
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

72494
10.1038/s41598-024-72494-w
Article
Exploring tourists’ preferences and willingness to pay for national park recreation improvements based on regret and utility comparison
Gao Qin 37778774@qq.com

1
Cui Songsong 2
Shi Pingping 3
Li Zhenrui 2
1 grid.443652.2 0000 0001 0074 0795 International Business College, Shandong Technology and Business University, Yantai, 264000 China
2 grid.443652.2 0000 0001 0074 0795 School of Management Science and Engineering, Shandong Technology and Business University, Yantai, 264000 China
3 grid.443652.2 0000 0001 0074 0795 School of Foreign Studies, Shandong Technology and Business University, Yantai, 264000 China
14 9 2024
14 9 2024
2024
14 2152421 3 2024
9 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Research on the improvement of national park recreation policies has attracted much attention to discrete choice experiments to obtain tourists’ preferences and willingness to pay. However, individual choice behavior is extremely complex, and the single Random Utility Maximization (RUM) model ignores anticipated regret and is insufficient to explain individuals’ actual choice behavior. To investigate whether regret influences tourists’ choices regarding the improvement of national park recreation attributes, this study introduces the Random Regret Minimization (RRM) model and explores the performance of polynomial logit models and hybrid latent class models in analyzing discrete choice models based on utility and regret. By constructing a hybrid utility-regret model, we examine how tourists trade off between attributes such as vegetation coverage, water clarity, amount of litter, and level of crowding in national park recreation. Results indicate that the RRM model has better goodness-of-fit and predictive ability than the RUM model, indicating that regret is a significant choice paradigm, and the hybrid model better explains respondents’ choices. Specifically, 62.5% of tourists’ choices are driven by regret, and regret-driven respondents are more inclined to increase vegetation coverage and improve water clarity, while utility-driven respondents are more inclined to reduce litter and crowding. This study not only provides a reference for managers to develop more optimal recreation improvement strategies but also offers theoretical insights for national park recreation improvement policies.

Keywords

National park recreation improvement
Discrete choice experiments
Utility maximization
Regret minimization
Willingness to pay
Preferences heterogeneity
Subject terms

Behavioural ecology
Ecological modelling
Environmental sciences
Environmental social sciences
Design and Application Research of China's Marine Economy Big Data Monitoring Systemnumber 21ATJ006 Gao Qin Shandong Business College Wealth Management Characteristic Research Project, China under Grantnumber 2022YB11 Gao Qin Research on the Path of Improving the Quality and Efficiency of Scenic Village Tourism in Shandong Province under the Background of Rural Revitalizationnumber 22CJJJ26 Gao Qin issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

With increasing concerns for environmental protection and ecotourism, national parks have rapidly emerged worldwide. National parks serve as crucial protected areas for natural ecosystems and cultural heritage, offering unique recreational experiences for visitors and significantly impacting local economies and social development1. However, with population growth, tourism development, and environmental degradation, national parks face growing numbers of visitors and substantial pressures and challenges that cannot be ignored2. Aiming at protecting the ecological environment of national parks, enhancing visitor experiences, and ensuring the sustainable development of recreational activities, improvements in national park recreation are essential.

National park recreation improvements involve effective resource conservation and visitor behavior guidance, among other aspects. However, making wise decisions in recreational improvements is a complex issue. Solely relying on the efforts of managers is by no means sufficient; it is also of crucial importance to comprehend visitor preferences and choice behaviors3. On the one hand, managers need to carry out effective resource conservation and implement reasonable measures for recreational improvements. On the other hand, understanding visitor preferences in recreational choices is equally important. Therefore, in order to attract more visitors to experience national park recreational services, researchers and decision-makers have been exploring scientifically effective decision methods and strategies conducive to visitor experiences.

The recreational resources of national parks are non-market environmental resources. In terms of individual preferences and values for the non-market environmental products, one of the most flexible and appropriate methods is the discrete choice experiment (DCE)4–6. It allows for estimating the Willingness to Pay (WTP) for improving recreational attributes in national parks, which serves as a basis for policy-making. WTP reflects the economic value of enhancing recreational experiences in national parks. Decision-makers can use information on tourists’ preferences, choice behavior, and WTP to guide national park development planning, destination management strategies, and conservation measures. By considering tourists’ preferences, policymakers can make informed decisions regarding infrastructure development, resource allocation, and sustainability measures while protecting natural and cultural assets, ultimately creating positive visitor experiences. Therefore, understanding tourists’ preferences and choice behavior from scientific, managerial, and policy perspectives is crucial7.

Discrete choice experiments (DCE) have been utilized across various fields, including transportation, health, ecosystem, ecological tourism and environmental economics8–10. With the increasing popularity of discrete choice models in different domains, more and more researchers are employing choice experiments to study behavior and public preferences for different attributes of services and goods11–13. In the context of tourism, travelers make various decisions during their trips, including the choice of destination, mode and timing of travel, accommodation, and activities at the destination. For instance, using choice experiments to address various aspects of tourists’ considerations regarding the attractiveness of competing tourist destinations14. It also investigates the relationship between tourists’ choices of different tourism product attributes and their socio-psychological characteristics15,16. Furthermore, the study assesses tourists’ preferences for national parks and nature parks as recreational travel destinations17.

The analysis of discrete choice experiment data is mostly estimated using the Random Utility Maximization (RUM) model, which assumes that respondents are totally rational and make decisions based on maximizing their utility, as guided by random utility theory18. However, the development of behavioral economics has raised questions about the assumption of complete rationality19. In psychological and behavioral economic research, it has been shown that anticipated regret can influence decision-making20, and that decision theorists have long been interested in this emotion21. This theoretical framework assumes that respondents are boundedly rational and their choices are influenced by the expected performance of alternatives that were not chosen. Previous studies have shown the introduction of the Random Regret Minimization (RRM) model to be superior to the RUM model in various domains, including public preferences for renewable energy projects and air quality improvement22,23, travel behavior24, transportation route choice25, food choices26, hotel location selection27, hospital bed28 and wetland management29, further demonstrating that anticipated regret is a significant determinant of decision-makers’ choice behavior30.

Different from the RUM specification, the RRM assumes that respondents make choices by comparing each attribute of the selected alternative with the best value of the unselected alternatives, aiming to minimize expected regret31–33. Unlike RUM, RRM suggests that individuals seem to minimize regret rather than maximize utility when making choices. This behavioral rule is applicable when different options have equally important attributes. In such cases, regret is defined as the experience when an unselected alternative performs better than the chosen alternative on one or more attributes.

However, few scholars have explored whether tourists’ choice of improving the recreational attributes of national parks is driven by minimizing the expected regret and whether it will affect the rationality of policy formulation. This study compares the performance of two decision rules (RUM and RRM) to explain tourists’ preferences for national park recreation attribute improvement schemes described by five attributes: vegetation coverage, water clarity, waste quantity, site congestion, and ticket price. First, the goodness-of-fit of the RUM and RRM decision rules is examined by estimating a multinomial logit (MNL) model. Then, according to the general hybrid model specification proposed by Chorus19 and Long et al.29, this hybrid model is based on the RUM rule for some attributes and the RRM rule for other attributes. This study constructs a hybrid RUM-RRM model to explore which decision rule tourists’ selection of recreational attributes is based on. Just as suggested by Hess et al.34, van Cranenburgh et al.35, and Mao et al.23, this study adopts the latent class (LC) model a hybrid model combining two choice paradigms, and the influence of relevant individual social characteristics on decision-making (utility maximization or regret minimization). At the same time, this study also analyzes tourists’ preferences and WTP for the natural resources and management attributes related to recreation in national parks. In general, this study explores whether regret minimization can be used as a supplement to traditional methods for the decision rules of improving recreational choices in national parks, improve the accuracy of evaluation results, and provide more scientific and effective policy suggestions for the formulation of improvement plans for recreation in national parks.

Based on the analysis above, the main research focuses of this study are as follows: 1. Does regret have an impact on tourists’ preferences regarding the improvement of recreation in national parks? 2. By comparing the differences between the RRM model and the RUM model, is it still reasonable to estimate the policy benefits of improving recreation in national parks based on the RUM framework? 3. Which decision rule do tourists use when choosing the improvement of recreation in national parks, and how do their individual socio-economic characteristics influence their preference for a particular choice paradigm?

Materials and methods

Research area

The Yellow River Estuary National Park is located on the northern bank of the Yellow River Estuary in Dongying City, Shandong Province, China. It is one of China’s second batch of national parks, covering a total area of approximately 3518 square kilometers. Its geographical location is shown in Fig. 1. The park consists of three main sections: the ecological conservation area, the cultural heritage area, and the tourism and leisure area. Within the park, there are abundant natural resources and cultural heritage sites, including the Yellow River Delta Wetland, the Yellow River Estuary, the Old Course of the Yellow River, and the Yellow River Estuary Fishing Village. The park is home to diverse vegetation and animal species, making it an important habitat and migration passage for birds in China. The park aims to achieve sustainable development in terms of ecology, economy, and society through various means such as protecting the ecological environment, inheriting cultural heritage, and developing tourism economy. As a result, it has gained increasing attention and popularity among tourists who seek to experience the recreational services offered by the Yellow River Estuary National Park.Fig. 1 Research area.

Random utility model

In choice experiments, the utility of a product or service is determined by its intrinsic characteristics36, and consumers strive to maximize their utility when making choices37. According to the Random Utility Model (RUM), consumers select alternative option i from choice set k in a rational manner, aiming to maximize their expected utility. Therefore, individual n’s utility-based selection of alternative i under RUM can be represented as:1 Unik=Vnik+εnik=β′Xnik+εnik

The probability that (Unik) respondents n chooses alternative i among the choice set k, considering the utility consisting of deterministic and random parts, which is a vector representing the improvement attributes of national park management, with β being the parameter to be estimated. Assuming that the error term ε follows the Gumbel distribution (i.e., extreme value type I), and that the error terms ε are independently and identically distributed, in this case, according to the multinomial logit (RUM) model described by McFadden37, the probability that respondent n chooses alternative i is:2 PnikRU=eVnik∑k=1KeVnik′

Random regret model

As a psychological factor, regret influences the choice behavior of respondents. The RRM model assumes that the choice between multi-attribute alternatives is driven by pairwise comparisons and requires weighing the attributes of the alternatives. Decision-makers (denoted as n) attempt to select options that minimize regret. When one or more unselected alternatives are more attractive than the chosen option on one or more attributes, regret arises32. Therefore, individual choices of alternatives are based on the expectation of minimizing regret rather than maximizing utility. Overall regret is considered to be the total regret when considering all competing alternatives k versus the chosen alternative i across all attributes m. The classic RRM model was proposed by Chorus31 and can be expressed as:3 RRnik=Rnik+εnik=∑k≠i∑mln1+expβmxkmn-ximn+εnik

This function maps the differences in attributes between alternative options and competitive alternative options to regret. Similar to RUM, RRnik is the total regret value of the respondent n choosing alternative option i from choice set k, consisting of a deterministic part Rnik and a random part εnik. Among them, The parameter to be estimated is βm, Rnik=∑k≠i∑mln(1+exp(βm[xkmn-ximn])) represents the sum of regrets when comparing all attributes m of the chosen alternative option i with its competitive alternative option k. Minimizing random regret is mathematically equivalent to maximizing negative random regret. Assuming that the random part of the error follows an IID extreme value distribution, the choice probability of respondent n selecting option i using the RRM paradigm is modeled using MNL choice probability.4 PnikRR=e-Rnik∑k=1Ke-Rnik

Hybrid RUM-RRM model

Both models presented above are based on single decision rules. However, individual choice behavior is extremely complex, and the heterogeneity of decision rules should be explored not only from the perspective of decision contexts and decision makers themselves but also from the attribute level of choice alternatives. Different decision rules may be employed by decision makers for different attributes, and a single decision mechanism may not accurately reflect reality. A more reasonable approach is to adopt a hybrid decision mechanism based on the combined effects of utility and regret23,25,29. The choice of decision rule (RUM or RRM) depends on the attribute19 proposed a generic hybrid model specification that employs RUM rules for some attributes and RRM rules for others. Therefore, to capture which rule is more effective for attributes, a fixed constant in the regret function can be replaced with a regret weight. The formula is as follows:5 Ri=∑k≠i∑mlnγm+expβmxkm-xim

When γm = 0, the generalized RRM model produces the same choice behavior as the traditional RUM model. Considering the independence of unrelated choices, the function can be simplified as the linearly additive attribute differences between alternatives. Therefore, when the regret weight γm approaches 1, the attribute adopts the RRM model decision rule, and when γm = 0, the attribute adopts the RUM model decision rule. The hybrid RUM-RRM model, which determines the decision rule for each attribute based on regret weights, is as follows:6 Hni=∑m=1..Qβmxim-∑k≠i∑m=Q+1..Mln1+expβmxkm-xim+εni

where Q represents the number of attributes determined by RUM rules, and M-Q represents the number of attributes determined by RRM rules.

Since the proposal of RRM model, many studies have compared the performance of the RRM and RUM mechanisms on specific datasets and their ability to better explain decision-making behavior23,25,26,38. Researchers typically use empirical analysis methods to compare the fitting and predictive ability of these two models by collecting and analyzing a large amount of actual decision data. Although the RRM model provides a framework for explaining decision-making behavior, it does not provide a detailed explanation of how respondents’ decision rules change. Therefore, understanding the mechanism of variation in respondents’ decision rules is still limited. According to the hypothesis proposed by Boeri and Longo22, one group of respondents chooses to follow the RUM decision rule, while another group selects the RRM decision rule. The combination of these two decision rules forms a latent class (LC) model with two classes, where individuals within each group exhibit homogeneous choice behavior, while the preference structure between groups is heterogeneous. The hybrid LC model is utilized to investigate which decision choices respondents are more inclined towards, as well as how respondents’ individual socioeconomic characteristics influence their preference for choice paradigms. The selection probability of the hybrid LC model is as follows:7 Pnik=πnsPnikRU+1-πnsPnikRR

The probability of members in the RUM class πns is usually a logit model.8 πns=expθs+φs′znexpθs+φs′zn+1

where θs represents class-specific constants,zn represents individual socio-economic characteristics that influence choices, and φs′ represents the parameter vector of class membership degrees.

Willingness to pay

The marginal rate of substitution (marginal rate of substitution, MRS) is an important output information in model selection, and one of the attribute variables is usually represented in monetary terms, so MRS can be expressed in monetary terms. MRS is often referred to as WTP. The marginal WTP of the two models is as follow:9 WTPkRU=-∂Vnik/∂Xj∂Vnik/∂Xp=-βkβp

10 WTPkRR=-∂Vnik/∂Xj∂Vnik/∂Xp

Discrete choice experiments and data collection

Discrete choice experiments

Discrete choice experiments provide an excellent technique for eliciting stated preferences and predicting the public’s preferences and choice behavior toward different features of environmental improvements5,6. In contingent valuation studies, respondents are typically presented with various options and asked to choose their preferred options. Different alternative options have varying levels of attributes36. By varying the attribute levels within choice sets, discrete choice experiments enable respondents to evaluate changes in environmental improvements under specific assumptions39. The design of the study’s DCE aims to investigate the impact of different features of national park recreation attributes on respondents’ preferences. The study uses different sets of attribute choices related to national park recreation improvements at varying levels to understand respondent preferences.

Recreation attributes and levels

The key to DCE design hinges on determining the attributes and levels. Typically, different alternative options have varying levels of attributes. Through extensive literature review and discussions with the staff from Dongying Ecological Environment Bureau and environmental science experts, the attributes, levels, and expressions to be used have been initially determined. Before the formal survey, a pilot study was conducted in March 2023, involving 70 respondents, to rectify any inaccuracies in the questionnaire and ensure the clarity and validity of the statements. Ultimately, five important recreational attributes were selected: vegetation coverage, water clarity, amount of garbage, level of crowding, and ticket price. The final choice set consists of different levels of one price attribute and four management attributes. Each attribute is described by different levels, as shown in Table 1.Table 1 Attributes and levels.

Attributes	Descriptions	Levels	
Vegetation (%)	Increasing the vegetation coverage of the park	40	
55*	
75	
Water (m)	Improving the visible depth of water quality	0.5	
1*	
1.5	
Garbage (items/20 m2)	Average quantity of litter per 20 square meters	0–3*	
3–10	
10–20	
Crowd (people/100 m2)	Average population density per 100 square meters	10*	
20	
30	
Price (people/CNY)	Ticket price for each visitor entering the National Park	40	
50	
60*	
70	
80	
Note: * represents current situation.

Experimental design

In our DCE, each choice set consists of one current status option and three experimental design options. A full factorial design would consist of all possible combinations of attribute levels, resulting in 405 (3*3*3*3*5) choice sets. However, providing such a large number of combinations for respondents to choose from would be both complex and unrealistic. Therefore, a D-efficient design is employed to reduce the experimental design to 7 choice sets, which included 21 alternative options and one current status option. Each questionnaire is assigned three choice sets and one current status option, resulting in seven versions of the questionnaire. Within each choice set, respondents are asked to select their most preferred option out of the four alternatives. An example of a sample choice set in the questionnaire is shown in Table 2.Table 2 Choice collection in the DCE.

Attributes	Current situation	1	2	3	
Vegetation (%)	55	40	70	70	
Water (m)	1	0.5	1.5	1.5	
Garbage (items/20 m2)	0–3	3–10	3–10	0–3	
Crowd (people/100 m2)	10	20	30	10	
Price (people/CNY)	60	40	60	80	
Solution selection	❏	❏	❏	❏	

Investigation and data collection

This study takes the Yellow River Estuary National Park in Dongying City, Shandong Province as an example. The formal survey was conducted during the “May Day” holiday from April 28th to May 7th, 2023. A total of 560 respondents were interviewed through one-on-one visits. The questionnaire was distributed in the form of the local language. These respondents were randomly selected from tourists in the Dongying scenic area by trained interviewers, including six undergraduate students and three graduate students from Shandong Business and Technology University. Respondents took approximately 15–30 minutes to complete the survey and received a small gift to enhance their motivation.

The content of the questionnaire survey designed for this study is as follows: In the first part, the purpose of the survey and visitors’ environmental awareness and level of development of the Yellow River Estuary National Park are investigated. The second part focuses on visitors’ satisfaction and importance ratings of the recreational services provided in the Yellow River Estuary National Park. The third part includes choice sets generated using the DCE method to explore individual preferences and WTP. The final part of the questionnaire collects information on respondents’ personal and social characteristics.

Descriptive statistics

After eliminating unreasonable and invalid questionnaires, a total of 524 responses are available for further exploration, resulting in a questionnaire validity rate of 93.6%. As shown in Table 3, the gender distribution of respondents is relatively equal, with 49.9% being male and 50.1% being female. Among all respondents, the majority (69.2%) are married. In terms of age structure, the respondents are generally younger, with the age group of 20–39 years representing 63.8% of the total. Most respondents have received a good education, with 52% having a bachelor’s degree or higher. In terms of income level, the average monthly income ranged from 3000 to 6000 CNY. The relatively diverse socio-economic characteristics of the respondents ensure the scientific nature of the data collected in this survey.Table 3 Description statistics of respondents.

Characteristics	Group	Frequency (%)	
Gender	Male	49.9	
Female	50.1	
Matrimony	Spinster	30.8	
Married	69.2	
Age	10–20	6.1	
20–29	27.3	
30–39	36.5	
40–49	16.4	
50–59	8.6	
Older than 60	5.0	
Education	Primary and below	3.3	
Junior high school	4.8	
Senior or vocational school	19.3	
Junior or vocational	20.7	
Undergraduate	40.9	
Bachelor & above	11.1	
Monthly income	Less than CNY2000	16.1	
CNY2001-3000	7.3	
CNY3001-4000	25.8	
CNY4001-6000	28.3	
CNY6001-8000	8.8	
CNY8001-10000	9.6	
More than CNY 10000	4.2	

Results and discussions

Estimates of the MNL models

In order to understand tourists’ preferences and choice behavior towards recreational improvement attributes in national parks, this study estimates the RUM and RR-RUM models, and the results are presented in Table 4. The results indicate that the parameters of both models are statistically significant and have the expected signs. However, since the coefficients of the two models have different meanings, the interpretations of the coefficients differ as well. In the RUM model, positive significant coefficients (vegetation coverage and water clarity) imply that increasing vegetation coverage and improving water clarity would enhance tourists’ overall recreational experience utility, while negative coefficients would decrease tourists’ recreational experience utility. In the RRM model, positive significant coefficients (vegetation coverage and water clarity) indicate that regret would also increase when the levels of these attributes in the unchosen hypothetical policies increase, compared to the chosen alternative. Similarly, negative significant coefficients (crowding level, amount of garbage, and ticket price) suggest that regret would decrease when the differences between chosen and unchosen alternatives increase.Table 4 Estimates of the RUM and RRM models.

Variables	RUM	RRM	
Coff	z	Coff	z	
Vegetation	0.409***	4.99	0.415***	3.91	
Water	0.321***	3.99	0.335***	3.56	
Garbage	− 0.179**	− 2.32	− 0.107***	− 2.78	
Crowd	− 0.227***	− 2.65	− 0.202**	− 2.45	
Price	− 0.011***	− 2.62	− 0.013***	− 3.08	
Log-likelihood	− 683.268		− 682.222		
AIC	1376.536		1374.444		
AIC/N	0.657		0.655		
Note: ***, **  represent significance at 1%, 5% level, respectively.

Based on our research findings, recreational improvement policies in national parks that involve increasing vegetation coverage, improving water clarity, reducing garbage, appropriately reducing crowding levels, and moderately lowering ticket prices would generate more utility and less anticipated regret.

Comparing the log-likelihood and Akaike Information Criteria (AIC) values of the two models in Table 4, it can be observed that the RRM model has a better fit than the RUM model. This suggests that regret minimization is more suitable for data analysis than utility maximization. Additionally, it indicates that the regret-based model is better at explaining the choice of national park improvement policies compared to the utility-based model, indicating that regret is the main driving factor for selecting national park improvement policies.

Choice probability prediction

Research has shown that choice probability predictions can be used to compare two models in decision-making and provide insights for decision-makers using the RRM approach22. Comparing the maximization of utility and minimization of regret in tourists’ choice for park recreation improvements can offer recommendations for park managers on which decision rule to use. Thus, based on the model estimations in Table 4, we can simulate and predict the choice probabilities for both paradigms, with Fig. 2 presenting the 7 choice sets included in the survey, consisting of 21 alternative scenarios and a status quo scenario.Fig. 2 Choice probability predictions.

The results clearly indicate that the probability of choice from Fig. 2 (RRM choice probability represented by red circles, and actual choice probability represented by blue stars, and RUM choice probability represented by green rectangles) shows that the red circles are closer to the blue stars, indicating that the choice probability of the RRM model is closer to the actual choice probability. This further demonstrates that the predictive ability of the RRM model is superior to the RUM model. The difference in choice probabilities between RRM and RUM implies the heterogeneity of preference for the hypothetical scenarios.

Estimates of the hybrid RUM-RRM model

To explore tourists’ choice rules regarding improvements in recreational attributes in national parks, Chorus et al.19 proposed that it is reasonable to assume that within attribute-choice rules exist, where some attributes are applied to the RUM rule and others to the RRM rule. Based on this, a hybrid RUM-RRM model has been constructed, and after multiple combinations, the final model is shown in Table 5. Vegetation coverage and water clarity were modeled using the RRM decision rule, while the number of garbage and crowding levels are modeled using the RUM decision rule. The fit of the hybrid RUM-RRM model is found to be superior to that of a single decision rule, as evidenced by the higher log-likelihood and lower AIC values.Table 5 Estimates of the hybrid RUM-RRM models.

Variables	RUM	RRM	
Coff	z	Coff	z	
Vegetation			0.408***	3.83	
Water			0.356***	3.64	
Garbage	− 0.168**	− 2.25			
Crowd	− 0.214***	− 2.75			
Price	− 0.009**	− 2.03			
Log-likelihood	− 673.191				
AIC	1356.382				
AIC/N	0.647				
Note: ***, ** represent significance at 1%, 5% level, respectively.

Preference and choice behavior analysis

To further explore the preference heterogeneity of respondents, a hybrid Latent Class (LC) model composed of RUM and RRM is constructed. The results are presented in Table 6, which includes the coefficient estimates of the attributes related to national park recreation improvement, demonstrating the preference heterogeneity towards these attributes. The coefficient estimates are consistent with those obtained from the individual choice paradigm, indicating that respondents have a preference for increasing vegetation coverage, improving water quality, reducing the amount of litter, moderately reducing crowding, and appropriately lowering ticket prices. Additionally, the membership probabilities suggest that the RRM model provides a better explanation for respondent choices, with 62.5% of respondents leaning towards the regret-based choice paradigm. The lower section of the table presents the coefficients of class membership, revealing the individual sources of preference heterogeneity. It is observed that older, married, and higher-educated respondents tend to favor the RUM decision rule, while female and higher-income respondents lean towards the RRM decision rule.Table 6 Estimates of the hybrid utility-regret MNL models.

Variables	RUM-class	RRM-class	
Coff	z	Coff	z	
Vegetation	0.415***	4.02	0.581***	3.56	
Water	0.388***	3.08	0.461**	2.32	
Garbage	− 0.208**	− 2.04	− 0.108**	− 1.99	
Crowd	− 0.245**	− 2.12	− 0.137**	− 2.09	
Price	− 0.016***	− 2.98	− 0.010**	− 2.13	
Membership probability	0.375		0.625		
Gender	− 0.51431**	− 2.14			
Age	0.29715**	2.06			
Matrimony	0.09103*	1.69			
Education	0.45410***	3.14			
Monthly income	− 0.2499***	− 2.68			
Log-likelihood	− 653.523				
AIC	1343.046				
AIC/N	0.640				
Note: ***, ** represent significance at 1%, 5%, 10% level, respectively.

According to the Log-likelihood and AIC values in Table 6, it can be observed that the fit of the hybrid RUM-RRM model is optimal, indicating that respondents’ economic characteristics influence the choice paradigms.

Estimation of marginal WTP

The practical significance of the results from the RUM and RRM models is based on the comparison of the willingness to pay (WTP), as evidences from Dekker40. This study utilizes the average marginal WTP to capture the economic value of parks in terms of benefits and losses. The WTP estimates for the single MNL model and the two categories of LC models are shown in Fig. 3.Fig. 3 (a) WTP by comparison of MNL and (b) by comparison of hybrid MNL.

From Fig. 3a, based on the WTP values in the MNL, it can be inferred that the preferences of tourists are almost identical. In the RUM model, the WTP of visitors for vegetation coverage and water clarity increases by 37.2 CNY and 29.2 CNY, respectively, for each level change in these attributes. This means that for every 10% increase in vegetation coverage, the economic value of the park will increase by 37.2 CNY, and for every 0.5 m improvement in water clarity, the economic value will increase by 29.2 CNY. On the other hand, for each level increase in garbage quantity (per 20 m2) and congestion level, the WTP of visitors decreases by − 16.3 CNY and − 20.7 CNY, respectively, indicating a loss in economic value. It can be seen from the figure that the marginal WTP of the RRM model is slightly lower than that of the RUM model. The WTP estimates derived from the RRM method merely constitute the lower or upper bound of the actual value. It is worth noting that the estimated WTP based on the assumptions of park recreation improvement in both models can provide some reference value for policy makers.

From Fig. 3b, it can be observed that there are significant differences in the hybrid RUM-RRM model across different categories. Among respondents who are more inclined towards regret-based choice paradigms, an increase in vegetation cover and improvement in water quality result in higher WTP. This suggests that these attribute levels are the primary drivers of increased regret in the context of competing alternative scenarios. Conversely, for respondents focused on maximizing utility, the quantity of garbage and level of crowding are the most important attributes.

Discussions

This study incorporates regret into the analysis of tourists’ choices in scenarios of recreational improvement in national parks. It utilizes the MNL, hybrid models, and LC models to explore the performance of the RUM and RRM models, analyzing tourists’ preferences and stated choices for recreational improvements in national parks. These models are estimated in the NLOGIT641 software, which can be used for various model estimations. The comparative results between the RUM and RRM models indicate that the RRM model slightly outperforms the RUM model in terms of goodness of fit and predictive validity, while the LC model demonstrates an even better fit. The RRM model is found to be more suitable for analyzing our data, providing a better explanation of individual preferences and choice behaviors. This aligns with previous research findings23,29. As discussed in many studies, discrete choice modeling based on regret minimization can enhance the explanatory power of individual behavior22,42. This also suggests that, in contrast to some RUM studies, regret is not merely an efficient response to decision outcomes but also serves as a motivating factor in decision-making. When alternatives that were not chosen perform better than the selected option, and respondents are motivated to avoid regret. In analyzing tourists’ preferences for recreational improvements in national parks, the bounded rationality of tourists leads them to favor choices that minimize the potential for regret.

Furthermore, the choice between RUM and RRM depends on the attributes19. The hybrid RUM-RRM model exhibits better fit than a single decision model, indicating heterogeneity in preferences among attributes. This is corroborated by previous studies24,43, showing that the hybrid RUM-RRM model can effectively explain respondent choices. In the context of wetland improvement, the hybrid model performs optimally in public decision-making regarding wetland attribute choices29.

Evidence from the LC model suggests that the socio-economic characteristics of respondents significantly influence choice paradigms and behaviors, supporting findings by Mao et al.23. For these two types of preference differences, the results indicate that older, married, and highly educated respondents are more concerned about less litter and lower crowding, while female and high-income respondents prioritize higher vegetation and better water quality. The heterogeneity in preferences among respondents with different socio-economic characteristics highlights differences in visitor preferences for national park recreation improvement. These findings are crucial for decision-makers to identify the interest groups affected by recreation improvement policies and take effective measures to enhance societal support for policy decisions.

Based on the research findings, when national parks are undertaking recreation improvements, they should focus on increasing vegetation coverage and improving water quality, while also appropriately reducing litter and managing crowding levels. Additionally, it is important to adjust ticket prices reasonably. Furthermore, park managers should consider the decision rule preferences of visitors and tailor the national park to be more appealing and satisfying to visitors based on their decision rule preferences.

Conclusions

This study explores tourists’ decision-making behavior in selecting hypothetical improvement scenarios for national park recreation, with a focus on minimizing regret and maximizing utility. We investigate the differences in choice behavior among tourists with different personal and social characteristics and under different model choice paradigms. The MNL and LC models are employed to examine the performance of the RUM and RRM models, while the hybrid RUM-RRM model explored the decision rules for attributes. The results demonstrate that regret influences tourists’ choice behavior, and the discrete choice model based on RRM can better explain and predict individual choice behavior, making it a valuable complement to traditional RUM-based choice models. The hybrid RUM-RRM model provides the best fit. Considering the significance of regret in selecting improvement scenarios for national park recreation, it is crucial for decision-makers to consider the choice paradigm used when analyzing respondent preferences, as the effectiveness of intervention measures may vary, potentially influencing policies.

This study also explores preference heterogeneity in individual choices, using two types of LC models RUM and RRM. Results indicate that choices are primarily driven by regret, as RRM explains 62.5% of the choices. Older, married, and highly educated respondents tend to prefer the paradigm of maximizing utility, while female and high-income respondents tend to prefer the paradigm of minimizing regret. Based on the heterogeneous preferences of respondents identified in the study, differentiated policies should be implemented for different groups to develop rational recreation services and management strategies to meet the needs of various visitor groups. For older, married, and highly educated respondents, policymakers can provide more ecological education and public facilities to meet their needs for leisure, learning, and socializing in the park. For female and high-income respondents, the government can offer more comfortable and convenient park facilities and services, along with tailored ecological experience projects. Additionally, decision-makers are advised to enhance promotion and education about national parks to raise visitor awareness of ecological conservation, guiding visitors to make more rational choices in recreational activities and collectively promote the sustainable development of national parks.

The multiple attribute marginal WTP related to national park recreation provides interesting policy suggestions for improving the Yellow River Estuary National Park. The RR-MNL model and RU-MNL model show that visitor preferences are consistent. The WTP from the RR-MNL model indicates the minimum economic value that recreational attribute improvements bring to the park. The hybrid RUM-RRM model shows significant differences between different categories. Among respondents more inclined towards regret-based choice paradigms, an increase in vegetation coverage and water quality leads to higher WTP. Therefore, policymakers should prioritize improving the park’s vegetation coverage and water quality when formulating comprehensive park management and improvement strategies to enhance the park’s economic value. Specifically, for individuals involved in maintaining or improving national park recreation, this can be achieved through initiatives such as increasing vegetation planting programs, wetland restoration projects, and water quality monitoring and management. Emphasizing the protection of the natural environment, enhancing landscape quality, and strengthening supervision and management of vegetation coverage and water quality in policies will help reduce visitor regret and enhance visitor satisfaction and recreation experiences. While higher levels of litter and crowding have a negative impact on visitors’ WTP, it is still important to address and improve these issues. Measures can be taken to enhance park litter cleaning and management, implement waste classification, reduce waste generation, and enhance visitor education to raise environmental awareness among visitors. These actions will contribute to increasing the overall attractiveness of the national park. Therefore, managers should pay attention to visitors’ negative emotions and formulate scientific and appropriate national park recreation management policies (Supplementary information).

While this study has yielded valuable findings, it also has certain limitations. The study focuses on the Yellow River Estuary National Park, with most respondents being local residents and tourists from within Shandong province, and fewer visitors from other provinces. The selection of attributes is limited, and potential bias in sample selection is unavoidable. Future research could consider multiple national parks, select a wider range of attributes from multiple dimensions to enhance sample representativeness. Furthermore, to enhance the generalizability of regret models, more regret-based models should be introduced to explore their application in national park management policies. Additionally, further investigation into more influencing factors (such as environmental awareness, environmental knowledge, geographic location, etc.) is needed to analyze the preference heterogeneity and decision heterogeneity among different groups in more depth. Nevertheless, our study results are of significant value for research and policy-making, providing more accurate and scientifically guided recommendations for national park recreation improvement policies, and assisting managers in making informed decisions. From a research perspective, the impact of regret on tourists’ choices regarding improvements in national park recreation suggests that RRM is a viable decision rule for analyzing tourists’ choices regarding improvement policies in national park recreation. From a policy-making perspective, it provides multiple mechanisms and decision rules for formulating sound management policies for national park recreation, there by offering potential public support for improving national park recreation management policies.

Supplementary Information

Supplementary Information.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-72494-w.

Acknowledgements

This study is supported by the Design and Application Research of China’s Marine Economy Big Data Monitoring System (number 21ATJ006); Shandong Business College Wealth Management Characteristic Research Project, China under Grant (number 2022YB11); Research on the Path of Improving the Quality and Efficiency of Scenic Village Tourism in Shandong Province under the Background of Rural Revitalization (number 22CJJJ26).

Author contributions

Q.G.: Resources, Data Curation, Writing-Reviewing and Editing, Supervision, Funding acquisition. S.C.: Conceptualization, Methodology, Software, Writing Original draft preparation Writing-review & editing. P.S.: Writing-review & editing. Z.L.: Investigation, Writing-review & editing.

Data availability

The processed data and code required to reproduce the above research results cannot be shared at present as the data also forms part of an ongoing study. If anyone needs the data and code for this study, please contact the corresponding author upon reasonable request. Author’s email: 37778774@qq.com.

Competing interests

The authors declare no competing interests.

Ethical approval

This study involving the collection of data from human participants is approved by the Ethics Committee of [International Business College, Shandong Technology and Business University]’s Institute for Ethical Research (Approval Number: [2024001]). The research was conducted in accordance with the ethical guidelines outlined by the [International Business College, Shandong Technology and Business University]’s Ethics Committee and complied with all relevant regulations. Prior to participating in the study, all participants provided written informed consent after being briefed about the nature and purpose of the research, their rights as participants, and the confidentiality measures in place to protect their data.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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