ARI Research Essay No. 02
Can Human Experience in Tourism Research Be Explained by Numbers Alone?
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ARI Research Essay No. 02
Can Human Experience in Tourism Research Be Explained by Numbers Alone?
⏤ Between What Can Be Measured and What Matters
Jenna Hong, Ph.D.
Authentica Research Institute (ARI)
August 2026
This research essay reflects on what measurement and interpretation can each reveal—and potentially obscure—in tourism research. Rather than arguing for the superiority of either quantitative or qualitative inquiry, it considers how research questions should guide the choice of methods when studying complex human experiences.
1. Why tourism research needs numbers
Tourism research needs numbers.
Researchers and policymakers need to know how many people visit a destination, how much they spend, how long they stay, how satisfied they are, and whether they intend to return. They may need to examine whether visitor numbers changed after a policy intervention, which factors are associated with travel decisions, or how particular forms of behaviour are distributed across different populations.
Numbers are exceptionally powerful for answering questions of this kind. They make it possible to compare large numbers of people using common measures, identify patterns that may not be visible in individual cases, and examine relationships among variables in a systematic way. With appropriate data and methods, researchers can assess the scale of a phenomenon, estimate the strength of observed relationships, and distinguish recurring patterns from isolated occurrences.
Such information is equally important for tourism management and public policy. Forecasting demand, allocating budgets, planning infrastructure, evaluating interventions, and comparing outcomes over time all require data that can be aggregated and compared.
Recognising the complexity of human experience therefore does not require rejecting measurement.
The more difficult question comes afterwards.
Should we treat a measured value as if it were the experience itself?
2. Does the same “4 out of 5” mean the same experience?
Imagine two travellers who both rate their trip four out of five on a satisfaction scale.
In a dataset, the two responses are equivalent. They can be aggregated into an average satisfaction score and used in statistical analysis alongside other variables.
Yet the journeys behind those identical numbers may have been entirely different.
One traveller may have loved the landscape and accommodation but spent much of the trip in conflict with a companion. Another may have been dissatisfied with transportation and facilities but experienced an unexpected encounter with a local resident that became the most memorable part of the journey. A third traveller might initially regard the trip as disappointing, only to reinterpret it years later as an important turning point in life.
Assigning these experiences the same numerical value does not make their meanings identical.
A score compresses different experiences into a comparable form. That compression is precisely one of the strengths of measurement. It allows researchers to examine patterns across hundreds or thousands of cases.
But every act of compression also involves some loss.
A single number cannot, by itself, tell us why a person selected that response, how satisfaction and disappointment coexisted within the same journey, or how the trip was connected to that person’s relationships, memories, expectations, and wider life history.
Numbers can condense traces of experience, but they do not explain by themselves what that experience meant to the person who lived it.
This is one of the central difficulties of studying tourism experience. What appears externally to be a single journey or activity may, on closer examination, involve a complex combination of place, people, relationships, memories, bodily sensations, expectations, and personal histories. Buchmann et al. (2010), in their examination of authenticity in film tourism, illustrate how tourist experience may emerge through the interplay of prior knowledge, relationships, embodied engagement, and encounters with place.
Measurement does not wrongly erase such complexity. It necessarily simplifies aspects of reality in order to make them analysable.
The important distinction is between a measured representation of experience and the experience it represents.
3. What can disappear when experience is converted into data?
To measure human experience is to select certain dimensions of reality and translate them into a form that can be analysed.
Consider something as familiar as “tourist satisfaction.” Before satisfaction can be measured, researchers must decide what counts as satisfaction. They may ask respondents to evaluate accommodation, food, transportation, attractions, service, price, or overall experience. In doing so, they transform a complex and potentially ambiguous experience into a set of defined categories.
This makes systematic research possible.
At the same time, whatever is not asked may remain outside the dataset.
One element that can be lost is context. The same event may mean very different things to different people. Getting lost during a first solo journey abroad is not necessarily the same experience as getting lost for someone who has travelled internationally dozens of times. Rain may be experienced differently by a honeymooning couple, a long-distance walker, and a parent travelling with a young child.
Another is time. The meaning of an experience is not always settled when the experience ends. Something perceived as a failure during a trip may later become a favourite memory. An event that seemed transformative at the time may later lose its significance. Assessments can change depending on when they are measured because people continually reinterpret past experiences in relation to their present lives.
Human experience also contains contradiction. A journey can be exhausting and rewarding at the same time. A traveller may feel freedom and anxiety simultaneously. Someone may deeply care for a travel companion while experiencing intense conflict during the trip. Satisfaction and dissatisfaction, pleasure and discomfort, connection and frustration do not always cancel one another out.
Tourism experience is also fundamentally relational. Travellers do not experience places as isolated units. Their experiences emerge through interactions with companions, residents, service workers, other tourists, landscapes, built environments, and natural settings.
Nor is experience purely cognitive. Bodies and senses matter. Heat, cold, fatigue, smell, music, unfamiliar voices, long periods of waiting, bodily movement, spatial atmosphere, and physical discomfort can all contribute to the meaning of a journey. Matteucci’s (2014) study of tourists’ experiences of flamenco demonstrates the importance of the body not merely as a vehicle for thought, but as an active part of sensing, participating in, and making meaning of the world.
No single survey can fully capture all of these dimensions.
Nor should it necessarily try to.
The more important question is whether researchers understand not only what their measures capture, but also what they leave outside the frame.
Another feature that may disappear in aggregated data is the exceptional case.
Identifying averages and general patterns is essential in large datasets. Yet an experience far from the average may sometimes reveal a problem that existing questions failed to capture. If 95 percent of visitors report no difficulty, the remaining 5 percent may still be experiencing a serious barrier worth understanding.
An outlying case may simply be statistical noise.
But it may also be the first visible sign of a phenomenon we have not yet learned how to ask about.
4. Does interpretation mean that researchers can simply say whatever they think?
Once the limitations of measurement are raised, an obvious objection follows.
“If numbers cannot fully explain experience, can researchers simply listen to people’s stories and interpret them however they wish?”
One of the most persistent criticisms of qualitative research concerns subjectivity. Two researchers may read the same interview differently. A researcher’s theoretical assumptions, social position, expectations, and prior experiences may influence what they notice and how they interpret it.
Acknowledging this problem, however, is not the same as reducing qualitative inquiry to personal opinion.
Qualitative researchers are still required to explain why particular materials were selected, how data were generated or collected, how interpretations developed, and how those interpretations remain connected to the evidence. They may need to examine cases that contradict their expectations, consider rival explanations, document analytical decisions, and reflect on how their own position shapes the research process.
Decrop’s (1999) discussion of qualitative tourism research is particularly useful here because it rejects the idea that quantitative and qualitative research should be treated as opposing methodological camps. The choice of research design, he argues, should be guided by what researchers seek to know and by the relationship between the phenomenon and the sources through which it can be known.
Drawing on Lincoln and Guba (1985), Decrop (1999) discusses four criteria for judging the trustworthiness of qualitative inquiry: credibility, transferability, dependability, and confirmability. The significance of these criteria lies not simply in naming them but in implementing them through research design and analytical practice.
Triangulation across data, methods, researchers, or theoretical perspectives is one way of strengthening interpretation. Researchers may also search for negative or atypical cases, test alternative explanations, and preserve the context in which observations and accounts were produced. Such practices do not remove interpretation from qualitative research; rather, they make the interpretive process more transparent and accountable.
Interpretation, then, is not a licence to substitute evidence with unrestricted opinion.
Interpretation is a research practice through which evidence and context are connected in order to explain how meaning is formed, while making the grounds of that interpretation available for scrutiny.
Statistical research requires standards appropriate to statistical inference.
Interpretive research likewise requires standards appropriate to interpretation.
Rigour does not belong exclusively to one methodological tradition.
5. Different questions require different methods
Perhaps the first methodological question should not be:
“Should this be a quantitative or qualitative study?”
A more useful starting point is:
What do we want to know?
Different questions call for different kinds of evidence.
These examples are not rigid methodological rules.
Quantitative research can address explanatory questions commonly expressed as “why,” and qualitative findings can help researchers understand phenomena that are also measured numerically. Mixed-method and multimethod designs can combine different forms of evidence within a single research programme.
The central issue is not the label attached to the method.
It is the fit between the question and the evidence required to answer it.
Decrop’s (1999) argument for methodological eclecticism rests on a similar principle: research questions and the nature of the phenomenon under investigation should direct the choice of research design, rather than allegiance to a particular methodological camp.
For this reason, identifying oneself primarily as “a quantitative researcher” or “a qualitative researcher” can become problematic if methodological identity begins to determine the questions one is willing to ask.
Good research should not search for questions that fit the method a researcher already knows best.
It should be willing to select methods that fit the question.
6. The people an average may fail to see
The relationship between measurement and interpretation is not only methodological.
It can also become an ethical question.
Imagine that a destination reports an average visitor satisfaction score of 4.6 out of 5. This is meaningful information. It suggests that a large proportion of visitors had broadly positive experiences.
Yet the average does not represent every experience connected to that destination.
Facilities that are convenient for most visitors may remain difficult or impossible to access for wheelchair users or people travelling with mobility constraints. High visitor satisfaction may depend partly on service arrangements that place substantial emotional or physical demands on tourism workers. Increased visitation may support local businesses while simultaneously contributing to congestion, noise, or changes in residential life.
Tourists may describe an encounter with wildlife or nature as highly satisfying while the ecological effects of that activity remain entirely absent from a visitor satisfaction questionnaire.
There is nothing inherently wrong with the number.
The question is who was asked.
If visitors were asked about satisfaction, the resulting number tells us something about visitor satisfaction. It does not automatically tell us about workers’ experiences, residents’ quality of life, accessibility barriers, animal welfare, or ecological consequences.
Problems arise when a measure of one dimension is treated as if it represented the success of the phenomenon as a whole.
At that point, experiences that were never measured can become difficult to see.
Choosing what to measure is therefore not only a technical act.
It also involves decisions about whose voices enter the dataset, what counts as an outcome, and which experiences are considered relevant enough to become evidence.
An experience that has not been measured is not an experience that does not exist.
And the decision to ask certain questions while leaving others unasked is itself one way of framing reality.
This is why studying small groups, exceptional cases, or experiences that are difficult to quantify should not automatically be regarded as secondary work with little relevance to the broader field. Sometimes the exception reveals precisely what an established indicator has failed to notice.
7. We do not have to choose between numbers and stories
Does this mean that tourism researchers should stop explaining human experience through numbers?
No.
Visitor numbers, expenditure, satisfaction, mobility patterns, and behavioural relationships are indispensable to understanding tourism. Quantitative evidence allows researchers to see scale, distribution, repetition, and patterned relationships across populations.
At the same time, in-depth attention to a person’s narrative, memories, relationships, bodily experiences, or social context cannot substitute for population-level evidence. A small number of interviews cannot demonstrate that all tourists experience a phenomenon in the same way.
Different approaches illuminate different dimensions of reality.
And every approach also leaves some dimensions less visible.
The more productive methodological question is therefore not whether numbers or stories are more scientific, but rather:
What must we be able to see in order to understand the phenomenon we are asking about?
Sometimes scale must be established first.
Sometimes one experience needs to be examined closely.
Sometimes both forms of evidence are necessary.
Tourism experience particularly resists reduction to a single dimension. People move through places while simultaneously sensing, remembering, relating, evaluating, and later reinterpreting what happened. Buchmann et al. (2010) emphasise the interaction of personal histories, knowledge, relationships, embodiment, and place in tourist experience, while Matteucci (2014) demonstrates the importance of bodily and sensory engagement in the construction of meaning.
Recognising such complexity does not mean that every study must attempt to capture everything.
It means that researchers should know what their chosen method makes visible, and what remains beyond what that method can legitimately claim.
The task of tourism research is therefore not to refuse to turn human experience into numbers.
It is to keep asking what becomes visible when experience is translated into numbers—and what may become less visible in the same process.
Good research does not assume that only what can be measured matters.
Nor does it permit unsupported interpretation simply because human experience is difficult to quantify.
Numbers have their forms of rigour. Interpretation has its forms of rigour.
For research concerned with human experience, the challenge is not to choose one and reject the other. It is to select the evidence and methods required by the question, and to remain clear about the limits of what those choices allow us to know.
References
Buchmann, A., Moore, K., & Fisher, D. (2010). Experiencing film tourism: Authenticity & fellowship. Annals of Tourism Research, 37(1), 229–248.
Decrop, A. (1999). Triangulation in qualitative tourism research. Tourism Management, 20(1), 157–161.
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. SAGE Publications.
Matteucci, X. (2014). Forms of body usage in tourists’ experiences of flamenco. Annals of Tourism Research, 46, 29–43.
Suggested Citation
Hong, J. (2026). Can human experience in tourism research be explained by numbers alone? Between what can be measured and what matters (ARI Research Essay No. 02). Authentica Research Institute. https://www.authenticaresearch.org/ari-publications/ari-research-essay/no-02
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