Rural geotourists segmentation by motivation in weekends and weekdays

Abstract

Rural geotourism is one of the geotourism products which is linked to rural tourism activities. More specifically, geo-villages especially stone villages are main forms of rural geotourism destinations and they are ideal for geotourists. Thus, the main study’s objective is to explore rural geotourists’ characteristics, revealing the differences in their motivations on weekends versus weekdays using a factor–cluster market segmentation approach. The sample population for this research consisted of tourists who have visited the Kandovan rocky village in Tabriz City. We collected push and pull motivation data from a questionnaire and separated respondents into weekend and weekday samples. The weekend and weekday motivation factors were derived from 30 motivation items through factor analysis. Among a total of 624 questionnaires distributed, 600 questionnaires were used for further data analyses. As a result of cluster analysis, weekend tourists were divided into three clusters and weekday tourists were segmented into four clusters. Through a comparison of the results for weekends and weekdays, we find both weekend and weekday tourists seek “novelty” and “geological attractions” when they travel to Kandovan.

Keywords

Rural geotourism geotourist motivations factor–cluster market segmentation weekends and weekdays

Introduction

Tourism is a dynamic and competitive industry, and the new tourism products will take more clear and certain form in the future (Costa and Buhalis, 2006). Geotourism, like other tourism products, is dynamic. One of the geotourism products which is growing under the umbrella of rural tourism is the recently named “Rural Geotourism.” Rural geotourism is a form of natural tourism that occurs in the countryside which includes unique geological and geomorphological landscapes. Geo-villages, in particular stone villages also are considered new rural geotourism destinations and they are ideal for those who are interested in local culture, natural, and geological sciences, particularly lithology and petrology, and also for those willing to gain knowledge about their places in our dynamic earth. As Robinson (2008) indicated that the visitors of geo-heritage such as geo-villages, geoparks, geo-sites, caves, mine, etc. are considered geotourists or geotravelers. Pralong (2006) further suggest that there are specific targeted groups, such as seniors, families, and schools whose needs and wants are satisfied by different geotourism activities. Tourist motivations have been central in tourism research for several decades (Huang, 2010). In fact, an improved understanding of travel motivations would help in segmenting the markets, thereby allowing tourism marketers to allocate scarce tourism resources more efficiently. As a result, segmentation has emerged as a very powerful and useful tool to market goods and services to targeted markets and targeted groups (Berrigan et al., 1992). It has been suggested that the field of tourism would greatly benefit from a segmentation technique based on motivations because it could provide cues and/or insights that destination marketers could use in developing and promoting their tourism destinations. The sign–gestalt paradigm, better known as the “push–pull factor” compendium theory by Tolman (1959) and later by Dann (1977), is perhaps the most recognized theory within the realm of tourism research. The literature on tourist motivation indicates that the examination of motivations based on the push and pull factors has been generally well accepted (Yuan & McDonald, 1990; Uysal & Hagan,1993).

Overall, the tourist’s motivations for geotourism’s participating have been explored to a very little extent. Furthermore, this significant issue in the broader tourism literature is still an undeveloped area of study although the range of implementation of motivation studies in the tourism literature is abundant (Allan, 2012). However, even if the rural tourism has got a lot of attention in the pertinent literature, investigating geotourists’ motivations in relation to rural tourism and based on day of visitation is under-researched and nearly nonexistent. This paper contributes with filling this gap by identifying the main drivers for travelers to participate in rural geotourist activities in weekdays and weekend.

The main purpose of this study is to identify rural geotourists’ motivations undertaking their rural geotourism experiences on weekends versus weekdays. For this purpose, market segmentation using a tourist motivation approach appears to be the most useful method. It is obvious that while the most common characteristics of the segment are revealed, destination brands and related companies can find their marketing strategies to cater more comprehensively to tourist requirements. The results of this study are expected to segment geotourists according to their motivations into meaningful and manageable groups and identify rural geotourism participants.

Literature review

Motivation

Travel motivation has gained considerable attention from tourism academia since the 1960s to better understand and predict factors that influence travel decision-making (Kim et al., 2007). According to Backman et al. (1995), motivation is conceptually viewed as “a state of need, a condition that serves as a driving force to display different kinds of behavior toward certain types of activities, developing preferences, arriving at some expected satisfactory outcome.” This driving force has dominated travel patterns to protect and satisfy one’s desire and to increase the self-value of an individual (Lee and Chen, 2005).

By reviewing the existing literature on tourist motivation, several theories or models have been developed to guide the empirical study of travel motivation, including push–pull (Dann, 1977), escape seeking (Dunn and Iso-Ahola, 1991), and travel career ladder (Pearce, 1988). Particularly, the push and pull theory provides an important theoretical framework (Dann, 1977; Klenosky, 2002). Simply, push forces “considering whether to go” (i.e. the desire to travel), while pull forces associate with the decision “where to go” (i.e. the choice of destination) (Kim et al., 2007). Based upon the push and pull approach, people are pushed by internal desires, such as personal escape, psychological or physical health, thrill and adventure, and social interactions (Baloglu and Uysal, 1996). Subsequently, they are then pulled by external resources, such as natural or artificial attractions that a destination may possess (Cha et al., 1995). It is worth mentioning that the two sets of forces are dependent, thus, peoples’ decisions to travel are formed consciously or unconsciously in a two-stage sequence (Kim et al., 2007; Wong et al., 2013).

To date rural tourism has been a popular research topic among tourism scholars. Tourists’ motivations in rural areas have previously been studied through surveys (Bansal and Eiselt, 2004; Bieger and Laesser, 2002, 2004; Chen and Chen, 2010; Devesa et al., 2010; Farmaki, 2012; Frochot, 2005; Kamata and Misui, 2013, 2015; Park and Yoon, 2009). Qualitative research could be more helpful in generating insightful information about people’s motivations for traveling (Huang, 2010).

In addition, segmentation is a good way to gain a better position in the markets compared to competitors because it provides valuable information on customers and makes it possible for a destination to adjust its supply to better match customers’ needs (Matzler et al., 2004). Earlier rural tourism segmentation studies have mainly compared what segments are like or what they do instead of studying what they would like. This information is particularly important in order to develop rural tourism businesses and destinations. Earlier studies have either segmented rural tourists based on their travel motivations (i.e. push motivations) (e.g. Park and Yoon, 2009), benefits (i.e. pull motivations) they seek from a destination (e.g. Kastenholz et al., 1999), or a combination of these two (e.g. Molera and Albaladejo, 2007). However, in rural tourism segmentation using both push and pull motivations has rarely been investigated in a single study. Instead, most studies have focused on what activities members of different segments want to do during their holiday (e.g. Park and Yoon, 2009).

Rural geotourism

Rural tourism typically refers to tourism outside densely populated areas and tourism centers. It is experiencing expansion in many countries due to the benefits it offers to the host community and the tourists (San Martin and Herrero, 2012). One of the benefits of rural tourism is the generation of income for local communities. Such income can then be used by these communities toward the sustainability of their traditional activities (Romera et al., 2011), the promotion and conservation of their local arts and cultures, and the prevention of rural–urban migration. Other benefits of rural tourism are well documented by Haldar (2007). Therefore, he postulates that local communities are benefiting from rural tourism in three aspects: economic, environmental, and sociocultural.

Rural geotourism is considered as a new niche market in tourism and also a new geotourism product. It not only encompasses characteristics of rural tourism, but also, with an emphasis on geology and geography, it is known as a strategy for universalization of earth sciences. It strives to explore and revive cultural identities and integrates them with geo-knowledge for educating locals and transferring knowledge to visitors. In addition, this new niche market integrates rural tourism holidays activities with earth sciences (e.g. geo-sports) (Farsani et al., 2013).

Furthermore, rural geotourism is not only transferring geo-knowledge from the professional to local level, schoolchildren, and visitors, but also a way for implementing sustainable principals and geo-conservation methods. Similarly, rural geotourism is a gateway for the entrance of public and private infrastructures and educational facilities into rural areas particularly in developing countries (Farsani et al., 2013).

In the Middle East context, there are some villages with hand-dug houses amidst the rocks like Cappadocia (Turkey), Kandovan village (Iran), and Meymand village (Iran). These rocky villages are rural geotourism destinations which can offer different experiences like rocky hotels, stone accommodation, cave restaurants, living like a caveman and living in the Stone Age, etc. to visitors. In these villages, geo-heritage and geo-landscapes fit in with the rural population’s lifestyle and culture. Moreover, visitors can not only be educated and touch the stones, but can observe the use of stones in architecture and culture as well (Farsani et al., 2013).

Study area

Kandovan village is one of the third rocky villages and the only inhabitable rocky village in the world which has caused its unique attractiveness (Lotfi et al., 2010).

Kandovan with ancient texture architectures is one of unique examples of rock monuments in the world which is located 65 km from Tabriz city and is in northwestern foot of Sahand Mountain mass. Kandovan village in the presence of conical houses, mineral waters fountains and animals life has prepared various tourism attractions for tourists. According to tourism organization report of east Azerbaijan province in 2012, about 600,000 people visited this village in 2012. Regarding resources and tourism attractions, Kandovan village has historical texture with troglodytic houses in heart of rocks and mountains. Hence, on the one hand these villages create an opportunity to meet needs and growing demands of tourists and those interested in historical and attractive spaces due to having abundant historical, cultural, and natural attractions and very long history. On the other hand, their economic, social, and cultural resurgence have been provided in this way, these villages have been able to attract a large number of domestic and international tourists (Ghasemzadeh, 2013).

Methodology

Questionnaire design and data collection

The sample population for this research consisted of tourists who have visited the Kandovan village in Tabriz City during the period surveyed. A convenience sampling method was used and an onsite survey was administered from 5 June to 8 July 2016. A self-administered questionnaire was employed and total of 624 questionnaires were distributed, but 24 questionnaires proved to be unreliable because of multiple missing values. As a result, 600 questionnaires were used for further data analyses. These data were separated into weekend and weekday samples. In this study, weekend tourists were defined as those who had undertaken a trip on Thursday or Friday.¹ The sample size was equally distributed between weekend visitors (n = 300) and weekday visitors (n = 300). The questionnaire structure consisted of two parts, which are “demographic characteristics of tourists” and “travel motivations.” It includes five questions relating to sociodemographic characteristics including gender, age, education, occupation, and marital status. The second part contained 30 statements relating to the purpose of the visit. Each motivation was rated using a five-point Likert scale, ranging from “1—strongly disagree” to “5—strongly agree.” The questionnaire was prepared in two versions: English and Persian.

The pilot study (N = 30) was conducted to provide vital feedback on the language, format, and content of the different items of the questionnaire (Thomas et al., 2005). After modification, there were total of 600 cases in good quality collected within two months and analyzed for further research results.

Data analysis

The data for the surveys were captured in Microsoft Excel and analyzed using the Statistical Programme for Social Sciences (SPSS 17.0).

This study used different types of statistical techniques as appropriate, thus it employs factor analysis and cluster analysis. Factor analyses with varimax rotation were performed on motivations to identify smaller sets of explanatory composite factors that define the fundamental constructs assumed to underlie the original variables. It enables the identification of some homogeneous groups (segments) using data that describe pull or push motivation (Kamata and Misui, 2015). Only those factors with an eigenvalue equal to or higher than 1.0 were considered. A factor loading of 0.30 is considered significant, while a factor loading of 0.50 is considered very significant (Field, 2005). However, variables with factor loading coefficients of 0.60 were considered for travel motivations. To ensure quality of measurement, the variables were also subjected to reliability (Cronbach’s alpha reliability test) and appropriateness (Bartlett’s test of sphericity and Kaiser–Meyer–Olkin measure of sampling adequacy) testing.

In the second step, we derived each tourist motivation factor for weekends and weekdays from 30 motivation items and then clarified each motivation factor for rural geotourism. The cluster analysis (K-mean cluster analysis) was conducted in two stages. In the first stage, the factor scores of each respondent were used to segment geotourists into homogeneous groups. Next, sociodemographic and travel profiles in each cluster were developed and compared using a chi-square test to find statistical differences among the clusters. This analysis of weekends and weekdays clarified the number of segments and their characteristics.

The results

Weekdays tourists

The sample size of weekday tourists was 300 tourists. The 30 push and pull motivation items were factor analyzed. Eventually, 12 push and eight pull motivation items were analyzed and eight factors were derived with eigenvalues of greater than one. The total variance accounted for was 70.88%. The results of the factor analysis are reported in Table 1. The eight factors generated were escape, relaxation, cost, novelty seek, geological attractions, activities, knowledge, and historical attractions.

Table 1.

Factor analysis: motivational dimensions, items, and statistics of weekday tourists.

Motivational dimensions and items	Factor loadings	Eigenvalue	Variance (%)	Cronbach’s Alpha
Escape		8.1	17.1	0.857
To escape from the daily life routine	.819
To escape from the life pressures	.790
Relaxation		6.4	13.5	0.910
To relax and reset in the site	.923
To improve my mental and physical state	.766
Cost		5.05	10.7	0.838
Because accommodation is cheap at the destination	.820
Because it is cheap destination	.740
Novelty seek		4.43	9.39	0.867
To visit a distinctive place	.912
To explore new places and experiences	.810
Geological attractions		3.49	7.39	0.755
Because it has many architectural features	.649
Because it has important geological attractions	.611
Activities		2.55	5.4	0.697
Because it provides a large variety of activities	.871
Because it provides many interesting activities for all family members	.665
Knowledge		2.03	4.3	0.829
To experience new places and countries	.712
To enhance my knowledge and learn new things	.700
Historical attractions		1.49	3.1	0.630
Because it has important historical and heritage sites	.641
Because many historical actions occurred there	.603
Total variance explained			70.88

According to the K-means clustering procedure, mean values of the eight factors were extracted. As a result of factor analysis for geotourism motivations, the four clusters were generated (Table 2). The four clusters were expected to show a variety of characteristics of weekday’s geotourists’ motivations. The clusters were characterized as follows: a group who perceived a high level on the “escape” but a low level on the “activities” and “cost” motivation dimensions (cluster 1); a group who perceived a high level on the “relaxation,” “cost,” and the “activities” motivation dimensions but a low level on the other motivation dimensions (cluster 2); a group who perceived a high level on the “novelty” motivation dimension but the lowest level on other motivation dimensions (cluster 3); and a group who perceived a high level on the “historical attractions” and “geological attractions” motivation dimensions (cluster 4). Thus, each cluster was named according to the motivation factors within that cluster that received the highest ratings such as escape seekers, multipurpose seekers, novelty seekers, and geological and historical attractions seekers.

Table 2.

Results of cluster analysis: weekday tourists (N = 300).

Factors/clusters	Clusters 1 (escape seekers) N:96	Clusters 2 (multipurpose seekers) N:44	Clusters 3 (novelty seekers) N:87	Clusters 4 (historical and geological attractions seekers) N:77	F-value
Escape	4.03	2.90	2.78	3.02	96.87
Relaxation	3.68	3.85	2.68	2.94	170.26
Cost	1.04	3.98	1.90	2.08	188.23
Novelty seek	2.88	2.06	3.88	3.18	153.58
Geological attractions	3.40	2.87	3.04	4.13	133.99
Activities	2.56	3.74	1.95	2.54	180.88
Knowledge	1.08	3.02	2.17	3.08	124.15
Historical attractions	2.78	1.46	2.88	4.10	132.55

Bold values show the motivation factors that received the highest ratings compared to other motivations.

A series of chi-square tests were conducted to examine whether there were statistically significant levels of association between the clusters and selected sociodemographic variables. These results were used to identify the distinctive sociodemographic characteristics of the clusters. The results are reported in Table 3. Significant differences (p < 0.05) were found between clusters regarding all sociodemographic variables, including gender, age, educational level, occupation, and marital status. The summary of each cluster is as follows:

Table 3.

Results of sociodemographic characteristics: weekday tourists (N=300).

Sociodemographic characteristics	Clusters 1 (escape seekers) N:96	Clusters 2 (multipurpose seekers) N:40	Clusters 3 (novelty seekers) N:87	Clusters 4 (historical and geological attractions seekers) N:77	Chi-square	p-value
Gender					9.08	0.000
Male (N:183)	58.4	47.3	41.6	61.5
Female (N:117)	41.6	52.7	58.4	38.5
Age					18.07	0.000
18–34 (N:36)	11.2	12.1	34.8	25.2
35–39 (N:78)	12.8	34.7	28.7	27.6
40–49 (N:100)	25.0	29.5	14.3	24.4
50–59 (N:50)	30.6	13.3	12.2	11.8
60 s or more (N:36)	20.4	10.4	10.0	11.0
Educational level					17.60	0.000
Primary (N:26)	6.8	12.1	4.9	12.5
Secondary/high school (N:74)	28.7	24.9	12.7	11.9
Undergraduate (N:156)	40.5	36.7	40.1	40.6
Postgraduate (N:44)	24.0	26.3	42.3	35.0
Occupation					16.90	0.000
Student (N:47)	28.2	14.1	34.4	18.7
Employed (N:116)	37.1	35.9	30.5	39.2
Pensioner (N:93)	29.9	37.2	24.3	26.0
Other (N:44)	4.8	12.8	10.8	16.1
Marital status					20.3	0.000
Single (N:76)	27.6	37.6	43.5	32.5
Married (N:166)	47.2	40.9	36.1	37.8
Divorced (N:32)	13.1	8.7	10.8	19.5
Other (N:26)	12.1	12.8	9.6	10.2

Cluster 1 (32%): We named this cluster “escape seekers” since tourists in this cluster seek soothing and release from the daily life routine and life pressure. These were mainly male tourists aged 40–59, undergraduate, employed, and married.

Cluster 2 (13.3%): We named this cluster “multipurpose seekers,” referring to tourists who had expectations pertaining to cost, activities, and relaxation factors. These were mainly female tourists aged 35–49, undergraduate, pensioner, and married.

Cluster 3 (29%): We named this cluster “novelty seekers” since tourists in this cluster went on the village with exploring and visiting distinctive place purposes. These tourists were mainly female, aged 18–39, postgraduate, student, and single.

Cluster 4 (25.6%): We named this cluster “historical and geological attractions seekers” since tourists in this cluster seek architectural features, geological attractions, historical and heritage sites. These were mainly male tourists aged 18–49, undergraduate, employed, and married.

Weekend tourists

As the same of weekday’s tourists, the sample size of weekend tourists was 300 tourists. The 30 push and pull motivation items were factor analyzed. Ultimately, 12 push and eight pull motivation items were analyzed and six factors were derived with eigenvalues of greater than one. The total variance accounted for was 66.47%. The summary of the factor analysis results is shown in Table 4. We identified and categorized the factors based on the items constituting each factor including geological attractions, novelty seek, enjoyment, socialization, knowledge, and historical attractions.

Table 4.

Factor analysis: motivational dimensions, items, and statistics of weekend tourists.

Motivations	Factor loadings	Eigenvalue	Variance (%)	Cronbach’s alpha
Geological attractions		10.2	21.6	0.766
Because it has many architectural features	.823
Because it has important geological attractions	.721
Novelty seek		8.4	17.8	0.617
To visit a distinctive place	.751
To explore new places and experiences	.616
Enjoyment		5.6	11.8	0.958
Because it is an exciting experience	.679
To have pleasant time	.645
Socialization		3.2	6.78	0.781
To accompany my relatives and friends	.852
To meet people with same hobbies, activities, and interests	.721
Knowledge		2.8	5.93	0.659
To experience new places and countries	.619
To enhance my knowledge and learn new things	.604
Historical attractions		1.25	2.65	0.884
Because it has important historical and heritage sites	.782
Because many historical actions occurred there	.684
Total variance explained			66.47

As a result of cluster analysis, we employed three clusters where statistical conditions were significant. Table 5 shows the results of the cluster analysis. The motivation factors that received the highest ratings in each cluster were bolded in table 5, including enjoyment–socialization, geological attractions, novelty. Each cluster was named according to the motivation factors within that cluster that received the highest ratings namely enjoyment and socialization seekers, geological attractions seekers, and novelty seekers.

Table 5.

Results of cluster analysis: weekend tourists (N = 300).

Factors/clusters	Clusters 1 (enjoyment and socialization seekers) N:70	Clusters 2 (geological attractions seekers) N:154	Clusters 3 (novelty seekers) N:96	F-value
Geological attractions	1.58	4.21	3.29	101.25
Novelty seek	2.78	3.48	3.97	88.42
Enjoyment	3.84	2.32	1.89	90.34
Socialization	3.68	3.90	2.74	69.31
Knowledge	3.09	3.74	3.31	79.35
Historical attraction	2.86	1.80	3.24	125.91

Bold values show the motivation factors that received the highest ratings in each cluster.

The results were also characterized by demographic variables and travel profile variables and compared using a chi-square test to find statistical differences among the clusters. The summary of each cluster is as follows:

Cluster 1 (23.3%): We named this cluster “enjoyment and socialization seekers” since tourists in this cluster seek enjoyment and relationships with friends and families or meeting people with same interests. These were mainly male tourists aged 40–59, undergraduate, employed, and married.

Cluster 2 (44.6%): We named this cluster “geological attractions seekers” referring to tourists who seek architectural features and geological attractions. These were mainly male tourists aged 18–49, undergraduate, employed, and married.

Cluster 3 (32%): We named this cluster “novelty seekers” since tourists in this cluster went on the village with exploring and visiting distinctive place purpose. These tourists were mainly female, aged 18–49, undergraduate, student, and married.

Discussion

So far, there has been no detailed investigation of rural geotourism motivations and segmentation. Thus, this research has found four different rural geotourist segments among weekdays tourists and three various rural geotourist segments among weekend ones by segmenting them according to their travel motivations using k-means clustering method (Table 7).

Table 6.

Results of sociodemographic characteristics: weekend tourists (N = 300).

Sociodemographic characteristics	Clusters 1 (enjoyment and socialization seekers) N:70	Clusters 2 (geological attractions seekers) N:134	Clusters 3 (novelty seekers) N:96	Chi-square	p-value
Gender				11.2	0.000
Male (N:154)	53.2	63.7	38.7
Female (N:146)	46.8	36.3	61.3
Age				17.43	0.000
18–34 (N:56)	10.2	20.2	26.7
35–39 (N:58)	21.2	25.7	23.6
40–49 (N:81)	32.3	24.5	25.1
50–59 (N:64)	27.1	13.2	11.2
60 s or more (N:41)	9.2	16.4	13.4
Educational level				20.8	0.000
Primary (N:24)	8.2	9.8	7.3
Secondary/high school (N:66)	23.7	19.9	15.2
Undergraduate (N:133)	53.2	37.5	45.2
Postgraduate (N:77)	14.9	32.8	32.3
Occupation				16.76	0.000
Student (N:95)	32.8	31.1	36.2
Employed (N:140)	34.4	36.4	33.8
Pensioner (N:42)	30.1	21.2	21.3
Other (N:23)	2.7	11.3	8.7
Marital status				19.65	0.000
Single (N:78)	35.2	33.6	28.3
Married (N:130)	41.1	42.9	40.1
Divorced (N:51)	15.3	13.2	18.2
Other (N:41)	8.4	10.3	13.4

Table 7.

Summarized table of the research findings.

	Weekday tourists	Weekend tourists
Cluster 1	Escape seekers	Enjoyment and socialization seekers
Cluster 2	Multipurpose seekers	Geological attractions seekers
Cluster 3	Novelty seekers	Novelty seekers
Cluster 4	Historical and geological attractions seekers

As a result of cluster analysis, we employed four clusters for weekdays tourists. The first segment, “escape seekers,” is the largest segment with 32% of 300 respondents. A very quiet, calm, and passive rural holiday is very important for such respondents. They will choose a destination that can offer beautiful landscapes and good opportunities to spend time in outdoor attractions in the nature. These were mainly male tourists aged 40–59, undergraduate, employed, and married. This result may be explained by the fact that older generations have stronger motivation to enjoy hobbies that are undemanding and provide opportunity for relaxation; therefore, they often indulge in passive forms of tourism. Actually, they believe having relaxing pastimes can alleviate their stress.

Whereas, the second segment, “multipurpose seekers,” is the smallest segment with 40 respondents. The members of this segment are very difficult to target for rural tourism companies as they choose the destination based on very different targets. As an example, this segment should be targeted with low-cost products as they valued low prices the most since they imagined it as a cheap destination. Moreover, in this segment some people want to feel relaxed, value privacy, and like calm and rush-free atmosphere more than other segments; therefore, they are quite passive and do not generally value other benefits as high as other segments. On the other hand, it could be deduced that other people chose Kandovan village because it provides a large variety of activities for them and their family members, thus they are active tourists. These were mainly female tourists’ aged 35–49, undergraduate, pensioner, and married. They are younger than first cluster. A possible explanation for this might be that the youth and younger generation often prefer to spend their free time actively as they are physically agile. On the other hand, most of people in this group are pensioners, that is why relaxation is other motivation in this category. In fact, among rural geotourists there is a special segment searching for a “lazy” relaxing holiday without an emphasis on any special activities.

The third segment, “novelty seekers,” is the second largest segment with 87 respondents. They seek novelty, new, and memorable experiences; get pleasure from adventure; and want to satisfy their curiosity since they consider it as a distinctive place. These tourists were mainly female, aged 18–39, postgraduate, student, and single.

Last segment in weekdays, “historical and geological attractions seekers,” is the third largest. For them, historical and geological aspects of the village play an important role in choosing this destination. These can be regarded as basic expectations for almost all rural geotourism, something that everyone expects when they come to this rural destination, as it is popular for having historical texture with troglodytic houses in heart of rocks and mountains. These were mainly male tourists aged 18–49, undergraduate, employed, and married.

On the other hand, the findings of this study showed three clusters in weekend. The first segment is the smallest segment with 70 respondents. They want to spend time with their family and friends, have fun, exciting experience, and pleasant time. These factors should be taken into account when planning marketing efforts for this segment. These were mainly male tourists aged 40–59, undergraduate, employed, and married.

The second segment is the largest segment with 44.6% of 300 respondents, referring to tourists who seek architectural features and geological attractions. What makes Kandovan village so unique is that many of its homes have been made in caves located in cone-shaped, naturally formed compressed volcanic ash formations that make the landscape look like a gigantic termite colony. In this village geo-heritage and geo-landscapes fit in with the rural population’s lifestyle and culture (Farsani et al., 2013). These were mainly male tourists aged 18–49, undergraduate, employed, and married.

The third and last segment in weekend cluster, “novelty seekers,” is the second largest segment with 96 respondents. They seek same characteristics that mentioned in weekdays novelty seekers except they are undergraduate and married, while they were postgraduate and single in weekdays cluster.

Conclusions

Examining segments has been a very important act recently because comprehensively studied segments provide specific information about tourists’ purchasing patterns to managers and destination administrators in the tourism industry (Park, 2009).

The goal of the current study was exploring the characteristics and segmentation of tourists who visited Kandovan village in different days including weekend and weekdays. The findings of this study are as follows:

First, the “geological attractions” segment and the “novelty” segment exist across both the weekend and weekday samples. Moreover, in both groups “novelty seekers” were mainly female aged 18–34 and student. Other segments, namely “historical and geological attractions seekers”—cluster 4 in weekday tourists and “geological attractions seekers”—cluster 2 in weekend tourists, had same results. It means visitors were mainly male tourists aged 18–49, undergraduate, employed, and married.

Second, “weekend tourists” have more motivations related to geotourism than “weekday tourists,” since 44.6% of them had geological purposes for their trip. They came to this destination because of its main attraction. It is reasonable to claim that such tourists choose their preferred destinations with detailed and sufficient information in order to enhance their architectural, historical, and scientific information. As it was mentioned, Kandovan is one of unique examples of rock monuments in the world with ancient texture architectures. From this result, it can be said that weekend tourists have more geotourism interests than do weekday tourists.

Whereas, weekday tourists tend to have wider range of motivations and “escape” is their main goal for coming to this place. They can enjoy from calm atmosphere during the week due to the fact that it is really crowded in weekend.

Third, as shown in the results of the weekday tourist segmentation, cluster 2 mainly consisted of pensioner: since they have more free time and freedom to travel during the weekdays while based on Table 6, most of the respondents in all clusters were employed or students which seem to reflect the fact that it is impossible for them to take a trip during the week.

In conclusion, the results of analyses to identify the characteristics of different motivational clusters offer distinctiveness of each segment and provide a good insight into understanding geotourism marketing. Thus, the research’s outcomes are expected to assist destination marketing practitioners who develop overall management of geotourism resources. For example, on the basis of the results of this study, one of the main goals for weekday tourists was cost, which seems to reflect the fact that all prices are lower on weekdays so this group may respond negatively to pricing at the entrance or accommodation.

On the other hand, the sociodemographic differences among the motivational clusters could be very helpful in understanding who they are. Tourism managers should distinguish between the weekend and weekday tourist markets in their promotional strategies.

Future studies on the current topic are therefore recommended. Thus, further work is required to examine motivational clusters in other geotourism forms, such as caving tourism, mountain tourism, and so on. In future investigations, it might be possible to use different motivational factors which were not applied in the current study.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Note

Author Biographies

Mamoon Allan is currently an Associate Professor at the Faculty of Archeology and Tourism at the University of Jordan in Amman. He completed his PhD in Tourism Marketing at Edith Cowan University in Perth, Western Australia. He conducts international research in the contexts of geotourism, accessible tourism in the Middle East, tourist behaviour, and e-tourism issues. His books include Geotourism in Jordan (2014), Geotourism: Toward a Better Understanding of Motivations for a Geotourism Experience: Self-Determination Theory Perspective (2012), and Tourism Industry in Libya (2006).

Mercede Shavanddasht has a Master Degree in Tourism Management from Allameh Tabatabaie University. Her research interests are geotourism and tourism marketing.

References

Allan

(2012) Geotourism: Toward a Better Understanding of Motivations for a Geotourism Experience: A Self-Determination Theory Perspective, Saarbrucken: LAP Lambert Academic Publishing.

Backman

Uysal

et al. (1995) Event tourism: An examination of motivations and activities. Festival Management and Event Tourism 3(1): 15–24.

Baloglu

Uysal

(1996) Market segments of push and pull motivations: A canonical correlation approach. International Journal of Contemporary Hospitality Management 8(3): 32–38.

Bansal

Eiselt

(2004) Exploratory research of tourist motivations and planning. Tourism Management 25(3): 87–396.

Berrigan

Finkbeiner

Feldman

(1992) Segmentation Marketing: New Methods for Capturing Business Markets, New York: HarperBusiness.

Bieger

Laesser

(2002) Market segmentation by motivation: The case of Switzerland. Journal of Travel Research 41: 68–76.

Bieger T and Laesser C (2004) Information sources for travel decisions: Toward a source process model. Journal of Travel Research 42(4): 357–371.

Cha

McCleary

Uysal

(1995) Travel motivations of Japanese overseas travelers: A factor-cluster segmentation approach. Journal of Travel Research 34(1): 33–39.

Chen C and Chen F (2010) Experience quality, perceived value, satisfaction and behavioral intentions for heritage tourists. Tourism Management 31(1): 29–35.

10.

Costa

Buhalis

(2006) Introduction. In: Buhalis

Costa

(eds) Tourism Management Dynamics Trends, Management and Tools, Oxford: Elsevier, pp. 1–7.

11.

Dann

(1977) Anomie, ego-enhancement and tourism. Annals of Tourism Research 4(4): 184–194.

12.

Devesa

Laguna

Palacios

(2010) The role of motivation in visitor satisfaction: Empirical evidence in rural tourism. Tourism Management 31(4): 547–552.

13.

Dunn

Iso-Ahola

(1991) Sightseeing tourists: Motivation and Satisfaction. Annals of Tourism Research 18(2): 226–237.

14.

Farmaki

(2012) An exploration of tourist motivation in rural settings: The case of Troodos, Cyprus. Tourism Management 2(3): 72–78.

15.

Farsani

Coelho

Costa

(2013) Rural geotourism: A new tourism product. Acta Geoturistica 4(2): 1–10.

16.

Field

(2005) Discovering Statistics Using SPSS, 2nd ed. London: SAGE Publications.

17.

Frochot

(2005) A benefit segmentation of tourists in rural areas: A Scottish perspective. Tourism Management 26(3): 335–346.

18.

Ghasemzadeh

(2013) An opportunity for tourism development with troglodytic architecture. Research Journal of Applied Sciences, Engineering and Technology 5(12): 3294–3297.

19.

Haldar P (2007) Rural tourism – Challenges and opportunities. In: International marketing conference on marketing and society, IIMK, 8–10 April. Kozhikode: Indian Institute of Management Kozhikode, pp.111–129.

20.

Huang

(2010) Measuring tourism motivation: Do scale matters? Tourismos 5(1): 153–162.

21.

Kamata H and Misui Y (2013) A comparison of Japanese tourists segmentation by push motivation at “peak” and “around the peak.” In: Proceedings of 2nd World Research Summit for Tourism and Hospitality, Orlando, FL, 15–17 December.

22.

Kamata

Misui

(2015) The difference of Japanese Spa tourists motivation in weekends and weekdays. Procedia – Social and Behavioral Sciences 175: 210–218.

23.

Kastenholz E, Duane D and Paul G (1999) Segmenting Tourism in Rural Areas: The case of North and Central Portugal. Journal of Travel Research 37(4): 353–363.

24.

Kim

Jogaratnam

(2007) College student travel: A revised model of push motives. Journal of Vacation Marketing 13(1): 73–85.

25.

Klenosky

(2002) The pull of tourism destinations: A means-end investigation. Journal of Travel Research 40(4): 385–395.

26.

Lee

Chen

(2005) Traveling motivation and satisfaction of tourists: An empirical study of Taroko National Park in Taiwan. The Business Review 4(2): 175–181.

27.

Lotfi H, Hemmati S and Pourali R (2010) An effective pattern for sustainable development of rural tourism: (a case study: Kandovan). Iranian Journal of Tourism and Hospitality 1(1): 105–116.

28.

Matzler K, Pechlaner H and Hattenberger G (2004) Lifestyle-typologies and marketing segmentation: The case of Alpine skiing tourism. Bozen: EUR. AC research, European Academy.

29.

Molera L and Albaladejo IP (2007) Profiling segments of tourists in rural areas of South-Eastern Spain. Tourism Management 28(3): 757–767.

30.

Park

D-B

Yoon

(2009) Segmentation by motivation in rural tourism: A Korean case study. Tourism Management 30(1): 99–108.

31.

Park Y (2009) A study of market segmentation management in the hotel industry: A customer equity approach. Doctoral Thesis. The Faculty of the Graduate College of the Oklahoma State University, USA, 403 pages; 3387606, http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:3387606.

32.

Pralong

(2006) Geotourism: A new form of tourism utilising natural landscapes and based on imagination and emotion. Tourism Review 61(3): 20–25.

33.

Pearce PL (1988) The Ulysses Factor: Evaluating Visitors in Tourist Settings. New York: Springer-Verlag.

34.

Robinson AM (2008) Geotourism: Who is a geotourist? In: Paper presented at the inaugural national conference green travel, climate change and ecotourism, 18 November. Australia: Edith Cowan University, pp. 1–12.

35.

Romera EB, Brida GJ, Martínez CA, et al. (2011) Rural tourism at Arqitectura Negra villages (Guadalajara Spain): Demand analysis. Tourism Management 27: 1011–1013.

36.

San Martin

Herrero

(2012) Influence of user’s psychological factors on the online purchase intention in rural tourism. Tourism Management 33: 341–350.

37.

Thomas

Nelson

Silverman

(2005) Research Methods in Physical Activity, 5th ed. Champaign, IL: Human Kinetics.

38.

Tolman

(1959) Principles of Purposive Behavior . In: Koch

(ed.) Psychology: A Study of a Science Vol. 2, New York: McGraw-Hill, pp. 92–157.

39.

Uysal M and Hagan LAR (1993) Motivation of pleasure travel and tourism. In: Khan M, Olsen M and Var T (eds) Encyclopedia of Hospitality and Tourism, pp. 798–810.

40.

Wong

Cheung

Wan

(2013) A study on traveler expectation, motivation and attitude. Contemporary Management Research 9(2): 169–186.

41.

Yuan S and McDonald C (1990) Motivational determinants of international pleasure time. Journal of Travel Research 24(1): 42–44.