Ways to Travel
The Ultimate Guide for Packing Cubes
As a seasoned traveler, I consider myself a good packer. I suspect you do, too. Most people do.
It’s human nature, I think, to presume our organizational skills are better than everyone else’s—a sort of Dunning-Kruger effect for human’s ability to cram a carry-on with a week’s worth of clothes. But unless you’re using packing and/or compression cubes, you’re doing it wrong. Trust me, that’s just the fact.
For years, I avoided compression cubes, mostly because I assumed they were more hassle than they were worth. I was also, to put it generously, an inveterate tightwad. Throwing down $50 dollars on a nylon bag to pack something inside it, which was then packed inside something else, felt ridiculous. Why waste the money, and the extra effort?
I was wrong. Once I started traveling every other week for work, I quickly realized I needed a better system than “always roll, never fold.” That wasn’t getting me there. And neither was jamming whatever didn’t fit in my suitcase into the bottom of my carry-on backpack like a chipmuck stockpiling nuts for winter. So I bought a couple of cheap packing cubes to help organize everything. I then made the full transition to packing-cube evangelist once it became clear how much easier compression cubes made everything.
Not only do they create more space, but they allow you to be more organized while on the go. No need to constantly unpack and repack every item each time you want a pair of socks from the bottom of your bag. They also allow you to cordon off certain items from others. For example, you can keep dirty and clean clothes in separate cubes or even stash hiking shoes next to your pants without worrying about mud stains.
Those benefits may seem basic, but over the course of a weeklong trip, one with multiple destinations, those little cubes can save valuable minutes and reduce re-packing headaches. Here’s a primer on how to deploy compression and packing cubes effectively, as well as our guide to the best ones on the market.
Basic Packing Cube Guidelines
➡️ Decide Between Compression vs. Packing Cubes
It may seem obvious, but too many people confuse general packing sacks with compression cubes. The latter actually compresses items inside them to make all your items fit in a smaller space. They do this through zippers that get progressively tighter, straps that cinch down, or even vacuum sealing. Having both a couple compression cubes and packing sacks is ideal. Compression cubes are particularly helpful for clothes, especially loose items like socks and underwear, which can be condensed into a much tighter space. General packing bags are good for keeping organized and, if they’re large enough, offering a place to stash items like shoes.
➡️ Invest in at Least Three Different Sized Cubes
Like Goldilocks, having a few bags will allow you to find an option that’s just right for whatever you’re packing, whether it’s a down jacket or four pairs of boardshorts for a surf trip. I often bring four or five cubes: a large compression cube for large clothing items; a large packing bag for a pair of shoes; one for socks and underwear; one for the bottom of my backpack to hold a complete outfit, just in case (pants, a long-sleeve shirt, socks, and underwear); and a toiletries bag, which is basically its own form of a packing cube. When I’m traveling overseas, I also have a little pouch for cords to keep my power adapter and other electronic necessities in one spot. As the trip unfolds, I eventually transition my large compression cube into a bag for dirty clothes, to keep them separate from clean ones (which has the added benefit of making unpacking easier once I get home).
➡️ A Dedicated Shoe Bag Is Highly Underrated
I’m a fanatic about traveling with just carry-on luggage, which means I limit myself to two pairs of shoes: the ones I wear on the plane and another pair packed in my roller bag. But if the trip involves basically any activity, especially hiking—or even strolling through a foreign city—chances are I’m going to get my boots or sneakers wet or muddy. Until I bought a shoe bag, I’d have to pack those soggy sneakers next to my pants or shirts and risk getting them dirty, too. But with the bag, I can pack them in my roller bag wherever it fits best and not worry about cross-contamination.
➡️ Get a Well-Designed Toiletry Bag
I like to keep my toiletries organized, too, and most dopp kits are simply fancied-up stash bags with a single pocket and a zipper. That means that everything, from a hairbrush to a deodorant to sunscreen, goes into the same large compartment. If anything leaks, it gets over everything. And even if it doesn’t leak, you’re basically forcing your toothbrush to intermingle with your toenail clipper. Get one that has multiple, dedicated compartments to keep things separate and organized. If you’re bringing sunscreen, make sure it has a screw top, or put it in a different container so it doesn’t leak.
➡️ Use a Small Bag for Cords
I almost always travel with my laptop, and because I travel frequently, I have everything ready to go in a small bag. It holds my charging cord, an extra cord for my phone, and two small wall chargers, just in case. By always keeping them in the same small ditty bag, it’s an easy grab and go scenario when I’m packing for a trip.
The Best Compression Cubes, Pouches, and Sacks
Best All-Around: Eagle Creek Pack-It Isolate Compression Cubes
💰 Cost: From $25
Eagle Creek has long offered some of the best compression and packing cubes on the market, and these ones are the latest, greatest version. They come in multiple sizes and even as a set of two (starting at $50). The cubes help shrink clothes stashed inside via a zipper that, as you close it, pulls the bag and its contents together. These compressions pouches are made from 100-percent ocean-recycled fabric that’s water resistant, which means it’s great for packing bathing suits or wet towels while shuttling between stops on a multi-destination trip. As an all-around packing cube, these ones are hard to beat.
Also Great: Yeti Crossroads Packing Cubes
💰 Cost: From $25
Like all things from Yeti, these packing cubes are over-engineered—in a good way. They are, without doubt, the burliest compression cubes on the market and will stand up to years of abuse (with a three-year warranty). They come in three sizes, too: small, medium, and large. The small one is very small, basically good for cords and cables and other small items. But the large is great, big enough to keep pants and shirts organized. The zippers are particularly durable, which means you can overload these bags and get them to close (and compress as it does). If the cubes get dirty, say from muddy shoes or wet clothes, simply turn them inside out and toss them in the washer.
Best Large Cube: Patagonia Black Hole Cube
💰 Cost: $45
If you want one large packing cube to store most of your clothes on a trip, this is the one to get. At 14 liters, it’s the size of a daypack (i.e. it has plenty of storage), but it’s in the shape of a rectangle, so it fits snugly in a roller bag or duffel. It opens like a clamshell to reveal a zippered compartment on each side, both of which are big enough for multiple pairs of pants and long-sleeve shirts, or even a down jacket. One side is mesh, to see your items, and the other is covered with taffeta fabric to keep dirty items separate from the other compartment. The cube doesn’t compress items but, in a pinch, it will work as an extra, separate bag if you pick up too many souvenirs.
Best Multi-Cube Bundle On a Budget: Cotopoxi Cubo Packing Travel Bundle – Del Día
💰 Cost: $50
If you’re looking for a quick, simple purchase to complete your organizational quiver, this three-cube travel set is perfect. The cubes come in three sizes—two liters, three liters, and ten liters—and each has mesh sides for breathability and a way to see what’s inside. Each also has a wrap-around zipper that makes packing and compressing a cinch. In short, this is a great starter set if you have any doubts about the efficiency of packing cubes.
Best for Clothes Organization: Thule Clean/Dirty Packing Cube
💰 Cost: $34
These days, plenty of companies offer packing cubes designed with “clean” and “dirty” compartments to keep your clothes organized and fresh while traveling. But this one from Thule is excellent, with a thermoplastic polyurethane divider between its two compartments that prevents dirt, moisture, and odor from transferring from one side to the other. At 13 liters, it’s huge, too, so you can pack a week’s worth of clothes inside. Made primarily from ripstop nylon, it’s both water repellant and durable, and because the exterior fabric is semi-transparent, you can even see the colors of the clothes inside to know which items are where. With a webbing handle, you can also use it as a separate bag, if needed.
Best for Shoes: Peak Design Shoe Pouch
💰 Cost:$25
This durable nylon bag is perfect for holding a pair of shoes and sandals for a long weekend trip. It’s also big enough to fit mid- and high-cut hiking boots if you need to bring along some serious ankle support for trekking. The burly zipper will withstand years of abuse, and when you’re not using it, the bag packs down into its own storage pouch that’s just three inches long.
Best Compression Sack: Sea to Summit Ultra-Sil Compression Sack
💰 Cost: From $35
Sometimes when traveling you need some serious compression capability—say if you’re traveling with a down parka or multiple fleece jackets or sweaters. This is when you need a compression sack like these from Sea to Summit. Essentially, they’re sleeping bag sacks, with buckle straps that you can crank down on to really shrink whatever is inside. These ones come in multiple sizes, from five to 35 liters. The diameter of each one is different, from 6 inches to 11.3, but they all fit inside a wheeled roller bag or most backpacks. It’s rare that you would need one when flying, but if you do need to compress something soft and bulky, this is the best way to do it.
Best Toiletries Gag: Gravel Toiletry Bag
💰 Cost: From $40
This toiletry bag comes in four sizes, but the slim, at 9 x 5 x 2 inches and with six individual pockets, will work for most people. The multiple interior pockets keep everything organized. For example, a dedicated toothbrush compartment means your comb or toenail clipper won’t rub up against the tool you’ll be putting in your mouth.
Ryan Krogh is a freelance writer and editor based in Austin, Texas. In the last decade, he’s traveled to 27 countries and every U.S. state, nearly all with just a carry-on suitcase, thanks to packing light and using compression cubes. He has recently written a guide to carry-on luggage, the best vacation spots abroad in 2025, and airports with amazing outdoor spaces.
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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