CN111768240A - Aero service recommendation method and system based on perceptual engineering and Kano model - Google Patents
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Abstract
The invention discloses an airline service recommendation method based on perceptual engineering and Kano model, which comprises the following steps: s11, obtaining the sensitive requirements and service requirements of customers on the airliners for the airlines; s12, constructing a client perceptual space according to the acquired perceptual requirements, and constructing a service attribute space based on a Kano model according to the acquired service requirements; s13, synthesizing the constructed client perceptual space and the service attribute space, and performing validity test on the synthesized client perceptual space and the service attribute space; s14, judging whether the validity passes the test, if not, re-executing the step S11; if yes, go to step S15; and S15, constructing a client sensory space, a service attribute space and a use intention model based on a decision tree algorithm, and outputting a recommended service combination according to the constructed use intention model.
Description
Technical Field
The invention relates to the technical field of service design, in particular to an airline service recommendation method and system based on perceptual engineering and Kano model.
Background
With the increasing competition of the aviation service industry, the expectations and demands of customers are increasing, and the customer preference is changing from past functional demands to emotional demands. The method has the advantages that the homogenization of on-board service of an airline company in China is serious, the service quality is insufficient, and the problem of customer loss is caused, and if an enterprise does not change the visual angle and performs service design and improvement by taking the perceptual demand of customers as the guide, the competitive advantage is difficult to maintain. In aviation services, the quality is not as simple as "specification compliant," and it should also meet the emotional needs of higher-level people. Currently, service design methods focus more on inherent service attributes, focus on improving existing services, and focus on easier-to-measure service technology aspects, but ignore emotional needs that will give customers satisfaction or even pleasure. In addition, although all service attributes are important, there is a need to determine service attribute priority policies due to the limited resources and non-linear relationship between the level of service attribute improvement and the perceptual perception of the customer. However, the conventional service design methods are difficult to realize the improvement priority of the improved service attribute, so that the airline company cannot correctly identify the real requirement of the client and cannot provide high-quality service.
Disclosure of Invention
The invention aims to provide an airline service recommendation method and system based on perceptual engineering and Kano model aiming at the defects of the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
an airline service recommendation method based on perceptual engineering and Kano model comprises the following steps:
s1, obtaining the sensitive requirements and service requirements of customers on an airline plane to an airline company;
s2, constructing a client perceptual space according to the acquired perceptual requirement, and constructing a service attribute space based on a Kano model according to the acquired service requirement;
s3, synthesizing the constructed client perceptual space and the service attribute space, and carrying out validity test on the synthesized client perceptual space and the service attribute space;
s4, judging whether the validity passes the test, if not, executing the step S1 again; if yes, go to step S5;
and S5, constructing a client sensory space, a service attribute space and a use intention model based on a decision tree algorithm, and outputting a recommended service combination according to the constructed use intention model.
Further, the step S2, after constructing the client perceptual space, further includes:
and quantizing the constructed customer perceptual space by adopting a 5-point semantic difference table.
Further, the step S3 of combining the constructed customer perception space and the service attribute space is performed by means of questionnaire design.
Further, the validity test of the combined customer perceptual space and the service attribute space in step S3 is performed by a factor analysis method.
Further, the step S5 of outputting the recommended service combination according to the built usage intention model specifically includes: determining the influence of service elements on the aircraft on a client perceptual space by adopting a partial least square method, taking perceptual words corresponding to perceptual requirements as target variables of a decision tree model of using willingness, taking attribute states corresponding to the service requirements as input variables of the decision tree model of using willingness, and outputting service combinations recommended to use and service combinations not recommended to use by using the willingness model.
Correspondingly, an airline service recommendation system based on perceptual engineering and Kano model is also provided, which comprises:
the acquiring module is used for acquiring the perceptual demand and the service demand of a client on the aircraft on an airline company;
the building module is used for building a client perceptual space according to the obtained perceptual requirement and building a service attribute space based on a Kano model according to the obtained service requirement;
the synthesis module is used for synthesizing the constructed client perceptual space and the service attribute space and testing the effectiveness of the synthesized client perceptual space and the service attribute space;
the judging module is used for judging whether the validity passes the test;
and the output module is used for constructing a client sensory space, a service attribute space and a use intention construction use intention model based on a decision tree algorithm, and outputting a recommended service combination according to the constructed use intention model.
Further, after the building module builds the customer perceptual space, the method further includes:
and quantizing the constructed customer perceptual space by adopting a 5-point semantic difference table.
Further, the synthesis module synthesizes the constructed customer perceptual space and the service attribute space by means of questionnaire design.
Further, the validity test of the synthesized customer perceptual space and service attribute space in the synthesis module is performed by a factor analysis method.
Further, the output module outputs the recommended service combination according to the constructed use intention model specifically includes: determining the influence of service elements on the aircraft on a client perceptual space by adopting a partial least square method, taking perceptual words corresponding to perceptual requirements as target variables of a decision tree model of using willingness, taking attribute states corresponding to the service requirements as input variables of the decision tree model of using willingness, and outputting service combinations recommended to use and service combinations not recommended to use by using the willingness model.
Compared with the prior art, the method and the device have the advantages that the relation between the perceptual perception of service clients on the aircraft and the attributes of the services on the aircraft is obtained, so that the priority strategy for improving the attributes of the services is determined, reference is provided for developing specific perceptual service on the aircraft, and a new idea is provided for improving a service design method and developing high-quality services.
Drawings
FIG. 1 is a flowchart illustrating an airline service recommendation method based on perceptual engineering and Kano models according to an embodiment;
FIG. 2 is a schematic diagram of an airline service recommendation based on perceptual engineering and Kano models according to an embodiment;
FIG. 3 is a schematic diagram of a Better-Worse coefficient analysis provided in the first embodiment;
FIG. 4 is a schematic diagram of a usage intention decision tree according to an embodiment.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
The invention aims to provide an airline service recommendation method and system based on perceptual engineering and Kano model aiming at the defects of the prior art.
Example one
The present embodiment provides an airline service recommendation method based on perceptual engineering and Kano model, as shown in fig. 1-2, including the steps of:
s11, obtaining the sensitive requirements and service requirements of customers on the airliners for the airlines;
s12, constructing a client perceptual space according to the acquired perceptual requirements, and constructing a service attribute space based on a Kano model according to the acquired service requirements;
s13, synthesizing the constructed client perceptual space and the service attribute space, and performing validity test on the synthesized client perceptual space and the service attribute space;
s14, judging whether the validity passes the test, if not, re-executing the step S11; if yes, go to step S15;
and S15, constructing a client sensory space, a service attribute space and a use intention model based on a decision tree algorithm, and outputting a recommended service combination according to the constructed use intention model.
In this embodiment, step S11 is preceded by:
s10, selecting a service domain.
The service domain is a service design object and comprises information such as target groups, user types, market positioning, product specifications and the like, wherein the perceptual perception and satisfaction of customers play an important role.
The present embodiment selects the onboard services of the domestic airline economy class as the service domain. The target customer base is limited to customers with domestic airline economy class onboard service experience within a year.
In step S11, the customer' S perceived needs and service needs of the airline to the airline are obtained.
The perceptual demands are perceptual words, and can be collected through magazines, related documents, advertisements, related forums, manuals, experienced users, user expectations, news media, social media, official websites and other channels.
The service requirements are to collect all service attributes that can represent the service domain on the aircraft from service manuals, company official networks, related documents, etc., select the attribute that has the greatest perceptual impact on the customer, and select a sample of services that can represent the selected service attribute.
In step S12, a customer perceptual space is constructed according to the obtained perceptual requirements, and a service attribute space based on the Kano model is constructed according to the obtained service requirements.
In this embodiment, the customer perceptual space is constructed according to the acquired perceptual requirements:
perceptual space is the perceptual term of a customer's experience, desire, and vision of services on an aircraft.
Constructing a customer perceptual space comprises: the perceptual words of the client to the on-machine service are collected (as in step S11), and 89 perceptual words are collected in the present embodiment.
And then screening and developing the perceptual word structure. The method for screening and developing the perceptual word structure specifically comprises the following steps: firstly, the perceptual words which do not belong to the service description field are deleted by adopting expert experience, and then the perceptual words with the same semanteme are grouped by using an affinity graph method which relates to 4 participants. Subsequently, one dominant word is selected as a representative for each group to develop a perceptual word structure. The final perceptual space contains a total of 17 perceptual words (K1, K2, …, K17), as shown in table 1.
| No | Perceptual word | No | Perceptual word | No | Perceptual word |
| k1 | Spacious | k7 | Easy to use | k13 | Fresh and clean |
| k2 | Security | k8 | Professional | k14 | Enthusiasm |
| k3 | Bright and bright | k9 | Intimacy of care | k15 | Ease for use |
| k4 | Is responsible for | k10 | Coming around | k16 | Rich in |
| k5 | Satisfaction | k11 | Neat and tidy | k17 | Is simple and compact |
| k6 | Happy | k12 | Comfort of the wearer |
TABLE 1 perceptual space
In this embodiment, after the step S12 of constructing the client perceptual space, the method further includes: and quantizing the constructed customer perceptual space by adopting a 5-point semantic difference table.
Quantifying customer perceptual space served on an aircraft: with the 5-point semantic difference scale, the interviewees were asked to assess their perception of the service description.
In this embodiment, a Kano model-based service attribute space is constructed according to the acquired service requirements:
when 24 service elements (i.e., services provided by the passenger cabin) are collected in the step S11, including 52 service attributes (i.e., whether the services are provided or not and specific contents), the onboard services are divided into six categories according to the onboard service flow: pre-flight service, preparation for take-off, catering service, cabin facilities, on-board entertainment, flight safety measures. And 12 passenger cabins were designed (i.e., stimuli a-L), where a is the baseline stimulus, as in table 2.
TABLE 2 stimulus design for service elements and attributes
After the service attribute space on the machine is finished, classifying the service elements according to a certain logic and determining the specific service element of each element, if the 1 st service element x1Is r is1An element
2 nd service element x2Is r is2An element
Mth service element xmIs r ismAn element
Therefore, it shares
A service element.
And introducing the mean value of the coefficient of the satisfactory influence and the unsatisfactory influence of the service attribute on the Kano model computer, determining the category of the service attribute on the computer, and further determining the priority of the service attribute to be improved or maintained so as to enhance the scientificity of the perceptual engineering method. And drawing a sensitivity matrix according to the Better-word coefficient of the service attribute so as to determine the improvement priority and perform repeated analysis on the service elements belonging to the expected attribute and the charm attribute.
The concrete operation method about the Better-word coefficient is shown as the following formulas (1) and (2):
Better/SI＝(A+O)/(A+O+M+I) (1)
Worse/DSI＝(O+M)/(A+O+M+I) (2)
the SI value range is 0-1, the closer the value is to 1, the more the satisfaction of the service element can be increased, and the satisfaction of the customer can be increased; when the value is close to 0, the satisfaction of the service element is shown to have little influence on increasing the customer satisfaction. The DSI value range is-1-0, and the closer the value is to-1, the dissatisfaction of the service element is shown to reduce the dissatisfaction of a client; when the value is close to 0, the unsatisfication of the service element is not caused to be unsatisfactory.
Taking a dissatisfaction degree word coefficient (DSI) of the client as an x axis and a satisfaction degree Better coefficient (SI) of the client as a y axis to obtain a quartering bitmap: pattern of Better-word analysis, as shown in FIG. 3.
In step S13, the constructed client perceptual space and service attribute space are combined, and validity of the combined client perceptual space and service attribute space is tested.
The synthesis of the constructed customer perceptual space and the service attribute space is performed by means of questionnaire design. The validity test of the synthesized customer perceptual space and the service attribute space is performed by a factor analysis method.
The synthetic machine sensitivity space and the service attribute space are as follows: 10-20 stimuli (service scenarios) are designed, each stimulus comprising one selected attribute for each service element in the attribute space. The attributes of each element are incompatible and occur at approximately a uniform frequency, where a "0" indicates that the service does not employ the attribute and a "1" indicates that the attribute is employed.
Assuming that the number of designed onboard service stimuli is 12, the perceptual word evaluation value, i.e., the dependent variable, is Y. First, whether a certain service element exists in a stimulation sample is analyzed, namely, the stimulation sample is determinedi(j,k)(i＝1,2,…,n；j＝1,2,…,m；k＝1,2,…,rj) The value is shown in formula (3).
From alli(j, k) constitutes the reaction matrix of n × p for the service, as shown in equation (4).
The effectiveness test is as follows: the factor analysis was performed using SPSS 24.0, extracting factors with eigenvalues greater than 1, and reliability analysis was performed with Cronbach's α values. As shown in table 3. Finally, three factors are analyzed and extracted, and are named as: factor 1: the sensory factor, factor 2: atmosphere factor, factor 3: and designing a factor.
TABLE 3 perceptual wording factor analysis
In step S14, it is determined whether the validity passes the test, and if not, step S11 is executed again; if yes, go to step S15;
if the validity test fails, the customer perceptual space and the service attribute space must be updated until the validity test is passed.
In step S15, a usage intention model is constructed based on the decision tree algorithm, the customer sensitivity space, the service attribute space, and the usage intention, and a service combination recommended for use is output according to the constructed usage intention model.
The method for outputting the recommended service combination according to the constructed use intention model specifically comprises the following steps: determining the influence of service elements on the aircraft on a client perceptual space by adopting a partial least square method, taking perceptual words corresponding to perceptual requirements as target variables of a decision tree model of using willingness, taking attribute states corresponding to the service requirements as input variables of the decision tree model of using willingness, and outputting service combinations recommended to use and service combinations not recommended to use by using the willingness model. The method comprises the following specific steps:
a model was constructed using the C4.5 decision tree of IBM SPSS Modeller 18.0 to analyze the relationship between the client-aware and on-board service attributes and usage intents.
The C4.5 algorithm comprises the following specific calculation steps:
(1) let | D | be the number of samples, p, in the training set Du＝freq(GuD)/| D | is the class G in the training sample DuThe formula for calculating the entropy of the training set D is shown in formula (5):
(2) the training set D is split into m subsets according to the variable V, | DrI/D represents the weight of the r-th subset, and the variable V is calculated in turnrSee formula (6):
(3) calculating the information gain as the difference between the entropy and the expected information, see formula (7):
Gain(V)＝Info(D)-InfoV(D) (7)
(4) and (4) calculating a splitting information value, specifically calculating the value shown in the formula (8):
(5) calculating the information gain rate of the training set D and the variable V (the perceptual word or the service attribute state), see formula (9), and selecting the variable with the maximum gain rate to divide the training set D:
Gain_ratio(V)＝Gain(V)/Split_infoV(D) (9)
the intention of use of the on-board service is taken as a target variable (the intention of use has three values: 3: agreed, 2: neutral, 1: not agreed), and the perceptual word is taken as an input variable. The method of cross validation is adopted to divide the sample data into two parts: a training data set for creating a model and a test data set for testing the performance of the model. Specifically, about 2/3 is used to create a training data set for the model and 1/3 is used to test a test data set for the performance of the model.
The effect on accuracy of the minimum record of each sub-branch of the decision tree modeling is explored, wherein the minimum record of each sub-branch is represented by theta. Each setup performed 10 experiments to observe average depth, average number of nodes, average training accuracy and average testing accuracy, where the three decision trees used in each experiment for the training and testing setup were different, and the appropriate θ generation decision tree and associated rules were chosen. Multiple experiments show that the accuracy of the decision tree with theta 7 is reasonable and easy to explain, the difference between the training precision and the test precision effect is minimal, and the decision tree result is shown in fig. 4.
And carrying out partial least squares analysis by using SmartPLS 3.0 to determine the influence of the on-board service element on the perceptual perception of the client. And performing partial least squares analysis by taking 52 service attributes as independent variables and 17 sensitive words as dependent variables to obtain important sensitive words (p < 0.05). Setting each important perceptual word as a decision tree target variable, setting all corresponding attribute states as input variables, and dividing each perceptual word into two categories according to the result of the association rule table.
And according to the corresponding rules of the relationship between the on-board service attribute state and the important perception vocabulary described by the decision tree. And observing the service attribute corresponding to the rule with the positive use intention (UI ═ 3). And (4) further screening the attributes influencing the use will by using the service attributes of which the category is attractive [ A ] and expected [ O ] in the kano model result to obtain the key on-machine service attribute combination recommended to be used. As in tables 4-5 below.
| Combination serial number | Recommended service attribute status |
| Attribute combination 1 | c7＝＝0，c11＝0，c29＝＝0，c35＝＝1 |
| |
c7＝＝0，c10＝＝1，c11＝1，c29＝＝0，c35＝＝1 |
| |
c7＝＝0，c11＝0，c27＝＝0，c29＝＝1，c30＝＝＝1 |
| Attribute combination 4 | c7＝＝0，c10＝＝1，c11＝1，c27＝＝0，c29＝＝1，c30＝＝＝1 |
| |
c7＝＝0，c11＝＝0，c29＝＝0，c35＝＝0 |
TABLE 4 rules 5 onboard service design recommendation combination
| Combination serial number | Recommended service attribute status |
| Attribute combination 1 | c7＝＝0，c27＝＝1，c29＝＝1，c35＝＝1 |
| |
c7＝＝0，c27＝＝0，c29＝＝1，c30＝＝0，c35＝＝1 |
TABLE 5 on-Board service design recommendation combinations for rule 7
Perceptual perceptions corresponding to rules with negative usage intent (UI ═ 1) and their corresponding service attribute combinations are observed, which are some combinations that the airline is not advised to employ in designing on-board services. As in tables 6-8 below.
TABLE 6 onboard service design non-recommended combinations of rule 2
TABLE 7 onboard service design non-recommended group of rule 4
TABLE 8 rules 6 onboard service design non-recommended combinations
The embodiment obtains the relation between the perceptual perception of service clients on the aircraft and the attributes of the services on the aircraft, thereby determining the improved priority strategy of the service attributes, providing reference for developing the services on the aircraft with specific perceptual, and providing a new idea for improving a service design method and developing high-quality services.
Example two
The present embodiment provides an airline service recommendation system based on perceptual engineering and Kano model, including:
the acquiring module is used for acquiring the perceptual demand and the service demand of a client on the aircraft on an airline company;
the building module is used for building a client perceptual space according to the obtained perceptual requirement and building a service attribute space based on a Kano model according to the obtained service requirement;
the synthesis module is used for synthesizing the constructed client perceptual space and the service attribute space and testing the effectiveness of the synthesized client perceptual space and the service attribute space;
the judging module is used for judging whether the validity passes the test;
and the output module is used for constructing a client sensory space, a service attribute space and a use intention construction use intention model based on a decision tree algorithm, and outputting a recommended service combination according to the constructed use intention model.
Further, after the building module builds the customer perceptual space, the method further includes:
and quantizing the constructed customer perceptual space by adopting a 5-point semantic difference table.
Further, the synthesis module synthesizes the constructed customer perceptual space and the service attribute space by means of questionnaire design.
Further, the validity test of the synthesized customer perceptual space and service attribute space in the synthesis module is performed by a factor analysis method.
Further, the output module outputs the recommended service combination according to the constructed use intention model specifically includes: determining the influence of service elements on the aircraft on a client perceptual space by adopting a partial least square method, taking perceptual words corresponding to perceptual requirements as target variables of a decision tree model of using willingness, taking attribute states corresponding to the service requirements as input variables of the decision tree model of using willingness, and outputting service combinations recommended to use and service combinations not recommended to use by using the willingness model.
It should be noted that the airline service recommendation system based on perceptual engineering and Kano model provided in this embodiment is similar to the embodiment, and will not be described herein again.
Compared with the prior art, the method and the device have the advantages that the relation between the perceptual perception of service clients on the aircraft and the attributes of the services on the aircraft is obtained, so that the priority strategy for improving the attributes of the services is determined, reference is provided for developing specific perceptual service on the aircraft, and a new idea is provided for improving a service design method and developing high-quality services.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. The method for recommending the airline service based on the perceptual engineering and Kano model is characterized by comprising the following steps of:
s1, obtaining the sensitive requirements and service requirements of customers on an airline plane to an airline company;
s2, constructing a client perceptual space according to the acquired perceptual requirement, and constructing a service attribute space based on a Kano model according to the acquired service requirement;
s3, synthesizing the constructed client perceptual space and the service attribute space, and carrying out validity test on the synthesized client perceptual space and the service attribute space;
s4, judging whether the validity passes the test, if not, executing the step S1 again; if yes, go to step S5;
and S5, constructing a client sensory space, a service attribute space and a use intention model based on a decision tree algorithm, and outputting a recommended service combination according to the constructed use intention model.
2. The method for recommending airline services based on perceptual engineering and Kano model of claim 1, wherein said step S2 further comprises, after constructing the client perceptual space:
and quantizing the constructed customer perceptual space by adopting a 5-point semantic difference table.
3. The method for recommending airline services based on perceptual engineering and Kano model of claim 2, wherein said step S3 for combining said constructed customer perceptual space and service attribute space is performed by means of questionnaire design.
4. The method for recommending airline services based on perceptual engineering and Kano model of claim 3, wherein the validation test of the combined customer perceptual space and service attribute space in step S3 is performed by a factor analysis method.
5. The method for recommending airline services based on perceptual engineering and Kano model of claim 4, wherein the step S5 of outputting the recommended service combination according to the built willingness-to-use model specifically comprises: determining the influence of service elements on the aircraft on a client perceptual space by adopting a partial least square method, taking perceptual words corresponding to perceptual requirements as target variables of a decision tree model of using willingness, taking attribute states corresponding to the service requirements as input variables of the decision tree model of using willingness, and outputting service combinations recommended to use and service combinations not recommended to use by using the willingness model.
6. An airline service recommendation system based on perceptual engineering and Kano model, comprising:
the acquiring module is used for acquiring the perceptual demand and the service demand of a client on the aircraft on an airline company;
the building module is used for building a client perceptual space according to the obtained perceptual requirement and building a service attribute space based on a Kano model according to the obtained service requirement;
the synthesis module is used for synthesizing the constructed client perceptual space and the service attribute space and testing the effectiveness of the synthesized client perceptual space and the service attribute space;
the judging module is used for judging whether the validity passes the test;
and the output module is used for constructing a client sensory space, a service attribute space and a use intention construction use intention model based on a decision tree algorithm, and outputting a recommended service combination according to the constructed use intention model.
7. The system of claim 6, wherein the construction module, after constructing the customer perceptual space, further comprises:
and quantizing the constructed customer perceptual space by adopting a 5-point semantic difference table.
8. The system of claim 7, wherein the composition module composes the constructed customer perceptual space and the service attribute space by means of a questionnaire design.
9. The system of claim 8, wherein the validation testing of the combined customer perceptual space and service attribute space in the composition module is performed by a factor analysis method.
10. The system of claim 9, wherein the output module outputs a recommended service combination according to the constructed willingness-to-use model, specifically: the method comprises the steps of determining the influence of service elements on an aircraft on a client perceptual space by adopting a partial least square method, taking perceptual words corresponding to perceptual requirements as target variables of a decision model of using will, taking attribute states corresponding to the service requirements as input variables of the model of using the will, and outputting service combinations recommended to use and service combinations not recommended to use by using the model of using the will.
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