Customer inspiration is defined as a state of temporary motivation evoked by corporate marketing efforts, promoting the generation of new ideas related to consumption, and driving consumers to take action on new ideas (Böttger et al., 2017). As an important concept of the inspiration theory in marketing, customer inspiration includes inspired-by and inspired-to states in its breadth and depth and focuses on the generation of new ideas inspired by corporate marketing efforts and customer consumption behavior (Böttger et al., 2017). Tourist inspiration and its cognitive importance in tourist decision-making have gained the widespread attention of scholars and practitioners from different fields (Khoi et al., 2019; Khoi et al., 2021; Dai et al., 2022). Dai et al. (2022) note the importance of travel inspiration at the tourist dreaming stage and refer travel inspiration as a motivational state that may influence tourist behavior, such as tourist destination choice. Gollwitzer (1990) proposed the mindset theory of action phases and divided the consumer decision-making process into two phases i.e., a pre-decision phase of deliberation and a post-decision phase of implementation, embodying a sequential relationship. Böttger et al. (2017) argue that the inspired-by state belongs to the deliberation phase, while the inspired-to-state reflects the transition to the implementation phase. In the stream of tourist inspiration, there is a causal and sequential relationship between the inspired-by state and the inspired-to state where the inspired-by-state exists before the inspired-to state (Böttger et al., 2017; Cao et al., 2021; Dai et al., 2022).

External stimuli, such as destination environment, destination events, and discovery of new possibilities in a tourist destination, may trigger tourist inspired-by-state in tandem with generating new ideas regarding tourist consumption with self-transcendence (Khoi et al., 2019). According to the theory of self-determination, the attractiveness of new ideas regarding tourist consumption drives tourist self-realization and fuels tourist unplanned purchases (Ryan and Deci, 2000), which is sketched as the inspired-to state of tourist inspiration on the canvas of this study. For example, when a tourist is inspired by a product with destination characteristics, the idea of purchasing such a product and giving the same to a friend as a gift may emerge naturally (Böttger et al., 2017). The appropriateness of the product as a gift becomes an internal driving force that generates tourist purchase intention, which is an inspired-to state of tourist inspiration (Thrash and Elliot, 2004). Some scholars argue that inspired-by state may also be a precursor to inspired-to state (Hinsch et al., 2020; Izogo et al., 2020; Izogo and Mpinganjira, 2020). For example, in a cross-cultural study, Izogo et al. (2020) found that inspired-by state significantly and positively affects the inspired-to state. Similar findings were found in research on augmented reality (Hinsch et al., 2020) and social media content (Izogo and Mpinganjira, 2020). However, most studies advocate the causal and sequential impact of inspired-by state on inspired-to state (Böttger et al., 2017; Dai et al., 2022). Thus, there is a need to testify the impact of inspired-by state on inspired-to state for conclusive evidence. Drawing on most studies reflecting the impact of inspired-by state on inspired-to state, we propose the following hypothesis.

The tourist experience is the application of the experience economy in a tourism context. The concept of the experience economy is widely accepted by tourism scholars and practitioners. According to the two dimensions of customer connections, namely absorption and immersion, and the level of customer participation, namely passive and active participation, four experience realms of tourist experience are identified in the extant literature for business promotion, i.e., education, esthetics, entertainment, and escapism (Pine and Gilmore, 1999). A unique tourist experience stems from an interaction between destination event and tourist cognitive reaction (Pine and Gilmore, 1998) that may stimulate tourist psychological arousal (Hosany and Witham, 2010) alongside provoking tourist inspiration (Khoi et al., 2019). Tourist experience influences tourist psychological arousal, which is the first step toward tourist inspiration (Loureiro, 2014).

As an important construct in the field of marketing, familiarity with a product or brand refers to consumer experience and knowledge about a product or brand that may influence consumer decision-making regarding a product or brand (Alba and Hutchinson, 1987). Previous studies present that consumer positive decision-making and resultant favorable behavior are linked to consumer familiarity with brand products or services, such as first-time and repeat purchases (Tam, 2008; Paasovaara et al., 2012), as compared to consumer unfamiliarity with brand products or services. Destination familiarity is defined as an individual’s subjective assessment of destination information and knowledge (Hernández Maestro et al., 2007). Tourists’ behavioral intentions are influenced by tourists’ subjective familiarity assessment of destination attributes (Rao and Sieben, 1992; Park et al., 1994). When tourists feel that they are familiar with a tourist destination, they are more confident in their decision-making to visit tourist destinations (Milman and Pizam, 1995).

Existing studies show that destination familiarity has an important impact on tourist information search behavior (Gursoy, 2019). Tourists first conduct an internal search to obtain desired information from their memory and experience (Coupey et al., 1998). Tourists who extract desired information from their memory attempt to make informed decisions and do not engage in additional information search from external sources (Brucks, 1985). Tourists who do not find the desired information from their memory and experience search for information from external sources for rational travel-related decision-making (Gursoy and McCleary, 2004b). Evocation as a salient feature of tourist inspiration means that tourist inspiration is spontaneously evoked by an external stimulus (Böttger et al., 2017; Dai et al., 2022). Tourist inspiration is more likely to be triggered in low-familiar tourists as compared to in high-familiar tourists, because information obtained from outside may help tourists learn new knowledge, make discoveries, and gain new insights, which stimulate tourist imagination (Winterich et al., 2019). High-familiar tourists extract information from their memory and experience for decision-making, which may not fully trigger tourist inspiration due to the lack of external new things (Böttger et al., 2017). Tourists who are familiar with destinations show the favorable evaluation of destination attributes and develop positive behavioral intentions as compared to unfamiliar destinations (Chaulagain et al., 2019; Sanz-Blas et al., 2019; Shi et al., 2022). Previous studies note that destination familiarity moderates the relationship between brand equity and tourist revisit intention (Shi et al., 2022), the relationship between logotype and tourist attitude toward a destination (Roy and Attri, 2022), and the relationship between perceived quality and tourist visit intention (Chi et al., 2020). As tourist familiarity with a destination will increase, novel experiences and discoveries from external stimulation will decrease, which will reduce tourist inspiration. Therefore, we also propose the following hypotheses.

The proposed theoretical associations among the constructs of this study are presented in Figure 1.

A survey questionnaire was designed to gather data from Chinese respondents having tourist experience. Screening questions were made a part of the survey questionnaire: (1) What type of destination was your last trip? (2) What is the name of your recently visited tourist destination? (3) How recent is your tourist experience? To measure the proposed constructs in this study, scale items were adapted from the previous studies. Scale items for education, esthetics, entertainment, and escapism dimensions of tourist experience were adapted from Oh et al. (2007), scale items for tourist inspiration (including the inspired-by state and the inspired-to state) were adapted from Böttger et al. (2017), and scale items for destination familiarity were adapted from Gursoy and McCleary (2004a). All scale items were measured using a seven-point Likert scale, which ranged from 1 (strongly disagree) to 7 (strongly agree) (see Appendix 1). Scholars document that the seven-point Likert scale is well-suited to conduct online surveys (Chaulagain et al., 2019). The survey questionnaire was originally developed in English. Three native Chinese doctoral students, who were proficient in English and had academic and industry experience in tourism marketing, were invited to translate the English version of the questionnaire into Chinese. By following the work of Majeed et al. (2020a), we used the blind-translation back-translation method to translate the English version of the questionnaire into the Chinese language. Two bilingual professors, who were unfamiliar with the field were invited to convert the translated Chinese version of the survey questionnaire into English. The quality of the English and Chinese versions of the survey questionnaires were compared for clarity and similar intended meanings of the questions and minor adjustments were made to the content and composition of the survey questionnaire before the full launch of the survey (Chen et al., 2020).

This study conducted an online survey at Wenjuan Xing¹, a professional online academic survey platform in China, between November 5, 2019 and November 10, 2019, to gather data from Chinese tourists who were at least 18 years old to ensure the consent requirement of the study respondents (Majeed et al., 2020b; Xue et al., 2020). Wenjuan Xing undertakes a random sampling method to administer surveys and records responses from its more than three million registered users, who demonstrate diverse backgrounds and belong to different cities in China (Cao et al., 2021). Wenjuan Xing adopts a multichannel approach to distribute questionnaires to randomly invited users to reflect a greater representation of the relevant study population for the survey (Cao et al., 2021). The participants in the online survey were Chinese adult tourists who had traveled to tourist destinations. A total of 626 responses were received during the data collection process. After removing responses carrying missing values and respondent age as less than 18 years, a total of 622 were retained for the final analysis.

The number of male and female respondents in this study was relatively balanced, accounting for 45.7 and 54.3%, respectively (see Table 1). Most respondents were under the age of 40 years (80.38%), corporate staff (66.1%), and were married (67.2%). Table 1 shows that approximately 67.4% of the study respondents mentioned their education level as undergraduate. Approximately 63.5% of the study respondents mentioned 2–3 trips per year, 62.8% of respondents traveled for the first time, 50.6% of respondents liked to travel with family, and 31.5% respondents liked to travel with friends. Approximately 94.1% of respondents traveled to domestic tourist destinations. Destinations related to natural and heritage landscapes were favored by most of the study respondents, i.e., 47.7 and 42.1%, respectively. Approximately 74.8% of the study respondents visited tourist destinations within the previous 3 months from the date of the survey.

The four realms of tourist experience and the two states of tourist inspiration are lower-order latent variables in this study. Partial least square structural equation modeling (PLS-SEM) was employed to test the proposed relationships among tourist experience, tourist inspiration, and destination familiarity. PLS-SEM is preferred to the co-variance-based approach because PLS-SEM is one of the most used methods in analyzing the structural relationships of latent variables (Chin and Newsted, 1999) and moderating roles (Wong, 2016; Nitzl and Chin, 2017). Smart PLS 3.2.7 was used for data analysis in this study.

Table 2 presents the items and reliability evaluation results of the constructs of the study. Except for three items in the range from 0.667 to 0.697, which is above the minimum acceptable value of 0.50 recommended for factor loadings (Hair et al., 2010), all factor loadings were above 0.70 and were considered acceptable (Chin, 1998). The Cronbach’s alpha and composite reliability values of all constructs were above the recommended threshold of 0.70 (Nunnally and Bernstein, 1994), indicating good internal reliability of the study constructs.

The convergent validity and discriminant validity of each construct were evaluated. The average variance extracted (AVE) of all constructs ranged from 0.582 to 0.772 (see Table 2), which is above the recommended threshold of 0.50, indicating that all constructs of the model have good convergent validity (Fornell and Larcker, 1981). The Fornell-Larcker criterion and the heterotrait-monotrait (HTMT) ratio were calculated to evaluate the discriminant validity of the constructs (Henseler et al., 2015; Hair et al., 2021). Table 3 shows that the square root of the AVE of each construct was found greater than the correlation coefficient of other constructs, indicating that all constructs have good discriminant validity and met the Fornell-Larcker criterion. The maximum value of the HTMT ratio (Table 4) was found as 0.857, which is less than the recommended threshold of 0.90 (Henseler et al., 2015; Rasoolimanesh et al., 2021), indicating that all constructs had acceptable discriminant validity.

Before the bootstrapping procedure, destination familiarity was set as a moderator, inspired-by state as a dependent variable, and education, entertainment, esthetics, and escapism dimensions of tourist experience as independent variables to generate four moderating impacts. Since destination familiarity is a reflective-reflective construct in this study, a product indicator calculation method was selected while generating the moderating impact on the relationship between dimensions of tourist experience and the inspired-by state of tourist inspiration. To exclude the influence of other factors, demographic and destination variables mentioned in Table 1 were operationalized in the model as control variables. Complete bootstrapping with 5,000 sub-samples was performed to examine the hypothesized relationships presented in the conceptual model. Findings (Table 5) show that R² values of the dependent variables are over 0.1 and Q² values are over 0, presenting the predictive ability and predictive relevance of the structural model (Falk and Miller, 1992). To solidify the investigation of the goodness of fit and the significance of hypothesized relationships in the model, path coefficients were examined. Findings (Table 5) show that inspired-by state has significantly positive impact on inspired-to state (β = 0.431, t = 10.456, p < 0.001). Thus, hypothesis H1 is supported. Findings for education experience (β = 0.373, t = 9.606, p < 0.001), entertainment experience (β = 0.184, t = 4.685, p < 0.001), esthetics experience (β = 0.218, t = 5.612, p < 0.001), and escapism experience (β = 0.231, t = 7.122, p < 0.001) show that education, entertainment, esthetics, and escapism dimensions of tourist experience exert significantly positive impact on the inspired-by state of tourist inspiration. Therefore, hypotheses H2, H3, H4, and H5 are supported. Findings show that destination familiarity has a significantly negative impact on the relationship between education experience and inspired-by state of tourist inspiration (β = −0.109, t = 3.095, p < 0.05), indicating that when tourist destination familiarity is high, the relationship between education experience and the inspired-by state of tourist inspiration is weak. This supports hypothesis H6. However, findings show that destination familiarity has no moderating impact on the relationship between entertainment experience and inspired-by state (β = 0.002, t = 0.051, p > 0.05), between esthetics experience and inspired-by state (β = 0.049, t = 1.523, p > 0.05), and between escapism experience and inspired-by state (β = 0.042, t = 1.38, p > 0.05). Thus, hypotheses H7, H8, and H9 are not supported. The structural model of the study is presented in Figure 2.

The objective of sensitivity analysis is to determine how much of the change in the dependent variable is caused by the change in the relevant independent variable (Han et al., 2021) and to obtain a ranking of the importance of the independent variable’s influence on the dependent variable (Ahani et al., 2017; Leong et al., 2020). Artificial neural network (ANN) modeling is the most used method for sensitivity analysis because of its obvious advantages over traditional statistical methods (Sharma et al., 2021), such as regression analysis. ANN does not require the data to follow a normal distribution and is also suitable for the analysis of non-linear relationship variables (Tan et al., 2014; Leong et al., 2020). An ANN model usually consists of three layers, namely the input layer, the hidden layer, and the output layer, and each layer is connected by an activation function (Sharma et al., 2021). In terms of activation function, the sigmoid function is generally considered by researchers due to its advantages of squeezing the original data (Chiang et al., 2006). The IBM’s SPSS 21 neural network module and its multilayer perceptron were employed to perform ANN analysis and a feed-forward-backward-propagation (FFBP) algorithm for training and testing data in this study (Taneja and Arora, 2019). In line with Leong et al. (2020), 90% of data was used for training, while the remaining 10% of data was used for testing where sigmoid is the activation function for the hidden and output layers (Sharma and Sharma, 2019). Referring to the method of Sharma et al. (2021), a 10-fold cross-validating procedure was used to avoid the overfitting problem in ANN analysis. The root mean square error (RMSE) is widely used by scholars to validate the results of the ANN analysis (Chong, 2013; Liébana-Cabanillas et al., 2017), and, hence, followed for ANN analysis in this study.

To analyze the importance of education, entertainment, esthetics, and escapism dimensions of tourist experience and their associated impact on the inspired-by state of tourist inspiration, we constructed one ANN model. In the ANN model, the four dimensions of tourist experience, i.e., education, entertainment, esthetics, and escapism, are in the input layer, the inspired-by state of tourist inspiration is in the output layer, and there are three hidden nodes in the hidden layer (see Figure 3). Table 6 shows that the average RMSE values for both training and testing processes were relatively small at 0.074, indicating an excellent model fit (Leong et al., 2019; Leong et al., 2020). To rank the predictive power of the input neurons, a sensitivity analysis was performed. Table 7 shows the importance and normalized importance of each input neuron, i.e., education, esthetics, entertainment, and escapism dimensions of the tourist experience. The value of normalized importance refers to the importance of each input neuron divided by the maximum importance and expressed as a percentage (Leong et al., 2020). The results of the sensitivity analysis showed that education experience had the greatest normalized importance at 98.1%, suggesting that education experience was the most powerful predictor of the inspired-by state followed by esthetics (59.3%), escapism (46%), and entertainment (37.7%) dimensions of the tourist experience.

This study makes several important theoretical contributions to the extant literature. First, from the general perspective of tourism, this study puts forth empirical evidence to demonstrate the impact of tourist experience, i.e., education, esthetics, entertainment, and escapism, on tourist inspiration, i.e., the inspired-by state, that provides a broader lens to DMOs to understand tourist cognitive responses for destination promotion, which previously remained context-specific and limited, such as wellness tourism (He et al., 2021) and international travel (Khoi et al., 2019). This study expands the critical understanding of the relationship between tourist experience and tourist inspiration by clarifying the influence of entertainment experience on tourist inspiration which remained inconsistent and mixed in the previous studies on conventional tourism and wellness tourism (He et al., 2021). Tourist inspiration reveals the psychological mechanism of tourist experience that promotes tourist purchase behavior. Tourist shopping or consumption behavior at a host destination is likely to be driven by tourist inspirations. Second, based on the transmission model of inspiration, which was proposed by Thrash et al. (2010) and Böttger et al. (2017) found that inspired-by state mediates the influence of marketing stimulus on inspired-to state. In the field of marketing, previous studies verified the significant impact of inspired-by state on the inspired-to state (Hinsch et al., 2020; Izogo et al., 2020). In the field of tourism, some empirical studies explored the antecedents and consequences of tourist inspiration (He et al., 2021; Khoi et al., 2021). However, the relationship between the inspired-by state and the inspired-to state, as the two components of tourist inspiration, remained deeply under-explored. This study explored tourist consumption inspiration and found that the inspired-by state of tourist inspiration can significantly and positively affect the inspired-to state in the context of tourism, thus, consolidating the theory of customer inspiration and extending the applicable boundaries of the general inspiration theory in the field of tourism. Third, He et al. (2021) found that education experience significantly affects tourist inspiration in the context of wellness tourism, and this relationship is positively moderated by openness to experience. Our findings show that the education experience can significantly and positively impact the inspired-by state of tourist inspiration and destination familiarity negatively moderates the relationship between education experience and the inspired-by state, which extends knowledge and understanding regarding the impact of tourist education experience on tourist inspiration for additional theoretical insights.

Destination marketing organizations can design and arrange destination performances and events to enhance tourist experience, which may motivate tourist inspiration and increase destination sales revenue. Although the discussed four dimensions of tourist experiences significantly and positively affect tourist inspiration, DMOs need to combine their advantages to strategically position tourist experience of destinations. Based on such a positioning strategy, DMOs can plan related marketing activities to highlight specific tourist experiences in tourist destination to strengthen destination brand image. For example, the southern Sichuan Bamboo Sea is in Yibin City, Sichuan Province, China, and is a 4A-level natural scenic spot in China. To highlight the educational experience and aesthetic experience, the scenic spot has built the largest bamboo professional museum in China by combining the advantages of rich bamboo resources to show tourists the long history of Chinese bamboo culture and various bamboo crafts. Visiting the museum may inspire tourists to purchase bamboo craft products to increase scenic spot sales revenue. To highlight the escape and entertainment experiences, the scenic spot plans bamboo raft water experience activities. This inspires tourists to consume bamboo raft experience projects and, ultimately, to increase scenic spot sales revenue. Thus, tourist inspiration uncovered under the empirical lens of this study provides new strategic directions for DMOs.

For destinations characterized by education experience, DMOs need to consider the differences in tourist destination familiarity between revisiting and first-time tourists and adopt different marketing strategies for different types of tourists. For revisiting tourists, DMOs can use two marketing strategies to stop declining purchase motivation caused by the negative moderating effect of destination familiarity to maintain destination sales revenue. The first strategy is that using sales promotions and designing creative marketing campaigns may improve tourist purchase motivation and tourist inspiration, respectively. The second strategy is to increase tourist novelty experience during tourist visits to destinations to trigger tourist inspiration, which weakens the negative moderating effect of destination familiarity, with the help of continuous innovation in destination performances and events.

COVID-19 pandemic may negatively impact tourist willingness to visit familiar and unfamiliar tourist destinations, resulting in a huge impact on the global tourism industry due to the sharp drop in the number of tourists (Rasoolimanesh et al., 2021). The COVID-19 pandemic will likely impact tourist consumption patterns worldwide, such as the growing popularity of free and independent travel, luxury trips, and health and wellness tourism (Majeed and Ramkisson, 2020). The ongoing COVID-19 pandemic may have influenced people to reconsider their travel decisions for familiar and unfamiliar tourist destinations to avoid the risk of catching the COVID-19 virus during travel. From the perspective of wellness tourism experience, He et al. (2021) examined the influence of tourist experience on tourist inspiration during the COVID-19 pandemic, and the conclusions drawn by He et al. (2021) are consistent with the findings of this study, providing support to the applicability of the findings of this study during the ongoing context of the COVID-19 pandemic. Since our study was conducted before the COVID-19 pandemic, a comparison between the findings of our study and of He et al. (2021) open doors for DMOs for interesting takeouts of this study during and post the COVID-19 pandemic. However, tourist destinations need to reconsider their service designs and distribution channels to match tourists’ changing cognitive reactions and behavioral intention during and post the COVID-19 pandemic. Destinations can strengthen prevention and control measures at tourist attractions, such as controlling the density of tourists, increasing the requirement of negative nucleic acid tests, and frequent disinfection of the tourist attractions in densely populated areas, to reduce the perceived risk of catching the COVID-19 virus during tourist visit to tourist attractions. Improved prevention and control measures to combat the risk of catching the COVID-19 virus will increase tourist sense of security during visits to destination alongside enhancing tourist experience to trigger positive tourist inspiration.

This study explores the relationship between the dimensions of tourist experience and tourist inspiration. However, there are some limitations of this study that open doors for future research on the topic under investigation. There are many antecedents to tourist inspiration (Khoi et al., 2019; Winterich et al., 2019). However, the current study only explored four dimensions of tourist experience as antecedents to tourist inspiration. Future research can explore other factors that may influence tourist inspiration based on the traits of tourists and the characteristics of destinations for a profound understanding of tourist inspiration. Our study examined the moderating impact of destination familiarity on the relationship between tourist experience and tourist inspiration and found that destination familiarity has no moderating impact on the relationship between the proposed facets of tourist experience and tourist inspiration except for the relationship between education experience and inspired-by state of tourist inspiration. Future research can extend the scope of our work to investigate the impact of other moderators, such as national culture, on the relationship between tourist experience and tourist inspiration for additional insights.

Since this study explored the inspired-to state of tourist inspiration with a focus on purchase motivation, the proposed theoretical framework of the study can be extended to reflect how tourist inspiration affects tourist revisit intention, tourist wellbeing, and tourist intention to recommend a destination (Filep and Laing, 2019). Future research may also focus on other perspectives to examine tourist inspiration at different stages of travel. For example, how can tourist inspiration influence tourist intention to visit/revisit specific tourist destinations during the pre-travel planning phase? Since a growing number of tourists collect destination information online when making travel plans (Majeed and Ramkissoon, 2022), it will be worth exploring how DMOs can inspire tourists through online information and fuel tourist travel intention for a specific tourist destination.