CN113742928A - Service robot acceptance degree inspection method based on technical acceptance model - Google Patents
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Abstract
The invention discloses a service robot acceptance degree inspection method based on a technical acceptance model, which can verify whether the use attitude of a consumer or a user can generate positive influence on the use will, and is realized based on the technical acceptance model and specifically comprises the following steps: based on the existing technical acceptance model, selecting and adding related influence variables, constructing a service robot acceptance model, proposing a basic hypothesis, collecting data, adopting a structural equation model inspection method to inspect the proposed basic hypothesis, judging whether the basic hypothesis proposed based on the service robot acceptance model is established, if so, indicating that the service robot can be accepted by a user to a higher degree, otherwise, indicating that the service robot cannot be accepted to a higher degree.
Description
Technical Field
The invention relates to the technical field of robots, in particular to a service robot acceptance degree inspection method based on a technical acceptance model.
Background
With the rapid development of artificial intelligence technology, a service robot plays an increasingly important role in personal and public lives, and the service robot is a robot for providing services to consumers and mainly realizes service interaction with the consumers based on an interface unit, an interaction unit, a communication unit and the like of an artificial intelligence system. At present, common robots comprise hotel robots, and how to apply service robots well to realize good interaction between consumers and service robots is very important for the hotel service industry.
However, human-robot interaction is still an immature research field at present, and scholars propose an acceptance model for service robots, for example, Davis (1989) a Technology Acceptance Model (TAM) published based on rational behavior theory is a classic model (Davis, 1989) in the field of human-computer interaction, and the model measures the acceptance degree of a technology by users from two dimensions of perception usability and perception usefulness of the technology, so that a theoretical basis is laid for application of the robot technology.
On the basis of a Technology Acceptance Model (TAM), Wirtz and the like (2018) are combined with social, emotion and relation characteristics in a service robot application scene, and a service robot acceptance model (sRAM) (Wirtz and the like, 2018) is provided according to requirements when a consumer interacts with a service robot and consistency of role cognition of the service robot, wherein the model indicates that the acceptance degree of the consumer on the service robot mainly depends on whether the robot can well meet functional requirements (namely requirements related to dominance) and social emotion and relation requirements (namely requirements related to warmth), so that role consistency is realized. However, most of the existing accepted models are conceptual researches, and specific technologies for implementing the models are lacked, so that whether the using attitude of a consumer or a user can generate positive influence on the using will cannot be determined, and the interaction effect of the consumer and the service robot is influenced.
Disclosure of Invention
The invention provides a service robot acceptance degree inspection method based on a technical acceptance model, which can verify whether the use attitude of a consumer or a user can generate positive influence on the use intention. In order to achieve the purpose, the invention adopts the following technical scheme:
a service robot acceptance degree inspection method based on a technical acceptance model is realized based on the technical acceptance model and is characterized by comprising the following steps: s1, selecting influence variables, wherein the influence variables comprise perception usefulness, perception usability, perception entertainment and perception safety, and constructing the service robot acceptance model based on the selected influence variables;
s2, basic assumptions are proposed, and the basic assumptions comprise: based on the basic assumption of the influence relationship among the influence variables of the technology acceptance model, the basic assumption of the influence relationship of the newly added influence variables to the use attitude and the use will of the user is respectively assumed;
and S3, collecting data, adopting a structural equation model checking method to check the basic hypothesis, and judging whether the basic hypothesis proposed based on the service robot acceptance degree model is true or not, wherein if yes, the service robot can be accepted by a user to a higher degree, otherwise, the service robot cannot be accepted to a higher degree.
It is further characterized in that the method further comprises the steps of,
the service robot is a hotel service robot;
in step S1, the influence variables of the service robot acceptance model are selected and determined in combination with actual application characteristics of the service robot, where the actual application characteristics of the service robot include: the technology acceptance model comprises a mutual influence relationship among the perception usefulness, the perception usability, the use attitude, the use will and the use behavior, and influence relationships of the perception usefulness, the perception usability, the perception entertainment and the perception safety on the use attitude and the use will;
the influencing variables further include adjusting variables, including gender, age, education level;
in step S2, based on the technology acceptance model, in combination with the entertainment brought to the user by the service robot in actual use and the perceived safety of the user, the basic assumptions are proposed, and the basic assumptions proposed based on the technology acceptance model include: h1, the perception usefulness generates positive influence on the using attitude, H2, the perception usability generates positive influence on the using attitude, H3, the perception usability generates positive influence on the perception usefulness, H4, the using attitude of the user generates positive influence on the using will, H5, the perception usefulness generates positive influence on the using will of the user, H6, the using will of the user generates positive influence on the using behavior, and the basic assumptions are provided in combination with the entertainment brought to the user by the service robot in actual use and the security perceived by the user, wherein the basic assumptions comprise: h7, the perception usability generates positive influence on the perception entertainment, H8, the perception entertainment generates positive influence on the using attitude of the user, H9, the perception entertainment generates positive influence on the using will of the user, H10, the perception safety generates positive influence on the using attitude of the user, and H11, the perception safety generates positive influence on the using will of the user;
the basic assumptions also include the significant impact of the tuning variables on the relationships between the perceived entertainment, perceived safety and usage attitude, willingness to use: h8a-c, sex difference, age difference and education degree difference can regulate the positive influence relationship of the perceived entertainment on the using attitude, H9a-c, sex difference, age difference and education degree difference can regulate the positive influence relationship of the perceived entertainment on the using will, H10a-c, sex difference, age difference and education degree difference can regulate the positive influence relationship of the perceived safety on the using attitude, and H11a-c, sex difference, age difference and education degree difference can regulate the positive influence relationship of the perceived safety on the using will;
in step S3, the step of determining whether the basic assumption holds includes: s31, collecting the data by adopting a questionnaire survey mode, and carrying out sample statistics on the collected data;
s32, checking the reliability and validity of the collected data, judging whether the collected data can be used for checking the proposed basic hypothesis, if so, entering the next step S33, and if not, collecting again;
s33, checking the proposed basic hypothesis based on the data and sample statistical data;
in step S31, after the relevant documents of the technology acceptance model are referred, a Likter seven-grade scale is adopted for questionnaire design; the data collection process takes the form of a web questionnaire;
in step S31, performing the sample statistics according to gender, age, education level, and/or service robot categories familiar to the user;
in step S32, the method for checking the reliability and validity of the collected data includes: the validity of the data is checked through a suitability check value and a chi-square significance probability value checked by a Bartlett sphere, and the reliability of the data is checked through a Cronbach's alpha coefficient;
in step S33, the method of testing the proposed basic hypothesis based on the data and the sample statistics includes: s331, checking the set data and the adaptation degree of the service robot acceptance model by using the structural equation model checking method, wherein the adaptation degree is judged through adaptation degree index values, the adaptation degree index comprises chi-square significance CMID/DF, an adaptation degree index GFI, an approximate residual mean square and square root RMSEA, a standard fitting index NFI, an increment fitting index IFI and a comparison fitting index CFI, the adaptation degree index values comprise recommended values and fitting values, the fitting values are compared with the recommended values obtained in advance, whether the fitting values corresponding to the adaptation degree indexes fall into the corresponding recommended value ranges or not is judged, if yes, the technical acceptance model is accepted, the step S332 is carried out, and if not, data collection is carried out again or the influence variables are selected again;
s332, a significance test method is adopted to test whether the basic hypothesis is established, the judgment indexes of the significance test method comprise a standardized path coefficient and significance, and the significance is divided into three levels: intensity, medium degree and weak degree, if the significance P value is less than 0.001, the significance is indicated as intensity, the corresponding basic hypothesis is significantly established at a confidence level of 0.001, if the significance P value is less than 0.01, the significance is indicated as medium, the corresponding basic hypothesis is significantly established at a confidence level of 0.01, if the significance P value is less than 0.05, the significance is indicated as weak, the corresponding basic hypothesis is significantly established at a confidence level of 0.05, if the significance P value is more than 0.05, the corresponding basic hypothesis is not established, the established basic hypothesis is used for testing the acceptance of the service robot, and the standardized path coefficient is the influence degree of the corresponding basic hypothesis.
By adopting the structure of the invention, the following beneficial effects can be achieved: the application constructs a service robot receiving model based on the perception usefulness, the perception usability, the perception entertainment, the perception safety, the usage attitude, the usage intention and the usage behavior of the influence variables, proposes a basic hypothesis based on the service robot receiving model, adopts a collected data and structural equation model checking method to check the proposed basic hypothesis, and checks the conclusion to show that the perception usability and the perception entertainment have positive influence on the usage attitude, the perception usability, the perception usefulness, the perception entertainment and the perception safety all have positive influence on the usage intention, the perception usability has positive influence on the perception entertainment to show that the attitude of a user on the service robot is positive, the service robot is willing to be used when facing a new technology of the service robot, otherwise, the service robot is not to be adopted, thereby realizing the verification whether the usage attitude of the consumer or the user can generate positive influence on the usage intention of the service robot, the technical problem of lack of the technology for detecting the acceptance of the service robot is solved.
Drawings
FIG. 1 is a model framework diagram of a service robot acceptance inspection method of the present invention;
FIG. 2 is a structural block diagram of a structural equation model of the service robot receptivity model of the present invention.
FIG. 3 is a block diagram of a path coefficient framework of a service robot receptivity model according to the present invention
Detailed Description
Referring to fig. 1, a service robot acceptance inspection method based on a technical acceptance model is implemented based on the technical acceptance model, and includes:
s1, selecting and determining the influence variables of the service robot acceptance model by combining the actual application characteristics of the service robot, wherein the characteristics influencing the acceptance degree of the service robot by the user specifically comprise: using attitude, willingness to use, using behavior, according to the technology acceptance model mentioned in the background art, the acceptance of the service robot by the user can be measured by two dimensions of perception usability and perception technology usefulness, and social emotion and relationship requirements further include perception entertainment, perception safety, etc., perception entertainment means the interest that an individual subjectively perceives when taking a specific action or performing a specific activity, and Moon and Kim (2001) proposes three aspects of perception entertainment: concentration, curiosity and pleasure, considering that when a user is in the entertainment state, the interaction process itself finds entertainment to perform a specific activity, rather than any extrinsic consideration (Moon and Kim, 2001); perceived security refers to the degree to which a user believes that a specific application can be used without risk (Xiaowen Fang, 2005), which has a certain influence on the decision making process of a consumer and largely influences the use will of the consumer, and in addition, the sex, age and education degree of the consumer also have a great influence on the use will of the service robot. Thus, the acceptance of the service robot by the user may also be measured by perceived entertainment, perceived safety, gender, age, and education, and, in summary, the determined influencing variables include, but are not limited to perceived usefulness, perceived ease of use, perceived entertainment, perceived safety, gender, age, and education, wherein sex, age and education degree are regulating variables, a service robot acceptance model is constructed based on the perception usefulness, the perception usability, the perception entertainment and the perception safety of the selected influencing variables, the service robot acceptance model comprises the mutual influence relationship among the perception usefulness, the perception usability, the perception entertainment and the perception safety, and the influence relationship of perception usefulness, perception usability, perception entertainment and perception safety on the using attitude, the using will and the using behavior is shown in figure 2.
S2, based on the technical acceptance model, and combined with the entertainment brought to the user by the service robot in actual use and the perceived safety of the user, basic assumptions are provided, and the basic assumptions comprise: based on the basic assumption provided by the technical acceptance model, adding the basic assumption that the influence variable perception entertainment and the perception safety respectively influence the use attitude and the use will, wherein the influence variable perception usability influences the perception entertainment; wherein the basic assumptions proposed based on the technology acceptance model include: h1, perception usefulness generates positive influence on using attitude, H2, perception usability generates positive influence on using attitude, H3, perception usability generates positive influence on perception usefulness, H4, user's using attitude generates positive influence on using will, H5, perception usefulness generates positive influence on user's using will, H6, user's using will generates positive influence on using behavior, the newly added influence variables perception entertainment, perception safety respectively have influence relationship between using attitude and using will and the basic assumption of the influence relationship of perception usability on perception entertainment: h7, the perception usability positively affects the perception entertainment, H8, the perception entertainment positively affects the use attitude of the user, H9, the perception entertainment positively affects the use will of the user, H10, the perception safety positively affects the use attitude of the user, and H11, the perception safety positively affects the use will of the user.
And S3, collecting data, adopting a structural equation model checking method to check the proposed basic hypothesis, and judging whether the basic hypothesis proposed based on the service robot acceptance model is true or not, wherein if yes, the service robot can be accepted by the user to a higher degree, otherwise, the service robot cannot be accepted to a higher degree. The step of determining whether the basic assumption proposed based on the service robot acceptance model holds includes: s31, collecting data by adopting a questionnaire survey mode, and carrying out sample statistics on the collected data; the data collection mode specifically comprises that after the relevant documents of the model are received by the reference technology, a Likter seven-grade scale is adopted for questionnaire design, and the questionnaire design mainly aims at the hotel service robot; in the embodiment, the network questionnaire is issued through a professional questionnaire website, a consumer logs in a corresponding website through a computer or a mobile phone to answer questions, 352 electronic questionnaires are recycled in the network questionnaire, and after invalid questionnaires are deleted, 303 effective questionnaires are finally determined, wherein the recovery rate of the questionnaires is 86.0%.
The specific mode of questionnaire data statistics includes: carrying out sample statistics according to gender, age, education level and familiar reception service robot categories; the basic cases of 303 effective samples determined by screening were 1) gender composition, which was 52.15% female; 2) the age distribution shows youthful characteristics, wherein the ratio of the visitors of 18-25 years old, 26-35 years old and 36-45 years old accounts for 33.99%, 29.04% and 18.81%; (3) the education level is relatively high, wherein the ratio of the subjects and the masters reaches 64.35 percent;
s32, checking the reliability and validity of the collected data, judging whether the collected data can be used for checking the proposed basic hypothesis, if so, entering the next step S33, otherwise, collecting again; the way of checking the credibility and validity of the data includes: checking the validity of the data through a suitability check value and a chi-square significance probability value checked by a Bartlett sphere, and checking the reliability of the data through a Cronbach's alpha coefficient; the results of the examination were obtained using the SPSS22.0 software in a computer: first, the KMO test value for the applicability test was 0.735, the chi-square significance P value for the Bartlett sphere test was 0.000, and the validity of the questionnaire was good. Secondly, the Cronbach's alpha coefficient of validity test is 0.942, which indicates that the reliability of the questionnaire is good and can be used for testing.
S33, testing the proposed basic hypothesis based on the data and the sample statistics, wherein the specific testing method includes: s331, the fitness of the set data and the service robot receiving model is checked by adopting a structural equation model checking method, the fitness is judged through fitness index values, the fitness indexes comprise chi-square significance CMID/DF, fitness index GFI, approximate residual mean square and square root RMSEA, standard fitness index NFI, value-added fitness index IFI and comparative fitness index CFI, the fitness index values comprise recommended values and fitted values, the fitted values are compared with the recommended values obtained in advance, whether the fitted values corresponding to the fitness indexes fall into the corresponding recommended value ranges is judged, and the table 1 shows the main fitness indexes obtained through structural equation model checking in detail.
TABLE 1 Adaptation index values for structural equation models
As can be seen from table 1, the fitting values of the data obtained through the structural equation model and the technical acceptance model are compared with the preset fitting values, and the fitting values of other adaptation indexes fall within the range of the recommended values except that the GFI value and the NFI value are closer to the recommended value of 0.9, so that the setting of the technical acceptance model of the application is acceptable, the step S332 is entered, and if not, data collection is performed again or the influence variable is reselected;
s332, whether the basic assumption of the influence variable is established or not is checked by adopting a significance test method, at the moment, the significance influence of the regulating variable on the technical acceptance model is not considered temporarily, the judgment indexes of the significance test method comprise a standardized path coefficient and significance, and the significance is divided into three levels: intensity, medium, weak, intensity indicating a significance P-value of less than 0.001, denoted by "×", in table 2, if said significance P-value is less than 0.001, indicating that the corresponding basic hypothesis is significantly established at a confidence level of 0.001, medium indicating a significance P-value of less than 0.01, denoted by "×" in table 2, if said significance P-value is less than 0.01, indicating that the corresponding basic hypothesis is significantly established at a confidence level of 0.01, weak indicating a significance P-value of less than 0.05, denoted by "×" in table 2, if said significance is less than 0.05, indicating that the corresponding basic hypothesis is significantly established at a confidence level of 0.05, if said significance P-value is greater than 0.05, indicating that the corresponding said basic hypothesis is not established, the established basic hypothesis is used to test the acceptance of the service robot.
TABLE 2 hypothesis test results
As can be seen from table 2, only the significance indexes of the hypothesis H1 and the hypothesis H10 in the whole basic hypotheses are greater than 0.05, that is, the significance of the hypothesis H1 perception usefulness on the use attitude and the significance of the hypothesis H10 perception safety on the use attitude are not tested by the significance test, and the significance of the other hypotheses is less than 0.05, and the significance is tested at the level of the confidence coefficient of 0.05, which indicates that the influence relationship between the influencing variables is established.
The structural equation model block diagram is shown in fig. 2, e 1-e 33 represent residual errors, Ease 1-Ease 4, Use 1-Use 4, Fun 1-Fun 4, Safe 1-Safe 3, wild 1-wild 5, Act 1-Act 5, and Att 1-Att 4 represent problems in survey related to perception usability, perception usefulness, perception safety, Use Will, Use behavior and Use attitude, and arrows between perception usability, perception entertainment, perception safety, Use Will, Use behavior and Use attitude represent positive influences generated between the two. The actually obtained model and path coefficients are shown in fig. 3, and it can be seen from fig. 3 that the perception usability and the perception entertainment have a positive influence on the usage attitude, the perception usability, the perception usefulness, the perception entertainment and the perception security all have a positive influence on the will of use, and the perception usability has a positive influence on the perception entertainment. Moreover, as can be seen from the path coefficients in table 2, the forward influence path coefficient of the entertainment on the using attitude is sensed, the forward influence path coefficient of the entertainment on the using will is sensed, and the forward influence path coefficient of the security on the using will is sensed to be higher, so that the forward influence degrees of the three paths are the maximum.
The sex, the age and the education degree in the adjusting variables are individual characteristics, the influence of the individual characteristics on the three paths which are obviously established is tested by a significance test method, and the following processing is carried out on the sex, the age and the education degree of the adjusting variables according to statistical data: sex was classified into male group and female group, age was classified into low age group (25 years and below) and high age group (26 years and above), education was classified into low cultural degree group (major and below) and high cultural degree group (university and department and above), a structural equation model test method was used to conduct a targeted group test, the results are shown in table 3,
TABLE 3 significance test results for individual characteristics (significance test results are expressed as significance P values)
Note: "" indicates significant differences at significant levels below 0.05; "x" means there was a significant difference at a significant level of 0.01 or less; "x" means that there was a significant difference at a significant level of 0.001 or less.
Since the perceived usefulness and perceived safety of the user have no significant impact on the usage attitude, it is assumed that H1 and H10 do not hold, and therefore there is no need to test the regulatory effect (i.e., significance impact) of the basic assumption that does not hold with individual features, as can be seen from table 3: in the three paths, the male and the female are not obvious on the significance level of 0.05, and do not pass the regulation effect test, which shows that the gender has no obvious regulation effect in the influence of sensing entertainment on the using attitude and the using will and the influence of sensing safety on the using will; the low and high age groups were also not significant at a significance level of 0.05, indicating that age had no significant regulatory effect in three pathways; in the path of perceiving the influence of entertainment on the use attitude, the low culture degree group and the high culture degree group pass the inspection on the significance level of 0.001, and the path coefficients are respectively 0.467 and 0.396, which shows that the entertainment perception of the user with lower culture level on the hotel service robot can more promote the change of the use attitude than the user with high culture level. From the adjustment effect of the individual characteristics, the attitude of the hotel service robot is positive for the user, so the hotel service robot is more willing to use when facing the new technology of the service robot, the conclusion also conforms to the idea of the technology acceptance model, the belief is considered to influence the attitude, and the attitude further influences the behavior idea and finally influences the actual action.
The above is only a preferred embodiment of the present application, and the present invention is not limited to the above embodiments. It is to be understood that other modifications and variations directly derived or suggested to those skilled in the art without departing from the spirit and scope of the invention are to be considered as included within the scope of the invention.
Claims (10)
1. A service robot acceptance degree inspection method based on a technical acceptance model is realized based on the technical acceptance model and is characterized by comprising the following steps: s1, selecting influence variables, wherein the influence variables comprise perception usefulness, perception usability, use attitude, use will and use behavior, adding the selected newly added influence variables into a model to construct a service robot acceptance model;
s2, basic assumptions are proposed, and the basic assumptions comprise: based on the basic assumption of the influence relationship among the influence variables of the technology acceptance model, the basic assumption of the influence relationship of the newly added influence variables to the use attitude and the use will of the user is respectively assumed;
and S3, collecting data, and adopting a structural equation model checking method to check the proposed basic hypothesis, and judging whether the basic hypothesis is true, wherein if yes, the service robot is indicated to have a high acceptable degree by the user, otherwise, the service robot is indicated to have a high unacceptable degree.
2. The service robot receptivity checking method based on the technical reception model as recited in claim 1, wherein the service robot is a hotel service robot.
3. The service robot receptivity checking method based on the technical reception model as claimed in claim 1 or 2, wherein in step S1, the influence variables for determining the service robot reception model are selected in combination with actual application characteristics of a service robot, the actual application characteristics of the service robot include the perception entertainment and the perception safety, the basic assumptions include the mutual influence relationship among the perception usefulness, the perception usability, the usage attitude, the usage willingness and the usage behavior, and the influence relationship of the perception entertainment and the perception safety on the usage attitude and the usage willingness, respectively.
4. The service robot receptivity test method based on a technical receptivity model as claimed in claim 3, wherein the influence variables further comprise adjustment variables, and the adjustment variables comprise sex, age, and education level.
5. The service robot receptivity checking method based on the technical acceptance model as claimed in claim 4, wherein in step S2, the basic assumptions are proposed based on the technical acceptance model in combination with the entertainment brought to the user by the service robot in actual use and the security perceived by the user, and the basic assumptions proposed based on the technical acceptance model include: h1, the perception usefulness generates positive influence on the using attitude, H2, the perception usability generates positive influence on the using attitude, H3, the perception usability generates positive influence on the perception usefulness, H4, the using attitude of the user generates positive influence on the using will, H5, the perception usefulness generates positive influence on the using will of the user, H6, the using will of the user generates positive influence on the using behavior, and the basic assumptions are provided in combination with the entertainment brought to the user by the service robot in actual use and the security perceived by the user, wherein the basic assumptions comprise: h7, the perception usability positively affects the perception entertainment, H8, the perception entertainment positively affects the use attitude of the user, H9, the perception entertainment positively affects the use will of the user, H10, the perception safety positively affects the use attitude of the user, and H11, the perception safety positively affects the use will of the user.
6. The service robot receptivity checking method based on the technical acceptance model as claimed in claim 5, wherein the basic assumption further comprises: h8a-c, sex difference, age difference and education level difference can regulate the positive influence relationship of the perceived entertainment on the using attitude, H9a-c, sex difference, age difference and education level difference can regulate the positive influence relationship of the perceived entertainment on the using will, H10a-c, sex difference, age difference and education level difference can regulate the positive influence relationship of the perceived safety on the using attitude, and H11a-c, sex difference, age difference and education level difference can regulate the positive influence relationship of the perceived safety on the using will.
7. The service robot acceptance check method based on the technical acceptance model as claimed in claim 6, wherein the step of determining whether the basic assumption proposed based on the service robot acceptance model holds in step S3 includes: s31, collecting the data by adopting a questionnaire survey mode, and carrying out sample statistics on the collected data;
s32, checking the reliability and validity of the collected data, judging whether the collected data can be used for checking the proposed basic hypothesis, if so, entering the next step S33, and if not, collecting again;
s33, testing the proposed basic hypothesis based on the data and sample statistics.
8. The service robot receptivity test method based on the technical acceptance model as claimed in claim 7, wherein in step S31, after referring to the technical acceptance model-related documents, a seven-level table of leckt is used for questionnaire design; the data collection process takes the form of a web questionnaire;
in step S31, the sample statistics are performed according to gender, age, education level, and service robot categories with which the user is familiar.
9. The service robot receptivity checking method based on the technical reception model as claimed in claim 8, wherein the means for checking the reliability and validity of the data in step S32 includes: the validity of the data is checked through a suitability check value and a Chi-square significance probability value checked by a Bartlett sphere, and the reliability of the data is checked through a Cronbach's alpha coefficient.
10. The service robot receptivity test method based on the technical acceptance model as claimed in claim 9, wherein the step S33 of testing the proposed basic hypothesis based on the data and the sample statistical data includes: s331, checking the set data and the adaptation degree of the service robot acceptance model by using the structural equation model checking method, wherein the adaptation degree is judged through adaptation degree index values, the adaptation degree index comprises chi-square significance CMID/DF, an adaptation degree index GFI, an approximate residual mean square and square root RMSEA, a standard fitting index NFI, a value-added fitting index IFI and a comparison fitting index CFI, the adaptation degree index values comprise recommended values and fitting values, the fitting values are compared with the recommended values obtained in advance, whether the fitting values corresponding to the adaptation degree indexes fall into the corresponding recommended value ranges is judged, if yes, the service robot acceptance model is accepted, the step S332 is carried out, and if not, data collection is carried out again or the influence variables are selected again;
s332, a significance test method is adopted to test whether the basic hypothesis is established, the judgment indexes of the significance test method comprise a standardized path coefficient and significance, and the significance is divided into three levels: intensity, medium degree and weak degree, if the significance P value is less than 0.001, the significance is indicated as intensity, the corresponding basic hypothesis is significantly established at a confidence level of 0.001, if the significance P value is less than 0.01, the significance is indicated as medium, the corresponding basic hypothesis is significantly established at a confidence level of 0.01, if the significance P value is less than 0.05, the significance is indicated as weak, the corresponding basic hypothesis is significantly established at a confidence level of 0.05, if the significance P value is more than 0.05, the corresponding basic hypothesis is not established, the established basic hypothesis is used for testing the acceptance of the service robot, and the standardized path coefficient is the influence degree of the corresponding basic hypothesis.
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