From Perceived to Measurable: A Fuzzy Logic Index of Authenticity in Rural Tourism

Abstract

Choosing a rural destination today often comes down to one thing: how authentic it feels. In countries like Romania, where tradition is still woven into daily life, travelers are looking for something real and sustainable—but what exactly does that mean? And how can we measure it? This study takes a different approach. We created an Authenticity Index using fuzzy logic, a method that makes space for in-between answers and soft boundaries. It helped us capture how people actually perceive things like local food, architecture, and natural scenery—without forcing their opinions into rigid categories. We tested the index with real guest feedback from rural accommodation. The results showed that guests consistently valued sensory experiences—like nature and food—more than activities that required deeper cultural involvement, such as workshops or folk demonstrations. Instead of just producing a number, the index turned out to be a guide. It gives hosts a better idea of what really matters to their guests—even when those preferences are not always easy to define. More than that, it brings together what theory says with what visitors actually feel, supporting more sustainable tourism practices. And in rural tourism, that connection can make all the difference.

1. Introduction

It is no surprise that more and more people are drawn to rural places when they travel. For many—especially those coming from crowded cities—it’s not just about taking a break. It’s about feeling something real. In places like Romania, where daily life still reflects old customs, that feeling of authenticity often comes through in simple things: slow meals, quiet streets, and people who still value tradition [1].

Still, trying to explain what makes a rural experience feel truly authentic isn’t all that simple. Is it the sound of the wind moving through the trees? The warmth of a home-cooked meal? Or maybe just being welcomed by someone who genuinely lives that life? Everyone brings different expectations into the experience. As Dobre et al. explain, authenticity shifts—it’s not something fixed, but something felt differently by each person in each moment [1]. This also makes it tricky to measure. Many tools rely on rigid scales with fixed categories. But people don’t always fit neatly into boxes. If someone gives a “3”, it might mean “maybe”, or “sort of”, or just “I’m not sure”. And that uncertainty might actually tell us more than the number itself.

Meanwhile, in Romania’s rural areas, more and more tourism hosts are trying to center their services around tradition—through the look of the buildings, the food they serve, and even the stories they tell. These efforts often come from a sincere desire to preserve identity and pass it on. But not all combinations work. Sometimes, blending old and new in clumsy ways ends up creating confusion instead of connection.

Interestingly, guests notice these things. At Satul Banului Guesthouse, for example, research showed that visitors usually preferred experiences tied closely to the local culture rather than generic activities. Dobre et al. found that over 90% of guests liked services reflecting real Romanian traditions [2]. Many were urban, middle-aged tourists who wanted quiet surroundings, wooden houses, and conversations with locals. They weren’t looking for flashy entertainment—they wanted something genuine.

All of this made us realize we needed a better way to hear what people truly feel about authenticity. That’s why we decided to use fuzzy logic. It doesn’t force people into fixed answers but lets them show uncertainty, partial agreement, or mixed feelings—just like they might express naturally. Instead of turning those shades of meaning into a single number, our model sees them as useful insights. What we ended up with isn’t just a score; it’s a guide that helps rural hosts see what guests care about in real life, not only on paper.

When hosts understand these honest reactions, they can create experiences that do more than impress tourists—they also help keep traditions alive. This way of listening doesn’t just support better guest satisfaction. It can make rural tourism more sustainable, giving communities a way to celebrate what makes them unique while ensuring they can keep sharing it for years to come.

What makes something feel ‘authentic’ when we travel to rural areas? It’s rarely about clear definitions. Most of the time, it’s a mix of impressions—things we notice, how we feel, the people we meet. This paper tries to capture that mix in a different way. Instead of using checklists or rating scales, we used fuzzy logic. It gave us a way to include hesitation, emotion, or partial agreement—elements that are often missing in regular surveys. The paper is built in five parts. We begin with a short overview of the main theories on authenticity in tourism. Next, we explain how we applied fuzzy logic and how the data were collected. Then come the results and our interpretation of what they mean. Finally, we end with a reflection on limits and a few ideas for what future studies could look in to.

2. Literature Review

A growing body of research has explored the role of authenticity in tourism, particularly in rural and culturally significant destinations. While various conceptual frameworks and methodological approaches have been proposed, there is still no consensus on how authenticity should be defined or evaluated. To understand how our study builds on and contributes to this literature, we begin by reviewing the main theoretical perspectives on authenticity in rural tourism.

2.1. Understanding Authenticity in Rural Tourism

When people talk about rural tourism, authenticity often comes up. It’s something travelers hope to feel, even if they can’t always explain it. In most cases, it’s not about the perfect photo or a historical landmark—it’s the quiet sense of being in a real place, where culture and everyday life still go hand in hand. Whether it’s a bowl of soup made from ingredients from a nearby garden or a conversation with a local craftsperson, these moments can create that sense of genuine connection visitors often seek. At the same time, communities around the world are trying to preserve what makes them unique while offering those experiences in ways that feel both personal and respectful.

Mkono [3] reminds us that there is no single definition of authenticity. Her research shows that what one tourist sees as genuine, another might miss entirely. That’s because perceptions are shaped by expectations, memories, and cultural backgrounds. Especially in rural settings, where daily life isn’t staged, this kind of emotional interpretation plays a big role.

Sidali et al. [4] look at food, specifically. They suggest that sharing meals made from local ingredients can help tourists feel included. Their “intimacy model” isn’t about luxury dining—it’s about connection. Being invited to eat what the locals eat can make people feel like more than just observers.

In the Alps and Carpathians, Lun et al. [5] show how farming still matters—not just economically, but culturally. The link between agriculture and tourism helps maintain landscapes and traditions that would otherwise be at risk. Visitors, in turn, often respond well to this mix of natural beauty and lived heritage.

On Bornholm Island in Denmark, Prince [6] speaks to the challenges local artists face. Many refuse to change their work just to please tourists. Instead, they double down on what feels true to their craft. This choice, though difficult, often strengthens the feeling of authenticity rather than diluting it.

Arbogast et al. [7] shift the conversation to destination management. In places like Tucker County, West Virginia, local tourism boards walk a tightrope between growth and identity. Their work shows how maintaining what makes a place special can be just as complex as promoting it.

Dimitrijević et al. [8] report on EU-backed initiatives in Serbia that support rural tourism without sacrificing tradition. These programs provide financial and logistical support while encouraging communities to stay rooted in their culture.

In Romania, Craciun et al. [9] explore how heritage tourism can reinforce community pride. Their study of the “Valley of the Kings” shows how events, storytelling, and local ownership can turn tourism into a form of cultural preservation, and Gajić et al. [10] return to the kitchen. Their research highlights how meals made with seasonal, regional ingredients aren’t just delicious—they tell a story. For visitors, this kind of dining is often what sets rural trips apart from more commercial experiences.

Mkono [3] also points out that what feels authentic doesn’t come from perfect planning. More often, it’s the small, unpolished details—the things that weren’t put there for show—that resonate the most. In rural China, Yuan et al. [11] note that real connections come from interaction, not performance. Tourists in their study responded most strongly when they had time and space to talk to locals, ask questions, and take part in simple, everyday activities. Zuo et al. [12] take this idea further, suggesting that authenticity builds loyalty. Tourists who feel welcomed—not just served—are more likely to return and recommend the experience to others. Soleymani et al. [13] remind us that none of this matters if the community or environment suffers. Their work ties authenticity to sustainability, arguing that cultural and environmental care must go hand in hand if tourism is to stay meaningful in the long run.

There is no single checklist for what makes rural tourism authentic. It is often a mix of things—home-cooked food, family history, farm work, or simply being treated like a guest instead of a customer. These moments stay with people long after the trip ends. For the communities that host them, authenticity is both something to protect and something to share. And for tourists, it is often what makes the difference between visiting a place and truly experiencing it.

When people talk about authenticity in tourism, Rickly [14] takes a different route. Instead of looking just at what can be seen or proven, she draws attention to how people feel when visiting a place. In rural areas especially, it is often the small personal moments—memories, emotions, or connections—that stay with travelers long after they leave.

Wang’s [15] existential model follows a similar logic. It doesn’t depend on locations or objects. It depends on how the traveler lives that moment. If a place sparks a deep feeling—calm, surprise, or a sense of belonging—that can be enough to make it feel genuine. There doesn’t need to be a sign or official label. The same author, in his 1999 constructivist model, also argues that authenticity is not a fixed quality but a personal and subjective experience. What resonates deeply with one traveler may leave another untouched, as individuals bring their own cultural background, memories, and expectations into each encounter.

On the other hand, MacCannell [16] takes a more rigid stance. In his opinion, authenticity has to be proven—linked to real facts or historical truth. A site is only authentic if it’s original; if it’s a copy, it doesn’t count. Although stricter, this idea still holds weight, especially when dealing with heritage. Rather than being in opposition, these models offer different paths. Some people connect through facts, others through feelings. Both are valid. And for scholars, having multiple ways to think about authenticity helps explain how it’s experienced and understood in tourism.

These frameworks not only shape how authenticity is conceptualized in research but also guide the practical choices of communities, planners, and visitors alike.

While this study focuses on perceived authenticity in rural tourism, we acknowledge the conceptual proximity and partial overlap with constructs such as place image and place identity. As highlighted in recent research, Matlovičová [17], place image refers to the subjective perceptions formed by visitors based on direct experience or mediated representations, while place identity reflects the intended and evolving character of a destination. In this context, the fuzzy-based model developed here captures how tourists interpret visible and experiential elements—such as landscape, architecture, and local customs—through their own filters. Although these impressions align with aspects of place image, our focus remains on perceived authenticity, understood as a blend of cultural rootedness, emotional resonance, and sensory immersion that visitors associate with genuineness.

This view aligns with the idea of “layering identity”, discussed by Matlovičová [17], which suggests that the blending of old and new elements may create a renewed, multi-layered destination identity rather than diminishing authenticity. Such evolving identities reflect cultural continuity rather than confusion.

2.2. Measuring Authenticity: Challenges and Emerging Approaches

Talking about authenticity is one thing. Trying to measure it? That’s a whole other story. Especially in rural tourism, where so much of what visitors feel comes down to mood, memory, and a dozen little details you can’t always plan for. The usual way of handling this—rating things from 1 to 5 on a standard scale—just doesn’t quite cut it. People aren’t robots. A “3” doesn’t mean the same thing to everyone. For some, it might mean “I guess it was fine”. For others, it could be “I’m not sure” or even “depends on the context”. We have seen this issue come up again and again [18]. You give someone a survey, they tick a number, and somehow that number is supposed to say something deep about how they experienced a place. But it rarely does. A lot gets lost in translation. Feelings like warmth, comfort, surprise—those don’t always show up cleanly on a graph.

Some researchers have tried different paths. Long interviews, open questions, or letting people describe things in their own words [19]. That works well when time and budget aren’t an issue. Others use structured tools, like rankings or comparative evaluations, but those still rely on firm numbers [20]. And that’s where the problem often returns: there is no wiggle room for uncertainty. So, here’s the thing. What if we didn’t fight the ambiguity, but accepted it as part of the process? [21]. That’s where fuzzy logic comes in. It’s a way to say, “This feels mostly important, but also a bit uncertain”, and not treat that as a flaw. Think of it as giving space to mixed feelings instead of forcing people to pick just one label. It lets us listen better. In our case, this idea felt right. Guests in rural places often respond emotionally—maybe they love the landscape but aren’t that interested in workshops. Or maybe the food reminded them of childhood, but the rooms felt too modern. These are layered reactions. Using fuzzy methods helped us make sense of them without flattening their meaning. That’s why, in this study, we didn’t just ask for scores—we looked at how people weighed their feelings. Not to force a clear answer, but to build a picture that feels closer to how they actually experience authenticity [22].

2.3. Vernacular Heritage and the Romanian Context

What makes a place feel real? In rural Romania, the answer is often found not in the obvious landmarks, but in the quiet, everyday details. A weathered gate, the scent of firewood, a handmade carpet, or the silence of a road without cars. These aren’t tourist attractions in the formal sense—but they carry something deeper. That sense of place. Of life shaped slowly, over generations. Vernacular heritage isn’t just architecture. It’s the rhythm of a community. The way food is made, how people speak, how they greet each other at the gate. It lives in small things—things easy to miss, but hard to fake. And in Romania, you’ll find it, still, in many villages. Not everywhere, and not always untouched, but enough to feel its presence. Maramures tells its story through wood and ritual [23]; Oltenia, through color and voice [24]; Muntenia, through order, vine-shaded stone, and measured hospitality [25]. Different places, different souls [26]. But that heritage is fragile. The countryside is changing. Young people leave, and houses stay empty. New buildings go up—often faster, cheaper, louder. And while they bring comfort, they sometimes erase what was quietly meaningful. Some guesthouses try to recreate the old charm but miss the mark. Plastic flowers, busy walls, folklore costumes from the supermarket. It’s not bad intention—it’s confusion. A mix between preserving and performing. There are still places that get it right. Not because they’re perfect, but because they stay true. They cook the same food their parents did. They let guests walk into the kitchen, hear a story, join the rhythm. The authentic, in these cases, doesn’t shout. It hums in the background. You feel it, often when no one’s trying.

Studies like those by Dobre et al. [27] or Preda (Alexe) et al. [28] show how much this authenticity matters, and how easily it can be diluted when tradition becomes tourism packaging. They also point out how important it is to understand the cultural roots of a place before trying to evaluate it. You can’t measure what you haven’t truly seen. And in Romania, seeing means slowing down, listening, and noticing what remains even when everything else changes. So, when we talk about authenticity, we’re not just talking about history. We’re talking about presence: about people who still live in rhythm with their land—and what they pass on without even knowing they’re doing it. That’s the heart of the vernacular. And that’s what tourists, more often than not, come looking for—even if they don’t know how to name it. Moreover, keeping and sharing authentic ways of living can directly support sustainable rural growth. When traditions are preserved and locals are involved in tourism, authenticity turns into a real tool that protects cultural heritage, brings people together, and helps communities earn income that goes beyond a single visit.

2.4. Gaps in the Literature and the Role of Fuzzy Logic

There’s been more and more talk lately about what makes rural tourism feel authentic. Researchers look at culture, food, landscape, people’s connections to places—but when it comes to measuring these things, that’s when it gets complicated. You can’t just break the experience into neat categories and assign them numbers. People don’t live like that. And neither do feelings.

Take a simple example: two people stay in the same rural guesthouse. One says it felt rooted in tradition. The other? Too curated. Too fake. So, who’s right? Honestly, probably both. Because authenticity doesn’t follow one clear path—it depends on your background, expectations, even your mood that day. And that makes it tough to capture with traditional tools.

Scales like “rate from 1 to 5” don’t really let people explain what they felt. What does a “3” mean? That they didn’t care? Or that they liked some parts but not others? Or that they weren’t sure yet? We never know. And sure, interviews help—people can say what they want. But they’re hard to compare, and harder to repeat.

Romanian researchers have been talking about this, too. Dobre et al., for example, suggest that we can’t define authenticity unless we look at it through local, vernacular culture—something flexible, not rigid [1]. Preda et al. found that most tourists don’t just want to see traditions. They want to feel them, live them, sometimes without even realizing it [28]. So maybe, instead of fixing the ambiguity, we stop fighting it. Fuzzy logic isn’t made for tourism—but strangely, it fits. It lets people say “somewhat true” or “mostly important”, without forcing a yes or a no. It gives room for hesitation. For the in-between. That’s where people usually are, anyway.

Other researchers—Nguyen and Cheung [29], Zuo et al. [12], or Chroneos-Krasavac et al. [30]—have used fuzzy logic to look at how emotion, place, and meaning overlap, and their results show that when you let people answer in degrees, not absolutes, you learn more about what actually mattered to them. Especially in rural Romania, where no two villages look or feel exactly the same, that flexibility matters. As Craciun et al. [9] point out, people don’t respond to “authenticity” as a checklist. They react to how it is lived—through food, tone, gestures, surroundings. Tools that try to make everything fit into rigid scores can miss that entirely.

Will fuzzy logic solve everything? No. But maybe it brings us a little closer to what people actually feel. And when it comes to something as personal as authenticity, that might be just what we need.

2.5. Building on Fuzzy Logic: Applications in Rural Tourism Research

The truth is, people don’t always know what they feel. Not exactly. Not in words. And when they go somewhere rural, the feeling they get—it’s not always simple. It’s not something you can tick off a list. That’s why fuzzy logic, which comes from fields far from tourism, oddly fits here too. Nguyen and Cheung [29] tried it. They asked tourists to rate satisfaction using fuzzy decision-making. It turns out, most people didn’t give black-and-white answers. Mody and Hanks [31] said the same. Visitors often feel something—but not all the way. It’s not 100%. And not zero either. It’s... a mix.

Zheng et al. [32] looked at fuzzy logic in tourism as well. They thought about how people react to more than just what they see. The mood. The locals. The atmosphere. All that plays a part. And you can’t really box that into numbers.

In Romania, Dobre et al. [2] worked with rural guesthouses. They asked visitors what they thought about authenticity. The answers weren’t clear-cut. Some said “pretty traditional”. Others thought it felt like it was arranged too nicely. And in between those opinions? A kind of pause. That hesitation meant something too. Dobre et al. [1] also say you can’t measure this through checklists. It’s daily gestures; repeated habits; how things sound. Preda et al. [28] noticed that people connect most when something touches them emotionally. Not because it’s impressive, but because it feels familiar, or warm.

Almadi et al. [33] said emotions aren’t noise. They’re actually the signal. That’s how fuzzy logic helps—it doesn’t erase what feels vague. It lets it stay. In our case, we followed that path. Built a fuzzy index. Looked at what people really said. If they hesitated—we left it. If they weren’t sure—that mattered. The blurry parts told us more than the clean ones. That’s what we kept.

Importantly, understanding these layered feelings about authenticity isn’t only about pleasing tourists; it can also inspire more sustainable ways of developing tourism. When what’s offered stays true to local culture, it helps keep identity strong, reduces the push for fake attractions, and creates lasting benefits for both visitors and rural communities.

We didn’t choose these nine authenticity criteria randomly. They came from what we observed in the field, as well as what kept coming up again and again in the literature. Studies like those by Mkono [3], Sidali et al. [4], and Crăciun et al. [9] often pointed toward recurring themes—things that tourists say feel “real”: food, scenery, handmade objects, and meaningful interactions. Alongside that, in our earlier work with Romanian guesthouses [1,2], we kept hearing similar feedback from guests and noticing what hosts emphasized most. These insights helped us settle on the current list—one that brings together both theory and lived experience, and that captures what people actually seem to associate with authenticity in rural tourism.

3. Materials and Methods

We designed our sampling approach to connect with people who had recently spent time in rural areas of the Eastern Carpathians. Rather than aiming for statistical representation, we wanted to hear from those who had genuine, personal experiences in these places. To gather input, we used two methods: some respondents filled out online surveys posted in rural tourism groups and communities, while others completed printed questionnaires during visits to four selected villages. Altogether, we collected 214 valid responses. Among those approached face-to-face, 104 out of 120 agreed to take part, giving us a response rate of 87%. Online, participation was estimated at about 30%, a rate in line with other studies using similar channels. When we compared the answers from both groups, we didn’t find notable differences—whether in rating scale responses or in open-ended comments.

3.1. Designing and Conducting the Fuzzy Logic-Based Survey

In order to explore how tourists perceive authenticity in rural areas of Romania, the study used fuzzy logic as the main tool to. Why fuzzy logic? Because people often hesitate when answering—things are not always black and white. Instead of picking a fixed number, this method lets them say “partly”, “almost”, or “somewhere in between”. That makes more sense when dealing with feelings like authenticity.

To apply this approach, we collected responses over two periods: one during in-person visits to a guesthouse (part of a university-linked initiative), and the other online, using Google Forms. The mix helped us to capture both tourists already staying there and people who might visit in the future.

Altogether, 214 people filled out the questionnaire. It focused on nine things that could make a rural guesthouse feel authentic: how the landscape looked; if the building kept a traditional style; furniture or objects inside the rooms; what kind of food was served; if guests could join local customs or rituals; whether crafts were shown or taught; if there were farm activities; traditional clothing being present; stories or history shared during the stay.

For each one, participants gave scores from 1 (not important at all) to 5 (very important). The way they answered held up well when we checked it—Cronbach’s Alpha was 0.89, which is a strong result.

3.2. Fuzzy-Likert Transformation

When we asked visitors to rate various aspects of authenticity in rural guesthouses, we used a 5-point Likert scale—from 1 (very unimportant) to 5 (very important). But we didn’t want to treat those numbers as fixed points. People don’t think in absolutes. A “3” can mean many things: maybe they’re unsure, maybe it depends, or maybe it’s a cautious yes. So, we needed something more flexible—something that captures that uncertainty.

That’s why we used fuzzy categories. Instead of placing a response in a single box, we let each answer “belong” a little to more than one category. We created five categories: Very Low, Low, Medium, High, and Very High. Each Likert response was mapped to a five-element vector: one degree of membership (μ) for each fuzzy category. These μ-values always add up to 1, so every answer is still whole—but it’s spread across interpretations. For example, a “1” clearly means Very Low importance, so the vector looks like this: (1, 0, 0, 0, 0). A “3” is more nuanced—it sits in the middle, but it also leans a little toward “Low” and “High”. In this case, the vector becomes: (0, 0.25, 0.5, 0.25, 0). In this way, we reflect that uncertainty instead of flattening it. The full fuzzification scheme is shown below, in

Table 1. Fuzzification of Likert scale responses into fuzzy categories.

Likert Score	Very Low (μVL)	Low (μL)	Medium (μM)	High (μH)	Very High (μVH)
1	1.00	0.00	0.00	0.00	0.00
2	0.25	0.50	0.25	0.00	0.00
3	0.00	0.25	0.50	0.25	0.00
4	0.00	0.00	0.25	0.50	0.25
5	0.00	0.99	0.00	0.00	1.00

Source: Own elaboration.

:

Let’s take another quick example. If someone gives a “2”, it mostly falls into Low importance (μ = 0.75), but there’s still a small part of them that leans toward Very Low (μ = 0.25). That’s more accurate than saying “just a 2”.

This fuzzification step helped us treat each response as a soft signal, not a fixed fact. And that’s closer to how people actually express feelings about authentic experiences—somewhat, kind of, nearly. In other words, it reflects real life.

3.3. Calculating Importance Scores for Each Criterion

Once we mapped all the Likert responses into fuzzy categories, the next step was to bring them back to a form that’s easier to work with—a single number that still reflects those shades of meaning. Defuzzification is the name of the process. To do the defuzzification, we applied the centroid method, a commonly method used in fuzzy logic. For each respondent and each criterion, we calculated a weighted average based on the degrees of membership across the five fuzzy categories. The formula looks like this:

$${Score}_{r,c}={\displaystyle \frac{{\sum }_{k=1}^5\left[kx{\mu }_k\right(L_{r,c}\left)\right]}{{\sum }_{k=1}^5{\mu }_k\left(L_{r,c}\right)]}}$$

(1)

where

• S_r,c is the defuzzified score for respondent r on criterion c;
• μ_k(L_r,c) is the membership value for category k (from 1 to 5);
• k corresponds to the linguistic categories: Very Low = 1, …, Very High = 5;
• r = 1 to 214 (respondents), c = 1 to 9 (authenticity criteria).

Imagine this scenario: one of the respondents gave a score of 3 when asked how important the authentic rural landscape was during their stay. Now, according to the fuzzification table we used (see Table 1), a score like that doesn’t land squarely in just one category. It hovers somewhere between Low, Medium, and High—a kind of middle ground. This is where fuzzy logic really shows its strength. Instead of assigning that “3” to a fixed category, we break it down into degrees of membership:

μ = (0, 0.25, 0.5, 0.25, 0)

That means this response is 50% aligned with Medium importance, but it also leans 25% in both directions—slightly lower and slightly higher. To turn this fuzzy response into a single value we can work with, we use the centroid method. It works like this:

S = (0 × 1) + (0.25 × 2) + (0.5 × 3) + (0.25 × 4) + (0 × 5) = 3.0

So, although the original answer was “3”, the model acknowledges the hesitation behind it and reflects that subtlety in the final result. We repeat this process for every response, then average them to see how that particular aspect of authenticity was perceived overall. In this case, if the final mean score for rural landscape comes out as 4.48, that becomes its weight in the larger index model.

This step gave us a numeric score for every response—still rooted in fuzzy logic, but now on a scale that is easier to interpret. Once we had all these individual values, we aggregated them by taking the average across all respondents, for each of the 9 authenticity criteria. That gave us one score per criterion, reflecting how important it was perceived to be overall. This aggregation was performed by computing the arithmetic mean across all 214 respondents, as expressed in the following equation:

$${ScoreCriterium}_c={\displaystyle \frac{1}{N}}\sum _{r=1}^N({Score}_{r,c}^{\left(final\right)})$$

(2)

where

• ${ScoreCriterium}_c$ is the average defuzzified score for criterion c;
• ${Score}_{r,c}^{\left(final\right)}$ is the final defuzzified score for respondent r on criterion c;
• N is the total number of respondents (in our case, 214);
• c = 1, …, 9 corresponds to the nine authenticity criteria.

This resulted in scores like 3.2 or 4.4, which show whether a criterion was seen as moderately or highly important. For example, if Authentic rural landscape scored 4.3, that tells us visitors considered it very significant in shaping their experience. But we didn’t stop there. These scores weren’t just informative—they became weights in our final Authenticity Index. The idea is simple: the more important a criterion is to tourists, the more influence it should have on the final score of a guesthouse. To do that, we normalized the scores to obtain values between 0 and 1.

3.4. Normalization and Weight Assignment

To make sure more valued aspects had more influence in the final index, we normalized the scores into weights. These weights reflect the relative importance of each criterion. This ensured that all weights added up to 1, making them comparable:

$$w_i={\displaystyle \frac{{ScoreCriterium}_i}{{\sum }_{j=1}^m{ScoreCriterium}_j}},i=1,\dots ,m$$

(3)

Here,

• $W_i$ is the normalized weight for criterion i;
• ${ScoreCriterium}_i$ is the average defuzzified score for criterion i.

This transformation made our index more than just a sum—it became a reflection of what tourists actually value most. A criterion with a higher score (e.g., 4.5) would have a bigger say in the final result than one with a lower score (e.g., 3.1). In the next step, we’ll show how these weights are used to calculate the final authenticity index for a rural accommodation—based on how guests rate it on each dimension.

3.5. Computation of the Authenticity Index

After deriving the fuzzy-based weights (w1, w2, …, w9), each accommodation was evaluated by tourists using a 1–10 scale for each of the nine authenticity dimensions. Let’s imagine that we have a number T, tourists. These tourists are rating the same accommodation across the 9 criteria i ∈ {1…9}. For each criterion, we first calculated the average score across all tourist ratings:

$${\overline{R}}_i={\displaystyle \frac{1}{T}}\sum _{t=1}^T{R}_{t,i}$$

(4)

where

• ${\overline{R}}_i$ is the average tourist rating for criterion i;
• $R_{t,i}$ is the score given by tourist t to criterion i;
• T is the total number of tourists who evaluated the accommodation.

This gave us a representative value for each dimension, showing how tourists rated that specific aspect of authenticity. Next, we combined these average scores with the fuzzy-based weights. The final Authenticity Index for a guesthouse was computed as follows:

$$AI\left(Accomodation\right)=\sum _{i-1}^9({\displaystyle \frac{1}{T}}\sum _{t=1}^T{R}_{t,i})\times w_i$$

(5)

Here,

• AI(Accommodation) is the overall authenticity score for the evaluated rural guesthouse;
• $R_{t,i}$ is the score given by tourist t to criterion i;
• $w_i$ is the normalized weight of criterion iii, derived from fuzzy importance scores;
• i = 1, …, 9 covers all nine authenticity criteria.

This provided a single score on a 1–10 scale that reflects both how tourists rated the accommodation and how important each criterion was perceived to be. The result is a meaningful, personalized indicator of authenticity that captures what matters most to rural visitors.

4. Results and Discussion

In this chapter we present the outcomes of the fuzzy-based analysis applied to the nine authenticity criteria, followed by the calculation of the overall authenticity index for each of the selected rural guesthouses. The data are structured and interpreted with a dual aim: to extract meaningful insights and to ground the findings in the broader context of authenticity studies in rural tourism.

4.1. Descriptive Results for Authenticity Criteria

Based on the responses collected from 214 participants, we computed defuzzified importance scores for each of the nine criteria. These are displayed in

Table 2. Score distribution, percentage importance and defuzzified values by fuzzy categories.

Criterion		Fuzzy Value
		Very Low (VL)	Low (L)	Medium (M)	High (H)	Very High (VH)	Total
1. Significance of the authentic rural landscape	Sum (k × μ) 1	4.00	14.50	43.50	179.00	717.50	958.50
	Percentage (%) 2	0.42	1.51	4.54	18.68	74.86	100.00
	Sum (μ) 3	4.00	7.25	14.40	44.75	143.50	214.00
	Sum (k × μ)/Sum (μ) 4						Criterion Score 4479
2. Significance of traditional guesthouse architecture	Sum (k × μ)	7.50	47.00	96.00	214.00	487.50	852.00
	Percentage (%)	0.88	5.52	11.27	25.12	57.22	100.0
	Sum (μ)	7.50	23.50	32.00	53.50	97.50	214.0
	Sum (k × μ)/Sum (μ)						Criterion Score 3981
3. Rooms decorated with traditional furniture and objects	Sum (k × μ)	12.25	70.50	117.00	234.00	345.00	778.75
	Percentage (%)	1.57	9.05	15.02	30.05	44.30	100.0
	Sum (μ)	12.25	35.25	39.00	58.50	69.00	214.0
	Sum (k × μ)/Sum (μ)						Criterion Score 3639
4. Culinary authenticity of local dishes	Sum (k × μ)	6.75	33.00	49.50	207.00	612.50	908.75
	Percentage (%)	0.74	3.63	5.45	22.78	67.40	100.0
	Sum (μ)	6.75	16.50	16.50	51.75	122.50	214.0
	Sum (k × μ)/Sum (μ)						Criterion Score 4246
5. Participation in local customs and folk traditions	Sum (k × μ)	15.50	51.50	73.50	256.00	421.25	817.75
	Percentage (%)	1.90	6.30	8.99	31.31	51.51	100.0
	Sum (μ)	15.50	25.75	24.50	64.00	84.25	214.0
	Sum (k × μ)/Sum (μ)						Criterion Score 3821
6. Organization of traditional craft workshops	Sum (k × μ)	19.50	78.00	99.00	228.00	327.50	752.00
	Percentage (%)	2.59	10.37	13.16	30.32	43.55	100.0
	Sum (μ)	19.50	39.00	33.00	57.00	65.50	214.0
	Sum (k × μ)/Sum (μ)						Criterion Score 3514
7. Participation in traditional rural household activities	Sum (k × μ)	33.00	84.50	91.50	208.00	281.25	698.25
	Percentage (%)	4.73	12.10	13.10	29.79	40.28	100.0
	Sum (μ)	33.00	42.25	30.50	52.00	56.25	214.0
	Sum (k × μ)/Sum (μ)						Criterion Score 3263
8. Display of the regional folk costume	Sum (k × μ)	24.75	78.00	94.50	213.00	327.50	737.75
	Percentage (%)	3.35	10.57	12.81	28.87	44.39	100.0
	Sum (μ)	24.75	39.00	31.50	53.25	65.50	214.0
	Sum (k × μ)/Sum (μ)						Criterion Score 3447
9. On-site cultural circuit at the accommodation	Sum (k × μ)	12.75	57.00	85.50	216.00	451.25	822.50
	Percentage (%)	1.55	6.93	10.40	26.26	54.86	100.0
	Sum (μ)	12.75	28.50	28.50	54.00	90.25	214.0
	Sum (k × μ)/Sum (μ)						Criterion Score 3843

Source: Own elaboration; Note: ⁽¹⁾ The totals reflect fuzzy selections (1–2–3–4–5) grouped by significance levels (k). ⁽²⁾ Σk = the sum of selections in each fuzzy category; ⁽³⁾ Σk×μk = the sum of each category k multiplied by its membership function value (μk); ⁽⁴⁾ Σμk = the total of membership values for each criterion. The defuzzified score was calculated as shown in Equation (5).

.

These findings show that “culinary authenticity” and “authentic rural landscape” were seen as the most important aspects by tourists, closely followed by “traditional architectural style”. On the other hand, “traditional costumes” and “agricultural activities” scored lower. This indicates less emphasis on visual and participatory elements and confirms the claims of previous research, by Nguyen et al. [29] or Zuo et al. [12], that sensory-rich experiences like food and visual landscapes contribute heavily to perceived authenticity.

4.2. Understanding the Weight Distribution

Looking at the results in

Table 3. Authenticity index scores by guesthouse.

Criterion	Score	Weight (wᵢ)
Significance of the authentic rural landscape	4.479	0.131
2. Significance of traditional guesthouse architecture	3.981	0.116
3. Rooms decorated with traditional furniture and objects	3.639	0.106
4. Culinary authenticity of local dishes	4.246	0.124
5. Participation in local customs and folk traditions	3.821	0.112
6. Organization of traditional craft workshops	3.514	0.103
7. Participation in traditional rural household activities	3.263	0.095
8. Display of the regional folk costume	3.447	0.101
9. On-site cultural circuit at the accommodation	3.843	0.112
TOTAL	34.235	1.000

Source: Own elaboration.

, one thing becomes clear: tourists really notice the view. The natural landscape has the highest weight (0.131), and that makes perfect sense—it’s the first thing you see when you arrive. Whether it’s a wide valley or a quiet hill, that setting shapes the whole experience. Right after that comes food (0.124), which often carries emotional weight.

On the other hand, things like participating in daily chores (0.095) or joining a craft workshop (0.103) received less attention. Maybe these require more effort, or maybe people just want to relax when they’re in the countryside. Not everyone goes on vacation to grind wheat or learn how to carve wood. These activities are still valuable—but maybe they are not what everyone is looking for on their first visit, and that is where the fuzzy model helps. Instead of assuming everything matters equally, it gives us a way to capture what stands out most. For anyone running a guesthouse, this means focusing on the top elements—scenery, food, and old-style buildings—might be the most effective way to create a strong, lasting impression. Especially if your visitors are seeing the place for the first time.

The chart below, Figure 1, offers a clearer picture of how participants ranked each element of rural authenticity. By turning the scores into a visual, it becomes easier to see which aspects—like food, landscape, or traditions—left the strongest impression on visitors.

Altogether, this visual and statistical synthesis provides deeper insight into what visitors perceive as most authentic during their rural stays—offering valuable guidance for enhancing guest experiences in line with genuine expectations.

4.3. Interpreting the Authenticity Index Through a Case Scenario

To see how the authenticity index plays out in real life, we looked at two guesthouses—let’s call them Accommodation A and Accommodation B. Both were rated by a small group of tourists, based on the same nine criteria we’ve used throughout the study. Each person gave scores from 1 to 10, and those were then combined with the fuzzy weights shown earlier.

Table 4. Fuzzy-weighted scores and final authenticity index for accommodations A and B.

		Criterion No.
Accomodation A	Turist	1.	2.	3.	4.	5.	6.	7.	8.	9.	Index
	1	5	6	8	7	9	4	7	8	9	8.4
	2	10	10	10	10	10	10	10	10	10
	3	8	8	8	9	10	8	7	5	10
Average		7.67	8.00	8.67	8.67	9.67	7.33	7.33	7.67	9.67
Weight		0.131	0.116	0.106	0.124	0.112	0.103	0.095	0.101	0.112
Average × Weight		1.003	0.930	0.921	1.075	1.079	0.753	0.699	0.772	1.085
		Criterion No.
Accomodation B	Turist	1.	2.	3.	4.	5.	6.	7.	8.	9.	Index
	1	5	4	5	7	5	4	5	5	5	4.7
	2	6	6	5	5	5	5	5	5	5
	3	3	4	4	4	4	4	4	4	4
Average		4.67	4.67	4.67	5.33	4.67	4.33	4.67	4.67	4.67
Weight		0.129	0.114	0.106	0.122	0.111	0.104	0.100	0.103	0.112
Average × Weight		0.611	0.543	0.496	0.662	0.521	0.445	0.445	0.470	0.524

Source: Own2 elaboration.

summarizes the average scores, fuzzy weights, and resulting authenticity index for both guesthouses:

In the end, Accommodation A scored higher on the most visible and emotionally resonant aspects: things like the natural landscape (0.131), traditional architecture (0.116), and local food (0.124). These are elements that guests notice immediately, and they tend to leave a strong impression. As a result, its overall authenticity score came out to 8.4.

Accommodation B, by comparison, did fairly well on more interactive elements—like hands-on activities or cultural workshops—but wasn’t rated as highly on the core, high-weight criteria. Its final index was 4.7, which shows that although the experience was still meaningful, it didn’t match tourist expectations in quite the same way.

What this tells us is that different types of authenticity matter to different people, but some features have broader appeal. Scenic views, traditional design, or local flavors seem to resonate with most visitors, especially those coming from outside the region or visiting for the first time. This small comparison also shows how flexible the fuzzy model can be. Instead of just combining numbers, it helps us see whether what a guesthouse offers actually matches what visitors are looking for. And that’s useful—not just for theory, but for real decisions. A host can look at this kind of feedback and say, “OK, here’s where we can improve”, without needing a full overhaul.

4.4. Practical Implications and Theoretical Reflection

This index provides rural guesthouse owners a clearer picture of what their visitors care about. If elements like the landscape or architecture don’t rank well, that’s a good sign of where attention is needed. Even small steps can improve how authentic a place feels to guests. Even small changes in these areas can improve how visitors experience the space. The exact same approach could also be useful on a larger scale. Because the scores follow a common structure, they can be used to compare different places or track how things evolve over time. This might help local tourism associations or public programs better understand what kinds of rural experiences people enjoy most.

From a methodological point of view, one of the strengths of using fuzzy logic in this type of analysis lies in its ability to reflect how people actually perceive and express authenticity. Unlike rigid scales that force responses into fixed levels, the fuzzy approach allows for a more nuanced assessment—one that accommodates hesitation, uncertainty, or overlapping feelings. In this context, scores approaching 5.0 can be interpreted as high authenticity scores, meaning a strong connection between a certain feature and the visitor’s sense of cultural value. Conversely, low authenticity scores, closer to 3.0, suggest that the criterion was either less noticed or less emotionally significant. Instead of imposing arbitrary thresholds, this approach lets the data reveal where the strongest impressions lie, and that flexibility fills a real gap in previous models that tended to flatten or oversimplify the tourist perspective.

To make the results easier to understand, we turned the index into something more visual—something that can be recognized at a glance. Figure 2 shows an example of how this might look when combined with branding or signage:

This is a conceptual visualization, not a real logo in use, and is intended to illustrate how the index could be represented visually for communication and awareness purposes.

A simple image like this can replace charts or numbers and tell a quick story. Whether it is on a website, in a brochure, or at the door, it gives people a feeling of what kind of cultural experience they’re about to have. From a theoretical point of view, the index also moves away from rigid thinking. Everyone sees authenticity in a different way. What feels important to one person might not strike the same chord with another person. That’s why this flexible model works. It makes room for different ways people connect with culture, rather than forcing everything into a fixed idea.

Recent findings suggest that sustainability practices can play an important role in shaping tourists’ overall satisfaction in rural destinations. As Boros and Korcsmáros [34] highlight in their study of the Danube region, visitors are increasingly attentive to initiatives such as local sourcing, environmental responsibility, and community-based services. These aspects not only reflect the destination’s values but also reinforce a sense of authenticity when aligned with traditional identity. In this context, perceived authenticity—understood as emotional and cultural coherence—appears to correlate with visitors’ appreciation of sustainable tourism efforts.

5. Conclusions

This study showed a new model based on fuzzy logic, created to reflect how visitors perceive authenticity in rural tourism settings across Romania. It uses nine key criteria drawn from both theory and field practice, weighted by tourist feedback. The result is a method that gives shape to subjective impressions in a way that can be consistently analyzed. What stood out most in our results were elements that engage the senses—things like scenery, architecture, and food. These were the clearest signals of authenticity for visitors. This supports earlier studies showing that such features leave strong emotional impressions and help visitors connect with the place [1,35]. Since the pandemic, interest in rural areas has grown, especially in places that feel authentic, peaceful, and meaningful. During this period, tourists often seek more than comfort. They want to feel something personal and culturally rooted. The index presented here helps reflect those expectations in a form that is both understandable and useful [28,36].

On the ground, the index can help accommodation providers understand where their services meet expectations—and where improvements might be needed. It also works well as a communication tool. By visualizing authenticity through clear symbols (as shown in Figure 1), rural destinations can present their identity in a way that is both informative and emotionally engaging [1].

From an academic perspective, the model contributes to current debates around what authenticity means in tourism. Rather than defining it in fixed terms, the fuzzy logic approach makes room for different ways of seeing, feeling, and relating to places [35,37]. It captures subtle differences without flattening them into a single definition.

Like any model, this one has limitations. The dataset was relatively small, and the approach doesn’t yet take into account personal backgrounds, seasonal variation, or regional differences. Future work might adapt this framework for cross-cultural comparisons or test how well it predicts visitor satisfaction and return rates. Even so, this authenticity index offers a helpful starting point. It brings together subjective experience and analytical structure, allowing rural communities, policymakers, and researchers to better understand and share what makes a place feel truly authentic.

Like any first attempt at modeling something this complex, our study has a few limitations worth mentioning. The responses we analyzed came from two different channels—some people filled out the form in person, others did it online. Naturally, that might have influenced how they understood the questions or how much time they took to think about their answers. Also, since all respondents came from the same area in Romania, the results can’t really speak for the whole country. Most of those who took part were urban and middle-aged, and it’s possible that other groups—like younger travelers or people from rural communities themselves—would have viewed authenticity in a different way. That is something that future research could explore more deeply: how different people, from different places, relate to the same rural setting.

While this model offers promising applications, it’s important to recognize that some responses may have been shaped by how and where the data was collected. For instance, differences between in-person and online participation might have influenced how individuals interpreted certain items. Additionally, because most respondents came from a specific demographic group, future studies could explore how perceptions of authenticity vary by age, background, or place of origin.

Despite its useful insights, this study has certain limitations. The sample is geographically concentrated in the Eastern Carpathians, and the results may not be generalizable to all rural areas in Romania or beyond. Furthermore, the cross-sectional nature of the survey provides only a snapshot in time. Future research could apply the fuzzy authenticity index longitudinally, to monitor evolving tourist perceptions, or comparatively, across different countries or cultural contexts. These extensions would help validate the robustness and adaptability of the proposed model in diverse rural tourism settings.

Future research could also explore the broader applicability of this fuzzy-based index across different geographical and cultural contexts, to validate its relevance and adaptability beyond the initial case study.