Ways to Travel
AI-Travel Planner For a Weekend Adventure: Fargo, North Dakota
I’ve long been fascinated with visiting the city of Fargo, North Dakota, ever since I saw the Coen Brothers’ 1996 film Fargo starring Steve Buscemi and Frances McDormand long ago. But what would I do when I arrive? To help plan a weekend itinerary, I turned to the Only In Your State AI-Travel Planning Dashboard to help. So, what will I do on a weekend getaway to Fargo with friends? Let’s find out!
How to use the AI Travel Planner
While AI can be intimidating to use, this AI Travel Planner is super easy. All I had to do was enter a destination, choose how long I’d like to be away, the month, who I’ll be traveling with, and select some interests from a list. For this example, I chose Fargo for a weekend with friends in September. For interests, I chose a range of natural and man-made attractions, dining, and hotels. Here’s what the AI tool came up with:
What to do in Fargo
The travel planner gave two interesting options for what to do in Fargo. As an aviation enthusiast, spending a few hours at the Fargo Air Museum is a great idea, especially since I would love to see the Wright Brothers’ flyer on display here. The second given option was the Buffalo River State Park in Minnesota, located just 27 minutes away from Fargo. Here, my friends and I can cross the border into Minnesota (another state I have yet to visit) for hiking, kayaking, and horseback riding.
Where to dine in Fargo
I love trying local hotspots when traveling, and Doolittle’s Woodfire Grill seems like a fantastic place to dine. Serving various rotisserie dishes like chicken, Thai meatballs, salmon, and more, my friends and I could order several plates to share. For a light lunch and cocktails, Pounds would be an enjoyable place to spend an afternoon. Maybe we could order burgers and Bloody Marys and have fun talking to locals at this popular establishment. For a Sunday brunch, Nichole’s Fine Pastry & Café would be ideal to visit with my friends. With quiche, galettes, and pastries, it’s the perfect way to end our weekend in Fargo.
Where to stay in Fargo
The travel tool suggested two options for places to stay: a vacation rental and a resort. The Luxury Downtown Fargo Loft on Airbnb only has one bedroom for up to two guests. While this is a fantastic place if I was traveling with just one friend or my significant other, I think a bigger rental or a hotel would be a better choice.
The second option I was given was Fair Hills Resort, an all-inclusive lakeside retreat located 50 miles away in Detroit Lakes, Minnesota. While this seems like a fantastic place to stay, I’d opt for something like the Hotel Donaldson in Fargo instead.
Final thoughts on the AI Travel Planning Dashboard
I love how easy it is to use the AI travel tool. It’s so simple and takes seconds to generate an itinerary. For a weekend in Fargo with friends, I feel that it gave a fantastic mix of accommodations, dining options, and things to do. However, I wish it gave more accommodation options within Fargo. If you haven’t tried it yet, you should, especially for your next getaway plans!
This article was made using our award-winning dashboard planner. Feel free to check it out at Only In Your State.
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Budget & Luxury Travel
Perfect Stay Guide: Must-Have Tips for Effortless Travel Style
Discover how the perfect stay guide can help you choose accommodations that match your travel style, whether you’re seeking luxury indulgence or budget-friendly comfort for an unforgettable trip.
How to Choose the Perfect Stay for Your Travel Style
Choosing the perfect stay for your travel style can make or break your entire trip experience. Whether you’re planning a relaxing retreat or an adventurous getaway, where you choose to stay sets the tone for your journey. The decision often hinges on several factors, including preferences like luxury vs budget accommodations, whether you’re traveling solo vs group, or if your destination is more urban or rural—city vs countryside. Navigating these choices can be overwhelming, but with the right guidance, you can tailor your accommodation to perfectly align with your travel style. Here’s how to approach this important aspect of travel planning.
Understanding Your Travel Priorities
Before diving into specific options, it’s crucial to identify what kind of travel experience you want. Are you aiming for rest and rejuvenation, cultural immersion, or social interaction? Your answers will reveal a lot about the kind of stay that will best suit you.
Luxury vs Budget: Finding the Right Balance
One of the most common dilemmas travelers face is choosing between luxury vs budget accommodations. Both have distinct advantages depending on your needs and style:
The Appeal of Luxury Stays
Luxury accommodations offer comfort, top-notch amenities, exclusive services, and often prime locations. These stays are perfect if relaxation and indulgence are top priorities. You’ll find five-star hotels, boutique resorts, or lavish villas equipped with spa services, gourmet dining, and concierge assistance. For travelers who value impeccable service and extra pampering—often a solo traveler looking to recharge or couples on romantic retreats—luxury stays can turn an ordinary trip into an extraordinary experience.
Why Budget Stays Are Sometimes Better
Budget options like hostels, guesthouses, or budget hotels appeal to those prioritizing cost savings without compromising on cleanliness and basic comforts. Backpackers, students, and group travelers often prefer budget stays to maximize their travel duration or spend more on experiences rather than lodging. Interestingly, budget accommodations can also foster community and social interaction, especially in dorm-style hostels that encourage mingling among solo travelers.
Striking the Balance
Sometimes, a mid-range option with boutique hotels or higher-end Airbnb units can combine comfort with affordability. Think about how much time you plan to spend in your room versus exploring the destination—this can help you decide how much to invest in your stay.
Solo vs Group: Tailoring Your Stay to Company
Your choice of accommodation should reflect whether you’re traveling solo vs group, as this can significantly influence comfort, privacy, and convenience.
Solo Travel: Emphasis on Security and Connection
Solo travelers often look for safe, comfortable places that also offer opportunities to meet others if desired. Hostels with social areas, small bed-and-breakfasts, or co-living spaces are great for creating connections. On the other hand, solo travelers seeking solitude might prefer boutique hotels or private rentals where they can enjoy peace and quiet. Importantly, solo stays require accommodations that are easy to navigate alone and offer good customer support in case of emergencies.
Group Travel: Focus on Space and Shared Experiences
Groups, whether friends or family, demand ample space and cost-effective options. Vacation rentals or serviced apartments with multiple bedrooms and communal areas work wonderfully for groups. These options often allow you to cook your meals and enjoy quality time together, enhancing the group dynamic. Hotels offering suites or adjoining rooms can also be convenient, providing individual privacy alongside shared space.
City vs Countryside: The Setting Matters
Your destination’s environment—whether city vs countryside—plays a key role in shaping your lodging preferences.
City Stays: Convenience and Connectivity
Urban destinations are typically bustling with activity, so accommodations here often prioritize proximity to transportation, nightlife, museums, and dining hotspots. Hotels or apartments in the city center or near major transit hubs make it easier for you to explore without wasting time commuting. For solo or group travelers alike, the city offers plenty of social opportunities and amenities.
Countryside Stays: Nature and Tranquility
If your aim is to disconnect and recharge, rural stays provide a serene atmosphere surrounded by nature. Farmhouses, cabins, or countryside inns often embody the essence of peace and offer authentic local experiences. These types of accommodations encourage relaxation and allow travelers to engage in outdoor activities like hiking or stargazing. However, keep in mind the potential trade-off in terms of fewer dining and entertainment options nearby.
Additional Tips for Choosing the Perfect Stay
- Read Reviews Thoroughly: Past guest experiences can offer invaluable insights into what to expect.
- Consider Amenities: Free Wi-Fi, kitchen facilities, and laundry services can significantly affect comfort.
- Check Accessibility: Make sure the accommodation suits your mobility needs and proximity to points of interest.
- Book Early: Especially in popular destinations or during peak seasons, early booking ensures availability and better rates.
Finding the ideal accommodation depends on a clear understanding of your travel style and priorities. Balancing luxury vs budget needs, choosing based on solo vs group dynamics, and factoring in the environment—city vs countryside—will guide you toward the perfect stay. Remember, your lodging isn’t just a place to sleep; it’s part of your travel adventure. Choose wisely, and your stay will enhance your journey, creating memories you’ll cherish forever.
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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