is essential for the long-term operations of a business. We used a questionnaire to collect consumer opinions and to present the actual expectations of consumers. Businesses must reassess their methods for managing consumer complaints to determine whether they deviate from the actual expectations of consumers. Thus, businesses can prevent a mistaken service recovery from offending the consumer a second time after the original service failure. Using MLPs and SVMs neural network to predict consumer expectations for service recovery is a feasible method. We obtained a prediction accuracy of 87.5% using MLPs and 95% using SVMs. The prediction model using consumer complaint type, easily determined consumer characteristics (gender, age: young, middle-aged, or elderly, and dress), and complaint expression style (language, tone, and mood) can help onsite service personnel make a correct service response.

6. References [1] R. L. Desatnick, Managing to Keep the Customer, Houghton Mifflin, USA, 1988 [2] F. F. Reichheld, W. E. Sasser, "Zero Defections: Quality Comes to Services", Harvard Business Review, Vol. 68, No. 5, pp. 105 ~ 111, 1990 [3] C. Fornell, "A National Customer Satisfaction Barometer: The Swedish Experience", Journal of Marketing, Vol. 56, No.1, pp. 6 ~ 21, 1992 [4] C. Goodwin, I Ross, "Consumer Response to Service Failure: Influence of Procedural and Interactional Fairness Perceptions", Journal of Business Research, Vol. 25, No.2, pp. 149 ~ 163, 1992 [5] M. A. McCollough, S. G. Bharadwaj, "The Recovery Paradox: An Examination of Consumer Satisfaction in Relation to Disconfirmation, Service Quality, and Attribution-Based Theories", Marketing Theory and Application, Vol. 65, No. 4, pp. 102 ~ 107, 1992 [6] B. B. Holloway, S. E. Beatty, "Service Failure in Online Retailing: A Recovery Opportunity", Journal of Service Research, Vol. 6, No. 1, pp. 92 ~ 105, 2003 [7] B B Chaudhuri, U. Bhattacharya, "Efficient Training and Improved Performance of Multilayer Perceptrons in Pattern Classification", Neurocomputing, Vol. 34, No. 1, pp. 11 ~ 27, 2000 [8] Lingling Song, "Improved Intelligent Method for Traffic Flow Prediction Based on Artificial Neural Networks and Ant Colony Optimization", JCIT, Vol. 7, No. 8, pp. 272 ~ 280, 2012 [9] Jun-Jun Cheng, Yun Liu, Hui Cheng, Yan-Chao Zhang, Xia-Meng Si, Chang-Lun Zhang, "Growth Trends Prediction of Online Forum Topics Based on Artificial Neural Networks", JCIT, Vol. 6, No. 10, pp. 87 ~ 95, 2011

321

Suggest Documents