Ways to Travel
I Asked Our Ai-Travel Planner for a Weekend Adventure in Branson, MO
Can AI plan the perfect travel itinerary for a family trip to Branson, Missouri? I decided to check it out using Only in Your State’s AI-powered Dashboard trip planner, which uses our curated travel posts to create a customized itinerary. Since I live nearby and visit Branson frequently, I was super curious about whether the AI would select the places I would actually recommend to visitors. Would it plan anything similar to what a local would recommend?
If you Google things to do in Branson, you’ll end up with hundreds of options. It’s easy to get overwhelmed while scrolling lists of scenic spots, eclectic museums, music shows, restaurants, and so much more. Instead, let Only In Your State plan your trip. The AI-travel planner easily cuts through the clutter, creating a customized itinerary for the perfect family-friendly trip—one that’s local approved!
To get started, I simply told the planner the length of my trip, who I would be traveling with, and what I enjoyed. Then, within seconds, I had my itinerary of attractions, restaurants, and accommodations. How did it do? Let’s take a look!
Top Attractions to Visit in Branson
The first stop on the trip planner’s itinerary was Dick’s 5 & 10, an old-fashioned variety store in old downtown Branson. I wholeheartedly agree with the AI, as this was a place we often visited with our kids on family trips to Branson. We loved winding our way through the eclectic mix of kitsch and treasures. You can find everything from comically oversized cowboy hats to lemon meringue pie flavored soda. These kinds of places are fading away, but this one endures.
My family hasn’t hit many of the music shows in Branson, but they are one of the town’s claims to fame. The AI picked Dolly Parton’s Stampede, and this one has actually drawn us in several times over the years for its one-of-a-kind dinner-and-show production. While you won’t see Dolly herself, you will see an extravaganza of performing animals, boisterous competitions, and lively musical productions. It gets a little sappy in places, but we forgive Dolly for that. Dolly can do no wrong.
While Branson is definitely a tourist trap (in the best way), there are places where you can escape the lights and noise to explore the natural beauty of the Ozark Mountains. The trip planner recommended Lakeside Forest Wilderness Area for its hiking trails and views of a little-known waterfall. My family honestly hasn’t been here, and now I’m eager to check it out!
Restaurant Recommendations in Branson
Branson has a lot of restaurants with overpriced but underwhelming food, but the AI trip planner hit the mark by recommending Downing Street Pour House, one of my local favorites. This off-the-beaten-path English pub in nearby Hollister offers massive burgers. Almost every item on the menu has unexpected elements brought together in a pleasing way.
Similarly, the AI nailed it by recommending Sugar Leaf Bakery for its decadent homemade hot chocolate. Even if it’s not hot chocolate season, you’ll find many tempting items, whether you’re craving a sweet dessert or a savory sandwich. Trust me when I say to try the coconut cake and s’mores cookies!
The final food recommendation the trip planner gave me was the White River Fish House. My family hasn’t visited yet, but I’m putting it on my list for our next trip since the AI was so right about the other recommendations. Plus, this restaurant floats on the river, which adds a cool experience to the highly rated food. We’ve clearly waited too long for a visit.
Where to Stay in Branson
The AI trip planner selected Driftwater Resort as a one-of-a-kind place to stay, and I think it looks great! While Branson offers everything from remote tent camping to luxury hotels, a place like Driftwater Resort harks back to Branson’s early days as a relaxed lakeside retreat. Take your pick from one of 17 updated cabins. Plus, you can rent boats and kayaks onsite and venture out on the waters of Lake Taneycomo. Driftwater Resort looks like a perfect spot to relax after a busy day in Branson.
My Verdict on the AI-Travel Planner Itinerary for Branson
I give our trip planner a 10 out of 10 for this family-friendly weekend itinerary for Branson, Missouri. It included well-known attractions along with local gems, and it even gave me fresh ideas for my next trip! If you’re heading to Branson, this itinerary is a great starting point, or you can create a customized one of your own.
Disclaimer: This article was made using our award-winning Dashboard planner. Feel free to check it out!
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Ways to Travel
Pursuit of entertainment or self-expression? Research on adventure tourism
Data collection
The study focused on domestic and foreign tourists aged 18 and above participating in rafting at Antalya Köprülü Canyon. The questionnaires were applied immediately after rafting in-person, and it was thought that the tourists’ experiences were reflected. In order to accurately measure tourist motivations, the literature was reviewed and scales were selected from the literature. In the process of selecting the scales, previously experienced ready-made scales were used, however, the scales were preferred from ready-made scales with high values in terms of validity and reliability. The aim here is to measure the constructs measured in the study in the most reliable way and in a way that can be distinguished from other constructs. For this purpose, scales with high Cronbach α or composite reliability values and AVE (average variance extracted) values were preferred. Then the convenience sampling method was used as the sampling method because there was no random selection. It is a statistical fact that the convenience sampling method does not represent the whole population because it is not random. However, it is easier to apply than random sampling in terms of reaching individuals with new experiences. In addition, as a result of studies that can be carried out in other countries or regions, although it is not a random sample, new literature becomes more debatable and converges to a scientific reality with the literature obtained with the convenience sampling method together with the developing literature.
Participants voluntarily participated in the survey after the rafting experience. Thus, it can be stated that the participants’ responses to the questionnaire were not influenced by any incentives. This situation causes the participants’ views on the subject to be more sincere. The questionnaires were collected in 2021. From 327 questionnaires, 31 were excluded for incomplete data, leaving 296 for analysis. The demographics included 68.1% Russian, 21.5% EU citizens, 9.5% Turkish, and 1% from other nationalities, reflecting general tourism trends in Turkey as reported by the World Travel and Tourism Council (2021). According to the World Travel and Tourism Council (2021) report, 12% of those who came to Turkey in 2019 were Russian, and 8% were German tourists, while in 2020, this rate was 13% for Russians and 7% for Bulgaria, Germany and Ukraine. In this case, it is predicted that the data and results obtained from the target audience will provide correct inferences. Therefore, bias in the study poses as much risk as bias that can occur in real life.
Although 50% of the participants have visited Antalya before, the rate of those who have visited Köprülü Canyon before is 24.3%. In this case, it can be said that individuals who have visited before have returned home without rafting in Köprülü Canyon. The rate of those who have rafted before is 29.7%. The fact that the rates of those who have visited Köprülü Canyon and those who have rafted are close may indicate that individuals tend to do it again after the first experience. While 62.4% of the participants were female, 37.6% were male. In this case, it can be stated that women are more oriented towards adventure tourism. 12.2% of the participants are high school graduates, 21.3% are associate degree graduates, 57.4% are bachelor’s degree graduates, and 9.1% are master’s and doctorate graduates. The average age of the participants was 33.36, while the median was 33.
Measures
Five-point Likert-type scales assessed all constructs. The scales covered “experiencing nature” (Perić et al., 2019), “escape” (Carvache-Franco et al., 2019), and “joy” (Pestana et al., 2020). The “WOM” influence (Sirakaya-Turk et al., 2015) and “self-image congruence” (Sirgy et al., 1997) were also measured, along with “revisit intention” (Zhang et al., 2018).
Data analysis and results
The data analysis validated the measurement model and evaluated relationships between the constructs.
Measurement model
The confirmatory factor analysis (CFA) indicated a satisfactory fit, with chi-square/df at 2.81, CFI at 0.92, SRMR at 0.059, and RMSEA at 0.078 (Hu and Bentler, 1999), as detailed in Table 1.
Construct validity was confirmed, with convergent and discriminant validity assessed and meeting established thresholds (Hair et al., 2014) as shown in Table 2.
According to Tables 1 and 2, the AVE values are greater than 0.50 and the correlation between the variables. Therefore, convergent and discriminant validity is provided. After this stage of the analysis, common method bias (CMB) or common method variance (CMV) was examined. According to Podsakoff et al. (2003), CMB analysis refers to the bias that emerges from external factors on the data set and occurs when the majority of the variance is explained by a single factor (Podsakoff et al., 2003; Gaskin and Lim, 2016). To measure whether the majority of the variance was gathered under a single factor, the single factor Harman test was performed, and the explained variance rate was calculated as 43%. Since the single factor Harman test is a weak analysis, CMB was re-examined using the Controlling for the effects of an unmeasured latent methods factor analysis suggested by Podsakoff et al. (2003), and each regression path was calculated as 0.64, and the explained variance rate was found to be 41%. Since CMB does not exceed 50% (Eichhorn, 2014: p. 8), it can be said that CMB does not exist or is insignificant (Büyükdağ and Kitapci, 2021).
Structural model
The structural model’s evaluation produced the following results: chi-square/df value at 2.81, CFI value at 0.92, SRMR value at 0.059, and RMSEA value at 0.078. These indices satisfy the criteria set by Hu and Bentler (1999), indicating a good fit between the theoretical model and the observed data.
Table 3 shows that push factors significantly and positively influence self-image congruence (β = 0.66), WOM (β = 0.55), and revisit intention (β = 0.32). Self-image congruity also significantly enhances WOM (β = 0.35) and revisit intention (β = 0.30), while WOM positively impacts revisit intention (β = 0.28). The model explains 44% of the variance in self-image congruity, 68% in WOM, and 66% in revisit intention (Fig. 2).
This figure shows the tested structural model with standardized regression weights, reflecting direct and indirect effects among variables. This figure illustrates the structural model with standardized path coefficients, examining the relationships between push factors, self-image congruity, revisit intention, and word-of-mouth. The push factors are measured through three dimensions: experience nature, escape, and joy. The arrows represent the hypothesized paths, and the numerical values indicate the standardized regression weights. The model shows that push factors significantly influence self-image congruity, revisit intention, and word-of-mouth, both directly and indirectly.
Multi-group structural equation modeling (SEM) and analysis results
Multi-group structural equation modeling (SEM) was employed to compare the regression paths between two variables based on socio-demographic and field-specific characteristics. Various studies have utilized this approach: Yada et al. (2018) to understand teachers’ attitudes and self-efficacy, Al-Swidi and Al Yahya (2017) to examine educational intention and work behavior differences by gender, and Babin et al. (2016), Huang and Ge (2019), Murray et al. (2017), and Aka and Buyukdag (2021) to analyze factors such as culture, household characteristics, store design, and marital status. In this study, multi-group SEM was applied to explore the effects of rafting experience (first-time vs. repeated) and gender (female vs. male model).
According to the multi-group SEM related to rafting experience, the model showed good fit indices with a chi-square/df value of 2.19, a CFI of 0.90, an RMSEA of 0.064, a GFI of 0.79, and an AGFI of 0.73. The comparative analysis between unconstrained and constrained models revealed a chi-square difference of 35.06 and a df difference of 25, indicating no significant variation between the effects of rafting experiences (p = 0.087). Consequently, the research model is applicable to both first-time and repeated rafters. The significance of each path’s rafting experience was further analyzed and is detailed in Table 4.
According to the multi-group structural equation modeling focused on gender, the model demonstrated good fit indices with a chi-square/df value of 2.17, a CFI of 0.90, an RMSEA of 0.063, a GFI of 0.79, and an AGFI of 0.73. This suggests that the multi-group SEM adequately represents the gender-based differences in the data. Comparative analysis between unconstrained and constrained models showed a chi-square difference of 24.83 and a df difference of 25, indicating no significant variance in gender effects (p = 0.472). Therefore, the research model is equally applicable to both female and male categories. Further analysis was conducted to determine if significant differences exist in local paths based on gender, with detailed results presented in Table 4.
Table 4 shows that push factors affect self-congruence differently for first-time versus repeated rafters. Rafting experience moderates how these factors influence self-image congruence, with a more pronounced effect on first-timers. While push factors significantly impact WOM for both groups, the effect is stronger for newcomers, but rafting experience doesn’t moderate this relationship. Similarly, push factors notably influence revisit intention for first-time rafters, but less so for experienced rafters, where experience doesn’t act as a moderator (Fig. 3).
This figure illustrates differences in structural paths across first-time and repeat visitors, as well as male and female participants, using varying line styles. This figure presents the multi-group analysis results based on visit frequency (first-time vs. repeated) and gender (female vs. male). The structural paths between push factors, self-image congruity, revisit intention, and word-of-mouth are illustrated with different line styles. Solid lines represent first-time visitors, dotted lines indicate repeat visitors, dash-dot lines show female participants, and dashed lines represent male participants. Path coefficients are shown along each arrow. The figure highlights how these variables interact differently across groups, revealing variations in motivational and behavioral responses based on experience and gender.
The influence of self-congruence on WOM is significant for both novice and seasoned rafters, more so for the latter. This suggests that rafters with prior experience, and with higher self-image congruity, are likelier to share their experiences. Self-congruence significantly affects intention to revisit among experienced rafters, but not for newcomers. However, rafting experience does not moderate these relationships in either case.
The impact of WOM on revisit intention was significant for first-time rafters but not for repeat rafters, with rafting experience not moderating this relationship. Table 4 shows variance differences between these groups. For first-timers, the explained variance is 51%, while only 26.9% for repeat rafters. For WOM, the variance is 70.5% for first-time users and 66.5% for repeat rafters. Regarding revisit intention, the variance is 65.3% for novices and slightly higher at 66.1% for experienced rafters.
The model showed no significant gender-based moderating effects, but coefficients highlight important relationship nuances. Both genders experience a positive, significant effect of push factors on self-image congruence, with males showing a higher coefficient. The impact of push factors on WOM is significant for both, yet stronger for males. Females, however, demonstrate a greater influence of push factors on revisit intention. The effects of self-congruence on WOM are similar across genders. Males exhibit a more substantial influence of self-image congruence on revisit intention. WOM’s impact on revisit intention is marginally higher in males. While gender doesn’t significantly moderate these paths, the data suggest males typically have higher values in consumer experiences involving adventure and risk-taking.
Self-congruence significantly influences WOM for both first-time and repeat rafters, more so for the latter. This suggests experienced rafters, likely with higher self-image congruity, are more prone to sharing their experiences. Self-congruence also impacts revisit intention significantly among experienced rafters, but less for novices. In both cases, rafting experience does not moderate these relationships.
The study shows gender differences in variance rates for self-image congruity, WOM, and revisit intention. Self-image congruity explains 49.3% of the variance in males and 38% in females. For WOM, the variance is 81.6% in males and 57.7% in females. Regarding revisit intention, males have a variance rate of 68.1% compared to 65.7% in females. These results imply that self-image congruence is more prominent in male first-time rafters, who also tend to discuss their adventurous experiences more, indicating higher communication about risk-taking and adventure among males.
Study 2
A multiple correspondence analysis examined relationships between push factors, self-image congruence, and demographics in adventure tourism for greater insight into consumer behavior dynamics.
Multiple correspondence analysis
Multiple Correspondence Analysis (MCA) is a robust multivariate technique used to examine relationships among nominal data. This method allows researchers to analyze data, interpret findings, and develop perceptual maps, facilitating a deeper understanding of the data structure (Hair et al., 2010; Hair et al., 2014). In this study, MCA was employed to analyze the relationships between individuals’ perceptions of push factors, self-image congruence, WOM, and revisit intentions, alongside demographic or social factors such as gender, nationality, rafting experience, and visiting status. The objective was to conduct in-depth research and derive meaningful inferences. The graphical representation from the Multiple Correspondence Analysis is provided in Fig. 4.
This plot visualizes the associations between categorical variables, such as nationality, gender, experience, loyalty, and satisfaction. Spatial proximity indicates stronger relationships. This joint plot of category points illustrates the relationships among categorical variables based on their positions along two dimensions extracted through correspondence analysis. The plot visualizes associations between destination-related experiences (e.g., visit status, experiential satisfaction, loyalty), demographic variables (e.g., nationality, gender), and motivational/behavioral outcomes (e.g., push/pull factors, revisit intention, WOM). For example, high revisit intention, high congruity, and high WOM cluster on the right side of Dimension 1, while variables like low satisfaction and low loyalty appear on the left. The spatial proximity between categories indicates stronger associations.
According to the multiple correspondence analysis (MCA) results, repeat visitors to Köprülü Canyon are predominantly Turkish, male, and have prior visits to Antalya and rafting experience. These individuals are notably influenced by push and pull factors and demonstrate high self-image congruity, WOM, loyalty, and satisfaction, indicating a strong intention to revisit. Conversely, first-time visitors to Antalya and Köprülü Canyon are primarily Russian and female tourists, characterized by their pursuit of excitement, unique experiences, and experiential pleasure in adventure and risk-taking activities. Despite showing a high intention to revisit, the likelihood of Russian and female tourists returning is relatively low. This pattern suggests that while tourists enjoy adventure tourism as part of their sea, sun, and sand vacation, it is not the primary purpose of their visit. The findings imply that although tourists have significant rafting experiences and entertainment, they are more inclined to explore different geographical regions rather than revisit the same location. Consequently, it is expected that these tourists will likely choose alternative destinations for their next vacation.
Therefore, emphasizing promotions targeting first-time visitors in rafting or adventure tourism is anticipated to yield significant benefits. Consequently, catering to the preferences of Russian and female tourists with diverse adventure and risk-taking tourism options is projected to create a vital market segment. However, the analysis indicates that European tourists exhibit lower levels of self-image congruity, WOM, revisit intention, and satisfaction with push and pull factors related to rafting. As such, understanding the specific expectations of tourists from the European Union and offering varied tourism alternatives could become a significant source of revenue. Addressing these preferences may lead to enhanced tourist experiences and increased revisit rates.
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