Shared automobile service satisfaction identification method considering traveler attitude latent variable

Abstract

The invention discloses a shared automobile service satisfaction degree identification method considering traveler attitude latent variables, and relates to the technical field of shared automobiles, and the method comprises the following steps: dividing the cognitive stages of a traveler on a shared automobile; based on the divided cognition stage, dividing travel attitude latent variables according to the use requirements of travelers on the shared automobiles and the service quality of the shared automobiles; on the basis of the divided travel attitude latent variables, investigating and acquiring travel information of travelers and the understanding degree of the travelers on the shared automobiles by utilizing a resident travel investigation mode; based on the travel information of travelers and the understanding degree of the travelers on the shared automobiles, the correlation between independent variables and dependent variables is fitted by utilizing a partial partial scale model, and factors influencing the service satisfaction of the shared automobiles are identified. The method solves the problems that the existing method neglects popularization of shared automobiles, cognition of travelers and conversion of service quality into a dynamic process, and neglects the influence of psychological attitude factors of travelers on service satisfaction.

Description

Technical Field

[0001] The present invention relates to the technical field of shared cars, and in particular to a shared car service satisfaction identification method taking into account latent variables of traveler attitudes. Background Art

[0002] With the continuous surge in the number of motor vehicles and residents' travel needs, traffic congestion and energy and environmental problems caused by traffic are getting worse. More and more studies have shown that the car-sharing model (car-sharing is a network platform for user travel services based on mobile Internet and global positioning technology, providing users with short-term rental services of self-service small and micro passenger cars with minutes or hours as the billing unit. This model has the basic characteristics of borrowing and returning at any time, time-sharing rental, self-service, automatic settlement, etc., and users only have short-term use rights for rental vehicles) has a significant positive effect in reducing the number of motor vehicles and the growth of traffic volume, thus providing new possibilities for alleviating traffic congestion. Moreover, because of its characteristics of applying green sustainable energy and near-zero pollution emissions, it is favored by users and is gradually becoming the main trend of the future development of car-sharing. However, due to the uneven rise of car-sharing, it is still a new thing and is in an immature development stage. The unclear development mechanism of car-sharing has greatly restricted the development of car-sharing, inhibiting the significant positive effect of car-sharing in reducing the number of motor vehicles, the growth of traffic volume, pollution emissions, and energy consumption. Among them, the identification of travelers' satisfaction with shared car services and its influencing factors is the core content of the shared car development mechanism.

[0003] However, whether in the field of scientific research or patent application, current research on the quality of shared car services focuses on the frequency of use by travelers, and the research factors are concentrated on the travelers' socio-demographic attributes and travel characteristics (such as travel time, cost, distance, etc.), ignoring that the popularization of shared cars, travelers' cognition, and service quality changes are a dynamic process, and ignoring the impact of travelers' psychological attitude factors on service satisfaction. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a shared car service satisfaction identification method that takes into account the latent variables of travelers' attitudes, which solves the problem that the existing methods ignore the fact that the popularization of shared cars, travelers' cognition, and service quality changes are a dynamic process, and also ignore the impact of travelers' psychological attitude factors on service satisfaction.

[0005] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a method for identifying shared car service satisfaction considering the latent variables of travelers' attitudes, comprising the following steps:

[0006] S1: Divide travelers’ cognitive stages of car-sharing;

[0007] S2: Based on the divided cognitive stages, the latent variables of travel attitudes are divided according to travelers’ demand for shared cars and the service quality of shared cars;

[0008] S3: Based on the divided travel attitude latent variables, the travel information of travelers and their understanding of shared cars are investigated and obtained by using the residents' travel survey method;

[0009] S4: Based on travelers’ travel information and their understanding of shared cars, the partial partial proportion model is used to fit the correlation between the independent variables and the dependent variables, identify the factors affecting shared car service satisfaction, and complete the identification of shared car service satisfaction considering the latent variables of travelers’ attitudes.

[0010] Furthermore, the cognitive stages in S1 are divided into: very dissatisfied, dissatisfied, average, satisfied, and very satisfied.

[0011] Furthermore, the travel attitude latent variables in S2 are divided into: comfort attitude, speed attitude, environmental awareness, usage preference, site barrier, vehicle barrier, usage barrier, personal barrier, subjective norm and transfer intention.

[0012] Furthermore, the travel information and understanding of shared cars of travelers in S3 include latent variables of socio-demographic characteristics, travel characteristics, shared car environment, and travel attitude.

[0013] Furthermore, the socio-demographic characteristics include gender, age, income, education level, and presence of children;

[0014] The trip characteristics include trip distance, trip time, and car availability;

[0015] The shared car environment includes the degree of understanding of shared cars, the distance to surrounding shared car stations, and the probability that there are cars at the shared car stations.

[0016] Further, the age is 18-70;

[0017] The income is divided into: less than 5,000, 5,001-7,000, 7,001-10,000, 10,001-15,000 and more than 15,000;

[0018] The educational background is divided into: junior high school and below, senior high school or vocational high school or technical secondary school, junior college or undergraduate, and master's degree and above;

[0019] The travel distance is divided into: more than 15km, 10-15km, 5-10km, 3-5km and less than 3km;

[0020] The travel time is divided into: more than 60 minutes, 45-60 minutes, 30-45 minutes, 15-30 minutes and less than 15 minutes;

[0021] The degree of understanding of car-sharing is divided into: very little understanding, no understanding, general understanding, understanding and very understanding;

[0022] The probability of having a car at the shared car station is divided into: less than 20%, 20-40%, 40-60%, 60-80% and 80-100%.

[0023] Furthermore, in S4, the partial partial proportion model is used to fit the correlation between the independent variable and the dependent variable to identify the factors affecting the satisfaction of shared car service, including the following sub-steps:

[0024] S4-1: Socio-demographic characteristics, travel characteristics, car-sharing environment, and travel attitude latent variables were used as independent variables, and the traveler's cognitive stage of car-sharing was used as the dependent variable;

[0025] S4-2: Input the independent and dependent variables into the partial partial proportion model and use the analysis software to output the fitting results of all variables;

[0026] S4-3: Based on the fitting results, identify the influencing factors and impact sizes of each adjacent stage.

[0027] Furthermore, the adaptation value of the travel attitude latent variable in S4-1 is calculated as follows:

[0028] η=Γx+ζ

[0029] y=Λη+v

[0030] Among them, η is the attitude latent variable vector, Г and Λ are the parameter matrices to be estimated, x is the exogenous variable vector that affects the attitude latent variable, y is the observable indicator vector of the attitude latent variable, ζ and v are error terms.

[0031] Furthermore, the partial proportional model in S4-2 is:

[0032]

[0033] Among them, P(S n >j) is the traveler's satisfaction with the shared car service S n The probability that the value is greater than j, where j is the satisfaction level of shared car service and β1 is the ... 1n The parameter vector that does not violate the parallel line construction is related to the explanatory variable subset X. 2n Related, the parameter vector that violates the parallel line construction, φ j is the cut-off point of the ordered model threshold.

[0034] Furthermore, the fitting results in S4-3 are expressed using the significance evaluation index Sig., specifically:

[0035] (1) When the Sig. of a variable is ≤0.05, it means that the indicator has a significant impact on the dependent variable and the variable should be retained in the partial proportional model;

[0036] (2) When the Sig. of a variable is greater than 0.05, it means that the indicator has no significant effect on the dependent variable, and the variable should be removed from some partial proportional models;

[0037] (3) When the Sig. of the variable is ≤ 0.05 and the parameter value is negative, it means that the factor has a negative impact on the dependent variable, and the smaller the parameter value, the stronger the inhibitory effect;

[0038] (4) When the Sig. of the variable is greater than 0.05 and the parameter value is positive, it means that the factor has a positive impact on the dependent variable, and the larger the parameter value, the stronger the promoting effect.

[0039] The beneficial effects of the invention are as follows: Based on travelers' potential attitudes toward shared cars, the invention divides travelers' satisfaction with shared car services into five ordered levels, comprehensively determines travelers' satisfaction with shared car services in turn, improves the accuracy of shared car service quality judgment, accurately analyzes travelers' satisfaction with shared car services, provides shared car development operators and policy makers with an accurate and effective identification method, and improves the scientific nature of shared car development. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Flowchart of a method for identifying car-sharing service satisfaction considering latent variables of travelers’ attitudes.

[0041] Figure 2 It is the technical roadmap of the present invention.

[0042] Figure 3 It is the relationship diagram between latent variables and observed quantities. DETAILED DESCRIPTION

[0043] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0044] like Figure 1 and Figure 2 As shown in FIG, a method for identifying the satisfaction of shared car service considering the latent variables of travelers' attitudes includes the following steps:

[0045] S1: Divide travelers’ cognitive stages of car-sharing;

[0046] S2: Based on the divided cognitive stages, the latent variables of travel attitudes are divided according to travelers’ demand for shared cars and the service quality of shared cars;

[0047] S3: Based on the divided travel attitude latent variables, the travel information of travelers and their understanding of shared cars are investigated and obtained by using the residents' travel survey method;

[0048] S4: Based on travelers’ travel information and their understanding of shared cars, the partial partial proportion model is used to fit the correlation between the independent variables and the dependent variables, identify the factors affecting shared car service satisfaction, and complete the identification of shared car service satisfaction considering the latent variables of travelers’ attitudes.

[0049] The present invention takes the potential psychological and attitude latent variables of travelers as the main research content, integrates the social characteristics of travelers and the characteristics of the shared car system, applies the structural equation model and the partial partial proportional advantage model, and discriminates the cognitive stage of travelers on shared cars, including the following steps:

[0050] The cognitive stages in S1 are divided into: very dissatisfied, dissatisfied, average, satisfied, and very satisfied.

[0051] The travel attitude latent variables in S2 are divided into: comfort attitude η AC , quick attitude η AQ , environmental awareness AW , using preference η AD , site obstacle η BS , vehicle obstacle η BV , use barrier η BU , personal obstacle η BI , subjective norm η SN , transfer intention η CI .

[0052] The travel information and understanding of shared cars of travelers in S3 include latent variables of socio-demographic characteristics, travel characteristics, shared car environment and travel attitude.

[0053] The sociodemographic characteristics included sex, age, income, education, and the presence of children;

[0054] The trip characteristics include trip distance, trip time, and car availability;

[0055] The shared car environment includes the degree of understanding of shared cars, the distance to surrounding shared car stations, and the probability that there are cars at the shared car stations.

[0056] The age stated is 18-70;

[0057] The income is divided into: less than 5,000, 5,001-7,000, 7,001-10,000, 10,001-15,000 and more than 15,000;

[0058] The educational background is divided into: junior high school and below, senior high school or vocational high school or technical secondary school, junior college or undergraduate, and master's degree and above;

[0059] The travel distance is divided into: more than 15km, 10-15km, 5-10km, 3-5km and less than 3km;

[0060] The travel time is divided into: more than 60 minutes, 45-60 minutes, 30-45 minutes, 15-30 minutes and less than 15 minutes;

[0061] The degree of understanding of car-sharing is divided into: very little understanding, no understanding, general understanding, understanding and very understanding;

[0062] The probability of having a car at the shared car station is divided into: less than 20%, 20-40%, 40-60%, 60-80% and 80-100%.

[0063] In one embodiment of the present invention, a resident travel survey method is used to investigate and obtain travelers' travel information and understanding of shared cars, including: socio-demographic characteristics (gender, age, income, education, whether there are children), travel characteristics (travel distance, travel time, whether there is a private car), shared car environment (degree of understanding of shared cars, distance from surrounding shared car stations, probability of shared car stations having cars), shared car usage attitude (comfort attitude, speed attitude, environmental awareness, usage preference, station obstacles, vehicle obstacles, usage obstacles, personal obstacles, subjective norms, transfer intention). The specific survey contents are shown in Tables 1 and 2.

[0064] Table 1 Exogenous variables and their values

[0065]

[0066] Table 2 Survey of latent variables and their observed variables

[0067]

[0068]

[0069] Note: The degree of latent variable intention can be represented by a value of 1 to 5 (1, 2, 3, 4, 5), where a larger value indicates a stronger attitude of approval, and a smaller value indicates a stronger attitude of opposition.

[0070] Based on the individual travel attitude classification in step S2, 1,000 experimental individuals (selected by random sampling) were surveyed on the shared car use attitude data (observable variables). The specific survey content and the assignment of each variable are shown in Table 3. The survey results of each investigator were counted one by one.

[0071] Table 3 Questionnaire survey results statistics

[0072]

[0073]

[0074] In S4, the partial partial proportion model is used to fit the correlation between the independent variables and the dependent variables to identify the factors affecting the satisfaction of shared car services, including the following sub-steps:

[0075] S4-1: Socio-demographic characteristics, travel characteristics, car-sharing environment, and travel attitude latent variables were used as independent variables, and the traveler's cognitive stage of car-sharing was used as the dependent variable;

[0076] The dependent variable selected in the study is the traveler’s cognitive stage of shared cars. According to the characteristics of the constructed model, the dependent variables and their values ​​are shown in Table 4.

[0077] Table 4. Machine assignment of dependent variables in the cognitive stage discrimination model

[0078]

[0079] S4-2: Input the independent and dependent variables into the partial partial proportion model and use the analysis software to output the fitting results of all variables;

[0080] S4-3: Based on the fitting results, identify the influencing factors and impact sizes of each adjacent stage.

[0081] The adaptation value of the travel attitude latent variable in S4-1 is calculated as follows:

[0082] η=Γx+ζ

[0083] y=Λη+v

[0084] Among them, η is the attitude latent variable vector, Г and Λ are the parameter matrices to be estimated, x is the exogenous variable vector that affects the attitude latent variable, y is the observable indicator vector of the attitude latent variable, ζ and v are error terms.

[0085] Latent variables are variables that reflect user psychology and attitudes and cannot be directly observed. Structural equation models can measure implicit latent variables through observed variables and effectively analyze the impact of latent variables on travel behavior. The weight relationship between observed variables and latent variables can be directly fitted by AMOS software, such as Figure 3 As shown (with comfort attitude ηAC For example), c i is the linear relationship weight between the observed variable and comfort attitude, e i is an automatically generated constant.

[0086] Based on the survey results in Table 1, the fitting values ​​of each latent variable were obtained by fitting with AMOS software as shown in Table 5.

[0087] Table 5 Latent variable adaptation values

[0088]

[0089] According to the binomial logistic model utility value calculation formula, as shown in the following formula, Y i represents the utility value of cognitive stage i, s i is the social demographic characteristic vector of observable travelers; i is the travel mode attribute vector (travel characteristics and objective environment); η i is the unobservable latent variable vector (comfort attitude η AC , quick attitude η AQ , environmental awareness AW , using preference η AD , site obstacle η BS , vehicle obstacle η BV , use barrier η BU , personal obstacle η BI , subjective norm η SN , transfer intention η CI ), in this scenario, it is the perceived usefulness, perceived ease of use, attitude, subjective norm, perceived behavioral control and behavioral intention towards car sharing; a i ,b i ,c i are unknown parameter vectors to be estimated; ε i is a random item.

[0090] Y i =a i s i +b i z i +c i η i +ε i

[0091] Substitute the survey results in Table 3 and the obtained fitting values ​​into the above formula. With the help of Stata analysis software, input all dependent variables and independent variables. Stata can directly output the fitting results of all variables, and the coefficient weights of each variable using this scheme can be obtained:

[0092] u i =a1X gen+a2X age +a3X inc +a4X edu +a5X chi +

[0093] bX dis +b2X tim +b3X car +b4X sta +b5X veh +b6X kno +

[0094] c1η AC +c2η AQ +c3η AW +c4η AD +c5η BS +c6η BV +c7η BU +c4η BI +c9η SI +c 10 η CI +ε i

[0095] The fitting results are shown in Table 6. The parameter value indicates the degree of influence of the factor on the dependent variable, and the Sig. value indicates the significance evaluation index of the factor on the dependent variable.

[0096] Table 6 The influence of independent variables on dependent variables and the significance results

[0097]

[0098] The partial proportional model in S4-2 is:

[0099]

[0100] Among them, P(S n >j) is the traveler's satisfaction with the shared car service S n The probability that the value of the shared car service is greater than j, where j is the satisfaction level of the shared car service, j = 1, 2, ..., 5, 1 means very dissatisfied, 2 means dissatisfied, 3 means average, 4 means satisfied, and 5 means very satisfied. 1n The parameter vector that does not violate the parallel line construction is related to the explanatory variable subset X. 2n Related, the parameter vector that violates the parallel line construction, φ j is the cut-off point of the ordered model threshold.

[0101] The fitting results in S4-3 are expressed by the significance evaluation index Sig., which is:

[0102] (1) When the Sig. of a variable is ≤0.05, it means that the indicator has a significant impact on the dependent variable and the variable should be retained in the partial proportional model;

[0103] (2) When the Sig. of a variable is greater than 0.05, it means that the indicator has no significant effect on the dependent variable, and the variable should be removed from some partial proportional models;

[0104] (3) When the Sig. of the variable is ≤ 0.05 and the parameter value is negative, it means that the factor has a negative impact on the dependent variable, and the smaller the parameter value, the stronger the inhibitory effect;

[0105] (4) When the Sig. of the variable is greater than 0.05 and the parameter value is positive, it means that the factor has a positive impact on the dependent variable, and the larger the parameter value, the stronger the promoting effect.

[0106] In this embodiment, the influencing factors of each adjacent stage and their influence magnitude are identified based on the fitting results of each factor obtained from the above four binomial logistic models. That is:

[0107] ①Whether it affects analysis:

[0108] The existence of each variable depends on the significance evaluation index Sig. of the fitting results of each variable. When the Sig. of the variable is ≤0.05, it means that the index has a significant impact on the dependent variable, and the variable should be retained in the fitting model; when the Sig. of the variable is >0.05, it means that the index has no significant impact on the dependent variable, and the variable should be removed from the fitting model. From the Sig. corresponding to each parameter in the above table, we can see that:

[0109] In the PC-C model, the Sig. of B11, B21, B31, B41, B11, B12, B13, B18, and B19 is ≤ 0.05. Therefore, factor X gen , X age , X inc , X edu , X kno , X IM1 , X IM2 , η AQ , η AW It has no effect on promoting the development stage of shared cars from the pre-PC intention stage to the C intention stage; the other factors all have an impact.

[0110] ② Effect size analysis

[0111] If the significance evaluation index Sig. of a certain variable is ≤0.05, and the fitting coefficient is negative, it means that the factor has a negative impact on the dependent variable, that is, the shared car development measures represented by the factor inhibit the traveler's cognition and use attitude from the current stage to another stage, and the smaller the fitting coefficient, the stronger the inhibitory effect; if the significance evaluation index Sig. of a certain variable is greater than 0.05, and the fitting coefficient is positive, it means that the factor has a positive impact on the dependent variable, that is, the shared car development measures represented by the factor promote the traveler's cognition and use attitude from the current stage to another stage, and the larger the fitting coefficient, the stronger the promotion effect. From Table 6 and the above analysis, we can see that:

[0112] In the model, factor X chi <X dis <X IM3 <X IM4 <η SN <X IM5 <η BU <0, indicating that these factors inhibit the development of shared cars from the C intention stage to the PA preparation stage, and X chi Strongest inhibitory effect; X tim >X car >X sta >X veh >η AC >η AD >η BS >η BV >η BI >η CI >0, indicating that these factors promote the development of shared cars from the C intention stage to the PA preparation stage, and X tim The most facilitative.

[0113] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the invention.

Claims

1. A method for identifying shared car service satisfaction considering travelers’ attitude latent variables, characterized in that: The following steps are involved: S1: Divide travelers’ cognitive stages of car-sharing; S2: Based on the divided cognitive stages, the latent variables of travel attitudes are divided according to travelers’ demand for shared cars and the service quality of shared cars; S3: Based on the divided travel attitude latent variables, the travel information of travelers and their understanding of shared cars are investigated and obtained by using the residents' travel survey method; S4: Based on travelers’ travel information and their understanding of shared cars, the partial partial proportion model is used to fit the correlation between the independent variables and the dependent variables, identify the factors affecting shared car service satisfaction, and complete the identification of shared car service satisfaction considering the latent variables of travelers’ attitudes.

2. The method for identifying shared car service satisfaction considering traveler attitude latent variables according to claim 1 is characterized in that: The cognitive stages in S1 are divided into: very dissatisfied, dissatisfied, average, satisfied and very satisfied.

3. The method for identifying shared car service satisfaction considering traveler attitude latent variables according to claim 1 is characterized in that: The travel attitude latent variables in S2 are divided into: comfort attitude, speed attitude, environmental awareness, usage preference, site barriers, vehicle barriers, usage barriers, personal barriers, subjective norms and transfer intention.

4. The method for identifying shared car service satisfaction considering traveler attitude latent variables according to claim 1 is characterized in that: The travel information and understanding of shared cars of the travelers in S3 include latent variables of socio-demographic characteristics, travel characteristics, shared car environment and travel attitude.

5. The method for identifying shared car service satisfaction considering traveler attitude latent variables according to claim 4 is characterized in that: The sociodemographic characteristics included sex, age, income, education, and the presence of children; The trip characteristics include trip distance, trip time, and car availability; The shared car environment includes the degree of understanding of shared cars, the distance to surrounding shared car stations, and the probability that there are cars at the shared car stations.

6. The method for identifying shared car service satisfaction considering traveler attitude latent variables according to claim 5 is characterized in that: The age stated is 18-70; The income is divided into: less than 5,000, 5,001-7,000, 7,001-10,000, 10,001-15,000 and more than 15,000; The educational background is divided into: junior high school and below, senior high school or vocational high school or technical secondary school, junior college or undergraduate, and master's degree and above; The travel distance is divided into: more than 15km, 10-15km, 5-10km, 3-5km and less than 3km; The travel time is divided into: more than 60 minutes, 45-60 minutes, 30-45 minutes, 15-30 minutes and less than 15 minutes; The degree of understanding of car-sharing is divided into: very little understanding, no understanding, general understanding, understanding and very understanding; The probability of having a car at the shared car station is divided into: less than 20%, 20-40%, 40-60%, 60-80% and 80-100%.

7. The method for identifying shared car service satisfaction considering traveler attitude latent variables according to claim 1 is characterized in that: In S4, the correlation between the independent variable and the dependent variable is fitted using the partial partial proportion model to identify the factors affecting the satisfaction of the shared car service, including the following steps: S4-1: Socio-demographic characteristics, travel characteristics, car-sharing environment, and travel attitude latent variables were used as independent variables, and the traveler's cognitive stage of car-sharing was used as the dependent variable; S4-2: Input the independent and dependent variables into the partial partial proportion model and use the analysis software to output the fitting results of all variables; S4-3: Based on the fitting results, identify the influencing factors and impact sizes of each adjacent stage.

8. The method for identifying shared car service satisfaction considering traveler attitude latent variables according to claim 7 is characterized in that: The adaptation value of the travel attitude latent variable in S4-1 is calculated as follows: η=Γx+ζ y=Λη+v Among them, η is the attitude latent variable vector, Г and Λ are the parameter matrices to be estimated, x is the exogenous variable vector that affects the attitude latent variable, y is the observable indicator vector of the attitude latent variable, and ζ and v are error terms.

9. The method for identifying shared car service satisfaction considering traveler attitude latent variables according to claim 7, characterized in that: The partial proportional model in S4-2 is: Among them, P(S n >j) is the traveler's satisfaction with the shared car service S n The probability that the value is greater than j, where j is the satisfaction level of shared car service and β1 is the ... 1n The parameter vector that does not violate the parallel line construction is related to the explanatory variable subset X. 2n Related, the parameter vector that violates the parallel line construction, φ j is the cut-off point of the ordered model threshold.

10. The method for identifying shared car service satisfaction considering traveler attitude latent variables according to claim 7, characterized in that: The fitting results in S4-3 are represented by the significance evaluation index Sig., which is: (1) When the Sig. of a variable is ≤0.05, it means that the indicator has a significant impact on the dependent variable and the variable should be retained in the partial proportional model; (2) When the Sig. of a variable is greater than 0.05, it means that the indicator has no significant effect on the dependent variable and the variable should be removed from some partial proportional models; (3) When the Sig. of the variable is ≤ 0.05 and the parameter value is negative, it means that the factor has a negative effect on the dependent variable, and the smaller the parameter value, the stronger the inhibitory effect; (4) When the Sig. of the variable is greater than 0.05 and the parameter value is positive, it means that the factor has a positive impact on the dependent variable, and the larger the parameter value, the stronger the promoting effect.

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