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Introduction
Motivation, often described as a causal force, significantly influences the behavior of consumers and tourists when they make decisions about purchasing products or services. According to Martínez et al. (2022), this motivational impulse arises from a combination of internal (push) and external (pull) factors that exert their influence on an individual’s behavior. To gain a deeper understanding of the driving forces behind consumer motivation, existing literature highlights several internal variables that shed light on tourist behavior from a psychological perspective (Liu, 2023).
One such alternative is the consideration of theoretical variables related to needs (Devesa et al., 2010; Chen et al., 2022). These needs align with Maslow’s hierarchy and include physiological, safety, social, esteem, and self-actualization needs (Hernández, 2021; Castro, 2018). Another relevant theory, proposed by Edwin Locke (Locke & Latham, 2002), emphasizes the role of motivation in achieving objectives. This theory explores the intention to fulfill goals or desires as a fundamental motivation source guiding an individual's actions (Barberá, 2002).
Secondly, to comprehend the aspects of attraction that impact an individual’s motivation, Devesa et al. (2010) emphasize the need to analyze external factors that influence purchasing behavior. This analysis is based on the characteristics of the products and services. Motivation is closely tied to a destination's existing tourist offerings in tourism.
According to Nasimba and Cejas (2015), motivation is not the sole factor influencing an individual’s behavior; other variables, such as age, income, and personality traits, also play significant roles. In particular, empirical studies conducted by García and Moral (2022) and Bano et al. (2019) have revealed a substantial relationship between certain personality traits or behavioral profiles at the psychological level and consumer motivations.
This research highlights the importance of considering personality traits within the context of consumer behavior. These traits are part of the well-known “Big Five model” initially proposed by Lewis Goldberg in the 1980s. The five key personality dimensions include openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism.
(Ruiz, 2003; Soliño and Farizo, 2014). Medina’s studies (2016) shed light on the limited number of literary contributions that specifically analyze the influence of personality traits on individual motivations (both needs and desires) as well as motivations associated with the attributes of a destination (offer). Understanding these connections is crucial for developing effective marketing strategies and enhancing the tourism experience.
For García and Moral (2022) or Bano et al. (2019), individuals with extraverted personality traits (extroverted/energetic vs. lonely/reserved) seek out spaces to strengthen relationships with others. Their motivation tends to lead them toward places where they can experience adventurous activities with fellow people. However, this decision-making process varies for individuals with different personality traits (Barra et al., 2019). Their interests span a wide range, including seeking unique experiences, taking things easy, distancing themselves from others, finding places for rest, pursuing personal growth, and engaging in intellectually stimulating activities.
Given this scenario, the research problem arises from the need to delve deeper into the relationship between personality and motivation, particularly within the context of Quito tourism.
This study aims to understand how an individual’s personality traits influence motivation when selecting a tourist destination and shaping their desires. To achieve this goal, we consider two critical variables that define motivation and significantly impact an individual psychologically: internal needs and external supply. By gathering information through this study, we establish a theoretical foundation to inform the design of targeted campaigns and advertisements for audiences with shared or individual interests. This optimization of resources and time allows for the creation of tailored offers suitable for specific tourist profiles.
Theory development
Consumer behavior
Consumer behavior is a systematic process that occurs within an individual while purchasing a product or service to fulfill a need. Researchers Hoyer et al. (2015) and Espinel et al. (2019) have explored this phenomenon. In tourism, destinations and companies striving to develop effective marketing strategies must consider the diverse factors influencing an individual’s purchasing behavior related to tourism products and services.
Understanding consumer behavior poses a significant challenge for marketing and sales professionals. The multifaceted nature of consumer behaviors, coupled with the everevolving market dynamics, constrains the effective formulation of commercial strategies that drive company growth and enhance the market presence of their products or services (Díaz, 2012).
Consumer behavior, as discussed by Kotler and Armstrong (2012) and referenced by Salazar (2020) and Moreno et al. (2022), is influenced by a multitude of factors. To devise effective marketing strategies tailored to individual consumer profiles, these factors must be analyzed independently and collectively. Critical criteria include cultural aspects (such as culture, subculture, and social class), personal characteristics (including age, occupation, and personality), social influences (such as reference groups, family, and roles), and psychological factors (such as motivation, perceptions, learning, and beliefs).
Motivation as a psychological aspect of consumer behavior
According to Durmaz (2014) and Ramírez et al. (2022), the human being is an intricate and unpredictable entity shaped by many variables that demand analysis from a psychological perspective. We must draw upon various disciplines, including psychiatry, chemistry, biology, and anthropology, to gain deeper insights. Consequently, the study of human beings spans diverse domains, adapting to their multifaceted needs-ranging from social and educational contexts to labor, marketing, and political arenas.
At the social level, psychology dedicates itself to studying and analyzing human behavior within society. Researchers focus on the factors influencing behavior, feelings, and thoughts (Molina et al., 2023). Meanwhile, marketing delves into the processes of consumer behavior, aiming to understand preferences and consumption patterns. In today’s information-rich landscape, where abundant data about products and services circulate through various media channels, the point of contact with the consumer becomes pivotal (Sare & Hallo, 2021).
Within this context, the significant challenge at the marketing level lies in comprehending which psychological aspects-such as motivation, lifestyle, perception, beliefs, and learning-play a pivotal role in effectively developing commercial activities within tourism (Pan et al., 2024).
Authors like Hoyer et al. (2015) and Salazar (2021) have demonstrated that a tourist’s or consumer’s behavior can be psychologically influenced by various factors, including motivation, learning, attitude, and perceptions related to products and services (Solakis et al., 2022). Our study focuses on motivation as a central axis for understanding tourist behavior from a psychological standpoint. Specifically, we aim to explore how personality traits influence motivation.
By unraveling these connections, we can inform marketing strategies tailored to individual profiles, ultimately enhancing the tourism experience.
Motivation
The concept of “motivation” plays a pivotal role in consumer decision-making, especially regarding travel and tourism. It represents a dynamic force that develops within an individual when they seek to fulfill specific needs (Martínez et al., 2022). In essence, motivation acts as a stimulus variable significantly influencing purchasing choices.
According to Mediano (2002) and Pan et al. (2024), tourist behavior is influenced by both psychological push criteria and attraction-based (pull) factors. These factors play a crucial role in shaping an individual’s desire to travel and ultimately impact their choice of destination.
On this topic, Devesa et al. (2010)) provide a more lucid explanation of how motivation is generated in tourists, focusing on the following aspects:
Internal Variables (Push Factors): These variables are determined by intrinsic needs and desires. They include Rest (the desire for a tranquil and rejuvenating experience), adventure (the urge to explore new places and engage in exciting activities), and social Interaction (the need for social connections during travel).
External Variables (Pull Factors): These factors are influenced by the destination itself and its offerings. They encompass Attractions (the unique features, landmarks, and points of interest in a place), recreational infrastructures (facilities and amenities for leisure and entertainment), and cultural and Natural Settings (the richness of cultural heritage and the natural environment).
Necessity as a push factor
The concept of ‘need’ has been extensively studied to understand its origins in human beings and how individuals strive to fulfill them. Abraham Maslow’s well-known hierarchy of needs theory defines these needs as essential deficiencies for human well-being. These needs span various dimensions: physiological, security, social, esteem, and self-actualization. Individuals prioritize and satisfy these needs based on their level of importance (Díaz, 2012; Durmaz, 2014).
From an educational perspective, Rojas (2021) explores how meeting needs impacts learning outcomes. Spiritually, Bayes and Borrás (2005) delve into the connection between needs and inner fulfillment. Economically, Cobedo (2020) investigates how needs influence consumer behavior.
In the context of tourism, researchers like Arce et al. (2020) and Chen et al. (2022) argue that tourists’ needs are met through available tourism products and services. Fonseca and Estela (2020) highlight that contemporary tourists seek cultural experiences, sustainability values, and seamless technological integration. Ultimately, this variable significantly influences tourists’ destination choices as they look for places and stories they can share and pass on.
The offer of tourism products and services as a pull factor
Naranjo and Martinez (2022) highlight that research evaluating tourism offers valuable insights into a destination's social, economic, and environmental development. By understanding the tourism market dynamics, destinations can adapt to new challenges and enhance their products and services. This responsiveness is crucial in the ever-evolving context of tourism (Ab Dulhamid, 2022).
As discussed by Arce et al. (2020), tourism products or services emerge organically in specific locations. When we refer to a tourist product, we encompass any resource (such as beaches, mountains, forests) that integrates various tourist services (transport, accommodation, travel guides). These services cater to tourists’ preferences, which span diverse categories: sun and beach, sports and adventure tourism, cultural experiences, ecotourism, and community-based tourism. To meet demand effectively, it is essential to structure tourism resources based on quality and value. These resources act as pull factors, motivating tourists to visit a place and immerse themselves in unique service experiences. Considerations must extend to natural elements, cultural offerings, and human activities that contribute to the overall appeal of a destination.
Personality traits as a personality variable
Personality is a fascinating field within psychology. Researchers aim to understand how distinct traits shape an individual’s day-to-day decisions (Guzmán & Salamanca, 2021). Crespo and Soria (2019) emphasize that personality plays a crucial role in consumer behavior. Researchers estimate a strong correlation between an individual’s personality type (personality traits) and their preferred products.
One widely used model for studying personality is the Five Factor Model, the Big Five. This model comprises five fundamental dimensions or traits that provide insights into consumer behavior:
Openness to Experience: Individuals high in openness are curious, creative, and willing to explore the unknown. They appreciate aesthetics, fantasy, and novel ideas. Their values may include re-examining social, religious, or political norms (Soliño & Farizo, 2014; Sanchez, 1995; Arévalo et al., 2019).
Extraversion: Extraverts thrive on social interactions. They are cordial, gregarious (preferring company), assertive, and seek stimulation. Positive emotions, such as happiness and joy, characterize their experiences (Sánchez, 1995; Arévalo et al., 2019).
Conscientiousness: This trait reflects organization, discipline, and responsibility. Conscientious individuals set goals, exhibit competence, maintain order, and havea strong sense of duty. They aspire to achieve and practice self-discipline (Ruiz, 2003).
Agreeableness: Agreeable individuals are cooperative, empathetic, and compassionate. They value harmony, avoid conflicts, and prioritize relationships. Their interactions are characterized by kindness and understanding.
Neuroticism: High neuroticism indicates emotional instability. Individuals with this trait experience anxiety, mood swings, and stress. They may be more sensitive to negative emotions and perceive threats easily.
Understanding these personality traits helps marketers tailor products, services, and marketing strategies to specific consumer segments. By recognizing how personality influences preferences and decision-making, businesses can create more effective campaigns and enhance customer satisfaction.
Desire as a mediating variable
The conceptualization of desire has received limited attention in contemporary literature and is often mentioned superficially without in-depth exploration. According to the Dictionary of the Spanish Language, desire is defined as an “affective movement toward something that is desired” (Real Academia Española, 2024). In simpler terms, desires represent what we yearn to have or achieve, driven by our needs. Notably, a close relationship exists between desires and goals (Barberá, 2002).
Acerenza (2003) contends that desires and needs play a pivotal role in shaping various types of tourism. These desires manifest in diverse ways: cultural tourism (the willingness to explore new places, immerse in local culture, and gain insights into history, art, and traditions); sun and beach tourism (the longing for relaxation, warm climates, and coastal experiences, often associated with leisure and recreation); ecotourism (a desire to connect with nature, appreciate biodiversity, and engage in sustainable practices); other desires (these may include seeking adventure, culinary experiences, or spiritual encounters).
From this perspective, the interplay between desires and tourism is a motivating factor in selecting a destination (Gonzalez et al., 2023). Travelers seek places that align with their desires, whether discovering cultural gems, basking in the sun, or exploring pristine natural landscapes.
Structuring of the study variables
Table 1 presents the parameters for conducting this research and collecting relevant information to achieve the stated objective. Notably, the table captures motivation-related dimensions, including desires and personality traits.
Table 1 Dimensions and indicators of the tourist's motivation, desire, and personality
| Variables | Code | Dimension | Code | Indicator | Code |
|---|---|---|---|---|---|
| Motivation | MOT | Tourist needs (push factor) | NEE | Physiological or vital needs
Security Needs Social Needs Appreciation Needs (esteem and notoriety) Needs Transitive Self-Actualization |
NEE1
NEE2 NEE3 NEE4 NEE5 |
| Offer (Pull Factor) | OFE | City tourism
Nature tourism Wellness tourism Cultural tourism Sun and beach tourism Shopping tourism |
OFE1
OFE2 OFE3 OFE4 OFE5 OFE6 |
||
| Desire | DES | Desire | DES | Desire for a product
Desire over price Desire about location Desire about promotion Personal desire |
DES1
DES2 DES3 DES4 DES5 |
| Personality | PER | Personality traits | PET | Openness to experience Extraversion Conscientiousness: Agreeableness Neuroticism |
PET1 PET2 PET3 PET4 PET5 |
| Source: adapted from Devesa et al. (2010) | |||||
Source: adapted from Devesa et al. (2010).
Building upon the established variables, we have formulated the following hypotheses for our study:
H1: Personality traits significantly impact the push factor of motivation, which is defined by tourist needs.
H2: Personality traits significantly influence the pull factor of motivation, which is shaped by the existing tourist offerings.
H3: Individual personality traits directly influence a tourist’s desires. H4: Tourist needs to play a role in shaping their desires.
H5: The available offerings at a destination influence a tourist’s desires.
Methodological analysis
For the development of this study, we have chosen a quantitative approach to investigate whether personality traits influence tourist motivation (Sánchez, 2019). To achieve this, we conducted quantitative, non-experimental, descriptive, and correlational research at a crosssectional level. This approach allowed us to describe, analyze, and contrast the relationship between personality traits and tourist motivation (Guevara et al., 2020; Cvetkovic-Vega et al., 2021)
Population context
For this study, we needed to collect information from a population whose profile consists of individuals older than 20 years residing in Quito, Ecuador. Given that the population size exceeds 100,000 units, we calculated a sample size using a statistical formula for infinite populations (Hernández & Carpio, 2019). The following criteria were considered for this calculation: an error margin of 5%, a proportion of 50%, and a confidence level of 95%. As a result, the sample size for this study was determined to be 384 participants.
Data collection and measurement of variables
The technique used to collect information for this study was a survey (Feria et al., 2020), which participants completed in a self-administered manner (Noy, 2008). The questionnaire gathered general sociodemographic information about sex, age, daily activity, and monthly income. Additionally, participants were asked to identify their personality traits based on the following criteria: openness to experience, conscientiousness, extraversion, agreeableness, or neuroticism.
Furthermore, we explored various criteria that can influence tourist motivation to travel, focusing on two dimensions: needs and existing offers. We also included the desired variable as a mediating factor.
We used nominal measurement scales for sociodemographic variables and personality traits. For assessing motivation and desire, we employed a 5-point Likert scale, where 5 corresponds to “totally agree,” 4 corresponds to “agree,” 3 corresponds to “neither agree nor disagree,” 2 corresponds to “disagree,” and 1 corresponds to “strongly disagree” (Arribas, 2004). The entire sample's data collection occurred officially between November and December 2023.
Data analysis
For the data analysis stage, a study of the sample distribution was first considered to identify the profile of the participants who contributed information to this research. The variables considered were personality traits, needs, desires, and offers. Afterward, a content validity analysis was carried out using Cronbach's alpha; this test was performed to observe if the measuring instrument used in this research measured what it intended to measure (Quero, 2010).
An exploratory factor analysis (EFA) was also considered to explore the observed variables' underlying dimensions, constructs, or latent variables more precisely. This last analysis confirmed that it meets the minimum requirements for its development: a sample size of more than 300 cases and the degree of determination of the factors (Mavrou, 2015). The optimal recommendation is a minimum of 100 cases and the number of variables per factor (dimension) from 3 to 4 items (Lloret et al., 2014).
To test the model, a confirmatory factor analysis (CFA) was considered to identify those variables (indicators) that do not contribute to the proposed measurement model and corroborate the results of the TFA. Through this analysis, the initial hypotheses were also corroborated for those that aim to verify the relationship or effect generated between two variables and for those where a mediating variable is included (Ramírez & Polack, 2020).
Results
This section presents the results obtained from data collection and their corresponding analyses.
Descriptive results of the sample and exploratory factor analysis
The initial results from this study, as presented in Table 2, were obtained using the SPSS version 19 statistical program. These results provide descriptive data on the profile of the Quito participants who contributed to this study. Notably, the participant pool exhibits a diverse sociodemographic profile, with significant contributions from women, public employees, and individuals earning an average salary between $501 and $1000.
Table 2 Sample Overview
| Criterion | Scale | Frequency | Percentage |
|---|---|---|---|
| Gender | Male
Female |
169
215 |
44,0 56,0 |
| Age | 20 to 24 years old
25 to 29 years old 30 to 34 years old 35 to 39 years old 40 to 44 years old 45 to 49 years old 50 to 54 years old 55 to 59 years old Over 60 years old |
46 54 47 47 77 43 40 17 13 |
12,0
14,1 12,2 12,2 20,1 11,2 10,4 4,4 3,4 |
| Daily activity | Retiree
Student Enterprising Public employee Private employee Unemployed Housewife |
14 66 69 66 176 11 41 |
3,6 17,2 18,0 17,2 45,8 2,9 10,7 |
| Monthly income | Less than $500
Between $501 to $1000 Between $1001 to $1500 Between $1501 to $2000 Over $2000 I have no income |
78
142 60 24 40 40 |
20,3 37,0 15,6 6,3 10,4 10,4 |
| Personality traits | Openness to experience Extraversion Conscientiousness: Agreeableness Neuroticism |
148 25 86 92 33 |
38,5
6,5 22,4 24,0 8,6 |
| Note: Frequency and percentage values based on 384 respondents | |||
Source: Own elaboration.
About personality traits, as indicated in Table 3, the most prominent trait among the participants is ‘openness to experience’ (38.5%), followed by ‘agreeableness’ (24%) and ‘conscientiousness’ (22.4%). However, traits associated with neuroticism (8.6%) or extraversion (6.5%) received very low scores below the average. Based on these findings, most quiteños (residents of Quito) with a high openness to experience tend to seek out destinations that offer interesting and unexpected tourist activities, providing them with new and enriching experiences. Additionally, they appreciate places where cordial service is provided.
Table 3 Description of the personality traits of the sample
| Criterion | Scale | Frequency | Percentage |
|---|---|---|---|
| Personality traits | Openness to experience
Extraversion Conscientiousness: Agreeableness Neuroticism |
148
25 86 92 33 |
38,5
6,5 22,4 24,0 8,6 |
| Note: Frequency and percentage values based on 384 respondents | |||
Source: Own elaboration.
Regarding the variables considered for the proposed measurement model, the results obtained from the information reflected in Table 4 are as follows:
Table 4 Description of the variables and measurement of the reliability of the scale
| Variable | Dimension | Indicator | Mode | Sum | Mean | Standard deviation | Mean | Cronbach’s Alfa | Cronbach’s Alfa if the element is removed |
|---|---|---|---|---|---|---|---|---|---|
| NEE | NEE1
NEE2 NEE3 NEE4 NEE5 |
5
5 5 5 5 |
1725 1750 1617 1634 1653 |
4,49
4,55 4,21 4,25 4,30 |
0,988
0,951 1,066 1,065 1,107 |
4,36 | 0,879 | 0,861 0,847 0,856 0,854 0,844 |
|
| OFE | OFE1
OFE2 OFE3 OFE4 OFE5 OFE6 |
5 5 5 5 5 5 |
1647 1601 1470 1605 1630 1356 |
4,28
4,16 3,82 4,17 4,24 3,53 |
1,070 1,100 1,203 0,975 1,073 1,290 |
4,04 | 0,792 | 0,756 0,766 0,741 0,745 0,780 0,773 |
|
| MOT DES | DES | DES1
DES2 DES3 DES3 DES5 |
5
3 5 5 5 |
1605
1320 1612 1445 1625 |
4,17
3,43 4,19 3,76 4,23 |
1,115
1,235 1,050 1,312 1,084 |
3,96 | 0,809 | 0,755
0,802 0,751 0,784 0,765 |
Source: Own elaboration.
Descriptively, all the items within the ‘needs of tourists’ dimension consistently received a mode of 5. Notably, the need for safety and protection (NEE2) stood out, generating the highest sum and exhibiting the least standard deviation. This finding suggests that while tourists may have diverse needs, their primary concern is seeking a tourist destination that offers security. Safety emerges as a pivotal factor, holding greater importance than other considerations.
Similarly to the previous dimensions, the values obtained for the ‘tourist offer’ consistently showed a mode of 5. This indicates a balanced interest across all tourism products or services destinations can provide. However, among these options, city tourism alternatives (OFE1) are most widely accepted by tourists, whereas shopping tourism (OFE6) is less favored.
On the other hand, it’s worth noting that some arithmetic mean values fell below 3.8. This suggests that a significant percentage of people do not consider wellness tourism (OFE3) or shopping tourism (OFE6) as their preferred alternatives. The standard deviation further supports these findings, revealing very high variability in responses.
Regarding ‘desires’ most of the indicators obtained a mode of 5, demonstrating the participants' high degree of approval. However, there was an exception with the price variable (DES2). It was revealed that tourists are willing to visit destinations regardless of the costs associated with transportation, accommodation, food, or attractions-provided these expenses align with their expectations. On the other hand, the desire that received the highest value was personal desire (DES5). This desire is expressed by tourists seeking destinations that allow them to rediscover or reconnect with themselves.
Finally, considering the theoretical reference that suggests Cronbach’s alpha should fall between 0.7 and 0.9 for a reliable measurement scale (Cronbach, 1951), our study yielded the following results: Entire Motivation Variable: Cronbach’s alpha = 0.913. This indicates high reliability for the entire motivation variable across the 16 indicators. Separate Dimensions: Need Dimension (Cronbach’s alpha = 0.879), Desire Dimension (Cronbach’s alpha = 0.809), Offer Dimension (Cronbach’s alpha = 0.792). These values affirm the reliability of each indicator.
To assess the suitability of applying factor analysis, we examined two key indicators: Kaiser-
Meyer-Olkin Index (KMO): The KMO index should ideally reach a value of ≥ 0.8. In our study, the KMO value obtained was 0.879, indicating that the data is suitable for factor analysis. Bartlett’s Sphericity Test: Bartlett’s test assesses the level of correlation among variables. A p-value less than 0.05 is desirable for this test. In our case, the Bartlett sphericity test yielded a p-value of 0.000, further supporting the suitability of factor analysis.
Subsequently, we conducted an Exploratory Factor Analysis (EFA). Table 5 presents the relationship between motivation indicators, grouping them into two motivational factors: Push Factors (Needs) (These are determined by internal variables. They drive motivation), Pull Factors (Offer) (These are influenced by external variables. They also contribute to motivation). These findings align with Devesa et al.'s (2010) position that both internal and external factors shape motivation.
Table 5 Matrix of Components by Construct (Rotated Factors)
| Variable | Dimension | Indicator | Push factors | Pull factors |
|---|---|---|---|---|
| Needs | Existing Offer | |||
| MOT | NEE | NEE1
NEE2 NEE3 NEE4 NEE5 |
0,783
0,873 0,596 0,580 0,617 |
|
| OFE | OFE1
OFE2 OFE3 OFE4 OFE5 OFE6 |
0,541
0,469 0,778 0,597 0,414 0,569 |
||
| Note: values obtained by maximum likelihood | ||||
Source: Own elaboration.
Results of Confirmatory Factor Analysis
After obtaining satisfactory indices from the Exploratory Factor Analysis, we proceeded with the Confirmatory Factor Analysis (CFA). For this purpose, we utilized AMOS version 26 software, employing the maximum likelihood method. The following steps were carried out and evaluated: specification of the model considered and identification, estimation of parameters, fit Evaluation, re-specification of the model, and interpretation of the obtained results (Medrano & Muñoz, 2017).
Model Analysis
Specification of the model considered and identification of the model: Figure 1 presents the measurement model proposed for this research. It encompasses three dimensions: need, offer, and desires, which are contrasted with the variable of personality traits. According to the AMOS program, there are a total of 115 degrees of freedom, allowing for estimation and contrast (Medrano & Muñoz, 2017).
Model estimation: For this study, we employed the maximum likelihood estimation method to analyze and calculate various estimates. These estimates include:
Standardized Regression Coefficients: These coefficients allow us to compare the effects of different predictor variables on the response variable. They are unitless and indicate how many standard deviations the dependent variable changes per standard deviation increase in the predictor variable.
Coefficients of Determination (R²): R² measures how well a statistical model predicts an outcome. It ranges from 0 to 1, with higher values indicating better prediction.
Indirect Effects: These represent the pathways through which intervening variables transmit effects from causal variables to outcome variables.
Direct Effects: These capture the direct relationship between the independent and dependent variables, excluding mediation through intermediate variables.
Total Effects: The total effect combines direct and indirect effects to explain the overall relationship between variables.
Evaluation of the model:Specifically, we considered the following criteria:
Root Mean Square Error of Approximation (RMSEA): RMSEA should be less than 0.05.
Comparative Fit Index (CFI): CFI should be greater than or equal to 0.95.
Goodness of Fit Index (GFI): GFI should exceed a cut-off point 0.89.
-
Normed Fit Index (NFI): The NFI should be greater than 0.90 (Jordán, 2021).
The initial results of this model, using the TFA (Theoretical Framework Analysis), yielded the following absolute adjustment indices:
Chi-Square (X²): 773.378, Degrees of Freedom (df): 115, p-Value: 0.000, RMSEA: 0.122
The incremental adjustment indices were CFI: 0.788, GFI: 0.810, and NFI: 0.761.
These indices provide insights into the model’s goodness of fit. While some values fall short of the ideal criteria, they still offer valuable information for model evaluation.
Re-specification of the model: While these recent values do not precisely match or exceed the acceptability criteria, they still yield results that align well with the theoretical recommendations. Considering the existing level of relationship, further adjustments to this model are unnecessary.
Relationship analysis: In this section, we delve into the relationship analysis by examining the variance within the measurement model for each dimension. Theoretical principles guide our interpretation of these variance values:
Low Variance: When variance is low, it suggests that the data points are generally clustered around the mean. In other words, there is less variability among the values.
High Variance: Conversely, high variance indicates greater dispersion among the data points. They deviate more significantly from the mean.
Considering the data collected and organized for this study, the variance values for each dimension are as follows: Needs (NEE): Variance = 0.000 (Low), Offer (OFE): Variance = 0.010 (Low), Desire (DES): Variance = 0.783 (High), Personality Trait (PET): Variance = 0.000 (Low).
The first two dimensions (needs and offer) exhibit low variance, indicating less variability among participants’ responses. In contrast, the desire dimension shows high variance, suggesting significant dispersion in participants’ desires.
Similarly, personality traits also exhibit low variance, implying consistency in participant responses.
Relationship Analysis and Hypothesis Testing
When considering relationship values, it is essential to adhere to theoretical principles of estimation. Ideally, these values should approach 1. Additionally, according to Calvo (2017), each relationship should yield a standardized coefficient (λ) and covariance (E) greater than 0, preferably exceeding 0.5. Furthermore, a critical ratio (C.R.) greater than 1.96 and a pvalue less than 0.05 indicate statistical significance.
As observed in Table 6, the values obtained from the AMOS program and the estimation considerations reveal varying relationships. Some relationships meet the minimum acceptance criteria, while others do not. Based on the initially formulated hypotheses, we can draw the following conclusions:
Accepted Hypotheses:
H4: Needs significantly influence tourists’ desires.
H5: The existing offerings of a destination significantly impact tourists’ desires.
Rejected Hypotheses:
H1: Personality traits do not significantly influence tourists’ needs.
H2: Personality traits do not significantly influence the existing tourist offer.
H3: Desires do not act as mediating variables between personality traits and either tourist needs or the existing offer.
Table 6 Hypothesis testing through the TFA
| Hypothesis | λ | E | S.E. | C.R. | P | Conclusion |
|---|---|---|---|---|---|---|
| H1: PET → NEE | -0,003 | 0,027 | -0,124 | -0,901 | Rejected | |
| H2: PET → OFE | -0,099 | -0.051 | 0,030 | -1,277 | 0,084 | Rejected |
| H3: PET → DES | 0.007 | 0,003 | 0,021 | 0,158 | 0,875 | Rejected |
| H4: NEE → DES | 0,641 | 0,662 | 0,072 | 9,238 | *** | Accepted |
| H5: OFE → DES | 0,610 | 0,607 | 0,070 | 8,724 | *** | Accepted |
Note: Standardized coefficient λ = >0.5; E=Estimated covariance; SE=standard error; CR=critical value >1.95; P=p-value<0.05 or ***<0.001; PET=personality traits; NEE = tourist needs; OFE= tourist offers; DES=desires
Source: Own elaboration.
From the same information, Figure 2 provides a more detailed view and complete values for the standardized coefficients obtained for each dimension considered in this study. Additionally, it illustrates the degree of relationship between these coefficients and their respective indicators.
Analysis of the model considering personality traits as a mediating variable
The correlation results shown above show a direct connection between the two variables. However, they do not show the degree of relationship when a third variable is involved. In this sense, in order to strengthen the study, from now on, a new analysis of the proposed measurement model is proposed to study what is the cause-effect that is generated between two variables directly or indirectly when there is the presence of a third variable that acts as a mediator; In this aspect, the values obtained in the previously presented relationship are discarded, and new relationships are formulated and calculated.
In essence, the model now considers the relationship that exists when desires (DES) function as a mediating variable between the criterion variable personality trait (PET) and the predictor variables needs (NEE) and offer (OFE), respectively (as depicted in Figure 3). We formulated the following hypotheses:
H6: Personality traits positively influence tourism needs when desires act as a mediating variable.
H7: Personality traits positively impact tourists’ decisions regarding available offerings when desires act as a mediating variable.
To conduct this type of analysis, our study followed the guidance of Sanz (2014), who emphasizes three essential conditions for mediation analysis:
Relationship Between Predictor (X) and Mediator (M): The predictor or independent variable (X) must be related to the mediating variable (M).
Relationship Between Mediator (M) and Criterion (Y): The mediating variable (M) should be associated with the criterion or dependent variable (Y).
Significant Relationship Between Predictor (X) and Criterion (Y): Initially, there should be a significant relationship between the predictor variable (X) and the criterion variable (Y). However, this significance may diminish or disappear once the mediating variable is introduced.
In this context, to verify whether these three conditions are met, the statistical program employed for this proposed measurement model produced the following results:
Between a) PET →NEE (r=0.034 - p=0.386); b) PET →DES (r=-0.048 - p=0.388); c) DES →NEE (r=0.834 - p=0.001)
Between a) PET →OFE (r=-0.055 - p=0.186); b) PET →DES (r=-0.048 - p=0.388); c) DES →OFE (r=0.846 - p=0.001)
Unfortunately, as evident from these values, some relationships do not meet the conditions necessary for mediation. Specifically, proceeding with the mediation analysis is irrelevant since no significant values (p-values) were observed. However, to demonstrate this lack of mediation, we further examined which values do not contribute to answering the H6 and H7 hypotheses formulated earlier, thereby impacting the development of the mediation analysis.
As observed in Table 7, the results based on the two assumptions considered for this study (H6 and H7) indicate that no significant p-values below 0.05 were found-either directly or 69
Table 7 Mediation Analysis
| Hypothesis | Total coefficient p-value | Direct coefficient beta | p-value | Indirect coefficient beta | p-value | Observed mediation |
|---|---|---|---|---|---|---|
| H6: PET→DES→NEE | -,0,007 0,910* | 0,034 | 0,305* | -0,040 | 0,516* | None |
| H7: PET→DES→OFE | -0,096 0,155* | -0,055 | 0,204* | -0,041 | 0,500* | None |
| Note: PET=personality traits; NEE=tourist needs; OFE=tourist offers; DES=desires; *=No significance | ||||||
Source: Own elaboration.
indirectly. This corroborates the absence of a significant relationship between the predictor and criterion variables when the desire variable acts as a mediator. Consequently, this test leads to the rejection of hypotheses H6 and H7.
Discussion
Based on the obtained results, several important aspects can be identified. First and foremost, consumer behavior is influenced by various external and internal factors that impact purchasing decisions regarding products or services. Motivation plays a crucial role in this context from a psychological perspective. This fact has been corroborated in various studies taking into account multiple motivations, such as well-being (Ahn & Kim, 2024), religious tourism (Carvache, 2024), cultural destinations (Parreira et al., 2021) or natural (Mzimela et al., 2024).
Specifically, the descriptive results from this study reveal that the residents of Quito exhibit strong motivation to visit tourist destinations based on their needs and the available offerings. Push factors (such as personal preferences) and pull factors (such as the appealing features of the destination) contribute significantly to this motivation. Interestingly, these findings closely align with those Medina (2016) reported in a different geographical context, such as Gran Canaria.
Secondly, regarding the indicators considered for each of the variables mentioned earlier, exploratory factor analysis revealed the existence of motivational aspects that can be categorized as both ‘push’ and ‘pull’ factors, as they argue (Martínez-Cañas et al., 2023). This finding aligns with the perspectives of Mediano (2002), Devesa et al. (2010), and Vanegas and Santa (2024). Specifically, it highlights that tourists’ behavior is influenced by psychological push criteria, which ignite an individual’s desire to travel. Additionally, pull factors-cultural or otherwise-play a significant role in determining the choice of one destination over another.
Thirdly, based on the initially formulated hypotheses regarding the H1 hypothesis, the results indicate that personality traits do not significantly impact tourists’ needs. While personality traits naturally vary among individuals, they do not play a substantial role in motivating tourists. Instead, specific needs related to tourism drive people to explore new places and embark on travel experiences. In light of this rejection of the hypothesis, Medina (2016) proposed that such results can emerge in various research studies conducted across different human contexts. This prompts several intriguing questions for future investigation: Why did the personality traits of Quito natives not influence their needs? What underlying factors contributed to these results?
Concerning the H2 hypothesis, we aimed to analyze whether personality traits significantly influence the pull factor of motivation, defined by the existing tourist offer. However, this study led to the rejection of the hypothesis. Despite personality traits being unique to each individual, they play a significant role in shaping the attraction of tourist offerings. Consequently, the availability and quality of tourism options emerge as critical factors influencing travelers’ decisions. These findings align with those of Medina (2016), who demonstrated an association between personality and preferences in various tourist activities-a factor significantly impacts tourist motivations. Based on this information, it becomes evident that formulating proposals for improving the current tourist offerings is of utmost importance.
About the H3 hypothesis, the study did not provide evidence that personality traits significantly influence tourists’ desires. While personality traits are not the sole determinants of these desires, they do not automatically dictate which destinations or tourist experiences qualify as travel intentions. However, it remains crucial for tourism-oriented destinations or companies to consider personality traits when designing marketing strategies. They can effectively engage potential travelers by tailoring their tourism campaigns to address different segments' individual needs, as Barzola et al. (2019) and Chen et al. (2022) suggested. Additionally, this approach allows us to explore the tourist’s personality from perspectives beyond the consumer’s psychological processes-such as social and cultural factors.
Regarding the H4 and H5 hypotheses, the study demonstrated that specific tourist needs directly impact desires, and consequently, the tourist offerings also influence desire formation. As a result, we can accept both of these hypotheses. These findings align with studies such as those conducted by Naranjo and Martinez (2022), which emphasize the need for adapting the tourist offerings to meet the evolving demands of tourists. Additionally, Acerenza (2003) postulates that desires and needs give rise to various tourism experiences. In this context, travelers, tourists, and consumers have become adept at making informed decisions. They evaluate the potential opportunities and threats different destinations present, aiming to minimize challenges or barriers they might encounter during their journeys. These challenges can be technical or practical, such as communication, accessibility to products/services, or infrastructure availability.
Furthermore, tourists’ decisions are influenced by their character. Their personality shapes their initial ideas about the type of tourism experience they seek. Contreras and Vargas (2021) support this perspective, asserting that a person’s character is malleable and can adapt to new situations and changes.
In summary, while a person’s traits-such as openness to experience, conscientiousness, extraversion, agreeableness, or neuroticism-serve as valuable tools for understanding how they might influence consumer decisions, studies have revealed that these traits are not directly related to various motivational factors. In the context of this study, confirmatory factor analysis tests demonstrated no significant relationship between these personality traits and the following aspects: Needs (H1): Personality traits do not significantly impact tourists’ needs.
Desires (H2): These traits are not automatically determinants of travel intentions or desires. Existing Tourist Offer (H3): While personality traits shape attraction to tourist offerings, they do not directly influence the overall willingness to travel.
Fourthly, considering the arguments presented, the results obtained in this study cannot be directly compared to the findings reported by Bano et al. (2019) or García and Moral (2022). These previous studies suggest a significant relationship between specific personality traits or behavioral profiles at the psychological level and their impact on consumer motivations. However, despite this disparity, we should not dismiss the results from our study entirely. One valuable framework for understanding how personality traits influence human motivation is the “five-factor model,” as highlighted by Ruiz (2003) and Shuai et al. (2023). While our study focused on tourism-related motivations, this model can help evaluate how personality traits come into play in other contexts beyond tourism. For instance, it can shed light on motivation in education, job search, philanthropic activities, and more.
Fifthly, concerning the initial measurement model, the data indicated that the variables considered in this study are appropriately aligned at the relationship level. Consequently, no adjustments to the indicators were necessary. This finding suggests that each study dimension's criteria (indicators) are relevant and warrant further investigation.
Sixthly, Regarding the proposed new measurement model, which considers the existence of desire as a mediating variable, this study revealed that desire does not mediate between personality traits and tourist needs (H6) or the existing tourist offer (H7). These results stem from the lack of correlation between the following variables:
Personality Traits (Predictor) and Needs (Mediator): Personality traits do not significantly impact tourists’ needs.
Personality Traits (Predictor) and Existing Tourist Offer (Mediator): Personality traits are not directly related to the existing tourist offerings.
Personality Traits (Mediator) and Desires (Criterion): While personality traits do not directly affect needs or the tourist offer, desires influence needs and existing tourist offerings.
Conclusions
First and foremost, this research confirms that motivation significantly influences the purchase decisions related to tourism products or services. It does so through two fundamental factors: Push Factors: These represent the basic needs of human beings. They act as driving forces that prompt individuals to consider travel and explore new destinations. Pull Factors: These are shaped by the existing offerings of a destination, including its infrastructure, cultural richness, and natural beauty. The interplay between these pull factors influences the final choice made by tourists.
Now, turning to the specific personality traits measured in this study-openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism-we find three key insights: trait influence on needs and offer: contrary to expectations, these personality traits do not necessarily impact the needs of Quito tourists or the existing tourist offerings.
In other words, an individual’s personality traits alone do not determine their travel needs or preferences. Needs and offers influence desires; however, both needs and the existing tourist offer significantly influence tourists’ desires. The interplay between these factors shapes what travelers aspire to experience. Desires as mediating variables: Surprisingly, desires do not act as mediating variables between personality traits and either tourist needs or the existing offer. Instead, desires directly connect to both needs and the available offerings.
To sum up, an individual’s personality traits do not automatically dictate their motivation to choose a tourist destination. Therefore, it becomes crucial for destinations to tailor their efforts efficiently. They can showcase the diverse range of tourism products and services available by optimizing resources, time, and budget. These campaigns should be personalized, considering that each tourist has unique desires based on their needs.
Destinations must allocate their efforts toward developing efficient tourism campaigns that optimize resources, time, and finances. These campaigns should showcase the diverse range of tourism products and services available, emphasizing the benefits tourists can derive from them. To achieve this, it is essential to tailor these campaigns to the unique profile of each traveler. Recognizing that every tourist has distinct personality traits and desires, destinations can better satisfy their needs.
In summary, it is essential to note that during the development of this research, no significant limitations were identified that could have adversely affected the study, theoretically and methodologically. However, it is essential to acknowledge a methodological limitation related to identifying personality traits as a valid variable in market research. Specifically, when participants self-identify their personality traits, certain biases may arise. For instance, a notable trend emerged among most participants who reported having an openness to experience trait. This group seeks novel experiences, engages with fantasy, and explores new places. Consequently, this self-identification process may have influenced the study’s results. To address this, researchers should explore alternative mechanisms or objective methods for assessing participants’ personality traits.
Lastly, for future research related to this topic, we recommend applying the same model in diverse contexts to verify whether the data obtained in this study holds across various human or social realities. By doing so, researchers can expand the scope of assessments and explore the nuances that define each personality trait.
